From a84ffaab82fc0848acb0cac92d7e64bcab65c458 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 13:50:21 +0100 Subject: [PATCH 01/61] add info on jobflow remote and lobster workflow --- docs/user/codes/script.py | 21 ++++++++ docs/user/codes/vasp.md | 107 ++++++++++++++++++++++++-------------- 2 files changed, 90 insertions(+), 38 deletions(-) create mode 100644 docs/user/codes/script.py diff --git a/docs/user/codes/script.py b/docs/user/codes/script.py new file mode 100644 index 0000000000..1e993de87d --- /dev/null +++ b/docs/user/codes/script.py @@ -0,0 +1,21 @@ +from atomate2.vasp.flows.lobster import VaspLobsterMaker +from pymatgen.core.structure import Structure +from jobflow_remote import submit_flow, set_run_config +from atomate2.vasp.powerups import update_user_incar_settings +from atomate2.vasp.powerups import update_vasp_custodian_handlers + +structure = Structure( + lattice=[[0, 2.13, 2.13], [2.13, 0, 2.13], [2.13, 2.13, 0]], + species=["Mg", "O"], + coords=[[0, 0, 0], [0.5, 0.5, 0.5]], +) + +lobster = VaspLobsterMaker().make(structure) + +resources = {"nodes": 3, "partition": "micro", "time": "00:55:00", "ntasks": 144} + +resources_lobster = {"nodes": 1, "partition": "micro", "time": "02:55:00", "ntasks": 48} +lobster = set_run_config(lobster, name_filter="lobster", resources=resources_lobster) + +lobster = update_user_incar_settings(lobster, {"NPAR": 4}) +print(submit_flow(lobster, worker="my_worker", resources=resources, project="my_project")) diff --git a/docs/user/codes/vasp.md b/docs/user/codes/vasp.md index d9788e1a98..f66088b31b 100644 --- a/docs/user/codes/vasp.md +++ b/docs/user/codes/vasp.md @@ -352,9 +352,76 @@ lobster = update_user_incar_settings(lobster, {"NPAR": 4}) run_locally(lobster, create_folders=True, store=SETTINGS.JOB_STORE) ``` -It is, however, computationally very beneficial to define two different types of job scripts for the VASP and Lobster runs, as VASP and Lobster runs are parallelized differently (MPI vs. OpenMP). -[FireWorks](https://github.com/materialsproject/fireworks) allows one to run the VASP and Lobster jobs with different job scripts. Please check out the [jobflow documentation on FireWorks](https://materialsproject.github.io/jobflow/tutorials/8-fireworks.html#setting-the-manager-configs) for more information. +There are currently three different ways available to run the workflow efficiently, as VASP and LOBSTER rely on a different parallelization (MPI vs. OpenMP). +One can use a job script (with some restrictions), or [Jobflow-remote](https://matgenix.github.io/jobflow-remote/) / [Fireworks](https://github.com/materialsproject/fireworks) for high-throughput runs. + +#### Running the LOBSTER workflow without database and with one job script only + +It is possible to run the VASP-LOBSTER workflow efficiently with a minimal setup. +In this case, you will run the VASP calculations on the same node as the LOBSTER calculations. +In between, the different computations you will switch from MPI to OpenMP parallelization. + +For example, for a node with 48 cores, you could use an adapted version of the following SLURM script: + +```bash +#!/bin/bash +#SBATCH -J vasplobsterjob +#SBATCH -o ./%x.%j.out +#SBATCH -e ./%x.%j.err +#SBATCH -D ./ +#SBATCH --mail-type=END +#SBATCH --mail-user=you@you.de +#SBATCH --time=24:00:00 +#SBATCH --nodes=1 +#This needs to be adapted if you run with different cores +#SBATCH --ntasks=48 + +# ensure you load the modules to run VASP, e.g., module load vasp +module load my_vasp_module +# please activate the required conda environment +conda activate my_environment +cd my_folder +# the following script needs to contain the workflow +python xyz.py +``` + +The `LOBSTER_CMD` now needs an additional export of the number of threads. + +```yaml +VASP_CMD: <> +LOBSTER_CMD: OMP_NUM_THREADS=48 <> +``` + + +#### Jobflow-remote +Please refer first to the general documentation of jobflow-remote: [https://matgenix.github.io/jobflow-remote/](https://matgenix.github.io/jobflow-remote/). + +```py +from atomate2.vasp.flows.lobster import VaspLobsterMaker +from pymatgen.core.structure import Structure +from jobflow_remote import submit_flow, set_run_config +from atomate2.vasp.powerups import update_user_incar_settings + +structure = Structure( + lattice=[[0, 2.13, 2.13], [2.13, 0, 2.13], [2.13, 2.13, 0]], + species=["Mg", "O"], + coords=[[0, 0, 0], [0.5, 0.5, 0.5]], +) + +lobster = VaspLobsterMaker().make(structure) + +resources = {"nodes": 3, "partition": "micro", "time": "00:55:00", "ntasks": 144} + +resources_lobster = {"nodes": 1, "partition": "micro", "time": "02:55:00", "ntasks": 48} +lobster = set_run_config(lobster, name_filter="lobster", resources=resources_lobster) + +lobster = update_user_incar_settings(lobster, {"NPAR": 4}) +submit_flow(lobster, worker="my_worker", resources=resources, project="my_project") +``` + +#### Fireworks +Please first refer to the general documentation on running atomate2 workflows with fireworks: [https://materialsproject.github.io/atomate2/user/fireworks.html](https://materialsproject.github.io/atomate2/user/fireworks.html) Specifically, you might want to change the `_fworker` for the LOBSTER runs and define a separate `lobster` worker within FireWorks: ```py @@ -425,42 +492,6 @@ for number, (key, cohp) in enumerate( plotter.save_plot(f"plots_cation_anion_bonds{number}.pdf") ``` -#### Running the LOBSTER workflow without database and with one job script only - -It is also possible to run the VASP-LOBSTER workflow with a minimal setup. -In this case, you will run the VASP calculations on the same node as the LOBSTER calculations. -In between, the different computations you will switch from MPI to OpenMP parallelization. - -For example, for a node with 48 cores, you could use an adapted version of the following SLURM script: - -```bash -#!/bin/bash -#SBATCH -J vasplobsterjob -#SBATCH -o ./%x.%j.out -#SBATCH -e ./%x.%j.err -#SBATCH -D ./ -#SBATCH --mail-type=END -#SBATCH --mail-user=you@you.de -#SBATCH --time=24:00:00 -#SBATCH --nodes=1 -#This needs to be adapted if you run with different cores -#SBATCH --ntasks=48 - -# ensure you load the modules to run VASP, e.g., module load vasp -module load my_vasp_module -# please activate the required conda environment -conda activate my_environment -cd my_folder -# the following script needs to contain the workflow -python xyz.py -``` - -The `LOBSTER_CMD` now needs an additional export of the number of threads. - -```yaml -VASP_CMD: <> -LOBSTER_CMD: OMP_NUM_THREADS=48 <> -``` (modifying_input_sets)= Modifying input sets From 3b65b231a6d7be561b6397992813f62c5bcb991b Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 16:43:11 +0100 Subject: [PATCH 02/61] remove file and fix linting --- docs/user/codes/script.py | 21 --------------------- docs/user/codes/vasp.md | 22 +++++++++++++++++++++- 2 files changed, 21 insertions(+), 22 deletions(-) delete mode 100644 docs/user/codes/script.py diff --git a/docs/user/codes/script.py b/docs/user/codes/script.py deleted file mode 100644 index 1e993de87d..0000000000 --- a/docs/user/codes/script.py +++ /dev/null @@ -1,21 +0,0 @@ -from atomate2.vasp.flows.lobster import VaspLobsterMaker -from pymatgen.core.structure import Structure -from jobflow_remote import submit_flow, set_run_config -from atomate2.vasp.powerups import update_user_incar_settings -from atomate2.vasp.powerups import update_vasp_custodian_handlers - -structure = Structure( - lattice=[[0, 2.13, 2.13], [2.13, 0, 2.13], [2.13, 2.13, 0]], - species=["Mg", "O"], - coords=[[0, 0, 0], [0.5, 0.5, 0.5]], -) - -lobster = VaspLobsterMaker().make(structure) - -resources = {"nodes": 3, "partition": "micro", "time": "00:55:00", "ntasks": 144} - -resources_lobster = {"nodes": 1, "partition": "micro", "time": "02:55:00", "ntasks": 48} -lobster = set_run_config(lobster, name_filter="lobster", resources=resources_lobster) - -lobster = update_user_incar_settings(lobster, {"NPAR": 4}) -print(submit_flow(lobster, worker="my_worker", resources=resources, project="my_project")) diff --git a/docs/user/codes/vasp.md b/docs/user/codes/vasp.md index f66088b31b..edfd7826ef 100644 --- a/docs/user/codes/vasp.md +++ b/docs/user/codes/vasp.md @@ -352,7 +352,7 @@ lobster = update_user_incar_settings(lobster, {"NPAR": 4}) run_locally(lobster, create_folders=True, store=SETTINGS.JOB_STORE) ``` -There are currently three different ways available to run the workflow efficiently, as VASP and LOBSTER rely on a different parallelization (MPI vs. OpenMP). +There are currently three different ways available to run the workflow efficiently, as VASP and LOBSTER rely on a different parallelization (MPI vs. OpenMP). One can use a job script (with some restrictions), or [Jobflow-remote](https://matgenix.github.io/jobflow-remote/) / [Fireworks](https://github.com/materialsproject/fireworks) for high-throughput runs. @@ -420,8 +420,18 @@ lobster = update_user_incar_settings(lobster, {"NPAR": 4}) submit_flow(lobster, worker="my_worker", resources=resources, project="my_project") ``` +The `LOBSTER_CMD` also needs an export of the threads. + +```yaml +VASP_CMD: <> +LOBSTER_CMD: OMP_NUM_THREADS=48 <> +``` + + + #### Fireworks Please first refer to the general documentation on running atomate2 workflows with fireworks: [https://materialsproject.github.io/atomate2/user/fireworks.html](https://materialsproject.github.io/atomate2/user/fireworks.html) + Specifically, you might want to change the `_fworker` for the LOBSTER runs and define a separate `lobster` worker within FireWorks: ```py @@ -456,6 +466,16 @@ lpad = LaunchPad.auto_load() lpad.add_wf(wf) ``` + +The `LOBSTER_CMD` can now be adapted to not include the number of threads: + +```yaml +VASP_CMD: <> +LOBSTER_CMD: <> +``` + +#### Analyzing outputs + Outputs from the automatic analysis with LobsterPy can easily be extracted from the database and also plotted: ```py From e7c2b21cf24d9da7685e82dc2f0361fb16e6cb87 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 20:29:49 +0100 Subject: [PATCH 03/61] add another phonon example --- docs/user/codes/vasp.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/docs/user/codes/vasp.md b/docs/user/codes/vasp.md index edfd7826ef..53f09edb98 100644 --- a/docs/user/codes/vasp.md +++ b/docs/user/codes/vasp.md @@ -248,6 +248,23 @@ adjust them if necessary. The default might not be strict enough for your specific case. ``` +You can use the following code to start the standard version of the workflow: +```py +from atomate2.vasp.flows.phonons import PhononMaker +from pymatgen.core.structure import Structure + +structure = Structure( + lattice=[[0, 2.13, 2.13], [2.13, 0, 2.13], [2.13, 2.13, 0]], + species=["Mg", "O"], + coords=[[0, 0, 0], [0.5, 0.5, 0.5]], +) + +phonon_flow = PhononMaker(min_length=15.0, store_force_constants=False).make(structure=struct) +``` + + + + ### Gruneisen parameter workflow Calculates mode-dependent Grüneisen parameters with the help of Phonopy. From 80f55e1b46156c2e11f78275c11586e3c6808b2a Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 21:08:09 +0100 Subject: [PATCH 04/61] add tutorial for phonons including mock_vasp --- tutorials/phonon_workflow.ipynb | 431 ++++++++++++++++++++++++++++++++ 1 file changed, 431 insertions(+) create mode 100644 tutorials/phonon_workflow.ipynb diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb new file mode 100644 index 0000000000..0dd900b9c0 --- /dev/null +++ b/tutorials/phonon_workflow.ipynb @@ -0,0 +1,431 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:", + "id": "5929afcd65d79f7e" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T20:06:54.620984Z", + "start_time": "2025-02-07T20:06:54.618214Z" + } + }, + "cell_type": "code", + "source": [ + "from mock_vasp import TEST_DIR, mock_vasp\n", + "\n", + "ref_paths = {\n", + " \"phonon static 1/1\": \"Si_phonons_2/phonon_static_1_1\",\n", + " \"static\": \"Si_phonons_2/static\",\n", + " }" + ], + "id": "d14d39451aac0e35", + "outputs": [], + "execution_count": 12 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Now, we load a structure and other important functions and classes for running the phonon workflow.", + "id": "49d7cc42166b990f" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T20:06:54.671783Z", + "start_time": "2025-02-07T20:06:54.664068Z" + } + }, + "cell_type": "code", + "source": [ + "from pymatgen.core import Structure\n", + "from atomate2.vasp.flows.phonons import PhononMaker\n", + "from jobflow import run_locally, JobStore\n", + "from maggma.stores import MemoryStore\n", + "\n", + "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", + "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")\n" + ], + "id": "17de20060b45220a", + "outputs": [], + "execution_count": 13 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction by considering `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_relax_maker`. This is not done here for simplicity.", + "id": "7e042abdd5362b80" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T20:06:54.820564Z", + "start_time": "2025-02-07T20:06:54.713475Z" + } + }, + "cell_type": "code", + "source": [ + "flow=PhononMaker(\n", + " min_length=3.0,\n", + " bulk_relax_maker=None,\n", + " born_maker=None,\n", + " use_symmetrized_structure=\"conventional\",\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + " ).make(si_structure)" + ], + "id": "94e80cd2cebc9183", + "outputs": [], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "The phonon run will first perform a bulk relaxation, then the displacements are generated and run. As we currently don’t have a way to compute BORN charges with such potentials, a non-analytical term correction is not performed here. We can visualize the flow first.", + "id": "224c77cd46856b91" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T20:06:54.957758Z", + "start_time": "2025-02-07T20:06:54.827434Z" + } + }, + "cell_type": "code", + "source": "flow.draw_graph().show()", + "id": "4c713e7285b0f2c4", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 15 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`", + "id": "9c97c5b1152715b0" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T20:07:02.177364Z", + "start_time": "2025-02-07T20:06:54.964006Z" + } + }, + "cell_type": "code", + "source": [ + "with mock_vasp(ref_paths=ref_paths) as mf:\n", + " run_locally(flow, create_folders=True,\n", + " ensure_success=True,\n", + " raise_immediately=True,\n", + " store=job_store)" + ], + "id": "3f4db4f33192b6a3", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-07 21:06:54,971 INFO Started executing jobs locally\n", + "2025-02-07 21:06:54,974 INFO Starting job - structure_to_conventional (e580b1ee-85ab-48a6-b816-a3b9a1019fb0)\n", + "2025-02-07 21:06:54,986 INFO Finished job - structure_to_conventional (e580b1ee-85ab-48a6-b816-a3b9a1019fb0)\n", + "2025-02-07 21:06:54,987 INFO Starting job - get_supercell_size (d14a6d0f-618d-4c14-9167-98b332061252)\n", + "2025-02-07 21:06:54,990 INFO Finished job - get_supercell_size (d14a6d0f-618d-4c14-9167-98b332061252)\n", + "2025-02-07 21:06:54,991 INFO Starting job - static (1cb5d5de-3168-4a94-915c-e177c5b482f1)\n", + "2025-02-07 21:06:55,178 INFO Finished job - static (1cb5d5de-3168-4a94-915c-e177c5b482f1)\n", + "2025-02-07 21:06:55,179 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 21:06:55,180 INFO Starting job - generate_phonon_displacements (ddc72302-3d27-4a16-91ca-9e6430f81c1a)\n", + "2025-02-07 21:06:55,229 INFO Finished job - generate_phonon_displacements (ddc72302-3d27-4a16-91ca-9e6430f81c1a)\n", + "2025-02-07 21:06:55,229 INFO Starting job - run_phonon_displacements (90815b96-01b1-4a4b-beb5-3bef9aa0242f)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-07 21:06:55,336 INFO Finished job - run_phonon_displacements (90815b96-01b1-4a4b-beb5-3bef9aa0242f)\n", + "2025-02-07 21:06:55,339 INFO Starting job - phonon static 1/1 (a2f4b416-548e-4671-b5ea-fe93d999d489)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmpl354ftgh/job_2025-02-07-20-06-55-339042-56749/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-07 21:06:56,219 INFO Finished job - phonon static 1/1 (a2f4b416-548e-4671-b5ea-fe93d999d489)\n", + "2025-02-07 21:06:56,220 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 21:06:56,221 INFO Starting job - store_inputs (90815b96-01b1-4a4b-beb5-3bef9aa0242f, 2)\n", + "2025-02-07 21:06:56,222 INFO Finished job - store_inputs (90815b96-01b1-4a4b-beb5-3bef9aa0242f, 2)\n", + "2025-02-07 21:06:56,223 INFO Starting job - generate_frequencies_eigenvectors (b6a6c784-b333-47ea-ab91-74b46373cfb9)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-07 21:07:02,169 INFO Finished job - generate_frequencies_eigenvectors (b6a6c784-b333-47ea-ab91-74b46373cfb9)\n", + "2025-02-07 21:07:02,170 INFO Finished executing jobs locally\n" + ] + } + ], + "execution_count": 16 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T20:07:02.190300Z", + "start_time": "2025-02-07T20:07:02.184401Z" + } + }, + "cell_type": "code", + "source": [ + "from pymatgen.phonon.bandstructure import PhononBandStructureSymmLine\n", + "from pymatgen.phonon.dos import PhononDos\n", + "from pymatgen.phonon.plotter import PhononBSPlotter, PhononDosPlotter\n", + "from jobflow import SETTINGS\n", + "\n", + "job_store.connect()\n", + "\n", + "result = job_store.query_one(\n", + " {\"name\": \"generate_frequencies_eigenvectors\"},\n", + " properties=[\n", + " \"output.phonon_dos\",\n", + " \"output.phonon_bandstructure\",\n", + " ],\n", + " load=True,\n", + " sort={\"completed_at\": -1} # to get the latest computation\n", + ")" + ], + "id": "1bea502d9378bf28", + "outputs": [], + "execution_count": 17 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T20:07:02.532206Z", + "start_time": "2025-02-07T20:07:02.228693Z" + } + }, + "cell_type": "code", + "source": [ + "ph_bs = PhononBandStructureSymmLine.from_dict(result['output']['phonon_bandstructure']) # get pymatgen bandstructure object\n", + "ph_dos = PhononDos.from_dict(result['output']['phonon_dos']) # get pymatgen phonon dos object\n", + "\n", + "# initialize dos plotter and visualize dos plot\n", + "dos_plot = PhononDosPlotter()\n", + "dos_plot.add_dos(label='a', dos=ph_dos)\n", + "dos_plot.get_plot()\n", + "\n", + "# initialize Phonon bandstructure plotter and visualize band structure plot\n", + "bs_plot = PhononBSPlotter(bs=ph_bs)\n", + "bs_plot.get_plot()" + ], + "id": "3f7ab2d88f97fba7", + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 18 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "One can run the same workflow with a forcefield as well. Here, we cannot consider BORN charges yet as there is no forcefield equivalent.", + "id": "c12bfc7daf5b541" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T20:07:02.842750Z", + "start_time": "2025-02-07T20:07:02.538799Z" + } + }, + "cell_type": "code", + "source": [ + "from atomate2.forcefields.flows.phonons import PhononMaker\n", + "flow=PhononMaker(\n", + " min_length=3.0,\n", + " born_maker=None,\n", + " use_symmetrized_structure=\"conventional\",\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + " ).make(si_structure)\n", + "run_locally(flow, store=job_store,create_folders=True, raise_immediately=True)" + ], + "id": "82bb21620a5679f1", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-07 21:07:02,623 INFO Started executing jobs locally\n", + "2025-02-07 21:07:02,627 INFO Starting job - structure_to_conventional (ee0c83c8-d4f0-4d8d-8773-c6b5a9e126e4)\n", + "2025-02-07 21:07:02,639 INFO Finished job - structure_to_conventional (ee0c83c8-d4f0-4d8d-8773-c6b5a9e126e4)\n", + "2025-02-07 21:07:02,643 INFO Starting job - Force field relax (993e9aa0-14d0-4066-b8e3-316499801914)\n" + ] + }, + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'torch'", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[19], line 11\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01matomate2\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mforcefields\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mflows\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mphonons\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PhononMaker\n\u001B[1;32m 2\u001B[0m flow\u001B[38;5;241m=\u001B[39mPhononMaker(\n\u001B[1;32m 3\u001B[0m min_length\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3.0\u001B[39m,\n\u001B[1;32m 4\u001B[0m born_maker\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 9\u001B[0m generate_frequencies_eigenvectors_kwargs\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtstep\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;241m100\u001B[39m},\n\u001B[1;32m 10\u001B[0m )\u001B[38;5;241m.\u001B[39mmake(si_structure)\n\u001B[0;32m---> 11\u001B[0m run_locally(flow, store\u001B[38;5;241m=\u001B[39mjob_store,create_folders\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, raise_immediately\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:180\u001B[0m, in \u001B[0;36mrun_locally\u001B[0;34m(flow, log, store, create_folders, root_dir, ensure_success, allow_external_references, raise_immediately)\u001B[0m\n\u001B[1;32m 177\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m encountered_bad_response\n\u001B[1;32m 179\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarted executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 180\u001B[0m finished_successfully \u001B[38;5;241m=\u001B[39m _run(flow)\n\u001B[1;32m 181\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFinished executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 183\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ensure_success \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m finished_successfully:\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:169\u001B[0m, in \u001B[0;36mrun_locally.._run\u001B[0;34m(root_flow)\u001B[0m\n\u001B[1;32m 167\u001B[0m job_dir \u001B[38;5;241m=\u001B[39m _get_job_dir()\n\u001B[1;32m 168\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m cd(job_dir):\n\u001B[0;32m--> 169\u001B[0m response, jobflow_stopped \u001B[38;5;241m=\u001B[39m _run_job(job, parents)\n\u001B[1;32m 171\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 172\u001B[0m response\u001B[38;5;241m.\u001B[39mjob_dir \u001B[38;5;241m=\u001B[39m job_dir\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:114\u001B[0m, in \u001B[0;36mrun_locally.._run_job\u001B[0;34m(job, parents)\u001B[0m\n\u001B[1;32m 111\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m 113\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m raise_immediately:\n\u001B[0;32m--> 114\u001B[0m response \u001B[38;5;241m=\u001B[39m job\u001B[38;5;241m.\u001B[39mrun(store\u001B[38;5;241m=\u001B[39mstore)\n\u001B[1;32m 115\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604\u001B[0m, in \u001B[0;36mJob.run\u001B[0;34m(self, store, job_dir)\u001B[0m\n\u001B[1;32m 601\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m bound \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(bound, types\u001B[38;5;241m.\u001B[39mModuleType):\n\u001B[1;32m 602\u001B[0m function \u001B[38;5;241m=\u001B[39m types\u001B[38;5;241m.\u001B[39mMethodType(function, bound)\n\u001B[0;32m--> 604\u001B[0m response \u001B[38;5;241m=\u001B[39m function(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_args, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_kwargs)\n\u001B[1;32m 605\u001B[0m response \u001B[38;5;241m=\u001B[39m Response\u001B[38;5;241m.\u001B[39mfrom_job_returns(\n\u001B[1;32m 606\u001B[0m response, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moutput_schema, job_dir\u001B[38;5;241m=\u001B[39mjob_dir\n\u001B[1;32m 607\u001B[0m )\n\u001B[1;32m 609\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response\u001B[38;5;241m.\u001B[39mreplace \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/forcefields/jobs.py:158\u001B[0m, in \u001B[0;36mForceFieldRelaxMaker.make\u001B[0;34m(self, structure, prev_dir)\u001B[0m\n\u001B[1;32m 143\u001B[0m \u001B[38;5;129m@forcefield_job\u001B[39m\n\u001B[1;32m 144\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmake\u001B[39m(\n\u001B[1;32m 145\u001B[0m \u001B[38;5;28mself\u001B[39m, structure: Structure, prev_dir: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m|\u001B[39m Path \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 146\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m ForceFieldTaskDocument:\n\u001B[1;32m 147\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 148\u001B[0m \u001B[38;5;124;03m Perform a relaxation of a structure using a force field.\u001B[39;00m\n\u001B[1;32m 149\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 156\u001B[0m \u001B[38;5;124;03m added to match the method signature of other makers.\u001B[39;00m\n\u001B[1;32m 157\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 158\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m revert_default_dtype():\n\u001B[1;32m 159\u001B[0m ase_result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrun_ase(structure, prev_dir\u001B[38;5;241m=\u001B[39mprev_dir)\n\u001B[1;32m 161\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtask_document_kwargs) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m:\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/contextlib.py:137\u001B[0m, in \u001B[0;36m_GeneratorContextManager.__enter__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 135\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mkwds, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunc\n\u001B[1;32m 136\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 137\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mnext\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgen)\n\u001B[1;32m 138\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m:\n\u001B[1;32m 139\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgenerator didn\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mt yield\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/forcefields/utils.py:117\u001B[0m, in \u001B[0;36mrevert_default_dtype\u001B[0;34m()\u001B[0m\n\u001B[1;32m 108\u001B[0m \u001B[38;5;129m@contextmanager\u001B[39m\n\u001B[1;32m 109\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mrevert_default_dtype\u001B[39m() \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Generator[\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m]:\n\u001B[1;32m 110\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Context manager for torch.default_dtype.\u001B[39;00m\n\u001B[1;32m 111\u001B[0m \n\u001B[1;32m 112\u001B[0m \u001B[38;5;124;03m Reverts it to whatever torch.get_default_dtype() was when entering the context.\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 115\u001B[0m \u001B[38;5;124;03m https://github.com/ACEsuit/mace/issues/328\u001B[39;00m\n\u001B[1;32m 116\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 117\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mtorch\u001B[39;00m\n\u001B[1;32m 119\u001B[0m orig \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mget_default_dtype()\n\u001B[1;32m 120\u001B[0m \u001B[38;5;28;01myield\u001B[39;00m\n", + "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'torch'" + ] + } + ], + "execution_count": 19 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "It is possible to switch to another force field as well!", + "id": "c9b438cee59176b0" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from atomate2.forcefields.jobs import ForceFieldRelaxMaker, ForceFieldStaticMaker\n", + "flow=PhononMaker(\n", + " min_length=3.0,\n", + " bulk_relax_maker=None,\n", + " use_symmetrized_structure=\"conventional\",\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + " bulk_makre=ForceFieldRelaxMaker(force_field_name=\"MACE\"),\n", + " static_energy_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", + " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE\")).make(si_structure)\n", + "\n", + "run_locally(flow, store=job_store,create_folders=True, raise_immediately=True)" + ], + "id": "e85fc4a0867a5a4d", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "id": "ed2c876bf8fa9353", + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From cfa2f73a8a71e300f4ba5666df1ed5a8cc986cef Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:15:50 +0100 Subject: [PATCH 05/61] add tutorial test --- tutorials/phonon_workflow.ipynb | 204 +++++++++++++++++++++----------- 1 file changed, 136 insertions(+), 68 deletions(-) diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index 0dd900b9c0..84a9d5115f 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T20:06:54.620984Z", - "start_time": "2025-02-07T20:06:54.618214Z" + "end_time": "2025-02-07T22:07:53.217643Z", + "start_time": "2025-02-07T22:07:49.995673Z" } }, "cell_type": "code", @@ -18,25 +18,63 @@ "from mock_vasp import TEST_DIR, mock_vasp\n", "\n", "ref_paths = {\n", - " \"phonon static 1/1\": \"Si_phonons_2/phonon_static_1_1\",\n", - " \"static\": \"Si_phonons_2/static\",\n", + " \"phonon static 1/1\": \"Si_phonons_3/phonon_static_1_1\",\n", + " \"static\": \"Si_phonons_3/static\",\n", + " \"tight relax 1\": \"Si_phonons_3/tight_relax_1\",\n", + " \"tight relax 2\": \"Si_phonons_3/tight_relax_2\",\n", + " \"dielectric\": \"Si_phonons_3/dielectric\",\n", " }" ], "id": "d14d39451aac0e35", - "outputs": [], - "execution_count": 12 + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Phonon Workflow", + "id": "cf25f03bbedfa1c5" + }, + { + "metadata": {}, + "cell_type": "raw", + "source": "This tutorial has been written based on a previous version from Aakash Naik.", + "id": "a0d30838dfd4147f" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Background\n", + "The Phonon workflow is based on the finite displacement approach as implemented in Phonopy.\n", + "\n", + "If you want to read more about Phonopy, please read Togo’s paper: https://doi.org/10.7566/JPSJ.92.012001" + ], + "id": "c0224ddf46cf7a38" }, { "metadata": {}, "cell_type": "markdown", - "source": "Now, we load a structure and other important functions and classes for running the phonon workflow.", + "source": [ + "## Let's run the workflow\n", + "Now, we load a structure and other important functions and classes for running the phonon workflow." + ], "id": "49d7cc42166b990f" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T20:06:54.671783Z", - "start_time": "2025-02-07T20:06:54.664068Z" + "end_time": "2025-02-07T22:07:53.663722Z", + "start_time": "2025-02-07T22:07:53.222550Z" } }, "cell_type": "code", @@ -51,37 +89,35 @@ ], "id": "17de20060b45220a", "outputs": [], - "execution_count": 13 + "execution_count": 2 }, { "metadata": {}, "cell_type": "markdown", - "source": "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction by considering `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_relax_maker`. This is not done here for simplicity.", + "source": "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction considering very long-ranging forces by computing the `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_relax_maker`. Please make sure this is done very accurately.", "id": "7e042abdd5362b80" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T20:06:54.820564Z", - "start_time": "2025-02-07T20:06:54.713475Z" + "end_time": "2025-02-07T22:07:53.983047Z", + "start_time": "2025-02-07T22:07:53.706240Z" } }, "cell_type": "code", "source": [ "flow=PhononMaker(\n", " min_length=3.0,\n", - " bulk_relax_maker=None,\n", - " born_maker=None,\n", - " use_symmetrized_structure=\"conventional\",\n", - " create_thermal_displacements=False,\n", - " store_force_constants=False,\n", - " prefer_90_degrees=False,\n", + " use_symmetrized_structure=None,\n", " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + " create_thermal_displacements=True,\n", + " store_force_constants=True,\n", + " born_maker=None,\n", " ).make(si_structure)" ], "id": "94e80cd2cebc9183", "outputs": [], - "execution_count": 14 + "execution_count": 3 }, { "metadata": {}, @@ -92,8 +128,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T20:06:54.957758Z", - "start_time": "2025-02-07T20:06:54.827434Z" + "end_time": "2025-02-07T22:07:54.148887Z", + "start_time": "2025-02-07T22:07:53.988417Z" } }, "cell_type": "code", @@ -105,13 +141,13 @@ "text/plain": [ "
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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 15 + "execution_count": 4 }, { "metadata": {}, @@ -122,8 +158,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T20:07:02.177364Z", - "start_time": "2025-02-07T20:06:54.964006Z" + "end_time": "2025-02-07T22:08:05.806535Z", + "start_time": "2025-02-07T22:07:54.154496Z" } }, "cell_type": "code", @@ -140,17 +176,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 21:06:54,971 INFO Started executing jobs locally\n", - "2025-02-07 21:06:54,974 INFO Starting job - structure_to_conventional (e580b1ee-85ab-48a6-b816-a3b9a1019fb0)\n", - "2025-02-07 21:06:54,986 INFO Finished job - structure_to_conventional (e580b1ee-85ab-48a6-b816-a3b9a1019fb0)\n", - "2025-02-07 21:06:54,987 INFO Starting job - get_supercell_size (d14a6d0f-618d-4c14-9167-98b332061252)\n", - "2025-02-07 21:06:54,990 INFO Finished job - get_supercell_size (d14a6d0f-618d-4c14-9167-98b332061252)\n", - "2025-02-07 21:06:54,991 INFO Starting job - static (1cb5d5de-3168-4a94-915c-e177c5b482f1)\n", - "2025-02-07 21:06:55,178 INFO Finished job - static (1cb5d5de-3168-4a94-915c-e177c5b482f1)\n", - "2025-02-07 21:06:55,179 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 21:06:55,180 INFO Starting job - generate_phonon_displacements (ddc72302-3d27-4a16-91ca-9e6430f81c1a)\n", - "2025-02-07 21:06:55,229 INFO Finished job - generate_phonon_displacements (ddc72302-3d27-4a16-91ca-9e6430f81c1a)\n", - "2025-02-07 21:06:55,229 INFO Starting job - run_phonon_displacements (90815b96-01b1-4a4b-beb5-3bef9aa0242f)\n" + "2025-02-07 23:07:54,165 INFO Started executing jobs locally\n", + "2025-02-07 23:07:54,170 INFO Starting job - tight relax 1 (10bbe269-f138-4ec5-ac16-c8b36c49c2c0)\n" ] }, { @@ -159,24 +186,65 @@ "text": [ "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n" + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-07 23:07:55,243 INFO Finished job - tight relax 1 (10bbe269-f138-4ec5-ac16-c8b36c49c2c0)\n", + "2025-02-07 23:07:55,244 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 23:07:55,245 INFO Starting job - tight relax 2 (9e4af4a2-84aa-44e6-bf6f-65ccab84dc9b)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmp3s_tphxi/job_2025-02-07-22-07-55-245159-41630/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 21:06:55,336 INFO Finished job - run_phonon_displacements (90815b96-01b1-4a4b-beb5-3bef9aa0242f)\n", - "2025-02-07 21:06:55,339 INFO Starting job - phonon static 1/1 (a2f4b416-548e-4671-b5ea-fe93d999d489)\n" + "2025-02-07 23:07:56,685 INFO Finished job - tight relax 2 (9e4af4a2-84aa-44e6-bf6f-65ccab84dc9b)\n", + "2025-02-07 23:07:56,685 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 23:07:56,686 INFO Starting job - get_supercell_size (44572fcd-84e9-47f1-a53e-2fa434369f15)\n", + "2025-02-07 23:07:56,698 INFO Finished job - get_supercell_size (44572fcd-84e9-47f1-a53e-2fa434369f15)\n", + "2025-02-07 23:07:56,699 INFO Starting job - static (204ab95a-b7ae-4990-9499-3d66bfc5037e)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmpl354ftgh/job_2025-02-07-20-06-55-339042-56749/POTCAR.spec is not gzipped, skipping...\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmp3s_tphxi/job_2025-02-07-22-07-56-699384-12259/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-07 23:07:57,446 INFO Finished job - static (204ab95a-b7ae-4990-9499-3d66bfc5037e)\n", + "2025-02-07 23:07:57,447 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 23:07:57,447 INFO Starting job - generate_phonon_displacements (588f56ac-2b21-481c-89e2-ad0fdd26a1cf)\n", + "2025-02-07 23:07:57,505 INFO Finished job - generate_phonon_displacements (588f56ac-2b21-481c-89e2-ad0fdd26a1cf)\n", + "2025-02-07 23:07:57,506 INFO Starting job - run_phonon_displacements (beeac6d5-8734-4dcf-a103-ab091dd1177c)\n", + "2025-02-07 23:07:57,618 INFO Finished job - run_phonon_displacements (beeac6d5-8734-4dcf-a103-ab091dd1177c)\n", + "2025-02-07 23:07:57,621 INFO Starting job - phonon static 1/1 (b673cf1a-5754-42e6-8471-d1c292300ece)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmp3s_tphxi/job_2025-02-07-22-07-57-621005-18878/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -184,11 +252,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 21:06:56,219 INFO Finished job - phonon static 1/1 (a2f4b416-548e-4671-b5ea-fe93d999d489)\n", - "2025-02-07 21:06:56,220 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 21:06:56,221 INFO Starting job - store_inputs (90815b96-01b1-4a4b-beb5-3bef9aa0242f, 2)\n", - "2025-02-07 21:06:56,222 INFO Finished job - store_inputs (90815b96-01b1-4a4b-beb5-3bef9aa0242f, 2)\n", - "2025-02-07 21:06:56,223 INFO Starting job - generate_frequencies_eigenvectors (b6a6c784-b333-47ea-ab91-74b46373cfb9)\n" + "2025-02-07 23:07:58,553 INFO Finished job - phonon static 1/1 (b673cf1a-5754-42e6-8471-d1c292300ece)\n", + "2025-02-07 23:07:58,554 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 23:07:58,556 INFO Starting job - store_inputs (beeac6d5-8734-4dcf-a103-ab091dd1177c, 2)\n", + "2025-02-07 23:07:58,558 INFO Finished job - store_inputs (beeac6d5-8734-4dcf-a103-ab091dd1177c, 2)\n", + "2025-02-07 23:07:58,559 INFO Starting job - generate_frequencies_eigenvectors (da446f69-6549-4de8-bc22-bbe31c13005f)\n" ] }, { @@ -216,18 +284,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 21:07:02,169 INFO Finished job - generate_frequencies_eigenvectors (b6a6c784-b333-47ea-ab91-74b46373cfb9)\n", - "2025-02-07 21:07:02,170 INFO Finished executing jobs locally\n" + "2025-02-07 23:08:05,797 INFO Finished job - generate_frequencies_eigenvectors (da446f69-6549-4de8-bc22-bbe31c13005f)\n", + "2025-02-07 23:08:05,798 INFO Finished executing jobs locally\n" ] } ], - "execution_count": 16 + "execution_count": 5 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T20:07:02.190300Z", - "start_time": "2025-02-07T20:07:02.184401Z" + "end_time": "2025-02-07T22:08:05.822233Z", + "start_time": "2025-02-07T22:08:05.817549Z" } }, "cell_type": "code", @@ -251,13 +319,13 @@ ], "id": "1bea502d9378bf28", "outputs": [], - "execution_count": 17 + "execution_count": 6 }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T20:07:02.532206Z", - "start_time": "2025-02-07T20:07:02.228693Z" + "end_time": "2025-02-07T22:08:06.173641Z", + "start_time": "2025-02-07T22:08:05.865372Z" } }, "cell_type": "code", @@ -282,7 +350,7 @@ "" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -291,7 +359,7 @@ "text/plain": [ "
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" 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" 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" 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" }, "metadata": {}, "output_type": "display_data" } ], - "execution_count": 18 + "execution_count": 7 }, { "metadata": {}, @@ -318,8 +386,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T20:07:02.842750Z", - "start_time": "2025-02-07T20:07:02.538799Z" + "end_time": "2025-02-07T22:08:06.933660Z", + "start_time": "2025-02-07T22:08:06.180727Z" } }, "cell_type": "code", @@ -342,10 +410,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 21:07:02,623 INFO Started executing jobs locally\n", - "2025-02-07 21:07:02,627 INFO Starting job - structure_to_conventional (ee0c83c8-d4f0-4d8d-8773-c6b5a9e126e4)\n", - "2025-02-07 21:07:02,639 INFO Finished job - structure_to_conventional (ee0c83c8-d4f0-4d8d-8773-c6b5a9e126e4)\n", - "2025-02-07 21:07:02,643 INFO Starting job - Force field relax (993e9aa0-14d0-4066-b8e3-316499801914)\n" + "2025-02-07 23:08:06,340 INFO Started executing jobs locally\n", + "2025-02-07 23:08:06,345 INFO Starting job - structure_to_conventional (82644e48-8b91-40f2-be39-6b120242d320)\n", + "2025-02-07 23:08:06,365 INFO Finished job - structure_to_conventional (82644e48-8b91-40f2-be39-6b120242d320)\n", + "2025-02-07 23:08:06,369 INFO Starting job - Force field relax (62f866d7-c139-4b63-b789-eada2f451a71)\n" ] }, { @@ -355,7 +423,7 @@ "traceback": [ "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[19], line 11\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01matomate2\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mforcefields\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mflows\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mphonons\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PhononMaker\n\u001B[1;32m 2\u001B[0m flow\u001B[38;5;241m=\u001B[39mPhononMaker(\n\u001B[1;32m 3\u001B[0m min_length\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3.0\u001B[39m,\n\u001B[1;32m 4\u001B[0m born_maker\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 9\u001B[0m generate_frequencies_eigenvectors_kwargs\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtstep\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;241m100\u001B[39m},\n\u001B[1;32m 10\u001B[0m )\u001B[38;5;241m.\u001B[39mmake(si_structure)\n\u001B[0;32m---> 11\u001B[0m run_locally(flow, store\u001B[38;5;241m=\u001B[39mjob_store,create_folders\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, raise_immediately\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", + "Cell \u001B[0;32mIn[8], line 11\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01matomate2\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mforcefields\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mflows\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mphonons\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PhononMaker\n\u001B[1;32m 2\u001B[0m flow\u001B[38;5;241m=\u001B[39mPhononMaker(\n\u001B[1;32m 3\u001B[0m min_length\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3.0\u001B[39m,\n\u001B[1;32m 4\u001B[0m born_maker\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 9\u001B[0m generate_frequencies_eigenvectors_kwargs\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtstep\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;241m100\u001B[39m},\n\u001B[1;32m 10\u001B[0m )\u001B[38;5;241m.\u001B[39mmake(si_structure)\n\u001B[0;32m---> 11\u001B[0m run_locally(flow, store\u001B[38;5;241m=\u001B[39mjob_store,create_folders\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, raise_immediately\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:180\u001B[0m, in \u001B[0;36mrun_locally\u001B[0;34m(flow, log, store, create_folders, root_dir, ensure_success, allow_external_references, raise_immediately)\u001B[0m\n\u001B[1;32m 177\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m encountered_bad_response\n\u001B[1;32m 179\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarted executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 180\u001B[0m finished_successfully \u001B[38;5;241m=\u001B[39m _run(flow)\n\u001B[1;32m 181\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFinished executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 183\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ensure_success \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m finished_successfully:\n", "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:169\u001B[0m, in \u001B[0;36mrun_locally.._run\u001B[0;34m(root_flow)\u001B[0m\n\u001B[1;32m 167\u001B[0m job_dir \u001B[38;5;241m=\u001B[39m _get_job_dir()\n\u001B[1;32m 168\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m cd(job_dir):\n\u001B[0;32m--> 169\u001B[0m response, jobflow_stopped \u001B[38;5;241m=\u001B[39m _run_job(job, parents)\n\u001B[1;32m 171\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 172\u001B[0m response\u001B[38;5;241m.\u001B[39mjob_dir \u001B[38;5;241m=\u001B[39m job_dir\n", "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:114\u001B[0m, in \u001B[0;36mrun_locally.._run_job\u001B[0;34m(job, parents)\u001B[0m\n\u001B[1;32m 111\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m 113\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m raise_immediately:\n\u001B[0;32m--> 114\u001B[0m response \u001B[38;5;241m=\u001B[39m job\u001B[38;5;241m.\u001B[39mrun(store\u001B[38;5;241m=\u001B[39mstore)\n\u001B[1;32m 115\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n", @@ -367,7 +435,7 @@ ] } ], - "execution_count": 19 + "execution_count": 8 }, { "metadata": {}, From ffb96fde32646b325ebf293d94f2fd9265c92d4a Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:26:32 +0100 Subject: [PATCH 06/61] test tutorial test --- tutorials/force_fields/__init__.py | 0 tutorials/force_fields/phonon_workflow.ipynb | 79 +++++++++++++ tutorials/phonon_workflow.ipynb | 112 +++++++++---------- 3 files changed, 129 insertions(+), 62 deletions(-) create mode 100644 tutorials/force_fields/__init__.py create mode 100644 tutorials/force_fields/phonon_workflow.ipynb diff --git a/tutorials/force_fields/__init__.py b/tutorials/force_fields/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tutorials/force_fields/phonon_workflow.ipynb b/tutorials/force_fields/phonon_workflow.ipynb new file mode 100644 index 0000000000..17578e62d4 --- /dev/null +++ b/tutorials/force_fields/phonon_workflow.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "TEST_ROOT = Path(__file__).parent.parent / \"tests\"\n", + "TEST_DIR = TEST_ROOT / \"test_data\"" + ] + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from pymatgen.core.structure import Structure\n", + "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")" + ], + "id": "59d1925b6558c9bb" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "from atomate2.forcefields.flows.phonons import PhononMaker\n", + "from jobflow import run_locally\n", + "flow=PhononMaker(\n", + " min_length=3.0,\n", + " born_maker=None,\n", + " use_symmetrized_structure=\"conventional\",\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + " ).make(si_structure)\n", + "run_locally(flow, store=job_store,create_folders=True, raise_immediately=True)" + ], + "id": "83e78705d60d7c9b" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "bf10b23db3655278" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index 84a9d5115f..44c954f77b 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T22:07:53.217643Z", - "start_time": "2025-02-07T22:07:49.995673Z" + "end_time": "2025-02-07T22:16:39.898651Z", + "start_time": "2025-02-07T22:16:36.708061Z" } }, "cell_type": "code", @@ -73,8 +73,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T22:07:53.663722Z", - "start_time": "2025-02-07T22:07:53.222550Z" + "end_time": "2025-02-07T22:16:40.294922Z", + "start_time": "2025-02-07T22:16:39.902796Z" } }, "cell_type": "code", @@ -100,8 +100,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T22:07:53.983047Z", - "start_time": "2025-02-07T22:07:53.706240Z" + "end_time": "2025-02-07T22:16:40.617378Z", + "start_time": "2025-02-07T22:16:40.335466Z" } }, "cell_type": "code", @@ -128,8 +128,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T22:07:54.148887Z", - "start_time": "2025-02-07T22:07:53.988417Z" + "end_time": "2025-02-07T22:16:40.782878Z", + "start_time": "2025-02-07T22:16:40.623327Z" } }, "cell_type": "code", @@ -141,7 +141,7 @@ "text/plain": [ "
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NTEnB19SVlTP0k+7BgwffPC9fv65vGTZsGAIDA9G2bVuUK1cO9vb2cHZ2hoODQ6Z9v/586ujooEyZMvLLmr/H19cXixcvxs2bN5Gamirf/uVn7u7duyhbtixKlCjx3XZy4+ekePHiqFChQqZtwP9/Ln7lc/uzz9qP/PXXXwgLC0Pjxo1hYmICe3t79O7dG82aNfvpsURE9HMsbImI8olMJoNEIsHBgweh/F/x8iUdHZ0Mj7+1DwD5fN30NuvWrYslS5Z8c9+v/7j/cmQ23YkTJ9CxY0e0aNECq1evRpkyZaCqqgpvb+8s3dhGJpMBAFxcXNCvX79v7lOvXr2ftvOrsnKecrO99LsNZ6f9rwtgeZvf2JYslWboJzcYGhoiKioKhw4dwsGDB3Hw4EF4e3ujb9++8PX1zXH7mzdvhqurKzp37owJEybA0NAQysrKmDt3rnyEO6ty6+fkZ+/jr3xuc/JZq1WrFm7duoV9+/YhJCQE27dvx+rVqzF16lR4eHj89HgiIvoxFrZERDlkaGgIDQ2NTJeOAsiwzdjYGIIgoEqVKvLRx5wyNjbG5cuX0apVqyyNrn7L9u3boaGhgUOHDmUopry9vTPt+60+DAwMUKxYMUilUtjZ2f1Shq/duXMHgiBk6O/27dsAkOluvz9TqVIlAMCtW7dQtWpV+faUlBTExsZmO3P6KPzzpCTIBCFD0fooIQFA5hHa7HgYHw+JRJLpCoBKlSrh2rVrmc5LVterVVNTg6OjIxwdHSGTyTBs2DCsWbMGU6ZMyXRlga2trfxxYmIinj17hnbt2n237eDgYFStWhU7duzIkO3rS46NjY1x6NAhvH379rujtnnxc/ItefG5BX58lYO2tjZ69OiBHj16ICUlBV27dsXs2bMxceJEaGho5FoGIqKiiHNsiYhySFlZGXZ2dti1axeePn0q337nzh0cPHhQ/rhr165QVlaGh4dHphEeQRDw5s2bbPft7OyMJ0+eYN26dZme+/jxIz58Yw7nt/JLJBJI/xspBD5f7vvlHZXTaWtrZ1oiR1lZGd26dcP27dtx7dq1TMd8a+mdn3n69Cl27twpfxwfHw8/Pz+YmZlluAw5K+zs7KCmpobly5dnOO8bNmzA+/fv5Xd9ziodHR0YliqFpNRUHP9i+RmpTIa9MTHQVFFB3e9cGp4VMXFxqFGtWqaRyXbt2uHp06cZlmFKSkr67mW0X/r6s6WkpCQfjfz6kue1a9dmuJTY09MTaWlpGeaLfy19JPPL83v27Fn8+++/Gfbr1q0bBEH45ghl+rF58XPyvcy5/bkFPv+MvH//PtP2r3OrqanB1NQUgiBkON9ERPRrOGJLRJQLpk+fjtDQUDRr1gxDhw6FVCrFypUrUadOHURFRQH4PBI1a9YsTJw4Effv30fnzp1RrFgxxMbGYufOnRg8eDDGjx+frX779OmDwMBAuLu7Izw8HM2aNYNUKsXNmzcRGBiIQ4cOwcLC4odttG/fHkuWLIGDgwN69+6Nly9fYtWqVTAxMckwnxcAGjZsiLCwMCxZsgRly5ZFlSpVYGlpiXnz5iE8PByWlpZwc3ODqakp3r59i0uXLiEsLAxvv7Fm649Ur14dAwcOxPnz51G6dGls3LgRL168+OYo8s8YGBhg4sSJ8PDwgIODAzp27Ihbt25h9erVaNSoUYYbRWVVNycnrPHywpLz53EnLg6ltbVx8vFjRL95g8FmZtD6ah5oVqVKpTj97BlcBg7M9JybmxtWrlyJvn374uLFiyhTpgw2bdoErSyMDg8aNAhv375Fy5YtUb58eTx48AArVqyAmZkZatWqlWHflJQUtGrVCs7OzvLz1Lx5c3Ts2PG77Xfo0AE7duxAly5d0L59e8TGxsLLywumpqZITEyU72dra4s+ffpg+fLliImJgYODA2QyGU6cOAFbW1uMGDEiT35Ovie3P7fA55+RgIAAjB07Fo0aNYKOjg4cHR1hb28PIyMjNGvWDKVLl8aNGzewcuVKtG/fXr78EBER5UB+3X6ZiKiwO3LkiGBubi6oqakJxsbGwvr164Vx48YJGhoaGfbbvn270Lx5c0FbW1vQ1tYWatasKQwfPly4deuWfB9ra+tvLg3y9TIugvB5aZ758+cLtWvXFtTV1QV9fX2hYcOGgoeHh/D+/Xv5fgCE4cOHfzP7hg0bhGrVqgnq6upCzZo1BW9vb2HatGnC178mbt68KbRo0ULQ1NQUAGRY+ufFixfC8OHDhQoVKgiqqqqCkZGR0KpVK2Ht2rVZPYWCIHxe7qd9+/bCoUOHhHr16skzBQUFZdgvfXma8+fPZ9ievjTLl8vfCMLn5X1q1qwpqKqqCqVLlxaGDh0qxMXFZdgnq+f9+vXrAgChjoGBoKumJqgoKQmVixcX/mjUKMPSPenL/QysVy/Tsj4AhN6mphm2TbC0FAAI0dHR3zw3Dx48EDp27ChoaWkJpUqVEkaPHi2EhIT8dLmf4OBgwd7eXjA0NBTU1NSEihUrCkOGDBGePXuW6XweO3ZMGDx4sKCvry/o6OgIv//+u/DmzZtM5+nL5X5kMpkwZ84coVKlSoK6urpgbm4u7Nu375uf17S0NGHhwoVCzZo1BTU1NcHAwEBo27atcPHixQz75eTnJP0z9LVv/Qxk5XOb/pn6+jMYGxubaQmhxMREoXfv3oKenp4AQP7616xZI7Ro0UIoWbKkoK6uLhgbGwsTJkzI8DNKRES/TiIIv3h3DSIi+qnOnTvj+vXriImJETuKwqhcuTLq1KmDffv2iR3lhxzatEHUv/9iVatWvzxC+6Wk1FQMP3IEZk2aIOTQoVxImD0+Pj7o378/zp8//9NRfiIiooKGc2yJiHLJx48fMzyOiYnBgQMHYGNjI04gylOeXl5ITEvDusuXc9yWIAhYd/kyEtPS4OnllQvpiIiIihbOsSUiyiVVq1aFq6urfG1UT09PqKmp4c8//xQ7WoHw6tWrDDeo+pqamtoP1zctaKpUqYJly5fDzc0Nhlpa6GVq+kvtpEil2HTtGg7FxmLx4sXQ1NTE8+fPM+xTvHjxby7VRERERJ+xsCUiyiUODg7w9/fH8+fPoa6ujiZNmmDOnDmoVq2a2NEKhEaNGuHBgwfffd7a2hoRERH5FygXDBo0CC9evMDkyZPxMikJbvXrZ+uy5KTUVMw/cwbn/ytkx40bh3HjxmXaz9vbG66urrkVm4iIqNDhHFsiIsoXp06dynS59pf09fXRsGHDfEyUczKZDGvWrMHbt28xd/Zs6KiooK+pKZqXLw9Vpe/P9kmVSnHyyRP4RUfjfUoKmjZrhr/++gtK3zmmdu3aKFOmTF69DCIiIoXHwpaIiCibZDIZgoOD8eeff+LBgwfo1asXZs+ejaHu7jgUGgp9LS00LVMG1fT1UVFXF+rKykiWSvEwPh4xcXE4/ewZ4pKS0MbeHmrq6ti7dy8qVaqEhQsXolu3bt8tcImIiOjbWNgSERFlkVQqRVBQEKZNm4bbt2/Lt+/cuROdO3cGAERHR8PLywuHDx3CrZgYfPlrViKRoEa1amjdpg2GDh2KWrVqYceOHejWrZt8nxo1asDDwwPdu3eHsrJyvr02IiIiRcbCloiIKAtiYmLQvn17xMTEQElJCTKZTP7cw4cPUaFChUzHJCYmon379pDJZFixYgVMTEygo6OTYZ+HDx+iUqVK8sfpbVerVg379+/nHG0iIqIs4M2jiIiIsiA+Ph6PHj3KVNQWK1YM5cuX/+YxOjo6KF68OADAzMzsm/tUqFABxYoVQ0JCAoDPlzkrKSnh0aNHSExMzN0XQUREVEhxEg8REVEWNGzYEBcvXkTZsmUzbDc3N4dEIvnldiUSCerXr59hW7ly5RAZGQlzc/NfbpeIiKgoYWFLRESURSVLlkRqaipKly4t39agQYMct/vl3aCNjIyQmpqqUGv6EhERiY2FLRERURZIpVK4uLgAACIjI7Fx40ZoamrC1tY2x223bNkSmpqa8Pb2xqVLlyAIAlxcXCCVSnPcNhERUVHAm0cRERFlgYeHBzw8PHD48GG0atUKwOdi92d3Lu7YsSMAYM+ePT/c78u2jhw5gtatW8PDwwNTpkzJhfRERESFG0dsiYiIfuLIkSPw8PDA9OnT5UUtgFxdjufLtlq1aoVp06Zh2rRpOHr0aK71QUREVFhxxJaIiOgHnj17BjMzM9SrVw8hISHZLmazOmL7NalUCgcHB1y9ehVRUVEwMjLK1vFERERFCUdsiYiIviMtLQ29evWCsrIytmzZkqsjtD+T3qeSkhJ69+7N+bZEREQ/wMKWiIjoO6ZPn44TJ07A398fhoaG+d6/oaEh/P39cezYMXh4eOR7/0RERIqChS0REdE3HDp0CHPmzMGsWbNgbW0tWg5ra2vMnDkTs2bNQmhoqGg5iIiICjLOsSUiIvrK48ePYW5ujkaNGmHfvn1QUvr174F/dY7tl2QyGdq3b48LFy4gKioK5cqV++W2iIiICiOO2BIREX0hNTUVvXr1goaGBvz8/HJU1OYWJSUlbNq0CRoaGujVqxfS0tLEjkRERFSgiP/bmoiIqACZPHky/v33X2zbtg2lSpUSO45cqVKlsG3bNpw+fZpr2xIREX2FhS0REdF/9u3bhwULFmDevHlo1qyZ2HEyadasGebOnYt58+Zh//79YschIiIqMDjHloiICMDDhw9hbm6OZs2aYffu3ZBIJLnSbm7Msf2STCZDp06dcPr0aURGRqJixYq50i4REZEi44gtEREVeSkpKejRoweKFSsGHx+fXCtq84KSkhJ8fX2ho6ODnj17IjU1VexIREREomNhS0RERd7//vc/XLx4EQEBAShRooTYcX6qRIkSCAwMxIULFzBx4kSx4xAREYmOhS0RERVpu3btwtKlS7Fw4UJYWlqKHSfLLC0tsWDBAixevBi7d+8WOw4REZGoOMeWiIiKrNjYWJibm6NVq1YIDg7Ok0uQc3uO7ZcEQUC3bt0QHh6OS5cuoUqVKrneBxERkSLgiC0RERVJycnJcHZ2RokSJbBhw4YCPa/2eyQSCTZu3Ah9fX306NEDKSkpYkciIiISBQtbIiIqksaPH48rV64gKCgIenp6Ysf5ZXp6eggKCsLly5cxYcIEseMQERGJgoUtEREVOUFBQVi5ciWWLl2Khg0bih0nxxo2bIglS5Zg+fLlCA4OFjsOERFRvuMcWyIiKlLu3LmDBg0aoF27dvD398/zS5Dzco7tlwRBQI8ePXDo0CFcunQJxsbGedofERFRQcIRWyIiKjI+ffoEJycnGBkZYe3atQo5r/Z7JBIJ1q9fD0NDQzg5OeHTp09iRyIiIso3LGyJiKjIGDNmDG7cuIHAwEDo6uqKHSfX6erqIigoCNHR0Rg7dqzYcYiIiPKNitgBiIiI8oO/vz/WrFmDtWvXwszMLN/67dWrV771BQBmZmZYvnw5hgwZAisrq3zvn4iISAycY0tERIXerVu3YGFhgU6dOmHTpk35eglyWloaAEBFJf++SxYEAS4uLtizZw8uXLiAGjVq5FvfREREYmBhS0REhVpSUhJ+++03pKam4vz589DR0RE7Ur5ITExEo0aNoKamhjNnzkBTU1PsSERERHmGc2yJiKhQGzlyJO7cuYOgoKAiU9QCgI6ODoKCghATE4NRo0aJHYeIiChPsbAlIqJCy8/PDxs3bsTq1atRp04dsePkuzp16mDVqlVYv349Nm3aJHYcIiKiPMNLkYmIqFCKjo5Go0aN4OzsDG9vb7HjiMrV1RVBQUE4f/48TE1NxY5DRESU61jYEhFRofPhwwc0atQISkpKOHfuHLS0tPKt7+nTp2PXrl2IiorK9bZ9fHwwZswYvHv3LlvHffjwAZaWlhAEAefOnYO2tnauZyMiIhITL0UmIqJCRRAEDB06FA8fPkRQUFC+FrUFlba2NoKCgvDgwQMMGzYM/E6biIgKGxa2RERUqGzcuBGbNm2Cl5cXatWqle3jk5OTMWrUKBgaGkJDQwPNmzfH+fPnAXweMdXT08uw/65du+TLB/n4+MDDwwOXL1+GRCKBRCKBj48PAEAikcDT0xNt27aFpqYmqlatiuDgYHk7ERERkEgkGUZjo6KiIJFIcP/+fURERKB///54//69vO3p06dn+XXVqlULXl5e8PPzK/KXZhMRUeHDwpaIiAqNK1euYMSIEXBzc4OLi8svtfHnn39i+/bt8PX1xaVLl2BiYoI2bdrg7du3Pz22R48eGDduHGrXro1nz57h2bNn6NGjh/z5KVOmoFu3brh8+TJ+//139OzZEzdu3MhSrqZNm2LZsmXQ1dWVtz1+/PhsvTYXFxcMGjQIw4cPx9WrV7N1LBERUUHGwpaIiAqFhIQEODk5oUaNGvjnn39+qY0PHz7A09MTCxcuRNu2bWFqaop169ZBU1MTGzZs+Onxmpqa0NHRgYqKCoyMjGBkZJRh/VgnJycMGjQI1atXx8yZM2FhYYEVK1ZkKZuamhqKFy8OiUQib/tXli9avnw5qlevDicnJyQkJGT7eCIiooKIhS0RESk8QRAwePBgPH36FIGBgRmKyey4e/cuUlNT0axZM/k2VVVVNG7cOMsjqz/SpEmTTI9zo93s0NTURFBQEJ48eQJ3d3fOtyUiokKBhS0RESm8NWvWYNu2bVi/fj2qV6+eZ/0oKSllKgRTU1NzrW0AGdrPrba/Vr16daxfvx5bt27F2rVr86QPIiKi/MTCloiIFFpkZCTGjBmDYcOGZZjP+iuMjY2hpqaGU6dOybelpqbK1381MDBAQkICPnz4IH/+62V91NTUIJVKv9n+mTNnMj1Ov8GVgYEBAODZs2e/1HZ29ejRA0OHDsXo0aMRGRmZK20SERGJhYUtEREprPfv38PJyQm1a9fGkiVLctyetrY2hg4digkTJiAkJATR0dFwc3NDUlISBg4cCEtLS2hpaWHSpEm4e/cutm7dKr/rcbrKlSsjNjYWUVFReP36NZKTk+XPBQUFYePGjbh9+zamTZuGc+fOYcSIEQAAExMTVKhQAdOnT0dMTAz279+PxYsXZ2o7MTERR44cwevXr5GUlJSj17tkyRLUrl0bzs7OiI+Pz1FbREREYmJhS0RECkkQBAwcOBCvXr1CYGAg1NXVc6XdefPmoVu3bujTpw8aNGiAO3fu4NChQ9DX10eJEiWwefNmHDhwAHXr1oW/v3+mJXe6desGBwcH2NrawsDAAP7+/vLnPDw8sG3bNtSrVw9+fn7w9/eHqakpgM9zef39/XHz5k3Uq1cP8+fPx6xZszK03bRpU7i7u6NHjx4wMDDAggULcvRaNTQ0EBgYiJcvX2LQoEGcb0tERApLIvC3GBERKaAVK1Zg1KhRCA4ORrdu3cSO81MSiQQ7d+5E586dxY6Syfbt29G9e3esWLFCPoJMRESkSDhiS0RECuf8+fMYN24cRo8erRBFbUHXrVs3jBo1CmPHjsWFCxfEjkNERJRtHLElIiKFEhcXhwYNGsDQ0BAnTpyAmpqa2JGypCCP2AJASkoKrKys8OrVK1y6dAl6enpiRyIiIsoyFrZERKQwBEFAly5dcOzYMURGRqJy5cpiRypU7t+/D3Nzc9jY2GDHjh2QSCRiRyIiIsoSXopMREQKY+nSpdi9ezd8fX1Z1OaBypUrw9fXF7t27cKyZcvEjkNERJRlHLElIiKFcObMGVhZWWHMmDFYuHCh2HEKtfHjx+Off/7BiRMn8Ntvv4kdh4iI6KdY2BIRUYH35s0bmJubo0KFCoiIiICqqqrYkQq11NRUWFtb48mTJ4iMjESJEiXEjkRERPRDvBSZiIgKNJlMhr59+yIpKQnbtm1jUZsPVFVVERAQgA8fPqBfv36QyWRiRyIiIvohFrZERFSgLVy4EAcOHMCmTZtQoUIFseNk24QJEzBhwgSxY2RbhQoVsGnTJuzbtw+LFi0SOw4REdEPsbAlIqIC6+TJk/j7778xceJEtG3bVuw4v+TWrVu4deuW2DF+Sdu2bfG///0PkyZNwsmTJ8WOQ0RE9F2cY0tERAXSq1evYGZmBhMTExw5cgQqKipiR/olHTt2BADs2bNH5CS/Ji0tDS1btsS9e/cQGRkJAwMDsSMRERFlwhFbIiIqcGQyGVxcXJCamgp/f3+FLWoLAxUVFfj7+yMlJQV9+vThfFsiIiqQWNgSEVGBM2fOHBw+fBhbtmxB2bJlxY5T5JUrVw5btmxBaGgo5s6dK3YcIiKiTFjYEhFRgRIeHo5p06ZhypQpaN26tdhx6D+tW7fG5MmTMXXqVERERIgdh4iIKAPOsSUiogLjxYsXMDMzg6mpKUJDQ6GsrCx2pBxT9Dm2X5JKpWjdujVu3LiBqKgolC5dWuxIREREADhiS0REBYRUKkXv3r0hCAK2bNlSKIrawkZZWRlbt26FIAj4/fffIZVKxY5EREQEgIUtEREVEDNmzEBERAT8/f1hZGQkdhz6DiMjI/j7+yM8PBwzZ84UOw4REREAFrZERFQAHD58GDNnzoSHhwdsbW3FjkM/YWtri+nTp2PGjBkICwsTOw4RERHn2BIRkbiePn0KMzMzmJub4+DBg1BSKlzfuRamObZfkkqlaNu2LS5fvoyoqCiUKVNG7EhERFSEFa6/HoiISKGkpaWhV69eUFVVxebNmwtdUVuYKSsrY/PmzVBRUUGvXr2QlpYmdiQiIirC+BcEERGJZurUqTh16hS2bdsGAwMDseNQNhkaGmLbtm04efIkpk2bJnYcIiIqwljYEhGRKA4ePIi5c+di9uzZsLKyEjsO/SIrKyvMmjULc+bMQUhIiNhxiIioiOIcWyIiynePHj2Cubk5LC0tsXfv3kJ9CXJhnWP7JZlMBkdHR5w9exZRUVEoX7682JGIiKiIKbx/SRARUYGUmpqKnj17QktLC35+foW6qC0qlJSU4OfnBy0tLfTs2ROpqaliRyIioiKGf00QEVG+mjRpEs6dO4eAgACULFlS7DiUS0qWLImAgACcPXsWf//9t9hxiIioiGFhS0RE+Wbv3r1YtGgR5s+fjyZNmogdh3JZkyZNMG/ePCxcuBD79u0TOw4RERUhnGNLRET54sGDBzA3N0eLFi2wc+dOSCQSsSPli6Iwx/ZLgiCgc+fOOHHiBCIjI1GpUiWxIxERURHAwpaIiPJcSkoKrKys8PLlS1y6dAn6+vpiR8pT9+/fx8qVKyGVSrF7924AQKdOnaCsrIwRI0agcuXK4gbMY3FxcWjQoAFKly6N48ePQ01NTexIRERUyLGwJSKiPDdmzBisXr0ap06dQqNGjcSOk+d27NiBbt26QVlZGTKZDMDnGyxJpVJs374dXbt2FTlh3jt//jyaNWuG4cOHY+nSpWLHISKiQo6FLRER5an0Im/58uUYOXKk2HHyRVpaGkxMTPDw4UOk/5qVSCSoWLEi7ty5AxUVFZET5o/ly5dj9OjR2LFjB7p06SJ2HCIiKsRY2BIRUZ65d+8eGjRogNatWyMwMLDIzKsFAB8fH/Tv3z/Ttn79+omUKP8JggAnJyeEhYXh0qVLqFq1qtiRiIiokGJhS0REeeLTp09o1qwZ3r9/j4sXL6J48eJiR8pX6aO2Dx48AABUqlSpSI3Wpnv//j0aNGgAfX19nDp1Curq6mJHIiKiQojL/RARUZ4YN24crl27hsDAwCJX1AKAiooKpk+fLn/s4eFR5IpaAChevDiCgoJw9epVjBs3Tuw4RERUSHHEloiIcl1AQAB69uwJT09PuLu7ix1HNGlpafKi/v3790WysE3n6emJYcOGISAgAM7OzmLHISKiQoaFLRER5aqYmBg0bNgQ7du3x9atW4vUvNqvJSYmIjAwEKmpqbC0tISJiQl0dHTEjiUKQRDQq1cvHDhwABcvXkS1atXEjkRERIUIC1siIso1Hz9+RJMmTfDx40dcuHABxYoVEztSvouOjoaXlxdCQ0Jw+84dfPlrViKRoLqJCewdHODu7g5TU1MRk+a/hIQEWFhYQEtLC6dPn4ampqbYkYiIqJBgYUtERLlm8ODB2LRpE86ePYt69eqJHSdfxcbGYqi7Ow6FhkJfSwtNy5RBNX19VNTVhbqKCpLT0vAwPh4xcXE4/ewZ4pKS0MbeHp5eXqhSpYrY8fPNlStXYGlpib59+2LNmjVixyEiokKChS0REeWKLVu2wMXFBevXr8fAgQPFjpOv1q9fjzGjRkFHRQV9TU3RvHx5qCp9//6MqTIZTj5+DL/oaCSmpWHZ8uUYNGhQPiYW1/r16+Hm5oYtW7agd+/eYschIqJCgHdFJiKiHLt58yaGDBmCPn36YMCAAfLtNjY2qFOnjojJ8t7s2bPh5uaG5kZGWNWqFWwrVsxU1C45dw6u+/fLH6sqKWHh2bNoUbYsmhsZwc3NDbNnz861TPfv34dEIoGPj0+utZmbBg4cCBcXFwwePBg3b94UOw4RERUCLGyJiChHkpKS0L17d1SsWBGenp5F6mZR69evx+TJk9Gndm2MtrCAlqpqto5XVVbGaAsLuNSujcmTJ2PDhg15lLRgkUgk8PT0RMWKFeHk5ISkpKR86zspKQnTp09HREREvvVJRER5j4UtERHlyPDhwxEbG4ugoCBoa2uLHSffxMbGYsyoUWhTpQp6/cJNoHZ17YpetWoBAHrVqoU2Vapg9MiRiI2Nze2oBZKOjg6CgoJw7949jBgxIt/6TUpKgoeHBwtbIqJChoUtEZGC+vDhg9gR4OPjAx8fH6xevRq1a9cWO06+GuruDh0VFbjVr/9Lx6spK0P5v0uWJRIJ3OrXh46KCoYWoXV/a9eujdWrV8Pb2xu+vr5ixyEiIgXGwpaISAFMnz4dEokE0dHR6N27N/T19dG8eXPY2NjAxsYm0/6urq6oXLmy/HH6nMtFixZh7dq1MDY2hrq6Oho1aoTz589nK0tERIS8LTc3N2hqamLYsGHo2LEjHj169M1joqOjYWtrCy0tLZQrVw4LFizItM/Lly8xcOBAlC5dGhoaGqhfv36mYie7r+Po0aOwsrKCtrY29PT00KlTJ9y4cSPDPunn9s6dO3B1dYWenh6KFy+O/v37f/cS2ejoaBwKDUVfU9NMlx+ffvIEQw8dQqft2zH00CGcfvLkm220CwrC5uvXM2wrr62NQ6GhUFNTg6GhIVq3bo1Lly7Jn0+fs3zx4kU0bdoUmpqaqFKlCry8vL7Zx5euXLkCV1dXVK1aFRoaGjAyMsKAAQPw5s2bTPs+efIEAwcORNmyZaGuro4qVapg6NChSElJke/z7t07jBkzBhUqVIC6ujpMTEwwf/58yGQy+T5fvl+rVq1C1apVoaWlBXt7ezx69AiCIODhw4fQ0tKCq6srWrZsibdv32bKc/DgQfn7WKxYMbRv3x7Xvzp3rq6u0NHRwZMnT9C5c2fo6OjAwMAA48ePh1QqlecxMDAAAHh4eEAikUAikWD69OkAgOfPn6N///4oX7481NXVUaZMGXTq1An379//6fklIiJxqYgdgIiIss7JyQnVqlXDnDlzIAgCAgMDs3X81q1bkZCQgCFDhkAikWDBggXo2rUr7t27B9Vszg+dPHkyVFRUMG3aNLx79w7Lli2DnZ0doqKiMqxPGhcXBwcHB3Tt2hXOzs4IDg7GX3/9hbp166Jt27YAPq9/a2Njgzt37mDEiBGoUqUKgoKC4Orqinfv3mH06NHZfh1hYWFo27YtqlatiunTp+Pjx49YsWIFmjVrhkuXLmUo/AHA2dkZVapUwdy5c3Hp0iWsX78ehoaGmD9/fqbX7uXlBX0tLTQvXz7D9kvPn2PO6dOooKsL17p1EZ+SgqXnz6NUFtZrXXnpEi6/fAl1FRU0s7JCmzZtcPLkSdy4cQMNGjTIcD7btWsHZ2dn9OrVC4GBgRg6dCjU1NQy3Ljra4cPH8a9e/fQv39/GBkZ4fr161i7di2uX7+OM2fOyOdGP336FI0bN8a7d+8wePBg1KxZE0+ePEFwcDCSkpKgpqaGpKQkWFtb48mTJxgyZAgqVqyI06dPY+LEiXj27BmWLVuWoe8tW7YgJSUFI0eOxNu3b7FgwQI4OzujZcuWiIiIwIwZMzB37lyEh4dj9OjR2LRpk/zYTZs2oV+/fmjTpg3mz5+PpKQkeHp6onnz5oiMjMzwPkqlUrRp0waWlpZYtGgRwsLCsHjxYhgbG2Po0KEwMDCAp6cnhg4dii5duqBr164AIF+aqlu3brh+/TpGjhyJypUr4+XLlzh8+DAePnyY6fNCREQFjEBERAXetGnTBABCr169Mmy3trYWrK2tM+3fr18/oVKlSvLHsbGxAgChZMmSwtu3b+Xbd+/eLQAQ9u7dm+UsR48eFQAIEolEuHDhgnx7YGCgAED4559/MuQDIPj5+cm3JScnC0ZGRkK3bt3k25YtWyYAEDZv3izflpKSIjRp0kTQ0dER4uPjs/06zMzMBENDQ+HNmzfybZcvXxaUlJSEvn37yreln9sBAwZkeJ1dunQRSpYs+c1zUKNaNaG9sbFwwMkpw7+qenpCCQ0NIahzZ/m2WS1aCAAEQy2tDPsCEHqbmsofa6uqCh2MjYX2xsZCzerVv9lv+vlcvHhxhvOZ/lpTUlIynCdvb2/5fklJSZna8/f3FwAIx48fl2/r27evoKSkJJw/fz7T/jKZTBAEQZg5c6agra0t3L59O8Pz//vf/wRlZWXh4cOHGXIYGBgI7969k+83ceJEAYBQv359ITU1VRAEQbhx44agrKwsKCkpCR8/fhQEQRASEhIEPT09wc3NLUM/z58/F4oXL55he79+/QQAwowZMzLsa25uLjRs2FD++NWrVwIAYdq0aRn2i4uLEwAICxcuzPS6iYio4OOlyERECsQ9h/Mve/ToAX19ffljKysrAMC9e/ey3Mb+/5atcXR0RMOGDeXbu3fvjjJlyuDAgQMZ9tfR0YGLi4v8sZqaGho3bpyhzwMHDsDIyAi9evWSb1NVVcWoUaOQmJiIY8eOZet1PHv2DFFRUXB1dUWJEiXk+9WrVw+tW7fOlBHIfG6trKzw5s0bxMfHZ9iekJCA23fuoNoX/QPA248fce/dO7SqXBnaX4x+NyhdGhV1dTP19zVtVVXcevsWZbS1cSsmBomJid/cT0VFBUOGDJE/VlNTw5AhQ/Dy5UtcvHjxu+1/OYr+6dMnvH79Gr/99hsAyC93lslk2LVrFxwdHWFhYZGpjfRR3aCgIFhZWUFfXx+vX7+W/7Ozs4NUKsXx48czHOfk5ITixYvLH1taWgIAXFxcoKLy+eKxmjVronfv3pDJZFi6dCmAz6PM7969Q69evTL0o6ysDEtLS4SHh2fK+K33MSufb01NTaipqSEiIgJxcXE/3Z+IiAoWFrZERAqkSpUqOTq+YsWKGR6nF4dZ/UP+8uXLWL58OQCgc+fOGZ6TSCQwMTHJNB+xfPnymZYA0tfXz9DngwcPUK1aNSh9tf5rrf/uGvzgwYNsvY70/WvUqJHpNdSqVQuvX7/OdPOtrJ6bu3fvQhCETMXqy//m45bT0cnUZ/lixTJt+9qAevXw4P17bLhyBYIgYPz48d8syMqWLZvp7tPVq1cHgB/OBX379i1Gjx6N0qVLQ1NTEwYGBvLP0/v37wEAr169Qnx8/E/XHo6JiUFISAgMDAwy/LOzswPweb70l74+t+lFboUKFTJsb9myJYDP856vXLmCmJgY+fav+woNDc3Uj4aGhnwObbqvP2vfo66ujvnz5+PgwYMoXbo0WrRogQULFuD58+c/PZaIiMTHObZERApE86u5mhKJBIIgZNov/WY5X1NWVv7m9m+18bX4+Hg4OTmhYsWKuHv3bhbS5rzPgthmcnIyAEBdJXd/hbaoUAF1SpXC3jt3EHDzJnx9feHr64sdO3bI5yLnhLOzM06fPo0JEybAzMwMOjo6kMlkcHBwyHDDp6yQyWRo3bo1/vzzz28+n15op/veuf3e9kqVKqF79+7o3bs3gM/zbI2MjDLtp/LVe/C99rJqzJgxcHR0xK5du3Do0CFMmTIFc+fOxdGjR2Fubp6jtomIKG+xsCUiUmD6+vrfHNX7eoQzpwRBgJubG54/fw5PT0+4uLjIR9O+3OfOnTvyG/FkR6VKlXDlyhXIZLIMo7Y3b96UP5/d9gDg1q1bmZ67efMmSpUq9ctr7qqrqwMAktPSMmw31NICADz5xiXEjxMSstR2CU1NWJYti4CbN7F//3707dsXs2fPzlDYPn36FB8+fMiQ//bt2wDw3RscxcXF4ciRI/Dw8MDUqVPl279+Dw0MDKCrq4tr1679MKexsTESExPlI7S5bd68eXB1dcXhw4cBAIaGhrnW19dXD3zN2NgY48aNw7hx4xATEwMzMzMsXrwYmzdvzpX+iYgob/BSZCIiBWZsbIybN2/i1atX8m2XL1/GqVOncrUfT09PBAYGYsOGDShXrhwAwM/PDwlfFGzBwcF49uzZL40utmvXDs+fP0dAQIB8W1paGlasWAEdHR1YW1tnq70yZcrAzMwMvr6+ePfunXz7tWvXEBoainbt2mU7YzoTExNIJBI8/GrubQlNTVTV08OR+/fxITVVvv3SixeZ9v2aVBDkxzyMj4dEIkHjxo1RtmxZ+QhxurS0NKxZs0b+OCUlBWvWrIGBgUGGOc9fSh/J/Hr0+eu7FyspKaFz587Yu3cvLly4kKmd9OOdnZ3x77//4tChQ5n2effuHdK+Kvqzq2LFitiwYQNOnz4NDQ0NzJkzB6lfnNN0X37us0rrvy8gvvxcAEBSUhI+ffqUYZuxsTGKFSuW6T0gIqKChyO2REQKbMCAAViyZAnatGmDgQMH4uXLl/Dy8kLt2rUz3fToV128eBF//PEHRowYAScnJ0RERAAASpQogebNm6N///548eIFli1bBhMTE7i5uWW7j8GDB2PNmjVwdXXFxYsXUblyZQQHB+PUqVNYtmwZimVhjurXFi5ciLZt26JJkyYYOHCgfLmf4sWLy9ct/RU6OjqobmKCmLg42H8159m1bl1MP3EC448ehX2VKkhIScHeO3dQSVcXH39Q7H1MTUXfffvQvHx5PElMRGkDAwwcOBDnz5/H4sWLM+xbtmxZzJ8/H/fv30f16tUREBCAqKgorF279rtLNunq6srnjKampqJcuXIIDQ1FbGxspn3nzJmD0NBQWFtbY/DgwahVqxaePXuGoKAgnDx5Enp6epgwYQL27NmDDh06wNXVFQ0bNsSHDx9w9epVBAcH4/79+yhVqtQvnN3/5+TkhOHDh8PLywsnTpxAgwYN0LNnTxgYGODhw4fYv38/mjVrhpUrV2arXU1NTZiamiIgIADVq1dHiRIlUKdOHaSlpaFVq1ZwdnaGqakpVFRUsHPnTrx48QI9e/bM0WshIqK8x8KWiEiB1apVC35+fpg6dSrGjh0LU1NTbNq0CVu3bpUXoDnx7t07ODs7o169eli0aFGG5yZNmoQrV65g7ty5SEhIQKtWrbB69Wr5iFh2aGpqIiIiAv/73//g6+uL+Ph41KhRA97e3nB1df2l7HZ2dggJCcG0adMwdepUqKqqwtraGvPnz8/xTbjsHRywecMGDJbJoPrFpdMWRkaY2KQJ/K5dg8/Vqyijo4M/GjXCmSdPcOUHo4vqKipob2KCS8+fI/b9e6iqquLWrVtYvXo1hg4dmmFffX19+Pr6YuTIkVi3bh1Kly6NlStX/vQLha1bt2LkyJFYtWoVBEGAvb09Dh48iLJly2bYr1y5cjh79iymTJmCLVu2ID4+HuXKlUPbtm3l762WlhaOHTuGOXPmICgoCH5+ftDV1UX16tXh4eGR4Q7IObF48WKcOXMGT58+RenSpbFw4UIkJyejXLlysLKyQv/+/X+p3fXr12PkyJH4448/kJKSgmnTpmHkyJHo1asXjhw5gk2bNkFFRQU1a9ZEYGAgunXrliuvh4iI8o5EyMmdNoiIqNASBAHdunXD0aNHERkZKS8GIyIiYGtri6CgIHTv3l3klOKIjo5G7dq1McHSErZf3fE3J8IfPsTCs2cRHR0tvyP0l2xsbPD69eufzoEtTO7du4cGDRqgVatWCA4O/ukcWSIiKpo4x5aIiL5p+fLl2LlzJ3x8fHI8wlnYmJqaoo29Pfyio5H0jbmfvyIpNRV+0dFoY2//zaK2qKpatSq8vb2xY8cOrFixQuw4RERUQLGwJSIiAJ9vQvT8+XM8f/4cBw8exPjx4zF48GD89ttv8u0fP34UO6Yo3r9/Lz8H6f9mzJyJ+NRUrLx4EW+/uulQdgmCgHWXLyMxLQ2eXl65lLrw6NKlC8aMGYPx48fj3LlzYschIqICiIUtEREBAE6fPo0yZcqgTJkyaNeuHdLS0rB27Vr5tjJlymS4a3FRMnr06AznoUyZMrC0tMTHT58Q8egRXPbu/eW2BUGA/40bOBQbi39WrODo+HfMnz8f5ubmcHZ2RlxcnNhxiIiogOEcWyIiAvB5rdMLFy5g6tSpuHbtGry8vFC6dOkM+9SuXRtlypQRKaF4oqOj8fTpUwCfl9uJjIzE9u3bcffuXXTu3Bk+Pj5oU6UK3OrXh9Z37kz8LUmpqVh3+TIOxcaiTp062Lp1K+rWrZtXL0PhPXjwAObm5rCyssKuXbs435aIiORY2BIRkdyiRYswYcIE7N27Fx06dBA7ToGRkpKCI0eOIDAwEDt27JAvpaSpqYkPHz5gw4YNGDNqFHRUVNDX1BTNy5fPcLfkr6VKpTj55An8oqORmJaGrt27Y9OmTQAAMzMzDBgwQL60DWW0b98+ODo6YtGiRRg3bpzYcYiIqIBgYUtERAA+X4rcokULjBs3DvPnzxc7ToHx6tUr1KpVC2/evIGKigrS/luPViKR4I8//pCvMxsbG4vBbm4IO3IEehoaaFauHKrp66Oiri7UlZWRLJXiYXw8YuLicPrZM8QlJaGNvT08/xsZL168eIa2lZSU4ODggIkTJ6JZs2aivf6C6M8//8TSpUtx/PhxNGnSROw4RERUALCwJSIivH79Gubm5qhUqRLCw8Ohmo3LaQu71NRUWFtb4+zZs5DJZBmeO3/+PCwsLOSPN2/ejD59+qBv3744d+YMbsXE4MtfsxKJBDWqVUPrNm0wdOjQDHc/bt++PQ4ePIivfy07Ojpiz549efTqFFNqaipsbGzw8OFDREZGolSpUmJHIiIikbGwJSIq4mQyGdq3b48LFy4gMjIS5cuXFztSgZOQkICGDRsiJiZGvq1ChQp48OBBhnmerVq1glQqRUREBAAgMTERjo6OkEqlWL58OUxMTKCjo/PNPtauXQt3d3d5YSuRSFCvXj2EhYWxcPuGx48fw8zMDI0bN8a+ffug9INLv4mIqPDjbwEioiJu/vz5OHToEDZt2sSi9jsOHTqEu3fvomTJklBWVoaysjJ69+6doah98OABjh49iv79+8u3vX79GhEREThx4gT09fW/W9QCQIcOHTIUtYIgYODAgSxqv6N8+fLYvHkzDh48iAULFogdh4iIRMbCloioCDt+/DgmT56MSZMmwcHBQew4BVJQUBB69uyJnj174vbt22jQoAGkUimcnZ0z7Ofr6wsdHR10795dvm327NmQSCSQSCSYPXv2D/spW7YszMzMAHwe+R00aBBGjx6NwMDAXH9NhYWDgwMmTZqEyZMn48SJE2LHISIiEfFSZCKiIurly5cwMzND9erVERYWBhUVFbEjFTgBAQH4/fff0atXL/j4+EBZWRkJCQk4c+YMWrduLd9PJpPBxMQENjY22LhxIwDg/v37MDExgVQqBQAoKyvj7t27qFSp0nf72759O44dO4aFCxdCVVUV/fr1w7Zt27B79260a9cub1+sgkpLS4OdnR1iYmIQGRkJQ0NDsSMREZEIWNgSERVBUqkUDg4OuHLlCqKioork2rQ/s23bNri4uKB3797w9vaGsrLyd/c9duwYbGxscPz4cVhZWQEA3Nzc4O3tnaGwHTBgANauXZvlDGlpaXByckJISAhCQkJgbW2dsxdVSD19+hTm5uaoX78+Dh48+MP3ioiICideikxEVATNnj0bR44cwdatW1nUfsPWrVvx+++/4/fff/9pUQsA3t7eMDY2RvPmzQF8Hq39sqgFPn+ZsHHjRjx48CDLOVRUVLBt2zZYWVmhQ4cOOHfu3K+9oEKubNmy2Lp1K8LCwjBnzhyx4xARkQhY2BIRFTFHjx7F9OnTMW3aNLRq1UrsOAXOli1b5Ev2bNy48adFbWJiIoKDg+Hq6iq/mdS5c+cyFLXppFJptotTdXV17Ny5E/Xq1YODgwOuXbuWreOLilatWmHq1KmYPn06wsPDxY5DRET5jJciExEVIc+ePYO5uTnq1q2LkJAQXrL5lc2bN6Nfv37o168f1q9fn6UlZLy9vTFw4EDcv38fFStWlG9/8eIFpFIpXF1dAUA+R7d06dK/lO3du3ewtbXF8+fPceLECZiYmPxSO4WZVCpFmzZtcO3aNURFRcHIyEjsSERElE9Y2BIRFRFpaWlo3bo1bt68iaioqF8usAorPz8/uLq6yufBZnVd1BYtWkBdXR2HDx/+5vMdO3YEAOzZsyfHGV++fIkWLVrg06dPOHHiBCpUqJDjNgubFy9ewMzMDLVq1cLhw4f55Q0RURHBS5GJiIoIDw8PHD9+HNu2bWNR+xVfX1+4urpi4MCB2Spq79y5gxMnTmRYuzYvGRoaIiwsDADQunVrvHz5Ml/6VSSlS5fGtm3bcOzYMXh4eIgdh4iI8gkLWyKiIiA0NBSzZ8/GzJkzeWfdr/j4+KB///5wc3PDmjVrslzUAp8LYl1dXXTp0iUPE2ZUvnx5hIWF4f3797C3t0dcXFy+9a0orK2tMWPGDMyaNQuhoaFixyEionzAS5GJiAq5J0+ewMzMDBYWFti/f3+2CrfCbuPGjRg0aBAGDx6M1atXZ+vcyGQyVK5cGW3btsWaNWu+u19uXor8pWvXrsHa2ho1atRAaGgodHR0crV9RSeTydCuXTtcunQJkZGRKFeunNiRiIgoD/GvGyKiQiwtLQ09e/aEuro6Nm3axKL2Cxs2bMDAgQPh7u6e7aIW+Hx36UePHuXbZchfq1OnDkJCQnDt2jV07twZnz59EiVHQaWkpIRNmzZBTU0NvXr1QlpamtiRiIgoD/EvHCKiQmzy5Mn4999/ERAQgFKlSokdp8BYt24dBg0ahGHDhmHVqlW/VPB7e3ujZs2asLS0zIOEWdOoUSPs3bsXp06dQs+ePZGamipaloLIwMAAAQEBOH36NKZMmSJ2HCIiykMsbImICqn9+/dj/vz5mDt3Lpo1ayZ2nAJj7dq1GDx4MEaMGIGVK1fK157Njvfv32PHjh0Z1q4Vi7W1NXbs2IEDBw6gf//+kMlkouYpaJo1a4Y5c+Zg3rx5OHDggNhxiIgoj7CwJSIqhB4+fIi+ffuiQ4cOGDdunNhxCgwvLy8MGTIEI0eOxPLly3+5KA0ICEBKSgr69OmTywl/Tdu2bbFlyxb4+/tj+PDh4O0zMho/fjzat2+PPn364NGjR2LHISKiPMDCloiokElJSUGPHj2go6MDX19fzqv9j6enJ4YOHYrRo0fjn3/+ydFIq7e3N9q0aYOyZcvmYsKccXJywrp16+Dl5YX//e9/LG6/oKSkBF9fX2hra6NHjx68ZJuIqBDiXztERIXMxIkTceHCBQQEBKBEiRJixykQVq1ahWHDhmHMmDFYunRpjoramzdv4syZM6LdNOpHBgwYgGXLlmHBggWYO3eu2HEKlJIlSyIwMBDnz5/HxIkTxY5DRES5TEXsAERElHt2796NJUuWYOnSpfjtt9/EjlMgrFy5EiNHjsTYsWOxaNGiHM+J9fHxgb6+vnwZn4Jm9OjRiI+Px99//41ixYph5MiRYkcqMH777TcsWLAAY8eORYsWLQrse0hERNnHwpaIqJCIjY2Fq6srunTpgtGjR4sdp0BYvnw5Ro8ejXHjxmHhwoU5LmqlUik2bdqE3r17Q11dPZdS5r7Jkyfj/fv3GDVqFHR1ddGvXz+xIxUYY8aMwfHjx9GvXz9ERkaicuXKYkciIqJcwEuRiYgKgeTkZPTo0QP6+vrYuHGj6HfqLQiWLVuG0aNHY8KECblS1AJAaGgonj59WiAvQ/6SRCLBwoUL4ebmhgEDBmD79u1iRyowJBIJNm7cCD09PTg7OyMlJUXsSERElAtY2BIRFQITJkzA5cuXERgYCD09PbHjiG7p0qX4448/8Ndff2H+/Pm5Vuh7e3ujbt26aNCgQa60l5ckEgk8PT3h7OyMXr164dChQ2JHKjD09fURFBSEy5cvY8KECWLHISKiXMDClohIwQUHB2PFihVYsmQJLCwsxI4juiVLlmDs2LH43//+h7lz5+ZaUfv27Vvs3r27QKxdm1XKysrw8/ODg4MDunTpghMnTogdqcCwsLDA4sWLsXz5co5oExEVAixsiYgU2N27dzFw4EA4OTlh2LBhYscR3aJFizBu3DhMmjQJc+bMydUC1N/fHzKZDC4uLrnWZn5QVVVFYGAgmjRpgg4dOuDixYtiRyowhg8fDicnJwwYMAB3794VOw4REeWAROBCd0RECunTp09o2rQpEhIScPHiRejq6oodSVQLFizAX3/9hcmTJ2PGjBm5PqpqYWGBcuXKYffu3dk6Lv3Ou3v27MnVPNmVmJgIOzs73LlzB8ePH4epqamoeQqK9+/fo2HDhtDV1cXp06ehoaEhdiQiIvoFHLElIlJQf/zxB6KjoxEUFFTki9r58+fjr7/+wpQpU/KkqL169SouXrxY4G8a9SM6Ojo4cOAAypUrh9atW+PevXtiRyoQihcvjqCgIERHR2Ps2LFixyEiol/EEVsiIgXk7++P3r17Y82aNRg8eLDYcUQ1d+5cTJo0CdOmTcP06dPzpI979+7h5cuXaNy4MZSUsved8PXr1wEAtWvXzoto2fby5Us0b94caWlpOHHiBMqVKyd2pAJhzZo1cHd3x7Zt29CjRw+x4xARUTaxsCUiUjC3bt2ChYUFOnbsiM2bNyvMjYzywuzZszF58mRMnz4d06ZNEzuOwnj8+DGaNm0KHR0dHDt2DAYGBmJHEp0gCPj999+xd+9eXLx4EdWrVxc7EhERZQMLWyIiBfLx40dYWloiJSUFFy5cgI6OjtiRRDNr1ixMmTIFHh4emDp1qthxFM7t27dhZWWF8uXL4+jRoyhevLjYkUSXkJAACwsLaGho4MyZM9DU1BQ7EhERZRHn2BIRKZCRI0fizp07CAoKKtJF7YwZMzBlyhTMnDmTRe0vql69Og4fPox79+6hQ4cOSEpKEjuS6IoVK4bg4GDcvn0bo0aNEjsOERFlAwtbIiIFsWnTJmzYsAGrVq1C3bp1xY4jmvTLjtMvQ6ZfV69ePRw8eBCRkZHo0qULkpOTxY4kurp162LVqlVYv349Nm/eLHYcIiLKIha2REQKIDo6Gu7u7ujXr59C35k3JwRBwLRp0+Dh4YE5c+Zg0qRJOW7TxsYGY8aMAQBUrlwZy5Yty3Gbiua3337Dnj17cOzYMfTu3RtpaWliRxJd//790bdvXwwZMgTR0dFixyEioizgHFsiogLuw4cPaNy4MQDg3Llz0NbWFjlR/ksvamfOnIl58+bhr7/+ypV2bWxsYGZmhmXLluHVq1fQ1taGlpZWrrStaPbu3YuuXbvi999/x8aNG7N99+fChj93RESKpWj/1iIiKuAEQcCwYcNw//59BAUFFck/rgVBkM+nXbBgQa4VtV8zMDD4YVGbmpqaJ/0WFI6OjvDz84Ofnx9Gjx6Nov69t7a2NoKCgnD//n0MHz5c7DhERPQTLGyJiAowb29v+Pn5Yc2aNTA1NRU7Tr4TBAGTJ0/G7NmzsXDhQkyYMOGX2/rw4QP69u0LHR0dlClTBosXL87w/NeXIkskEnh6eqJjx47Q1tbG7Nmzf9h+REQEJBIJjhw5AgsLC2hpaaFp06a4deuWfJ+7d++iU6dOKF26NHR0dNCoUSOEhYVlyjFr1ix51kqVKmHPnj149eoVOnXqBB0dHdSrVw8XLlzIcNzJkydhZWUFTU1NVKhQAaNGjcKHDx+ydY569eqFNWvWYOXKlZgyZUq2ji2MTE1N4eXlBV9fX3h7e4sdh4iIfoCFLRFRAXX16lUMHz4cgwYNgouLi9hx8p0gCJg0aRLmzJmDxYsXY/z48Tlqb8KECTh27Bh2796N0NBQRERE4NKlSz88Zvr06ejSpQuuXr2KAQMGZKmfv//+G4sXL8aFCxegoqKS4bjExES0a9cOR44cQWRkJBwcHODo6IiHDx9maGPp0qVo1qwZIiMj0b59e/Tp0wd9+/aFi4sLLl26BGNjY/Tt21c+qnr37l04ODigW7duuHLlCgICAnDy5EmMGDEim2cJcHNzw6JFizB79mzMnz8/28cXNn369MHAgQMxfPhwXL16Vew4RET0PQIRERU48fHxQo0aNYR69eoJSUlJYsfJdzKZTPjzzz8FAMKSJUty3F5CQoKgpqYmBAYGyre9efNG0NTUFEaPHi0IgiBUqlRJWLp0qfx5AMKYMWOy3Ed4eLgAQAgLC5Nv279/vwBA+Pjx43ePq127trBixQr540qVKgkuLi7yx8+ePRMACFOmTJFv+/fffwUAwrNnzwRBEISBAwcKgwcPztDuiRMnBCUlpR/2/SNTp04VAAirV6/+peMLk6SkJKFu3bpCjRo1hPj4eLHjEBHRN3DEloiogBEEAUOGDMGTJ08QFBQETU1NsSPlK0EQ8Ndff2HBggVYtmwZ/vjjjxy3effuXaSkpMDS0lK+rUSJEqhRo8YPj7OwsMh2X/Xq1ZP/d5kyZQAAL1++BPB5xHb8+PGoVasW9PT0oKOjgxs3bmQasf2yjdKlSwNAhiWe0relt3v58mX4+PhAR0dH/q9NmzaQyWSIjY3N9msAPo9Wjx49GsOHDy/yy95oamoiKCgIT548gbu7e5Gff0xEVBCpiB2AiIgyWrt2Lfz9/bFt2zZUr15d7Dj5ShAETJgwAYsXL8by5csxcuRIUfP8ys26VFVV5f8tkUgAADKZDAAwfvx4HD58GIsWLYKJiQk0NTXRvXt3pKSk/LSNH7WbmJiIIUOGYNSoUZnyVKxYMduvIb2PJUuWICEhAa6urtDR0UHnzp1/qa3CoEaNGli3bh169eoFa2trDB48WOxIRET0BRa2REQFSGRkJEaPHo2hQ4eiR48eYsfJV4IgYNy4cVi6dClWrFjxS/NDv8fY2Biqqqo4e/asvNCLi4vD7du3YW1tnWv9/MypU6fg6uqKLl26APhckN6/fz/H7TZo0ADR0dEwMTHJcVtfUlJSwtq1a5GQkIAePXpg//79sLOzy9U+FEnPnj1x7NgxjBo1Co0bN4aZmZnYkYiI6D+8FJmIqIB4//49nJ2dYWpqiiVLlogdJ18JgoCxY8di6dKlWLlyZa4WtQCgo6ODgQMHYsKECTh69CiuXbsGV1fXfF+rtVq1atixYweioqJw+fJl9O7dWz7qmhN//fUXTp8+jREjRiAqKgoxMTHYvXt3rpxHZWVlbN68Ga1atUKnTp1w+vTpHLepyJYuXQpTU1M4OTkhPj5e7DhERPQfFrZERAWAIAgYNGgQXr58iaCgIGhoaIgdKd8IgoAxY8Zg2bJlWL16dZ6tGbpw4UJYWVnB0dERdnZ2aN68ORo2bJgnfX3PkiVLoK+vj6ZNm8LR0RFt2rRBgwYNctxuvXr1cOzYMdy+fRtWVlYwNzfH1KlTUbZs2VxIDaipqSE4OBgWFhZo164doqKicqVdRaShoYHAwEC8fPkSgwYN4nxbIqICQiLw/8hERKJbuXIlRo4cieDgYHTr1k3sOPlGEASMHj0aK1asgJeXF4YMGSJ2JPqB+Ph4tGrVCg8ePMDx48dRs2ZNsSOJZvv27ejevTtWrlyZZ1/GEBFR1rGwJSIS2YULF9C0aVMMHToU//zzj9hx8o0gCBg5ciRWrVqFNWvW8GY8CuLNmzewtrbGu3fvcPLkSVSuXFnsSKIZNWoU1qxZg1OnTv3SHbSJiCj3sLAlIhJRXFwcGjRoAAMDA5w8eRJqampiR8oXMpkMI0aMgKenJ9atW4dBgwaJHemn3N3dv7vsjYuLC7y8vPI5kXiePXsGKysrAMCJEyfkyxoVNcnJybCyssLr169x6dIl6OnpiR2JiKjIYmFLRCQSQRDQpUsXHDt2DJGRkUVm5Esmk2H48OFYs2YN1q1bh4EDB4odKUtevnz53ZsF6erqwtDQMJ8Tiev+/fto3rw59PT0cOzYMZQsWVLsSKKIjY1FgwYNYGtri+3bt8uXYiIiovzFwpaISCRLly7F2LFjsXv3bnTs2FHsOPlCJpNh2LBhWLt2LdavX48BAwaIHYly4ObNm2jRogUqV66MsLAw6Orqih1JFLt370bnzp2xdOlSjBkzRuw4RERFEgtbIiIRnDlzBlZWVhg9ejQWLVokdpx8IZPJ4O7ujvXr12Pjxo1wdXUVOxLlgsjISNja2qJ+/foICQmBpqam2JFEMW7cOCxfvhwnTpzAb7/9JnYcIqIih4UtEVE+e/PmDRo0aIBy5crh2LFjUFVVFTtSnpPJZBgyZAg2bNgAb29v9OvXT+xIlItOnz6N1q1bw8bGBjt37iwyc8W/lJqaihYtWuDp06eIjIxEiRIlxI5ERFSkcB1bIqJ8JJPJ0K9fPyQmJiIgIKDIFLVubm7YsGEDfH19WdQWQk2bNsWuXbsQFhYGFxcXSKVSsSPlO1VVVQQEBCAxMRH9+vWDTCYTOxIRUZHCwpaIKB8tWrQI+/fvx6ZNm1ChQgWx4+Q5qVSKgQMHwsfHB35+fujTp4/YkTKZMGECmjZtmmeFyIABA4rEXOLWrVsjICAAO3bswODBg4tkYVexYkX4+flh3759WLx4sdhxiIiKFBa2RET55OTJk5g0aRL+97//oV27dmLHyXPpRa2fnx82bdoEFxcXsSNl8uHDB3h5ecHe3h5KSnnzK/H169d4/fp1nrRd0HTu3Bk+Pj7YuHEjxo4di6I426l9+/b466+/MHHiRJw6dUrsOERERYaK2AGIiIqCV69eoWfPnmjatClmzpwpdpw8J5VKMWDAAGzevBmbN29Gr169xI70TTt27JBfOkq5w8XFBQkJCRg2bBiKFy8ODw8PsSPlu1mzZuHUqVPo0aMHIiMjYWBgIHYkIqJCjyO2RER5TCaToU+fPkhJSYG/vz9UVAr3d4pSqRSurq7YsmULtm7dWmCLWgDw9vaGra0tqlSpInaUQmXo0KGYN28eZsyYUSQvyVVRUcG2bduQnJyMPn36FMnLsomI8hsLWyKiPDZ37lyEhoZi8+bNKFeunNhx8pRUKkW/fv3g7++PrVu3okePHmJH+q779+8jPDycyw7lkb/++guTJk3C+PHjsW7dOrHj5Lty5cphy5YtCA0Nxbx588SOQ0RU6BXuYQMiIpFFRERg6tSpmDx5Muzt7cWOk6fS0tLQr18/BAQEwN/fH05OTmJH+iFfX1/o6OigW7duYkcptGbNmoX4+HgMGTIExYoVQ8+ePcWOlK/s7e3x999/Y8qUKWjWrBmsra3FjkREVGhxHVsiojzy4sULmJmZoVatWjh8+DCUlZXFjpRn0tLS0KdPHwQHB2Pbtm0FvliUyWQwNjZGy5YtsWHDhjztq2PHjgCAPXv25Gk/BZVMJkP//v2xdetW7Ny5Ex06dBA7Ur6SSqWws7PDzZs3ERUVhdKlS4sdiYioUOKlyEREeUAqlaJ3794QBAFbt24t9EWti4sLgoODERAQUOCLWgA4fvw47t+/j/79+4sdpdBTUlLChg0b0LFjR3Tv3h3h4eFiR8pXysrK2Lp1KwRBwO+//14k1/glIsoPLGyJiPLAzJkzERERAX9/fxgZGYkdJ8+kpqaid+/e2L59OwIDA9G1a1exI2WJt7c3TExM0KxZM7GjFAkqKirYunUrrK2t4ejoiLNnz4odKV+VKVMGW7duxdGjRzFr1iyx4xARFUosbImIcllYWBhmzJiB6dOnw9bWVuw4eSa9qN25cyeCgoLQpUsXsSNlSUJCAoKDg+Hq6gqJRCJ2nCJDXV0dO3bsgJmZGdq2bYsrV66IHSlftWzZEtOnT4eHhweOHDkidhwiokKHc2yJiHLR06dPYW5ujvr16+PgwYOF9hLk1NRU9OrVC3v27EFQUBA6deokdqQs27hxIwYNGoQHDx6gQoUKed5fUZ9j+7X379+jZcuWePLkCY4fP47q1auLHSnfSKVSODg44MqVK4iKikKZMmXEjkREVGhwxJaIKJekpaWhV69eUFFRwebNmwttUZuSkoIePXpgz5492L59u0IVtcDny5Bbt26dL0UtZVa8eHGEhISgRIkSsLOzw8OHD8WOlG+UlZWxZcsWKCsro1evXkhLSxM7EhFRocHClogol0ybNg2nTp2Cv78/DA0NxY6TJ9KL2v3792PHjh1wdHQUO1K23LlzBydPnuTatSIzMDDA4cOHoaKiAjs7O7x48ULsSPnG0NAQ27Ztw4kTJzB9+nSx4xARFRosbImIckFISAjmzJmDWbNmoUWLFmLHyRMpKSlwdnbGgQMHsGPHDoVctsXHxwfFixdH586dxY5S5JUrVw5hYWFITEyEvb094uLixI6Ub1q0aIFZs2Zh9uzZCAkJETsOEVGhwDm2REQ59OjRI5ibm8PS0hJ79+6FklLh+84wOTkZTk5OCA0Nxc6dO9G2bVuxI2WbVCpF5cqV0b59e3h5eeVbv5xj+2PR0dFo0aIFTExMcPjwYRQrVkzsSPlCJpOhQ4cOOHfuHKKiolC+fHmxIxERKbTC99cXEVE+Sk1NRc+ePaGpqQlfX99CW9R2794doaGh2LVrl0IWtQBw9OhRPH78mGvXFjCmpqY4dOgQoqOj0blzZ3z69EnsSPlCSUkJfn5+0NTURM+ePZGamip2JCIihVb4/gIjIspHf//9N86dO4eAgACUKlVK7Di5Ljk5Gd26dcPhw4exe/duODg4iB3pl3l7e6NWrVpo3Lix2FHoKw0bNsT+/fvx77//wtnZucgUeaVKlUJAQADOnj2LyZMnix2HiEihsbAlIvpF+/btw8KFCzFv3jw0bdpU7Di57tOnT+jatSuOHDmCPXv2oE2bNmJH+mXv3r3Dzp07uXZtAWZlZYUdO3YgJCQE/fr1g1QqFTtSvmjatCnmzp2LBQsWYN++fWLHISJSWCxsiYh+wYMHD9C3b1907NgRY8eOFTtOrvv06RO6dOmCo0ePYu/evbC3txc7Uo4EBAQgNTUVffr0ETsK/YCDgwP8/f0REBCAoUOHoqjcBmTcuHFwdHRE37598eDBA7HjEBEpJBa2RETZlH53YF1dXfj4+BS6EcBPnz6hc+fOOHbsGPbt2wc7OzuxI+WYt7c3HBwcUKZMGbGj0E9069YNGzZswLp16zBhwoQiUdxKJBL4+PhAV1cXPXr0QEpKitiRiIgUDgtbIqJs+uuvvxAZGYnAwEDo6+uLHSdXffz4EZ06dcLx48exb98+tGrVSuxIOXbjxg2cPXuWa9cqEFdXVyxfvhyLFy/GrFmzxI6TL0qUKIGAgABcunQJ//vf/8SOQ0SkcFTEDkBEpEh27tyJZcuW4Z9//il0NyFKSkpCp06dcOrUKezfvx+2trZiR8oVPj4+KFGiBBwdHcWOQtkwcuRIxMfHY/LkydDV1cXo0aPFjpTnLC0tsXDhQowZMwZWVlbo0qWL2JGIiBQGC1sioiy6d+8e+vfvj27dumHkyJFix8lVSUlJ6NixI/79918cOHAANjY2YkfKFWlpafDz80Pv3r2hrq4udhzKpkmTJiE+Ph5jxoxBsWLFMGDAALEj5blRo0bh+PHj6N+/P+rXr4+qVauKHYmISCHwUmQioixITk6Gs7MzSpYsiQ0bNhSqebVJSUlwdHTEmTNncPDgwUJT1AJAaGgonj9/zrVrFZREIsG8efPg7u4ONzc3BAUFiR0pz0kkEmzYsAElSpSAs7MzkpOTxY5ERKQQWNgSEWXBuHHjcPXqVQQFBaF48eJix8k1Hz58QIcOHXD27FkcPHgQLVq0EDtSrvL29ka9evVgbm4udhT6RRKJBKtWrUKvXr3w+++/4+DBg2JHynN6enoICgrC1atXMX78eLHjEBEpBBa2REQ/ERgYiFWrVmHZsmVo0KCB2HFyTXpRe/78eYSEhMDKykrsSLnqzZs32LNnD9euLQSUlJTg7e2Ndu3aoWvXrjh27JjYkfJcw4YNsXTpUqxcubJIjFQTEeUUC1sioh+IiYnBoEGD0KNHD7i7u4sdJ9ckJiaiXbt2uHDhAkJCQtC8eXOxI+U6f39/yGQyuLi4iJYhMTER79+/R1xcHKKiopCYmChaFkWnqqqKbdu2oVmzZnB0dMT58+fFjpTnhg4dih49emDgwIGIiYkROw4RUYEmEYrCAnFERL9AKpUiOjoaMpkMderUgbKystiRckV6URsVFYWQkBA0bdpU7Eh54tq1a1BTU0P16tXztd/o6Gh4eXkhNCQEt+/cybAOq0QiQXUTE9g7OMDd3R2mpqb5mq0wSExMhL29PW7duoVjx46hTp06YkfKU/Hx8bCwsIC2tjb+/fdfaGhoiB2JiKhAYmFLRFSEJCQkoF27drh8+TIOHTqEJk2aiB2p0IiNjcVQd3ccCg2FvpYWmpYpg2r6+qioqwt1FRUkp6XhYXw8YuLicPrZM8QlJaGNvT08vbxQpUoVseMrlLi4ONja2uLFixc4ceIETExMxI6Upy5fvgxLS0u4urrCy8tL7DhERAUSC1sioiIiISEBbdu2xdWrV3Ho0CH89ttvYkcqNNavX48xo0ZBR0UFfU1N0bx8eagqfX+2T6pMhpOPH8MvOhqJaWlYtnw5Bg0alI+JFd/Lly/RokULfPr0CSdPnkT58uXFjpSn1q1bh8GDB2PLli3o3bu32HGIiAocFrZEREVAfHw82rZti2vXriE0NBSWlpZiRyo0Zs+ejcmTJ6NNlSpwq18fWqqqWT42KTUV6y5fxqHYWMyaNQupqanw8PAAfzVnzaNHj9C8eXNoaWnh2LFjMDQ0FDtSnhEEAX369MGuXbtw4cIF1KxZU+xIREQFCgtbIqJCLj4+Hg4ODoiOjkZoaCgaN24sdiSFtnXrVrx8+RJjxozB+vXr4ebmhj61a6NXFufLfkpLQ/CtW6hnYIB6/xViW6Ojsfn6dTg6OmLv3r0sbLPhzp07sLKygpGREcLDw6Gnpyd2pDyTmJiIRo0aQUVFBWfPnoWWlpbYkYiICgzeFZmIqBB7//492rRpgxs3biAsLIxFbS7YunUrli1bhtjYWIwZNQptqlTJclELAMlSKbZGR+PKq1fybb1q1UKbKlVw5PBh3LhxIy9iF1omJiYIDQ3FgwcP0L59e3z48EHsSHlGR0cHQUFBuHv3LkaOHCl2HCKiAoWFLREVGdOnT4eZmVmetO3j41PgRorevXsHe3t73Lx5E2FhYbCwsBA7Uq4pCO/lUHd36KiowK1+/Rz3KZFI4Fa/PoqpqmLM6NE5bq+oqVu3LkJCQnDlyhV06dIFycnJYkfKM3Xq1MHq1auxceNG+Pr6ih2HiKjAYGFLRFQIpRe1MTExOHLkCBo2bCh2JIXx8eNHfPz4EZUrV4a6ujoMDQ3RunVrXLp0CTY2Nti/fz8ePHiAQ6GheJGQgGGhoQA+3xBq07VrGHX4MLrv3IkuO3ZgQng4Lr98KW/7xYcP6LVnD4DPlx+3CwpCu6AgbL5+HVqqqjDW1cWh0NBMo7abN29G48aNoaWlBX19fbRo0QKh//VLnzVu3Bh79+7FiRMn0LNnT6SlpYkdKc+4urrC1dUVQ4cOxfXr18WOQ0RUILCwJSKFkZycjFGjRsHQ0BAaGhpo3rw5zp8/D+Dbo2y7du2CRCKRP+/h4YHLly9DIpFAIpHAx8cHwOfRMk9PT7Rt2xaampqoWrUqgoOD5e1ERERAIpHg3bt38m1RUVGQSCS4f/8+IiIi0L9/f7x//17e9vTp0/PyVPxQXFwcWrdujbt37+LIkSNo0KCBaFm+pyC/l8OGDUNKSgoePHiAlJQU1K9fH5qamrhx4wb+/vtvmJmZQUNDA1pqavijUSMM/m/kOCk1FYdiY1HX0BD969XD77Vr431yMqYcP467//VXXF0dw/97P5qWK4fxjRtjfOPGaFauHACgYrFiAABPT095Pg8PD/Tp0weqqqqYMWMGPDw8UKFCBRw9ejRX3ovCxMbGBsHBwdi3bx/69+8PmUwmdqQ8s2rVKlStWhVOTk5ITEwUOw4RkehUxA5ARJRVf/75J7Zv3w5fX19UqlQJCxYsQJs2bXDnzp2fHtujRw9cu3YNISEhCAsLAwAUL15c/vyUKVMwb948/PPPP9i0aRN69uyJq1evolatWj9tu2nTpli2bBmmTp2KW7duAfg8F04M6UVtbGwsjhw5kmeX6+ZUQX4vNTQ0IJVK8fDhQwCf38sv389y5crh2rVraFOpElpXrizfrqOmBu/27TMs8+NQpQqGhIRgb0wMxjRqBA0VFTQvXx6rLl1C5eLF0bJSpQz9K/137OFDhwB8vjHSjBkz0KVLFwQHB8ufB8AbTH1H+/btsXnzZvTq1QvFihXDqlWr5F+KFCZaWloICgpCo0aNMGzYMPj6+hbK10lElFUcsSUihfDhwwd4enpi4cKFaNu2LUxNTbFu3Tpoampiw4YNPz1eU1MTOjo6UFFRgZGREYyMjKCpqSl/3snJCYMGDUL16tUxc+ZMWFhYYMWKFVnKpqamhuLFi0MikcjbFqOwffv2Lezs7HD//n0cPXq0wBa1Bf291NbWhkwmg0wm++Z7mZaWhrS0NFTT18+wXVkikRe1MkFAQkoKpIIAkxIlcOeLEeKsuBUTg8TEROzatQsymQxTp07NUNQCYBHzAz169MC6devg6emJSZMmiR0nz9SqVQteXl7YtGkTNm7cKHYcIiJRccSWiBTC3bt3kZqaimbNmsm3qaqqonHjxrhx4wYMDAxy1H6TJk0yPY6KispRm/kpvah99OgRjh49inr16okd6bsK+nvp7OwMT09PVKhQAQ0bNkS7du3Qt29fVK1aFQCQlJQEAKioq5vp2LD797Hj9m08jo9H2hcjqkba2tl6DYIg4M6dO7h79y6UlJRgmo27LtNnAwcOREJCAv744w/o6upi4sSJYkfKEy4uLjh+/DhGjBiBRo0aFeiffSKivMQRWyIqFJSUlDJdmpmampprbQMZL/3MrbZzw5s3b9CqVSuFKGqzQuz3snHjxihWrBhWrFiBsmXLYuHChahduzYOHjwIAPJ5m+oqGb8bPvrgAZacP48y2toY3agRZlpZYXaLFqhvaAjZL1w2XJjv7JtfxowZg+nTp2PSpElYtWqV2HHyzD///IMaNWrAyckJCQkJYschIhIFC1siUgjGxsZQU1PDqVOn5NtSU1Nx/vx5mJqawsDAAAkJCRnWsPx6lE5NTQ1SqfSb7Z85cybT4/Q5mekjiM+ePfultvPS69ev0apVKzx58gTh4eGoW7duvmfILkV4LwVBwLBhw7Br1y7ExsaiZMmSmD17NgBAWVkZAJD81V13Tz5+DCNtbUxu2hStKlVCQyMjmJcujZSvcmb1AmJ1dXUYGxtDJpMhOjo6i0fR16ZOnYqxY8dixIgR8PPzEztOntDU1ERgYCCePn2KwYMHc/41ERVJLGyJSCFoa2tj6NChmDBhAkJCQhAdHQ03NzckJSVh4MCBsLS0hJaWFiZNmoS7d+9i69at8jvlpqtcuTJiY2MRFRWF169fZxgRCwoKwsaNG3H79m1MmzYN586dw4gRIwAAJiYmqFChAqZPn46YmBjs378fixcvztR2YmIijhw5gtevX8svV81L6UXts2fPEB4ejjp16uR5n7mhIL+XUqkUpUqVyvBe6ujooGzZsvI+0ovjh/HxGTIp/Tfn9cuS4uabN7j55k2G/dJHej/8YBRaIpHAxMQEnTt3hpKSEmbMmJHpDr8sXrJGIpFg0aJFGDRoEPr3748dO3aIHSlPVK9eHevXr8e2bduwZs0aseMQEeU/gYhIQXz8+FEYOXKkUKpUKUFdXV1o1qyZcO7cOfnzO3fuFExMTARNTU2hQ4cOwtq1a4Uv/zf36dMnoVu3boKenp4AQPD29hYEQRAACKtWrRJat24tqKurC5UrVxYCAgIy9H3y5Emhbt26goaGhmBlZSUEBQUJAITY2Fj5Pu7u7kLJkiUFAMK0adPy8lQIL1++FOrWrSsYGhoK169fz9O+8kJBfS/j4uIEbW1toXr16oKWlpYAQKhdu7YAQFi8eLEgCIKwYMECAYBQpXhx4U9LS2Fas2bCAScnYYyFhQBA+K1sWWFkw4aCU82ago6qqlBJV1cw1NISDjg5yf9V1NUV9DU0hGHm5sJflpbCant74YCTk9Db1FQAINSsXl2ed8qUKQIAoWnTpsKiRYuEFStWCH379hX+97//5eE7VPikpaUJPXr0EFRVVYWQkBCx4+SZYcOGCWpqasLFixfFjkJElK8kgsCvfImoaJNIJNi5cyc6d+4sdpQsefnyJVq1aoVXr17h6NGjvLHQF3L6XqakpGDy5MkIDQ3FvXv3IJPJYGJigiFDhmDo0KEAPt/VuUGDBoi5fRsCAEMtLfi0bw9BEBB48yYO3ruHuE+fUFFXF33q1MHJR49w5dUr+LRvL+/nxps38IyMxP3375Emk6G3qSlcateG39Wr2HbzJkaOHInly5fL9/f29saKFSsQHR0NLS0t1KtXD5MnT4adnV1OTleRk5qaii5duuDo0aMIDQ1F8+bNxY6U65KTk9G0aVO8e/cOly5dyrAUFhFRYcbCloiKPEUqbF++fImWLVvizZs3CA8PR82aNcWOVKDk13sZHR2N2rVrY4KlJWwrVsy1dsMfPsTCs2cRHR2dpXV3Kfs+fvyIdu3a4dKlSwgPD0eDBg3EjpTr7t69iwYNGqB169YICgri0lBEVCRwji0RkYJ48eIFbG1tWdQWAKampmhjbw+/6Ggk5dIdm5NSU+EXHY029vYsavOQpqYm9uzZg5o1a6JNmza4ceOG2JFynbGxMby9vbF9+3asXLlS7DhERPmCI7ZERArg+fPnaNmyJd69e4fw8HDUqFFD7EhFXmxsLOrWro3mRkYYbWGRo7YEQcDyixdx4vlzXIqM5PubD96+fQtra2u8ffsWJ0+eRJUqVcSOlOvGjBmD1atX4+TJk2jcuLHYcYiI8hQLWyKiAu7Zs2do2bIl4uPjER4ejurVq4sdif6zfv16uLm5oU/t2uj1i3OdBUGA/40b2Hz9OoDPl1Obm5ujefPmaNy4MRo3bgwTExNeTpoHnj9/DisrK8hkMpw4cQJly5YVO1KuSklJgZWVFV68eIHIyEjo6+uLHYmIKM+wsCUiKsCePXsGW1tbJCYmIjw8HNWqVRM7En1l4sSJmDdvHtpUqQK3+vWhpaqa5WOTUlOx7vJlHIqNxezZs7Fjxw5cvHgRAKCiooK0/9bK1dPTw5EjRwrlfFCxPXjwAFZWVihWrBiOHTuGUqVKiR0pV92/fx/m5uawtrbGzp07+QUJERVanGNLRFRAPX36FDY2Nvjw4QMiIiJY1BZAUqkUFy9ehI6ODk48f47hR44g/OFDpH615uzXUqVShD98iOFHjuDk8+dYv349Jk2alOFOyOlFrUQigUwmk6+fS7mrUqVKCAsLw+vXr+Hg4ID379+LHSlXVa5cGb6+vti9ezeWLl0qdhwiojzDEVsiKpSkUilSUlKgoqIC1WyMoBUUT548ga2tLT59+oTw8HAYGxuLHalASU1NRVpaGjQ0NEQdgZo5cyamTZuG0NBQGBsbY6i7Ow6FhkJfSwtNy5RBNX19VNTVhbqyMpKlUjyMj0dMXBxOP3uGuKQktLG3h6eXV4b5nU2bNsXZs2ch+6I4PnjwIBwcHMR4iUXG5cuXYWNjg7p16yIkJARaWlpiR8pVEyZMwLJly3D8+HE0adJE7DhERLmOhS0RFTqvX7+Gubk5KlasiIiICIUrbB8/fgxbW1skJycjIiICVatWFTtSgSIIAqpXr45mzZrBx8dHtBzh4eGws7PD5MmT4eHhId8eHR0NLy8vHD50CLdiYvDlr1mJRIIa1aqhdZs2GDp06Dfvfrx//3506NAhw7YuXbrA19cXxYoVy7sXRPj333/RunVrNG/eHLt374a6urrYkXJNamoqbGxs8OjRI0RGRqJkyZJiRyIiylUsbImoUJHJZOjQoQPOnTuHqKgolC9fXuxI2fL48WPY2NggNTUV4eHhLGq/4eTJk7CyskJ4eDhsbGxEyfD8+XOYmZmhdu3aCA0NhbKy8jf3S0xMxJ07d5CcnAx1dXWYmJhAR0fnh20LggBTU1PcvHkTXbp0Qb9+/dCnTx+UL18eO3fu5B2T89iRI0fQrl07ODo6Ytu2bVBRURE7Uq559OgRzM3NYWlpib1790JJiTPSiKjw4P/RiKhQWbBgAQ4ePIjNmzcrXFH76NEj2NjYIC0tjSO1P+Dt7Y0qVaqgRYsWovQvlUrRu3dvSCQSbNmy5btFLQDo6OjAzMwMlpaWMDMz+2lRC3we1V26dCnat28PX19fdOrUCefPn4cgCGjcuDH27NmTmy+HvtKqVSsEBQVh165dcHNzy3BJuKKrUKECNm3ahAMHDmDhwoVixyEiylUsbImo0Dhx4gQmT56MSZMmKdx8xIcPH2Yoagvjmpq54cOHDwgMDES/fv1EG23y8PDAsWPH4O/vDyMjozzpw8HBAfv27ZNfelyjRg2cPXsWrVq1QqdOnTBt2rRCVXAVNB07doSfnx98fX0xZswYFKaL29q2bYuJEyfi77//xokTJ8SOQ0SUa3gpMhEVCi9fvoS5uTmqVauGsLAwhbp88MGDB7C1tYUgCIiIiEClSpXEjlRg+fn5oV+/foiNjUXlypXzvf/Q0FA4ODhgxowZmDx5cr73L5PJMG/ePEyePBnt27fHpk2boKenl+85ioo1a9bA3d0dkydPxsyZM8WOk2vS0tLQqlUr3LlzB5GRkTA0NBQ7EhFRjrGwJSKFJ5VK0bZtW0RFRSEqKgply5YVO1KW3b9/H7a2tpBIJIiIiEDFihXFjlSg2draQklJCUeOHMn3vp88eQJzc3OYm5vj4MGDos5PPHjwIHr37g0DAwPs3LkTtWvXFi1LYbdo0SJMmDABCxYswIQJE8SOk2uePn0KMzOzAvF5JiLKDfy/GBEpvDlz5iAsLAxbt25VuKLWxsYGSkpKLGqz4N69e4iIiICrq2u+952WloZevXpBTU0NmzdvFr0IaNu2LS5cuAANDQ1YWloiODhY1DyF2fjx4zF58mT8+eefWLNmjdhxck3ZsmWxZcsWHD58GHPmzBE7DhFRjrGwJSKFFh4ejunTp2Pq1Kmws7MTO06WxcbGwtraGioqKixqs8jPzw/FihVD165d873vKVOm4PTp09i2bRsMDAzyvf9vMTY2xr///gtHR0c4OTlh4sSJkEqlYscqlGbMmIFRo0Zh6NCh2Lp1q9hxck3r1q0xZcoUTJs2DeHh4WLHISLKEV6KTEQKK33JlTp16uDQoUM/vDttQXLv3j3Y2tpCTU0N4eHhCnf3ZjHIZDJUrVoVdnZ2WL9+fb72feDAAbRv3x7z5s3DX3/9la99Z4UgCFiyZAn+/PNP2NnZwd/fHyVKlBA7VqEjk8kwcOBAbNq0Cdu3b0enTp3EjpQrpFIp7O3tcf36dURFReXZDdGIiPIaC1siUkhSqRStW7fGjRs3EBUVhdKlS4sdKUvu3r0LW1tbaGhoIDw8HOXKlRM7kkIIDw9Hy5YtcfLkSTRr1izf+n306BHMzMzw22+/Ffh1P48cOYIePXpAV1cXO3fuRP369cWOVOikX5K+Z88e7N+/X6GuEvmR9C8JTU1NcfjwYYX5kpCI6EsF9zc0EdEPfLnkiqIUtXfu3IGNjQ00NTVZ1GaTt7c3qlWrhqZNm+Zbn6mpqejRowe0tbXh5+dXoIta4PP6qxcvXoSenh6aNGlSqC6ZLShUVFSwZcsWtGzZEp06dcK///4rdqRcYWRkBH9/fxw7dgwzZswQOw4R0S8p2L+liYi+ITQ0FLNmzcKMGTNgY2MjdpwsSS9qtbS0WNRmU3x8PIKDg+Hq6gqJRJJv/U6cOBHnz59HYGAgSpYsmW/95kSlSpVw6tQpODk54ffff8fYsWORlpYmdqxCRU1NDdu3b0fDhg3Rrl07REVFiR0pV9ja2sLDwwMzZ85EaGio2HGIiLKNlyITkUJJX3KlQYMGOHDgQIEfRQOAmJgY2NjYoFixYggPD0eZMmXEjqRQNmzYgMGDB+PBgwf5Nh95z5496NSpExYvXoyxY8fmS5+5SRAErFy5EmPHjkXz5s0REBDAtUpzWXx8PFq2bImHDx/ixIkTqFGjhtiRckwmk6Ft27aIjIxUuKXTiIhY2BKRwkhLS0PLli1x7949REZGFpi70/7I7du3YWNjg+LFiyM8PJw3ZvkFzZs3h46ODkJCQvKlv/v378Pc3BzW1tbYuXNnvo4S57bjx4/DyckJ6urq2LFjBywsLMSOVKi8fv0a1tbWiI+Px8mTJ1GpUiWxI+XYq1evYGZmBmNjYxw9ehQqKipiRyIiypKCP9RBRPSfgrjkyo/cunULNjY20NPTQ0REBIvaX3D79m2cOnUq39auTUlJgbOzM/T09ODt7a3QRS0AtGjRAhcvXkSZMmXQvHlz+Pj4iB2pUClVqhQOHz4MNTU12NnZ4fnz52JHyjEDAwNs27YNp0+fxtSpU8WOQ0SUZSxsiUghHDhwAPPmzcOcOXPQvHlzseP81M2bN2FjYwN9fX2Eh4crzA2uChpfX18UL14cnTt3zpf+JkyYgKioKAQGBkJfXz9f+sxr5cuXx/Hjx9GnTx/0798fw4cPR0pKitixCo2yZcsiLCwMHz9+ROvWrfH27VuxI+WYlZUVZs+ejblz5+LAgQNixyEiyhJeikxEBV76kitNmjTBnj17Cvy82hs3bsDW1halSpXC0aNHObfxF0mlUlSqVAmOjo7w9PTM8/62b9+O7t27Y/ny5Rg5cmSe9yeGtWvXYsSIEWjcuDGCg4N5FUEuunHjBlq0aIEqVargyJEjKFasmNiRckQmk8HR0RFnzpxBVFQUKlSoIHYkIqIfYmFLRAVaamoqrK2t8fjxY0RGRhb4u9NGR0ejZcuWMDAwwNGjRxXikumCKjQ0FG3atMHZs2fRuHHjPO3r7t27aNCgAezt7REYGKjwlyD/yL///otu3boB+FzMN2nSROREhcelS5dga2sLc3NzHDx4EJqammJHypE3b97A3Nwc5cuXx7Fjx6Cqqip2JCKi7yrYwx5EVOQp0pIr169fh62tLQwNDVnU5gJvb2+YmpqiUaNGedrPp0+f4OTkBAMDA6xfv75QF7UA0KRJE1y8eBFVq1aFtbU11q5dK3akQiP9bu3nz5+Hk5OTwl/yXbJkSQQEBOD8+fOYNGmS2HGIiH6IhS0RFVh79uzB4sWLsWDBAvz2229ix/mha9euwdbWFkZGRixqc0FcXBx27tyZL2vXjh07FtHR0QgMDETx4sXztK+CokyZMjh69CgGDx6MIUOGwM3NDcnJyWLHKhSaNWuGnTt34vDhw+jTpw+kUqnYkXKkSZMmmD9/PhYtWoQ9e/aIHYeI6Lt4KTIRFUjpS67Y2Nhgx44dBXoU7erVq2jVqhXKli2LI0eOFPiRZUXg5eWFESNG4PHjx3k6DzQgIAA9e/aEp6cn3N3d86yfgszHxwfu7u4wMzNDcHBwvq0VXNjt3LkTTk5OcHV1xbp16wr0/8N+RhAEdOnSBceOHUNkZCQqV64sdiQiokxY2BJRgZOSkoLmzZvj1atXuHTpUoG+O+2VK1fQqlUrlC9fHmFhYSxqc4mlpSUMDQ2xd+/ePOvj9u3baNiwITp06ICtW7cqdOGRUxcuXEDXrl2RnJyMoKAgtGjRQuxIhYKfnx/69euHP/74A4sXL1boz1hcXBwaNGgAQ0NDnDhxAmpqamJHIiLKgJciE1GBM2HCBFy+fBlBQUEFuqi9fPkyWrZsiQoVKnCkNhdFR0fj3Llzebp27cePH+Hk5ISyZcti7dq1Cl1w5AYLCwtcvHgRpqamaNWqFZYvXw5+751zffv2xcqVK7F06VLMmDFD7Dg5oq+vj8DAQERGRuLPP/8UOw4RUSYqYgcgIvrS9u3bsXz5cqxYsQIWFhZix/muqKgo2NnZoVKlSjh8+DBKlCghdqRCw8fHByVLloSjo2Oe9TFq1Cjcvn0bZ8+eVfhlWXKLgYEBDh8+jL/++gujR4/GhQsXsGbNGoW/s6/Yhg8fjoSEBEycOBG6urr4448/xI70yxo1aoTFixdj1KhRsLKykt9dm4ioIOClyERUYKQvudKmTRsEBAQU2FG0yMhI2NnZoUqVKjh8+HCBHlVWNGlpaahQoQKcnJywfPnyPOlj8+bN6NOnD9avX4+BAwfmSR+KbuvWrRg0aBBq1qyJHTt2cE5lLpg4cSLmzZuHdevWYdCgQWLH+WWCIMDZ2RmhoaG4dOkSjI2NxY5ERASAhS0RFRCfPn1C06ZNER8fj4sXLxbYu9NeunQJdnZ2MDExQWhoKPT09MSOVKjs378fHTp0wKVLl2Bubp7r7d+4cQMWFhbo1q0bfH19C+yXJwXB5cuX0aVLF8THxyMgIACtWrUSO5JCEwQBI0aMgKenJ/z9/dGjRw+xI/2y9+/fo2HDhihevDhOnToFDQ0NsSMREXGOLREVDOlLrgQFBRXYovbixYuws7NDtWrVWNTmEW9vb9SvXz9PitoPHz7AyckJlSpVgqenJ4van6hfvz4uXLiAhg0bwt7eHosWLeK82xyQSCRYsWIFXFxc4OLigv3794sd6ZcVL14cgYGBuHbtGsaNGyd2HCIiAByxJaICIH3JFS8vLwwZMkTsON+UnJyMly9fQlVVFYaGhlBS4veCuU0mkyExMRHq6upQV1fP9fY/fvyI1NRUaGtrQ1lZOdfbL6ykUikmT56MefPmoUePHtiwYQO0tbXFjqWw0tLS4OzsjAMHDuDgwYOwtbUVO9IvS0hIQFxcHEqWLMnPBBGJjoUtEYkqfckVR0dHbNmyhaNoRAVUcHAwXF1dUbVqVezcuZNzK3MgOTkZjo6O+PfffxEWFgZLS0uxIxERKTwWtkQkmo8fP+K3337Dp0+fcOHCBd6dlqiAu379Orp06YJXr17B398fDg4OYkdSWB8+fIC9vT1u3LiBY8eOoW7dumJHIiJSaLyWjohEk77kSnBwMItaIgVQu3ZtnDt3Ds2aNUO7du0wZ84czrv9Rdra2ti/fz8qVaqE1q1bIyYmRuxIREQKjYUtEYli8+bNWL9+PVatWsWRCiIFoqenhz179mDKlCn4+++/0a1bNyQkJIgdSyHp6ekhNDQU+vr6sLOzw6NHj8SORESksFjYElG+i46OxpAhQ9C3b1/0799f7DgKwcbGBhKJBBKJBFFRUWLHUSjTp0+HmZmZ/LGrqys6d+4sWp785urqKv/s7Nq1K1faVFJSgoeHB3bv3i2fI3rr1q1cabuoMTAwwOHDh6GkpAQ7Ozu8ePFC7EhERAqJhS0R5av0JVcqV66M1atX5+vNor4ucHKTj49Pni//4+bmhmfPnqFOnToAgPv37xe6m21VrlwZERERWd4/IiIClStXzlYf//zzD3x8fLJ1jCKZPn06XF1d5Y//+ecfPHv2LE/66tixI86fPw8AaNy4Mfbs2ZMn/RR25cuXR1hYGBISEtCmTRvExcXla//5/WVPbn7JQkSUjoUtEeWr4cOH4/7/sXfn4VC1/x/A32NfK61SoqJFG1GeFiEVbU9F0ibaVbK0Pq1oXx7RvilKkZC0KLKVtAqtkkRSSrtIlpn798fzbX5NJIShPq/r6rqaM+fc5z0zzsx85tznvtPT4e/vT9NDVJCMjAwUFRUhJiYm7CgCCgsLhR2hQurXr1/mjxB17fH8TP369aGoqFht7bdv3x7Xr1/HgAEDMGLECDg5OYHH41Xb/n5Xbdu2xYULF/Ds2TMMGTIEubm5wo5UQlFRkbAjCPjdjlVCyK+hwpYQUmM8PT1x6NAh7NmzBxoaGhXevqCgAHZ2dmjatCmkpKTQt29f/tmi0s6Ynjx5kn9G08vLCy4uLrh9+za/W+bXs3YcDge7d+/G4MGDIS0tjTZt2iAgIIDfTnR0NDgcDj58+MBflpiYCA6Hg/T0dERHR2Py5Mn4+PEjv21nZ+cKP76qcPnyZejp6UFaWhrKysqws7NDXl4e/35VVVWsW7cOU6ZMgby8PFq1aoV9+/YJtPHs2TOMGTMGDRo0QMOGDTFixAikp6fz7/96dmft2rVQUlJC+/btAQBXrlyBpqYmpKSkoKOjw3/+ExMTwRiDmpoa/v33X4F9fX0eHz9+XGXPwYYNG9CsWTPIy8tj6tSp+PLli8D935+dMjAwgK2tLRwcHNC4cWMYGxv/dB8cDgceHh4YNWoUZGRkoK6uLnC2ksvlYurUqWjdujWkpaXRvn17bN26tdQc69atQ7NmzdCgQQOsWrUKxcXFWLhwIRo2bIiWLVvC09NTYLufvT7CIC8vj4CAAKxbtw6rV6/GiBEjBI4XUj6dOnVCaGgo7t+/jxEjRpT42/1VAQEB6NKlC6SlpdGoUSMMGDAACxcuxKFDhxAcHMx//4qOjub3CPHz84O+vj6kpKRw9OjRUnu+uLu7l+g5cfDgQXTq1AmSkpJo3rw5bG1tAYC/3qhRo8DhcPi3Sztr7ODgAAMDA/7tHx2r9+7dw+DBgyEnJ4dmzZrB0tISb968qaqnjRBSR1BhSwipEXfv3sWcOXMwdepUWFpaVqqNRYsWITAwEIcOHUJ8fDzU1NRgbGyMd+/e/XRbCwsLzJ8/H506dUJWVhaysrJgYWHBv3/FihUwMzPD7du3MWHCBIwdOxZJSUnlytW7d2+4u7ujXr16/LYXLFhQqcf4K1JTU2FiYgIzMzPcuXMHfn5+uHz5Mv8L5Veurq7Q0dFBQkICZs+ejVmzZvGvjywqKoKxsTHk5eURExOD2NhYyMnJwcTERODsSEREBJKTk3HhwgWcOXMGOTk5GD58OLp06YL4+HisXr0aixcv5q/P4XAwZcqUEkWap6cn+vXrBzU1tSp5Do4fPw5nZ2esW7cOcXFxaN68OXbt2vXT7Q4dOgQJCQnExsZiz5495dqXi4sLxowZgzt37mDIkCGYMGEC/2+Rx+OhZcuW8Pf3x4MHD7By5UosXboUx48fF2gjMjISL168wKVLl7BlyxY4OTlh2LBhUFBQwPXr12FjY4OZM2ciMzMTQPlfH2HgcDhYsmQJQkJCcPnyZfTs2RP3798Xaqa6SEdHB2fOnMGVK1dgYWFRZWdJs7KyMG7cOEyZMgVJSUmIjo6GqakpnJycMGbMGJiYmPDfv3r37s3f7p9//oG9vT2SkpLK9aMPAOzevRtz5szBjBkzcPfuXZw6dYp/jH/9MdLT0xNZWVn82+X1/bH64cMH9O/fH1paWoiLi8P58+fx6tUrjBkzpkLtEkJ+A4wQQqpZTk4Oa9++PevSpQv7/PlzpdrIzc1l4uLi7OjRo/xlhYWFTElJiW3atIl5enqy+vXrC2wTFBTEvn2bc3JyYt26dSvRNgBmY2MjsExXV5fNmjWLMcZYVFQUA8Dev3/Pvz8hIYEBYGlpaYwxVur+q5K+vj6zt7cvc52pU6eyGTNmCCyLiYlhIiIiLD8/nzHGmIqKCps4cSL/fh6Px5o2bcp2797NGGPM29ubtW/fnvF4PP46BQUFTFpamoWGhjLGGLOysmLNmjVjBQUF/HV2797NGjVqxN8PY4zt37+fAWAJCQmMMcaeP3/OREVF2fXr1xlj/71+jRs3Zl5eXhV8Nn6sV69ebPbs2QLLdHV1BV53KysrNmLECP5tfX19pqWlVaH9AGDLly/n387NzWUA2Llz5364zZw5c5iZmZlADhUVFcblcvnL2rdvz/T09Pi3i4uLmaysLPP19WWMle/1KStzUFBQuR/jr3j8+DHr0qULk5WVZf7+/jWyz99NSEgIExcXZ+PHjxf4G6msW7duMQAsPT29xH3fHxOMMZaWlsYAMHd3d4Hlpb2Purm5MRUVFf5tJSUltmzZsh9mKe1vsbQM9vb2TF9fn3+7tGN19erVbNCgQQLLnj17xgCw5OTkH2YghPx+6IwtIaRaMcZgY2OD58+fw9/fH9LS0pVqJzU1FUVFRejTpw9/mbi4OHr27FnuM6tl6dWrV4nbVdFuTbp9+za8vLwgJyfH/2dsbAwej4e0tDT+el27duX/n8PhQFFREdnZ2fw2Hj9+DHl5eX4bDRs2xJcvX5CamsrfrkuXLpCQkODfTk5ORteuXSElJcVf1rNnT4F8SkpKGDp0KA4ePAgAOH36NAoKCmBubl5lz0FSUhJ0dXUFln3/2pZGW1u7wvv69nmUlZVFvXr1+M8jAOzcuRPa2tpo0qQJ5OTksG/fPmRkZAi00alTJ4iI/P9HcbNmzQSmvxIVFUWjRo0q/PoIW9u2bXH16lUMHz4c5ubm+Oeff8DlcoUdq04ZPHgwjh49imPHjmH27Nm/PF9wt27dYGRkhC5dusDc3Bz79+8v1yBVOjo6FdpPdnY2Xrx4ASMjo8pGLdP3x+rt27cRFRUl8L7XoUMHAKhVxwQhpPrVrhFICCG/nf3798PHxwe+vr78azGrg4iISIkvflXVhe9r4fFt+7VtEBUAyM3NxcyZM2FnZ1fivlatWvH/Ly4uLnAfh8PhD/aTm5sLbW1tHD16tEQbTZo04f+/sgN/TZs2DZaWlnBzc4OnpycsLCwgIyNTqbaqUmUeT1nP47Fjx7BgwQK4urqiV69ekJeXx+bNm3H9+vWftlEVr09tICsrCx8fH+jo6GDRokVISEiAj48PGjVqJOxodYa5uTlyc3MxZcoU1KtXDxs3bqz0SOiioqK4cOECrly5grCwMGzfvh3Lli0r8Tf5ve+PjZ+911b2x8vyvod/nyc3NxfDhw/Hxo0bS6zbvHnzSmUhhNRNVNgSQqpNYmIi7OzsYGNjg7Fjx/5SW23btuVfV6WiogLgvy89N2/ehIODA5o0aYJPnz4hLy+P/8Xn+/leJSQkfnjW6Nq1a5g0aZLAbS0tLQD/XzBkZWVBQUGhwm3XlO7du+PBgwe/dL1q9+7d4efnh6ZNm6JevXrl3q59+/Y4cuQICgoKICkpCQClXjs3ZMgQyMrKYvfu3Th//jwuXbpU6ayl6dixI65fv17itaxpsbGx6N27N2bPns1fVhVnjyr7+ggLh8PB/PnzoampCQsLC+jo6CAoKKjapt36HU2ePBk5OTlwcHBA/fr1sWzZskq3xeFw0KdPH/Tp0wcrV66EiooKgoKCKvT+1aRJE7x8+RKMMX6R/e37oby8PFRVVREREQFDQ8NS2xAXFy+xvyZNmuDevXsCyxITE0v80PO97t27IzAwEKqqqrVuxHhCSM2irsiEkGqRk5MDc3NzaGhowM3N7Zfbk5WVxaxZs7Bw4UKcP38eDx48wPTp0/H582dMnToVurq6kJGRwdKlS5GamgofH58Sc5WqqqoiLS0NiYmJePPmDQoKCvj3+fv74+DBg3j06BGcnJxw48YN/qBLampqUFZWhrOzM1JSUnD27Fm4urqWaDs3NxcRERF48+YNPn/+/MuPuaIWL16MK1euwNbWFomJiUhJSUFwcHCJwaPKMmHCBDRu3BgjRoxATEwM0tLSEB0dDTs7O/4ARqUZP348eDweZsyYgaSkJISGhvJHQP72DJOoqCisra2xZMkSqKurl6ubcEXY29vj4MGD8PT05L+WwhjASF1dHXFxcQgNDcWjR4+wYsWKCg+SU5rKvj7CZmRkhFu3bkFBQQG9e/eGj4+PsCPVKfb29li9ejWWL1+Obdu2VaqN69ev8wdVy8jIwIkTJ/D69Wt07NgRqqqquHPnDpKTk/HmzZsye6QYGBjg9evX2LRpE1JTU7Fz506cO3dOYB1nZ2e4urpi27ZtSElJQXx8PLZv386//2vh+/LlS3536P79+yMuLg6HDx9GSkoKnJycShS6pZkzZw7evXuHcePG4ebNm0hNTUVoaCgmT54s9B8bCSE1iwpbQkiVY4xh2rRpyM7OxvHjxwWuu/wVGzZsgJmZGSwtLdG9e3c8fvwYoaGhUFBQQMOGDXHkyBGEhISgS5cu8PX1LTHljpmZGUxMTGBoaIgmTZrA19eXf5+LiwuOHTuGrl274vDhw/D19eVPSSQuLg5fX188fPgQXbt2xcaNG7FmzRqBtnv37g0bGxtYWFigSZMm2LRpU5U85oro2rUrLl68iEePHkFPTw9aWlpYuXIllJSUyt2GjIwMLl26hFatWsHU1BQdO3bkT5lT1hnCevXq4fTp00hMTISmpiaWLVuGlStXAkCJ13/q1KkoLCzE5MmTf5rHwMAA1tbW5c5vYWGBFStWYNGiRdDW1sbTp08xa9ascm9fVWbOnAlTU1NYWFhAV1cXb9++FTh7W1mVfX1qAxUVFcTGxsLc3BwTJkzAvHnzUFxcLOxYdcayZcuwYMEC2NvblxhdvDzq1auHS5cuYciQIWjXrh2WL18OV1dXDB48GNOnT0f79u2ho6ODJk2aIDY29oftdOzYEbt27cLOnTvRrVs33Lhxo8Qo8FZWVnB3d8euXbvQqVMnDBs2DCkpKfz7XV1dceHCBSgrK/N7xhgbG/OP3R49euDTp08CPS9+RElJCbGxseByuRg0aBC6dOkCBwcHNGjQQOD6dULI74/DfnU0AkII+c7OnTtha2uLgIAAmJmZCTvOT3E4HAQFBZWYQ7E2MTAwgKamJtzd3YUdpdyOHj3Kn9/32+vuYmJiYGRkhGfPnqFZs2ZltqGiogIXF5cKFbekdLXl75wxhh07dmDevHno27cvv2s1+bmvg/F5eHjAz88Po0ePFnYkQgipNeinLEJIlYqLi8O8efMwd+7cOlHU1iW7du2CnJwc7t69K+wopTp8+DAuX76MtLQ0nDx5EosXL8aYMWP4RW1BQQEyMzPh7OwMc3Pznxa19+/fR/369ct11ob8mI2NDeTk5IQdg4/D4WDu3LmIiIjAgwcPoKOjg7i4OGHHqhM4HA527dqFsWPHYvz48Th//rywIxFCSK1BZ2wJIVXmw4cP6N69Oxo3boyYmBj+IEK1XW05k1WW58+fIz8/H8B/Ixx/O9VObbFp0ybs2rULL1++RPPmzTFy5EisXbuWP+qxl5cXpk6dCk1NTZw6dQotWrQQcuLSHT16FDNnziz1PhUVFaFcs/srsrOzkZOTA+C/UWIrO6J1dcjMzISZmRlu376NPXv20Jn5cioqKoKZmRnCw8Nx/vx59OvXT9iRCCFE6KiwJYRUCcYYzMzMEBUVhfj4eLRu3VrYkQiplE+fPuHVq1el3icuLs4flZtUjYKCAtja2sLDwwOzZ8+Gm5tbrfzhprb58uULhg4dips3byIyMrLC880SQsjvhgpbQkiVcHd3h6OjI06ePIkRI0YIOw4hpI7Zt28fbG1t0bNnTwQEBEBRUVHYkWq93NxcDBw4ECkpKbh48SI6deok7EiEECI0VNgSQn7ZtWvXoKenBzs7uxLT4BBCSHldvXoVo0ePBmMMgYGBVT4d1O/o/fv3/Cl4Ll++jDZt2gg7EiGECAUVtoSQX/Lu3TtoaWlBSUkJly5dgri4uLAjEULqsJcvX8Lc3BzXr1/H9u3bMWPGDIG5kElJr169gp6eHoqKinD58uVae/06IYRUJxoVmRBSaTweD1ZWVsjNzYWfn5/Qi9r8/Hw8e/YMb968Af1mV/vk5eUhLy+vwtvxeDx8+vQJeXl59Lr+ARQVFREREYEZM2bAxsYG06dPx5cvX4Qdq1Zr1qwZwsPDwePxMGDAALx+/VrYkcqtuLgYz58/x+vXr+n4JoT8EipsCSGV5urqijNnzsDb2xutWrUSapagoCDUq1cP8+bNQ/369ekMTy2TkZEBeXl5HD9+vELbcblcGBsbQ11dHTk5OfS6/iEkJCSwY8cOeHp64siRI9DX10dmZqawY9VqrVq1Qnh4ON6/fw9jY2N8+PBB2JHKRUxMDAkJCWjatCn+/fdfYcchhNRhVNgSQiolNjYWS5YsweLFizFkyBChZjlx4gTGjBkDU1NT+Pj4CP3MMSnp8OHDkJGRgbm5eYW2W7NmDSIiInD06FE0b968mtKR2sra2hqXL19GVlYWtLW1cenSJWFHqtXU1dURFhaG9PR0DBs2rFI9JIRh2LBhWLRoEZYsWYLY2FhhxyGE1FF0jS0hpMJev34NLS0ttG7dGlFRURATExNalsDAQIwdOxZmZmY4cuSIULOQ0jHGoK6ujr59+8LLy6vc20VERGDgwIFwcnKCk5NT9QUktd7r168xZswYXL58Ga6urpg7dy6dvS/D9evXYWRkhN69e+P06dN1Yk7xoqIiGBoaIj09HYmJiWjcuLGwIxFC6hg6Y0sIqRAejwdLS0sUFBTg2LFjQi0k/f39YWFhgdGjR1NRW4vFxMQgNTUVkydPLvc2WVlZGD9+PIyMjLB8+fJqTEfqgiZNmuDChQuws7ODvb09rKyskJ+fL+xYtZauri5Onz6NS5cuYdy4cSguLhZ2pJ8SFxfHsWPHUFBQAEtLS/B4PGFHIoTUMVTYEkIqZMOGDQgLC8PRo0eFOvLm8ePHMW7cOFhYWMDb25uK2lrMy8sLbdq0gZ6eXrnWLy4uxvjx4yEqKoqjR49CVFS0mhOSukBMTAyurq44evQoAgIC0KdPH6Snpws7Vq1laGiIgIAAnD59GlOmTKkThWLLli3h7e2N0NBQbNiwQdhxCCF1DBW2hJByu3jxIlasWIFly5Zh0KBBQsvh5+eH8ePHY+zYsTh8+DAVtbVYbm4ujh8/DisrK4iIlO8jx8XFBZcuXYKvry+aNm1azQlJXTN+/HhcvXoVHz58gI6ODiIiIoQdqdYaNmwYvL29ceTIEdjZ2dWJUYdNTEywdOlSrFixAhcvXhR2HEJIHULX2BJCyuXVq1fQ1NREhw4dEB4eLrSzaL6+vpg4cSImTJgAT09POptXyx06dAjW1tZIT0+HiorKT9cPDQ3F4MGDsWbNGixdurQGEpK66t27dxg3bhzCw8OxceNGzJ8/n667/YH9+/djxowZWLJkCdatWyfsOD9VXFyMAQMG4NGjR0hISECzZs2EHYkQUgdQYUsI+amvU67cu3cPCQkJQhud1sfHB5aWlpg4cSIOHjxIRW0dYGBgAFFR0XKdVcvMzISWlhZ0dHRw9uzZcp/hJX8uLpeL5cuXY8OGDbCwsMCBAwcgKysr7Fi10pYtWzB//nysX78e//zzj7Dj/FRWVhY0NTXRtWtXnD9/nt7vCSE/Rd8aCCE/tWbNGkRGRsLHx0doRe3Ro0dhaWmJSZMmUVFbRzx58gQXL14s16BRxcXFGDduHKSkpODt7U1FLSkXUVFRrF+/Hv7+/jhz5gx69eqF1NRUYceqlebNmwcnJycsWbIEu3btEnacn2revDl8fHwQERGBtWvXCjsOIaQOoG8OhJAyRUREwMXFBc7Ozujfv79QMnh7e2PSpEmwsrKCh4cHFbV1xKFDhyAvLw9TU9Ofrrt8+XJcvXoVx44do2k+SIWNHj0a169fx5cvX6Cjo4Pz588LO1Kt5OTkBAcHB8yZMwfe3t7CjvNTRkZGcHJygrOzM11LTQj5KeqKTAj5odrQFezw4cOwtrbGlClTsG/fPjqTV0fweDy0adMGAwcOxP79+8tc98yZMxg+fDg2b96MBQsW1FBC8jv68OEDLC0tcfbsWaxZswZLliyh626/wxjD9OnT4eXlBX9/f4waNUrYkcrE5XJhYmKCO3fuIDExUWi9hgghtR8VtoSQUn07eEdiYqJQRqc9dOgQJk+ejKlTp2Lv3r1U1NYhERERGDBgAGJjY9G7d+8frpeRkQEtLS306dMHwcHBVISQX8bj8bBq1Sq4uLhg1KhR/J4D5P9xuVyMHz8eJ0+exJkzZzBw4EBhRyrTt4MXXrhwgUbCJ4SUir4lEkJK5ezsjJiYGBw7dkwoRa2npycmT56MadOmUVFbB3l5eaFdu3bo1avXD9cpLCyEhYUF5OXl4eXlRUUtqRIiIiJwdnZGcHAwIiIi0LNnTyQnJws7Vq0iKioKb29vDBgwACNHjkRsbKywI5WpWbNmOHbsGC5dugQXFxdhxyGE1FL0TZEQUsL58+exdu1arFmzBv369avx/R88eBBTp07FjBkzsGfPHipq65iPHz8iMDAQ1tbWZRarS5Yswa1bt+Dn54eGDRvWYELyJ/j7779x48YNcDgc9OjRA8HBwcKOVKtISEggICAAPXr0wJAhQ5CQkCDsSGXS19fH6tWrsXbtWoSGhgo7DiGkFqKuyIQQAZmZmdDU1ETPnj1x5syZGi8qPTw8MH36dMyaNQs7duygorYO8vDwwMyZM5GRkYEWLVqUus7JkycxatQouLu7w97evoYTkj/Jp0+fYG1tjRMnTmDFihVwdnam95Vv5OTkYMCAAUhLS8OlS5fQsWNHYUf6IR6Ph6FDhyIuLg4JCQlo2bKlsCMRQmoRKmwJIXxFRUUwNDTE06dPkZCQUOOj0+7btw8zZ87E7NmzsWPHDuqaWkf16dMH8vLyPxyZNi0tDVpaWjAyMkJAQAC9zqTaMcawYcMGLFu2DEOGDMGRI0fQoEEDYceqNd6+fQsDAwO8f/8eMTExaN26tbAj/dCbN2+gqamJ1q1bIyoqiq63JYTw0U+WhBC+5cuX4/r16/Dz86vxonbv3r2YOXMmbG1tqaitw5KTk3HlypUfzl1bUFCAMWPGoGHDhjhw4AC9zqRGcDgcLFmyBCEhIYiNjUWPHj1w//59YceqNRo1aoSwsDBISUlhwIABePHihbAj/VDjxo3h5+eHq1evYvny5cKOQwipRaiwJYQA+G/KlU2bNmH9+vVljmJbHfbs2QMbGxvMnTsX27Zto2KnDjt06BAaNGiAESNGlHr/woULcefOHfj7+9MZM1LjTExMEBcXB2lpaejq6iIgIEDYkWqN5s2bIzw8HIWFhRg4cCDevHkj7Eg/1KdPH6xfvx4bN27EmTNnhB2HEFJLUFdkQgiePn0KLS0t9O3bt8anXNm1axfmzJkDe3t7uLm5UVFbh3G5XKioqODvv//Grl27StwfEBAAc3Nz7Ny5E7NnzxZCQkL+k5eXh2nTpuHYsWNYvHgx1q5dK5R5umujhw8fol+/fmjVqhUiIyNRr149YUcqFY/Hw4gRI3DlyhUkJCSgVatWwo5ECBEyKmwJ+cMVFhaiX79+ePnyJeLj42t0dNqdO3fC1tYWDg4O2LJlCxW1dVBaWhrc3d0xfPhwFBYWYujQobhx4wZ69OghsN7jx4/RvXt3DBkyBL6+vvRaE6FjjGHLli1YtGgRBgwYAB8fHzRq1EjYsWqFxMREGBgYoFu3bjh37hxkZGSEHalU7969g5aWFpSUlHDx4kVISEgIOxIhRIiosCXkDzdv3jzs2LEDMTEx0NXVrbH9bt++HXZ2dpg3bx7+/fdfKnTqKB8fH0yYMAEAICUlBTk5OVy9ehVqamr8db58+YJevXohLy8PcXFxtfYMEPkzRURE8OdTDgoKgqamprAj1QpXrlzBwIED0a9fPwQHB9faovH69evQ09PD3Llz4erqKuw4hBAhomtsCfmDnTx5Em5ubti8eXONFrVbt26FnZ0d5s+fT0VtHaekpMT//5cvX/D27Vuoq6ujefPmiIuLAwA4OjoiKSkJ/v7+VNSSWsfIyAi3bt2CgoICevfuDR8fH2FHqhV69+6N4OBgREZGYsKECSguLhZ2pFLp6upi06ZN2LJlC06ePCnsOIQQIaIztoT8oZ48eYLu3bvX+JQr7u7ucHR0xMKFC7Fx40Yqauu4lJQUtGvXrtT7JCUlMXnyZOzZswf79u3D9OnTazgdIeWXn58PGxsbHD58GI6Ojti0aRNNJQMgODgYZmZmmDRpEjw8PGrlHMCMMZiZmSEyMhIJCQm1eroiQkj1ocKWkD9QQUEB+vTpg3fv3iE+Pr7GRqd1c3PDvHnzsHjxYqxfv56K2t9Abm4u5OXl+bdFRUXRsGFDvHv3DlwuFwDQpk0b3L17t9Zep0fIV4wx7Ny5E46Ojujbty/8/PzQtGlTYccSuiNHjmDSpEmYO3cu3N3da+V794cPH9C9e3c0atQIly9fhqSkpLAjEUJqWO372Y0QUu0WLFiAu3fv1uiUK66urpg3bx7++ecfKmp/I3JycpCWlgbw31yhvXv3hoaGBr+oBYD09HTo6OggPT1dSCkJKR8OhwNbW1tERkbiwYMH0NHR4Xep/5NNnDgRu3btwrZt2+Dk5CTsOKVq0KABjh8/jjt37mDhwoXCjkMIEQIqbAn5w/j7+2PHjh1wc3ODtrZ2jexz8+bNWLBgAZYuXYp169ZRUfub+XpmZMiQIQgLC8Pdu3dLrJOUlIRz587VdDRCKkVPTw/x8fFQUlJC37594enpKexIQmdjY4ONGzdi9erV+Pfff4Udp1Q6OjrYsmULtm/fDn9/f2HHIYTUMOqKTMgfRBhTrmzatAmLFy/G8uXLsWrVKipq67Dc3Fw8fvwYBQUFkJSUhJqaGuTk5NCvXz8wxhAVFYU3b96gefPmAAARERHweDz06NEDq1evxqBBg+j1J3VKQUEB5s6di/3792P27Nlwc3OrtaMD15Rly5Zh3bp12Lt3L2bMmCHsOCUwxmBhYYHz588jPj5eYIR2QsjvjQpbQv4QwphyZcOGDViyZAlWrFgBFxcXKmrqoAcPHmDPnj0IO38ejx4/xrcfGRwOB+3U1DDIxAQ2NjbQ0NDAoUOHYG1tDQD466+/sGrVKgwYMIBee1Kn7du3D7a2tujZsyf8/f35P978iRhjKCoqApfLhbi4eK0cYIsxhoKCAgD/9Sih9x9C/gxU2BLyh7CxsYGXlxeuX7+Obt26Vfv+1q1bh2XLlsHJyQnOzs7Vvj9StdLS0jDLxgahYWFQkJFB7+bNoa6ggFb16kFSTAwFxcXIyMlByvv3uJKVhfefP8N40CBMtLSEs7Mztm7diiFDhtAXSvLbuHr1KkaPHg3GGAIDA9GrVy9hRyKEEPINKmwJ+QP4+vpi/PjxNTblytq1a7F8+XI4OzvX2oFGyI95eHjAwc4OcmJimKShgb4tW0K8jCk+ing8XM7MxOEHD5BbXAz3bdswbdq0GkxMSM14+fIlzM3Ncf36dWzfvh0zZsygH28IIaSWoMGjCPnNPXz4ENOnT8eECRNqpNhYvXo1li9fDhcXl9++qN28eTPatGkDUVFRaGpqCjtOlVi7di2mT5+OvoqK2GlkBMNWrcosagFAXEQEhq1aYaeREfoqKmL69OlYu3YtoqOjweFwEB0dXTPhfwGHw6GeBeSnFBUVERERgRkzZsDGxgbTp0/Hly9fhB2LEEIIqLAl5Lf2+fNnmJubQ1lZGXv27Kn2MwsuLi5YuXIlVq9ejZUrV1brvsqybt06nDx5slr3ERYWhkWLFqFPnz7w9PTEunXrqnV/NcHDwwPLly+HZadOsNfRgYy4eIW2lxEXh72ODiZ26oTly5fj7Nmz1ZSUVIeaOG5+BxISEtixYwc8PT1x5MgR6OvrIzMzU9ixCCHkj0ddkQn5jU2dOhW+vr64ceMGOnfuXK37cnZ2houLC9auXYulS5dW675+Rk5ODqNHj4aXl1e17eOff/7B5s2bkZ+f/1uMkpqWloYunTqhr6Ii7HV0fqktxhi23bqFmKws3EpIQLt27SDyk7O+wvblyxeIiYnVyoFwakpNHDe/m7i4OJiamqKgoAD+/v7o16+fsCMRQsgfq3Z/0yCEVNrhw4dx8OBB7Nq1q1qLWsYYnJyc4OLignXr1lV5Ucvj8WplV7/s7GxIS0v/tKitrfm/N8vGBnJiYpheBQOLcTgcTO/WDfLi4nCwt6/1RS0ASElJ/dFFbXXJy8sTdoRqpaOjg1u3bkFDQwNGRkbYtm0b6uL5Amtra4wcObLG9sfhcITWO4DL5Qplv4SQ6lf7v20QQirs/v37mDVrFqytrflTr3wVHR0NHR0dSElJoW3btti7dy+cnZ1LdFM+cuQItLW1IS0tjYYNG2Ls2LF49uyZwDoGBgZo1qwZVq1ahTZt2mD16tVo0aIFNm3aVCJTQUEBnJycoKamBklJSSgrK2PRokX8KRm+4nA4sLW1xdGjR9GpUydISkri/PnzAIB///0XvXv3RqNGjSAtLQ1tbW0EBASU2D4vLw+HDh0Ch8MBh8MReA6eP3+OKVOmoFmzZpCUlESnTp1w8ODBCj2/HA4Hnp6eyMvL4+/j61musvKXd9+ZmZkYOXIkZGVl0bRpUzg6OiI0NLTE9aqqqqolXl/gv9fFwMBAYFlZz/+DBw8QGhaGSRoaGH3yJHbFx+PK8+eYFRqKvwMDYRMairiXL0vs501+Ptxv3sTE06fxd2AgJp89ix23bqGIx4OMuDgMWrZEaFgYDh06JLDd9evXYWJigvr160NGRgb6+vqIjY0VWOfTp09wcHCAqqoqJCUl0bRpUwwcOBDx8fHleIX+34cPH+Dg4ABlZWX+3LsbN24Ej8cTWK+0a2zLe6wA5T9eOnfujAcPHsDQ0BAyMjIljpdXr15BTEwMLi4uJfaRnJwMDoeDHTt2VPjx8Xg8bN26FV26dIGUlBSaNGkCExMTxMXF8R9/WcdNQkICBg8ejHr16kFOTg5GRka4du2awD68vLzA4XBw8eJFzJ49G02bNkXLli0BVN3rWRs1adIEFy5cgJ2dHezt7WFlZYX8/Hxhx6oWRUVFwo4goLCwsMLbcLlcvH79uhrSEEKEjhFCfiufPn1iHTt2ZJ06dWJ5eXkC98XHxzNJSUmmqqrKNmzYwNauXcuUlJRYt27d2LdvB2vWrGEcDodZWFiwXbt2MRcXF9a4cWOmqqrK3r9/zxhjjMfjsVatWjEArEGDBsze3p7t2rWL9e/fnwFgISEh/Pa4XC4bNGgQk5GRYQ4ODmzv3r3M1taWiYmJsREjRghkBMA6duzImjRpwlxcXNjOnTtZQkICY4yxli1bstmzZ7MdO3awLVu2sJ49ezIA7MyZM/ztvb29maSkJNPT02Pe3t7M29ubXblyhTHG2MuXL1nLli2ZsrIyW7VqFdu9ezf7+++/GQDm5uZW7ufY29ub6enpMUlJSf4+UlNTy8xf3n1//vyZtWvXjklJSbFFixYxd3d3pq2tzbp27coAsKioKP66KioqzMrKqkQ+fX19pq+vX+7nf+7cuUxBRoYFm5kxAKxN/fqsoZQUs+zUic3Q1GSKsrJMUlSUHfv7bxZibs5CzM2Z97BhrJGUFJMUFWUj1dWZbffubFzHjkxZXp4dHzGChZibszV6egwAGzVqFD9LREQEk5CQYL169WKurq7Mzc2Nde3alUlISLDr16/z1xs/fjyTkJBg8+bNYx4eHmzjxo1s+PDh7MiRI+V+nfLy8ljXrl1Zo0aN2NKlS9mePXvYpEmTGIfDYfb29gLrAmBOTk782+U9Vhgr3/Hy9XVRUlJiysrKZR4v/fv3ZxoaGiUej4uLCxMVFWUvX76s8OOztrZmANjgwYOZu7s7+/fff9mIESPY9u3bGWNlHzf37t1jsrKyrHnz5mz16tVsw4YNrHXr1kxSUpJdu3aNvw9PT08GgGloaDB9fX22fft2tmHDBsZY1byedcHRo0eZtLQ009LSYmlpacKOU4K/vz/r3Lkzk5KSYg0bNmRGRkZswYIFDIDAv6ioKJaWlsYAsGPHjrF+/foxSUlJ5unpyZycnFi3bt0E2nVzc2MqKioCyw4cOMA0NDSYhIQEU1RUZHPmzGGM/fe+9e2+vm5nZWVV4vPA3t5e4L1MX1+fzZkzh9nb27NGjRoxAwMDxhhjd+/eZSYmJkxWVpY1bdqUTZw4kb1+/brU56CwsJDt3r2b5ebmVvp5JITUTlTYEvIb4fF4zNLSksnKyrIHDx6UuH/48OFMRkaGPX/+nL8sJSWFiYmJ8b+sp6enM1FRUbZ27VqBbe/evcvExMTY2rVrGY/HY0uWLOF/MTl8+DB/vYKCAqaoqMjMzMz4y7y9vZmIiAiLiYkRaHPPnj0MAIuNjeUvA8BERETY/fv3S+T//PmzwO3CwkLWuXNn1r9/f4HlsrKypRZ8U6dOZc2bN2dv3rwRWD527FhWv379Eu2XxcrKisnKypZY/qP85d23u7s7A8COHz/OXycvL4+pqalVurD92fPfqmVLNrRtWxZibs4AMDEREXZg8GB+Ebtz4EAGgM3S0uIvM1JRYSIAczcy4i/7+u/s6NEsxNycbdDXZwCYcsuWjLH//j7V1dWZsbEx4/F4/ByfP39mrVu3ZgMHDuQvq1+/Pv+LcGWtXr2aycrKskePHgks/+eff5ioqCjLyMjgL/u+sC3PscJY+Y6Xr/T/93z87HjZu3cvA8Du3r0r0KaGhobA33p5H19kZCQDwOzs7Eo8R9++Dj86bkaOHMkkJCT4P94wxtiLFy+YvLw869evH3/Z18K2b9++rLi4WKCNqng964rExETWunVr1qhRIxYeHi7sOHwvXrxgYmJibMuWLSwtLY3duXOH7dy5k3369ImNGTOGmZiYsKysLJaVlcUKCgr4ha2qqioLDAxkT548YS9evChXYbtr1y4mJSXF3N3dWXJyMrtx4wb/B7zs7GwGgHl6erKsrCyWnZ3NGCt/YSsnJ8cWLlzIHj58yB4+fMjev3/PmjRpwpYsWcKSkpJYfHw8GzhwIDM0NCz1eeDxeGz9+vXM0tJS4O+fEFL3UVdkQn4jBw8ehLe3N/bu3YuOHTsK3MflchEeHo6RI0dCSUmJv1xNTQ2DBw/m3z5x4gR4PB7GjBmDN2/e8P8pKipCXV0dUVFRWLJkCdavX4+2bdtCTk4OEydO5G8vISGBnj174smTJ/xl/v7+6NixIzp06CDQZv/+/QEAUVFRAln19fWhoaFR4vFJS0vz///+/Xt8/PgRenp65erOyBhDYGAghg8fDsaYQA5jY2N8/PixyrpFfp+/IvsOCQlB8+bNMXr0aP72MjIymDFjRqXz/Oz5z8jMhLqCAn99raZN0VxOjn+7dYMGkBETQ1ZuLgCAxxiuPn+OnkpKaNewYYn9fd9V91lmJnJzc5GYmIiUlBSMHz8eb9++5efIy8uDkZERLl26xO9C26BBA1y/fh0vXrz4pcetp6cHBQUFgcc9YMAAcLlcXLp0qdTtynusAOU7Xr5VnuPF1NQUYmJi8PPz4y+7d+8eHjx4AAsLiwo/vsDAQHA4nFKn3/rZSOlcLhdhYWEYOXIk2rRpw1/evHlzjB8/HpcvX0ZOTo7ANtOnT4eoqKjAsqp4PeuKbt26IS4uDtra2hg0aBD+/fffWnHdbVZWFoqLi2FqagpVVVV06dIFs2fPhpycHKSlpSEpKQlFRUUoKioKjB3g4OAAU1NTtG7dGs2bNy/XvtasWYP58+fD3t4e7dq1Q48ePeDg4ADgv67bwH9/E4qKivzb5aWuro5Nmzahffv2aN++PXbs2AEtLS2sW7cOHTp0gJaWFg4ePIioqCg8evSoxPYcDgdDhw6Ft7d3hS9DIYTUbjRSBiG/iTt37sDW1pY/Z+33srOzkZ+fDzU1tRL3fbssJSUFjDGoq6uXup83b94gPDwcW7ZsQXBwMMTFxUt8OVZQUMCdO3cE2kxKSvrhF5js7GyB261bty51vTNnzmDNmjVITEwUuDa3PNMYvX79Gh8+fMC+ffuwb9++cuWorO/zV2TfT58+hZqaWonH1L59+0rn+dnzDwCt6tXj/7+JjEyJ++UkJJD7v+vrPhYU4HNxMVTq1y93hsePHyMlJQUAYGVl9cP1Pn78CAUFBWzatAlWVlZQVlaGtrY2hgwZgkmTJgkUVz+TkpKCO3fulPvv7tvl5TlWvu6jrONF/Lspk1q2bPnT46Vx48YwMjLC8ePHsXr1agCAn58fxMTEYGpqWuHHl5qaCiUlJTQs5UeIn3n9+jU+f/5c6t9fx44dwePx8OzZM3Tq1Im/vLTjtypez7qkYcOGCAkJwYoVK7Bw4ULExcXhwIEDkJWVFVqmbt26wcjICF26dIGxsTEGDRqE0aNHQ+GbH7VKo1PBUdKzs7Px4sULGBkZ/UrcH9LW1ha4ffv2bURFRUHumx/jvkpNTUW7du1KLO/SpQumTZsGW1tb9OjRA127dq2WrISQmkWFLSG/gU+fPsHc3Bzt27fH1q1bf6ktHo8HDoeDc+fOCZx1YYxh//798Pf3h7u7O+zt7REcHFzizMy363/bZpcuXbBly5ZS11VWVha4/e2Z2a9iYmLw999/o1+/fti1axeaN28OcXFxeHp6wsfHp1yPCwAmTpz4w8Kqqr7cfJ+/uvb9o4Key+UKvC5lPf9JSUmws7OD5DcjAov86IeCXzjrVFBQwH8eNm/eDE1NzVLX+/rldMyYMdDT00NQUBDCwsKwefNmbNy4ESdOnChx1vRHeDweBg4ciEWLFpV6f2lfeCvqR8fLV99/2S7P8QIAY8eOxeTJk5GYmAhNTU0cP34cRkZGaNy4scC+q/vxVUZpx29VvJ51jaioKNatWwdtbW1YWVmhV69eCAoKQtu2bYWW58KFC7hy5QrCwsKwfft2LFu2DNevXy9zu++LcRERkRJ/r98OKlXa618eP2v3R3lyc3MxfPhwbNy4scS6ZZ1h3rZtG27cuAFzc3PExcVBXl6+UrkJIbUHFbaE1HGMMcyYMQNZWVmIi4v74ZeKpk2bQkpKCo8fPy5x37fL2rZtC8YYWrduzf9izBjDggUL4O/vj23btmHu3LkVyti2bVvcvn0bRkZG5Tq7WprAwEBISUkhNDQUkpKS/OWenp4l1i1tH02aNIG8vDy4XC4GDBhQqQyVVZF9q6io4N69e2CMCTyO5OTkEusqKCjgw4cPJZY/ffpU4ExYWc//10KpoLi43I+nvqQkZMTE8PTjx3JvIykpyf9CX69evXK9Bs2bN8fs2bMxe/ZsZGdno3v37li7dm25C6G2bdsiNze3wq93eY+Vr/v4/nipCiNHjsTMmTP53ZEfPXqEJUuWlNh3eR5f27ZtERoainfv3pV51vZHx42MjEypf38PHz6EiIhIiR+mfuRXX8+6yszMDB06dMCoUaOgo6MDHx8foT1mDoeDPn36oE+fPli5ciVUVFQQFBQECQmJck+D06RJE7x8+VLgPSoxMZF/v7y8PFRVVREREQFDQ8NS2xAXFy+xvyZNmuDevXsCyxITE0v0evhe9+7dERgYCFVV1QpN2SUtLQ1/f39oa2tj5syZOHr0aKU/nwghtQNdY0tIHbd3714cO3YMHh4eZX6xFhUVxYABA3Dy5EmB69weP36Mc+fO8W+bmppCVFQULi4uYP8NMIf58+djy5Yt2LZtG8aPH1/hjGPGjMHz58+xf//+Evfl5+eXa65LUVFRcDgcgS9D6enppc6FKCsrW6LgExUVhZmZGQIDA0t8eQJQrdM/VGTfQ4YMwYsXLwSmMfr8+XOpXZjbtm2La9euCUx5cebMmRLTzJT1/Ldo0QIAkPHddZJlEeFw0KtFC9x48QKP3r0rcX9p1xOqqalBW1sbbdu2xb///ovc/12v+62vzwOXy8XH74rmpk2bQklJqcT0UGUZM2YMrl69itDQ0BL3ffjwAcU/KObLe6wAJY+XbzHG8Pbt23Ln/VaDBg1gbGyM48eP49ixY5CQkCgxz2h5H5+ZmRkYY6VOIfRt5h8dN4MGDUJwcDDS09P5y1+9egUfHx/07dsX9b7pxl6aqno967JOnTrhxo0b6Nu3L4YOHYq1a9eWmJKpul2/fh3r1q1DXFwcMjIycOLECbx+/RodO3aEqqoq7ty5g+TkZLx586bMaX0MDAzw+vVrbNq0Campqdi5c2eJ48LZ2Rmurq7Ytm0bUlJSEB8fj+3bt/Pv/1r4vnz5Eu/fvwcA9O/fH3FxcTh8+DBSUlLg5ORU6vvl9+bMmYN3795h3LhxuHnzJlJTUxEaGorJkyf/tFhv164dPDw84Ovr+8PLRAghdQedsSWkDouPj4e9vT1mz56NMWPG/HR9Z2dnhIWFoU+fPpg1axa4XC527NiBzp07839xb9u2LdasWYMlS5YgPT0dIiIiuHz5MgYMGIDt27ejoKAACxYsqFBOS0tLHD9+HDY2NoiKikKfPn3A5XLx8OFDHD9+HKGhoT+9jmvo0KHYsmULTExMMH78eGRnZ2Pnzp1QU1MTuD4R+O8arK/XASspKaF169bQ1dXFhg0bEBUVBV1dXUyfPh0aGhp49+4d4uPjER4ejnelFGlVpbz7nj59Onbs2IFJkybh1q1baN68Oby9vSFTynWv06ZNQ0BAAExMTDBmzBikpqbiyJEjJbo6/uz5V1FWRsr79xj0g2ubS2PVpQviX73C4uhoDG7TBsr16uFdfj4uZ2Zis6Eh5L4ZfEa5ZUt+l1wPDw8MHjwYnTp1wuTJk9GiRQs8f/4cUVFRqFevHk6fPo1Pnz6hZcuWGD16NLp16wY5OTmEh4fj5s2bcHV1LXfGhQsX4tSpUxg2bBisra2hra2NvLw83L17FwEBAUhPTxfo2vut8hwrQMnjZeTIkZCXl0daWhqCgoIwY8aMCh8vX1lYWGDixInYtWsXjI2N0aBBg0o9PkNDQ1haWvKLDBMTE/B4PMTExMDQ0BC2trYAfnzcrFmzBhcuXEDfvn0xe/ZsiImJYe/evSgoKCh1zurvVdXrWdc1aNAAwcHBWLVqFZYvX464uDgcOnTopz8MVJV69erh0qVLcHd3R05ODlRUVODq6orBgwdDR0eHP29zbm4uoqKioKqqWmo7HTt2xK5du7Bu3TqsXr0aZmZmWLBggUBhaGVlhS9fvsDNzQ0LFixA48aNBQbEc3V1xbx587B//360aNEC6enpMDY2xooVK7Bo0SJ8+fIFU6ZMwaRJk3D37t0yH5eSkhJiY2OxePFiDBo0CAUFBVBRUYGJiQlERH5+/sbCwgIXL16Evb09evbsCS0trfI9oYSQ2qfGxl8mhFSpDx8+sDZt2rDu3buzL1++lHu7iIgIpqWlxSQkJFjbtm2Zh4cHmz9/PpOSkhJYLyAggCkpKTEATFJSknXo0IHNmTOHJScn89fR19dnnTp1KrEPKyurEnMaFhYWso0bN7JOnToxSUlJpqCgwLS1tZmLiwv7+PEjfz0AP5wW5MCBA0xdXZ2f5+ucit+/lT18+JD169ePSUtLMwACU5i8evWKzZkzhykrKzNxcXGmqKjIjIyM2L59+8r7FPIf44+m+/lR/vLu++nTp+zvv/9mMjIyrHHjxsze3p6dP3++xHQ/jDHm6urKWrRowSQlJVmfPn1YXFxciel+CgoK2OLFi5menh5r0qQJExMTYzIyMkxVVZUNHjyYTZ06VWAe22H/m/rn239NZWTYABUVgWVeQ4cyIxUVVl9SkomLiDBFWVk2rG1bFmxmJjCPrba2NvP09GR79+5lrq6uzMbGhqmrqzMpKSkmISHBVFRU2JgxY1hERAQ/78KFC1m3bt2YvLw8k5WVZd26dWO7du2q0GvE2H/zOi9ZsoSpqakxCQkJ1rhxY9a7d2/277//ssLCQoHX7dvpfhgr/7HCGGOBgYGsb9++TFZWlsnKyv7y8cIYYzk5Ofy/4R/N91rex1dcXMw2b97MOnTowCQkJFiTJk3Y4MGD2a1bt/jrlHXcxMfHM2NjYyYnJ8dkZGSYoaEhf57br75O93Pz5k2B5VX5ev4ugoODWb169ViHDh3Yw4cPhR3nj5efn8+0tLRY27Zt2YcPH4QdhxBSSRzGasEY9ISQCmGMwdzcHOHh4YiPj//lkUVHjhyJ+/fv80etZYzBzs4OO3bswJ49ezBz5syqiE1+QXR0NAwNDREVFQUDA4MKbfvx40c0btwYXC4XYmJi4PF4Al305s2bhy1btmChri4MW7WqssxRGRnY/N3ANKKiohAREeFnCAoKKtHFtjb7/lghpLKSk5MxatQoZGZmwtvbGyNGjBB2pD9aamoqunfvjkGDBuH48eN0vS0hdRBdY0tIHbRjxw4EBgbC09OzwkVtfn6+wO2UlBSEhITwiyXGGGxtbbFjxw7s3buXitrfQP369TF27FiIiIigqKhIoKj966+/sHHjRhgPGoTDDx7gcxnX1lXE56IiHH7wAMaDBmHlypX85Vwul59BUVERQ4cOrZL9VYefHSuE/Ir27dvj+vXrGDhwIEaOHImVK1fW+HW35P+1bdsWBw8eREBAAHbu3CnsOISQSqBrbAmpY27evMmf+H7UqFEV3r5NmzawtrZGmzZt8PTpU+zevRsSEhJYtGgReDwebG1tsXv3buzfvx/Tpk2rhkdQu71+/brMAUckJCQqNR+oMN2+fRsfP34UeFyioqJQV1dHWFgYxMTEsHvPHnTp1An7b9+GfQXnrfweYwz7b99GbnExdu/ZA1VVVWRlZcHDw0NgsCIZGRmcO3cOw4YNK9e1cF/l5+eXGIzoew0bNoTEN9f5VkZZxwohVUFeXh4BAQHYsGEDli1bhvj4eBw5cqTE9dSkZpiZmcHOzg7z5s2Drq4uevToIexIhJAKoK7IhNQh79+/h5aWFpo1a4aYmJhKfXGfPHkyoqKi8PLlS0hKSqJXr15Yt24dNDU1MWfOHOzduxf79+/H1KlTq+ER1H6qqqp4+vTpD+/X19dHdHR0zQX6n4p2RebxeAgJCYGbmxsiIyOhrKwMUVFRZGRkAPhvIJn4+Hi0/mbAKA8PD0yfPh2WnTphnIZGpXIyxuCblIQj9+/Dw8OD/3dUWFgIIyMjXL16FVwuF+Li4tDU1MTNmzehpqYGe3t7WFtbl5j3tTReXl6YPHlymetUpsv29350rHTv3v2X2iWkNOfPn8e4cePQuHFjnDx5Ep06dRJ2pD9SYWEh+vbti9evXyM+Ph4KCgrCjkQIKScqbAmpIxhjGDVqFC5duoT4+PgfjlhZGTweD7NmzcL+/fvh4eGBKVOmVFnbdU1sbGyJLqjfUlBQgLa2dg0mqpi8vDwcOnQIW7duxaNHj6CrqwtHR0eYmpri7NmzGDVqFERFRREZGYl+/fqV2H7t2rVYvnw5jFu3xvRu3SDzkzkkv/W5qAj7b99GaFoa1q5di6VLlwrc/+bNG3Tv3h3Pnj3DrFmzsGvXLly/fh1ubm4ICAiAvLw8pk+fjrlz55Y5N2pWVhbu379fZhZtbW36QkrqnNTUVIwaNQpPnjyBl5eXwEjCpOakp6dDS0sL+vr6CAoKouttCakjqLAlpI7YsmUL5s+fj1OnTmH48OFV1i6Px8PMmTNx4MABHDx4ENbW1lXWNqk5mZmZ2LFjB/bt24ePHz/CzMwMjo6O6NWrF38dLpeL0aNHw8zMDBMnTvxhWx4eHnCws4OcmBgmaWigb8uWEC+jq3ARl4vLz5/j8IMHyC0uxtbt2394xv/+/fuwtbXFwYMHBc4WZ2Rk8PPn5uZi9OjRcHR0hK6ubiWeDULqrry8PEybNg3Hjh3D4sWLsXbtWoiKigo71h/n1KlTGDFiBLZs2QJHR0dhxyGElAMVtoTUAVevXkW/fv3g4OCAzZs3V1m7PB4PM2bMwMGDB+Hp6QkrK6sqa5vUjJs3b8LNzQ3+/v6QkZHhn/FUUVH5pXbT0tIwy8YGoWFhUJCRQe/mzaGuoIBW9epBUlQUBVwuMnJykPL+Pa5kZeH9588wHjQIu/fsEShYKyo3Nxeenp7YunUrUlNT0atXLzg6OmLUqFEQE6NhIcifgTEGNzc3LFy4EAMGDICPjw8aNWok7FjVqri4GElJSSgqKoKGhgakpKSEHQkLFizA1q1bERMTg7/++kvYcQghP0GFLSG13Nu3b6GlpQVlZWVER0dDvAJdQ8vC4/Ewbdo0eHl54dChQ7C0tKySdkn143K5OHnyJNzc3BAbG4s2bdrA3t4ekydPhry8fJXu68GDB9izZw8uhIYiOSVFYPAnDoeD9urqGGhsjFmzZqFjx45Vtl8ul4szZ87Azc0NFy9ehIqKCubOnYtp06ahfv36VbYfQmqzyMhIWFhYQE5ODkFBQdDU1BR2pGqVnZ2Nfv364cuXL4iJiSnzkoSaUFRUBH19fWRmZiIhIeG3/3GBkLqOCltCajEej4fhw4fj+vXrSEhIqLIPeS6Xi2nTpuHw4cM4dOhQmd1SSe2Rk5ODAwcOYNu2bUhPT0e/fv3g6OiI4cOH10hXxdzcXDx+/BgFBQWQlJSEmppauQZ7+lUJCQlwc3PDsWPHICkpiSlTpsDe3v6X528mpC54+vQpTE1NkZSUhP3792PChAnCjlStnj17Bj09PUhKSiImJgZNmzYVeh4tLS389ddfOHXqVIVGcCeE1DBGCKm1NmzYwACwkJCQKmuzuLiYWVlZMREREXb06NEqa5dUnydPnjAHBwcmLy/PxMTE2MSJE1lcXJywY9W458+fs2XLlrFGjRoxDofDRo0axS5dusR4PJ6woxFSrT5//swmTZrEADAHBwdWWFgo7EjVKiUlhSkqKrJu3bqxd+/eCTsOCwkJYQDYxo0bhR2FEFIGOmNLSC0VExMDQ0NDLFq0COvWrauSNrlcLiZPnoyjR4/iyJEjGDduXJW0S6oeYwyxsbFwc3PDyZMn0aBBA8ycORNz5sxBixYthB1PqD5//owjR47A3d0dSUlJ0NbWhqOjI8zNzX957lpCaivGGHbu3AlHR0f06dMHx48fF/rZzOp079496Ovro3379ggLC6uR3iFlWbJkCTZv3ozo6Gj07dtXqFkIIaWjwpaQWig7OxtaWlpQU1NDRERElQyaw+VyYW1tDV9fXxw9ehQWFhZVkJRUtaKiIvj7+8PNzQ1xcXFo3749HBwcMGnSJMjIyAg7Xq3C4/EQFhYGNzc3hIWFQUlJCba2tpgxYwZdC0d+WzExMRg9ejQkJCRw4sQJ9OjRQ9iRqs3NmzfRv39/6Orq4syZM0IdUKq4uBj9+/dHamoqEhMT0aRJE6FlIYSUji4UIKSW4fF4sLS0RFFREXx9faukqC0uLsakSZPg6+sLHx8fKmproffv32Pjxo1o3bo1JkyYgAYNGuDs2bN48OABbGxsqKgthYiICExMTBAaGop79+5hyJAhcHFxgbKyMmbNmoXk5GRhRySkyunp6SE+Ph4tWrSAnp4ePD09hR2p2vTo0QNnzpxBbGwsxo4di6KiIqFlERMTg6+vL4qKimBpaQkejye0LISQ0lFhS0gts27dOly4cAE+Pj5QUlL65fa+FrV+fn7w9fXFmDFjqiAlqSqPHj3CnDlz0LJlSzg5OcHY2Bh37tzBhQsXMGTIEBqopJw6deqE/fv349mzZ1iyZAmCgoLQoUMHDB06FOHh4aDOSeR30qJFC1y8eBGTJk3ClClTMGfOHBQWFgo7VrXQ19dHYGAgzp49i8mTJwu1oGzRogWOHj2KsLAwrF+/Xmg5CCE/IMTrewkh34mMjGQiIiJs5cqVVdJeUVERGzt2LBMTE2MBAQFV0ib5dTwej0VERLBhw4YxAKxp06bM2dmZvXr1StjRfhtfvnxhXl5erFu3bgwA69KlCztw4ADLz88XdjRCqtTevXuZuLg469OnD3vx4oWw41QbPz8/JiIiwmxsbIQ+YNyKFSuYiIgIi4yMFGoOQoggusaWkFri5cuX0NTURKdOnRAWFvbL07cUFxdjwoQJOHHiBPz8/GBqalpFSUllFRQUwNfXF25ubrhz5w66dOkCR0dHjBs3TqjXjv3OGGOIjo6Gm5sbzpw5gyZNmmDWrFmYNWsWmjVrJux4hFSJq1evYvTo0WCMITAwEL169RJ2pGpx8OBBTJ06FYsWLcKGDRvA4XCEkoPL5WLgwIFISkpCYmIivZcQUktQHzdCagEul4vx48eDw+Hg6NGjv1zUFhUVYfz48Thx4gSOHz9ORa2QvX79GqtWrYKKigomT54MZWVlhIeH4/bt25g8eTIVtdWIw+HA0NAQp06dQnJyMszNzbF582a0atUKU6ZMwd27d4UdkZBf1qtXL9y6dQtt27aFvr4+9u7d+1t2v58yZQrc3NywadMmoXYFFhUVhY+PDxhjGD9+PLhcrtCyEEK+IczTxYSQ/6xcuZKJiIiwqKioX26rsLCQmZmZMXFxcXby5MlfD0cq7e7du2zq1KlMUlKSSUtLs1mzZrGHDx8KO9Yf7+3bt2zDhg2sRYsWDAAzMjJiZ86cYVwuV9jRCPklBQUFbM6cOQwAmzp16m/b9d7FxYUBYNu2bRNqjq+XDzk5OQk1ByHkP1TYEiJkYWFhjMPhsNWrV/9yW4WFhczU1JSJi4uz4ODgKkhHKorL5bKQkBA2cOBABoApKSmx9evXs7dv3wo7GvlOYWEh8/HxYT169GAAWPv27dmuXbtYbm6usKMR8ks8PT2ZpKQk69mzJ3v27Jmw41Q5Ho/H5s+fzwAwLy8voWZZtWoV43A4LCwsTKg5CCF0jS0hQvXixQtoampCS0sL586d+6URcAsLCzF27FicOXMGgYGBGD58eBUmJT/z+fNneHt7w93dHQ8fPoSOjg4cHR1hbm4OcXFxYccjZWCM4cqVK3Bzc0NQUBDq168PGxsbzJkzBy1atBB2PEIqJS4uDqampigoKIC/vz/69esn7EhVijGGmTNn4sCBAzh+/DjMzMyEkoPL5WLw4MFITExEYmJilcxmQAipHCpsCRGSr5O9P3nyBAkJCb802XthYSHGjBmDc+fOITAwEMOGDavCpKQsL168wM6dO7F37168f/8eI0eOhKOjI/r06SO0gU1I5aWlpWH79u3w8PBAfn4+LCws4OjoCG1tbWFHI6TCXr9+DQsLC8TExMDV1RVz5879rd6XuFwuJk6ciMDAQJw+fRrGxsZCyZGdnQ0tLS2oqakhIiKiSuafJ4RUHA0eRYiQrFy5EleuXMGxY8d+uag1NzfHuXPnEBQUREVtDYmPj4elpSVUVVWxfft2WFpaIiUlBYGBgejbt+9v9eXxT9K6dWts2bIFmZmZ2Lx5M65cuQIdHR3069cPQUFBNEgMqVOaNGmCsLAw2Nvbw97eHlZWVsjPzxd2rCojKiqKw4cPw9jYGKNGjUJMTIxQcjRt2hTHjh1DbGwsnJychJKBEEJnbAkRinPnzmHIkCHYsGEDFi9eXOl2CgoKYG5ujrCwMAQFBWHw4MFVmJJ8j8vl4vTp03Bzc8OlS5egoqICOzs7TJ06FfXr1xd2PFINuFwugoOD4ebmhsuXL6NNmzaws7PDlClTIC8vL+x4hJSbr68vpk6dig4dOuDEiRNQVVUVdqQqk5+fj6FDhyIuLg5RUVFC62GxYcMGLFmyBCEhIfR5TIgQUGFLSA179uwZNDU18ddff+H06dOVvq62oKAAZmZmCA8Px8mTJ2FiYlLFSclXnz59gqenJ7Zt24bU1FT07t0bjo6OGDlyJHU5+4PExcXBzc0Nx48fh4yMDKZNm4a5c+f+VgUC+b3dvn0bo0aNQk5ODo4dO4YBAwYIO1KV+fTpEwYOHIjHjx/j0qVL0NDQqPEMPB4Pw4cPx/Xr15GQkABlZeUaz0DIn4wKW0JqUFFREfT19ZGZmYmEhAQ0atSoUu18+fIFZmZmiIiIQHBwsNCuK/rdZWRkYPv27di/fz9yc3Nhbm4OR0dH9OzZU9jRiBBlZmZix44d2LdvHz5+/AhTU1M4OjqiV69e1AWd1Hrv3r3DuHHjEB4ejg0bNmDBggW/zd/tu3fvYGBggDdv3vB7WNS0t2/fQktLC8rKyoiOjqbBAwmpQXSNLSE1aOnSpbh58yaOHz/+S0WtqakpIiMjcerUKSpqq8G1a9dgYWGBNm3awMPDAzNnzkRaWhp8fX2pqCVo2bIlNmzYgGfPnmHHjh24c+cO+vTpg7/++gvHjh1DUVGRsCMS8kMNGzZESEgIFi9ejEWLFmHs2LHIy8sTdqwq0bBhQ4SFhUFOTg4DBgzA8+fPazxDo0aN4Ofnhxs3bmDZsmU1vn9C/mR0xpaQGnLq1CmMGDECrq6umDdvXqXa+PLlC0aNGoXo6GicPn36t+pGJmzFxcU4ceIE3NzccO3aNaipqcHBwQFWVlaQk5MTdjxSi/F4PJw7dw5ubm6IiIhAy5YtMXfuXEyfPh0KCgrCjkfIDwUGBsLKygqtW7dGUFAQ1NTUhB2pSmRkZKBv376Qk5PDxYsXf2mAxspydXXFggULcOrUKZp+j5AaQoUtITUgPT0dWlpa0NfXR1BQUKW6feXn52PkyJGIiYnB6dOnYWRkVA1J/zwfPnyAh4cHtm/fjoyMDBgaGsLR0RFDhw79pXmFyZ/p9u3bcHd3h4+PD8TFxWFtbQ17e3uoq6sLOxohpbp//z5GjRqF169fw8fH57cZ9OjRo0fQ09NDy5YtERkZWeMD/DHG+J/ZCQkJUFFRqdH9E/InosKWkGpWWFgIPT09ZGdnIz4+vlJncPLz8zFixAhcvnwZZ86cQf/+/ash6Z8lNTUVW7duxcGDB1FYWIjx48fDwcEBmpqawo5GfgMvX77E7t27sXv3brx58wbDhg2Do6MjDAwMfpvrGcnv48OHD7C0tMTZs2exevVqLFmy5Lf4Ye/OnTvQ19dH586dERoaChkZmRrd//v379G9e3c0bdoUMTExkJCQqNH9E/KnqfvvWoTUcosWLUJCQgKOHz9eqaL28+fP+PvvvxEbG4uQkBAqan8BYwwXL17EyJEjoa6uDl9fX8ybNw8ZGRnw8vKiopZUGUVFRbi4uCAjIwP79+/HkydP0L9/f3Tv3h2HDh1CQUGBsCMSwtegQQMEBwdj5cqVWL58OczMzJCTkyPsWL+sa9euOHfuHBISEjBq1KgaP+4UFBRw/PhxJCQk/NLUfoSQ8qEztoRUoxMnTsDMzAzbtm3D3LlzK7z916L26tWrCAkJgb6+fjWk/P0VFhbCz88Pbm5uSEhIgIaGBhwdHTFhwgRIS0sLOx75AzDGEB4eDjc3N5w7dw6KioqYM2cObGxs0LhxY2HHI4Tv1KlTsLS0hJKSEk6ePIn27dsLO9Ivi4yMxJAhQzB06FD4+fnV+DRt27Ztg729PQIDA2Fqalqj+ybkT0KFLSHVJDU1Fd27d8egQYNw/PjxCnc/zMvLw/Dhw3Hjxg2EhISgX79+1ZT09/XmzRvs3bsXO3fuRFZWFkxMTODo6IiBAwdSd1AiNElJSdi6dSsOHz4MxhgsLS3h4OAglHk3CSlNcnIyRo0ahczMTHh7e2PEiBHCjvTLTp8+jVGjRmHChAnw9PSs0a7WjDGYm5sjPDwc8fHxQpmGiJA/ARW2hFSDL1++oE+fPvj48SNu3bpV4UEr8vLyMGzYMNy8eRPnzp2Dnp5eNSX9PSUlJcHd3R2HDx8GACocSK309u1b7N27Fzt27EBWVhaMjY3h6OiIQYMG0Q8vROg+ffoEa2trnDhxAitWrICzs3Odv+7W19cXEyZMwJw5c7Bt27YaPc4+fvyI7t27Q0FBAbGxsZCUlKyxfRPyp6jb71CE1FLz58/H/fv34e/vX6midujQoYiLi8P58+epqC0nxhguXLiAIUOGQENDA6dOncKyZcvw7Nkz7Nu3j4paUus0atQIS5cuRXp6Ory9vfH69WuYmJigc+fO2L9/P/Lz84UdkfzB5OXlERAQgO3bt8PLywtTpkzBx48fhR3rl4wbNw7e3t44efIkNm/eXKP7rl+/Pk6ePIn3799j9erVNbpvQv4UdMaWkCrm5+eHsWPHYvfu3bCxsanQtrm5uRg6dCji4+Nx/vx59OnTp5pS/l4YY7h37x527dqFa9euwdHRERYWFvSLOKlTGGOIiYmBm5sbgoOD0ahRI9jY2GDOnDlQVFQUdjxCCCGkVqPClpAq9OjRI2hra2PYsGHw8fGpUDen3NxcDBkyBImJiTh//jx69+5djUl/PzweDyIiImCMUTdOUuc9fvwY27Zt409HNW7cODg6OtLI3YQQQsgPUGFLSBXJz8/HX3/9hS9fviAuLg7y8vLl3vbTp08YMmQIbt++jdDQUPTq1asakxJC6ooPHz7Aw8MD27dvR0ZGBgwMDODo6Ihhw4bV+esdCSGEkKpEn4qEVBF7e3s8evQI/v7+FSpqc3JyYGJigjt37iAsLIyKWkIIX4MGDbBgwQKkpqbCz88PX758wYgRI9C+fXvs3LkTubm5wo5ICCGE1Ap0xpaQKnDkyBFYWlrCw8MDU6dOLfd2X4vaBw8eICwsDD179qzGlISQ38G1a9fg5uaGwMBAyMvLY8aMGbC1tYWysrKwoxFCCCFCQ2dsCflFSUlJmDlzJiwtLTFlypRyb/fx40cYGxvjwYMHuHDhAhW1hJBy+euvv+Dn54cnT55g2rRp2Lt3L1q3bo1x48bhxo0bwo73WzIwMACHwwGHw0FiYqKw4/wxVFVV4e7uzr/N4XBw8uRJoeWpaV//5ho0aCDsKITUCVTYEvIL8vLyYG5uDhUVFezevbvcgxZ9LWofPnyI8PBw9OjRo5qT1ixnZ+dqG+TGy8uLPuQJAdCqVSts3rwZz549g5ubG27evAldXV306dMHAQEBKC4uFnbE38r06dORlZWFzp07AwDS09PL/Z4fHR0NDoeDDx8+VGPC/2dtbY2RI0fWyL6A/x6fqqpqhbaxtraGs7NzhbbJysrC4MGDK7RNXaKqqoro6Gj+7aysLIHCnhBSNipsCfkFtra2SEtLQ0BAAGRlZcu1zYcPHzBo0CA8evQI4eHh0NHRqeaUhJDfmby8PObOnYvk5GScPHkS4uLiMDc3h5qaGrZs2VLn5x6tLWRkZKCoqAgxMbFq20dhYWG1tV0ZXC4XPB5P2DH4FBUVy5zGraioqAbTVD9FRUXUr19f2DEIqTOosCWkkjw9PeHl5YXdu3dDQ0OjXNt8LWpTUlIQHh4ObW3tak5ZOQUFBbCzs0PTpk0hJSWFvn374ubNmwBKP2N68uRJ/pkLLy8vuLi44Pbt2/xuVF5eXgD+61a1e/duDB48GNLS0mjTpg0CAgL47ZR2ViMxMREcDgfp6emIjo7G5MmT8fHjR37bFf3Fn5DflaioKEaMGIHo6GjcunUL/fr1wz///ANlZWU4ODjgyZMnwo74W3v69CmGDx8OBQUFyMrKolOnTggJCUF6ejoMDQ0BAAoKCuBwOLC2tgbwXxdnW1tbODg4oHHjxjA2NuafCf62y/OHDx/A4XAEzubdv38fw4YNQ7169SAvLw89PT2kpqbC2dkZhw4dQnBwMP99Mjo6+qfvr8D/v7+fOnUKGhoakJSUREZGBgoKCrBgwQK0aNECsrKy0NXVFchSFbKzszF8+HBIS0ujdevWOHr0aIl1vu2K/PV58vPzg76+PqSkpErd5ltfH19oaCg6duwIOTk5mJiYICsri7/OzZs3MXDgQDRu3Bj169eHvr4+4uPjS+TYu3cvhg0bBhkZGXTs2BFXr17F48ePYWBgAFlZWfTu3RupqakC2wUHB6N79+6QkpJCmzZt4OLiQj0rCKlCVNgSUgn37t3DnDlzMGXKFEyaNKlc27x//x4DBw5EamoqIiIi0L1792pOWXmLFi1CYGAgDh06hPj4eKipqcHY2Bjv3r376bYWFhaYP38+OnXqhKysLGRlZcHCwoJ//4oVK2BmZobbt29jwoQJGDt2LJKSksqVq3fv3nB3d0e9evX4bS9YsKDSj5OQ31X37t1x+PBhpKenw87ODkeOHIG6ujpMTU0RExMDGjey6s2ZMwcFBQW4dOkS7t69i40bN0JOTg7KysoIDAwEACQnJyMrKwtbt27lb3fo0CFISEggNjYWe/bsKde+nj9/jn79+kFSUhKRkZG4desWpkyZguLiYixYsABjxozhF2xZWVkVmhf98+fP2LhxIzw8PHD//n00bdoUtra2uHr1Ko4dO4Y7d+7A3NwcJiYmSElJqdiTVAZra2s8e/YMUVFRCAgIwK5du5Cdnf3T7f755x/Y29sjKSkJxsbGP13/8+fP+Pfff+Ht7Y1Lly4hIyND4HPk06dPsLKywuXLl3Ht2jWoq6tjyJAh+PTpk0A7q1evxqRJk5CYmIgOHTpg/PjxmDlzJpYsWYK4uDgwxmBra8tfPyYmBpMmTYK9vT0ePHiAvXv3wsvLC2vXrq3As0QIKRMjhFTIp0+fWIcOHVjnzp1ZXl5eubZ59+4d09bWZg0bNmQJCQnVG/AX5ebmMnFxcXb06FH+ssLCQqakpMQ2bdrEPD09Wf369QW2CQoKYt++nTg5ObFu3bqVaBsAs7GxEVimq6vLZs2axRhjLCoqigFg79+/59+fkJDAALC0tDTGGCt1/4SQsuXl5bG9e/eyjh07MgBMW1ubHTlyhBUUFAg7Wp2gr6/P7O3ty1ynS5cuzNnZudT7Sntv+9qulpaWwLK0tDQGQOCz4v379wwAi4qKYowxtmTJEta6dWtWWFhY6v6srKzYiBEjfpqhtPdXACwxMZG/ztOnT5moqCh7/vy5QHtGRkZsyZIlpe6/opKTkxkAduPGDf6ypKQkBoC5ubnxlwFgQUFBjLH/f57c3d3LvZ+vj+/x48f8ZTt37mTNmjX74TZcLpfJy8uz06dPC+RYvnw5//bVq1cZAHbgwAH+Ml9fXyYlJcW/bWRkxNatWyfQtre3N2vevPlPM9NnHiHlQ2dsCakAxhhsbGyQmZmJgIAAyMjI/HSbd+/eYcCAAUhPT0dkZGS1DapUVVJTU1FUVIQ+ffrwl4mLi6Nnz57lPrNalu/n6e3Vq1eVtEsI+TEZGRnMmDED9+7dw7lz59CwYUNMnDgRrVu3xvr168vVG4OUzc7ODmvWrEGfPn3g5OSEO3fulGu7ylySkpiYCD09PYiLi1d425+RkJBA165d+bfv3r0LLpeLdu3aQU5Ojv/v4sWLJbraVlZSUhLExMQEnosOHTqUa6DAio5TISMjg7Zt2/JvN2/eXODM8KtXrzB9+nSoq6ujfv36qFevHnJzc5GRkSHQzrfPUbNmzQAAXbp0EVj25csX5OTkAABu376NVatWCTyHXwck+/z5c4UeAyGkdNU3AgIhvyEPDw8cPXoUPj4+aN++/U/Xf/v2LQYMGIDMzExERkYKfBDWVSIiIiW6MVbVgB0iIv/91vZt+7/bYCCECJOIiAhMTExgYmKCe/fuwd3dHS4uLli9ejWsrKzg4OBQrvc2UtK0adNgbGyMs2fPIiwsDOvXr4erqyvmzp1b5nbfDzxYnvdBaWnpCucr7/urtLS0wGjPubm5EBUVxa1btyAqKiqwrpycXIVzVLXyDtz41fc/BnA4HIHnxMrKCm/fvsXWrVuhoqICSUlJ9OrVq8TAXt+28/X5Km3Z18G3cnNz4eLiAlNT0xKZpKSkKvQYCCGlozO2hJRTYmIi5s6di5kzZ2LcuHE/Xb+uFrVt27blX+/1VVFREW7evAkNDQ00adIEnz59Ql5eHv/+7+d1lJCQAJfLLbX9a9eulbjdsWNHAECTJk0AQGAgj4q0TQgpv86dO8PDwwMZGRn4559/EBQUhA4dOmDo0KEIDw+n63ArQVlZGTY2Njhx4gTmz5+P/fv3A/jvfQtAud67yvM+2LVrV8TExPzwh7/S3ifL025ptLS0wOVykZ2dDTU1NYF/ioqKP92+PDp06IDi4mLcunWLvyw5ObnGpkf6VmxsLOzs7DBkyBB06tQJkpKSePPmzS+32717dyQnJ5d4DtXU1Pg/OhBCfg0dSYSUQ05ODszNzdGxY8dyzSn35s0bGBkZ4fnz54iKihLonlTbycrKYtasWVi4cCHOnz+PBw8eYPr06fj8+TOmTp0KXV1dyMjIYOnSpUhNTYWPjw9/1OOvVFVVkZaWhsTERLx58wYFBQX8+/z9/XHw4EE8evQITk5OuHHjBn+ADTU1NSgrK8PZ2RkpKSk4e/YsXF1dS7Sdm5uLiIgIvHnzhrpwEfKLmjZtipUrV+Lp06fw9PREZmYmBg4ciG7dusHT0xNfvnwRdsQ6wcHBAaGhoUhLS0N8fDyioqL4P9qpqKiAw+HgzJkzeP36NXJzc3/YjrS0NP766y9s2LABSUlJuHjxIpYvXy6wjq2tLXJycjB27FjExcUhJSUF3t7eSE5OBvDf++SdO3eQnJyMN2/eoKioqFzvr6Vp164dJkyYgEmTJuHEiRNIS0vDjRs3sH79epw9e/YXnrH/1759e5iYmGDmzJm4fv06bt26hWnTplXqzPSvUldXh7e3N5KSknD9+nVMmDChSnKsXLkShw8fhouLC+7fv4+kpCQcO3asxGtLCPkFQry+l5A6gcfjsTFjxjB5eXmWkpLy0/Wzs7NZly5dWNOmTdm9e/dqIGHVy8/PZ3PnzmWNGzdmkpKSrE+fPgKDegQFBTE1NTUmLS3Nhg0bxvbt2ycweNSXL1+YmZkZa9CgAQPAPD09GWP/Dbixc+dONnDgQCYpKclUVVWZn5+fwL4vX77MunTpwqSkpJienh7z9/cXGNyEMcZsbGxYo0aNGADm5ORUnU8FIX8cHo/HIiIi2LBhwxgA1rRpU+bs7MxevXol7GhCU57Bo2xtbVnbtm2ZpKQka9KkCbO0tGRv3rzh379q1SqmqKjIOBwOs7KyKrPdBw8esF69ejFpaWmmqanJwsLCBAaPYoyx27dvs0GDBjEZGRkmLy/P9PT0WGpqKmPsv8+hgQMHMjk5OYHtfvb++qOBigoLC9nKlSuZqqoqExcXZ82bN2ejRo1id+7cKfW5+Dqw07d5fyYrK4sNHTqUSUpKslatWrHDhw8zFRWVnw4eVZEBGcsz+GF8fDzT0dFhUlJSTF1dnfn7+5eZ40dZShus6/z586x3795MWlqa1atXj/Xs2ZPt27evwpkJIaXjMEZ9jQgpy65duzBnzhz4+/tj9OjRZa77+vVrGBkZITs7G5GRkeWe3/ZPweFwEBQUhJEjRwo7CiGkHB49eoStW7fCy8sLXC4XEyZMgIODQ53qhVIVDAwMoKmpWa4eOwSIioqCqakpnjx5AgUFBWHHqdO8vLzg4OAglG7ZhNQ11BWZkDLcunULjo6OsLW1/WlRm52djf79+yM7OxtRUVFU1BJC6rx27dph586dePbsGVxcXBAaGoquXbti4MCBCAkJ4Q+M8yfYtWsX5OTkcPfuXWFHqfVCQkKwdOlSKmp/kZycHGxsbIQdg5A6g87YEvIDHz58QPfu3dGoUSNcvnwZkpKSP1z31atX6N+/P969e4eoqCh06NChBpPWHXTGlpC6raioCAEBAdiyZQvi4uLQvn17ODg4YNKkSeWa/qyuev78OfLz8wEArVq14g8GRWqXwYMHIyYmptT7li5diqVLl9Zwol/z+PFjAICoqChat24t5DSE1H5U2BJSCsYYzMzMEBkZiYSEhDI/UL4Wte/fv0dUVBRNlUEI+e0xxhAbGws3NzecPHkSDRo0wMyZMzFnzhy0aNFC2PHIH+rbHyC+17BhQzRs2LCGExFCahIVtoSUYuvWrXBwcPjp2cWXL1+if//++PDhAxW1hJA/UlpaGrZt24YDBw4gPz8fFhYWcHR0hLa2trCjEUII+YNQYUvId65fvw49PT3Y2tpiy5YtP1wvKysL/fv3R05ODqKiotCuXbsaTEkIIbVLTk4ODhw4gG3btiE9PR16enpwdHTE33//DVFRUWHHI+S38XWOYjquCBFEg0cR8o13795hzJgx6N69OzZs2PDD9bKysmBoaIhPnz4hOjq6Tha1XC4XDx8+BAAUFxeDy+WCMQb6rYsQUhn16tWDo6MjHj9+jICAAPB4PJiamqJdu3bYunUrPn36JOyIhNR5jDEUFxdj/PjxePfunbDjEFKrUGFLyP8wxmBtbY3c3FwcP378h4ODvHjxAgYGBsjLy0N0dDTU1dVrOOmvyc3Nxfbt29G+fXt07NgREydOxKNHj8DhcPj/CCGkskRFRWFmZobLly/jxo0b0NXVxYIFC9CyZUssWLAAT58+FXZEUge8fv0asbGxuHXr1g+vm61NeDwe7t69i9jYWOTk5FTbfjgcDl6/fo0LFy5g8uTJ9GM0Id+gwpaQ/3F1dcXp06dx+PBhtGrVqtR1nj9/DgMDA+Tn5yM6Ohpqamo1nLLyMjIysHDhQrRs2RKOjo7o0aMHrl+/jiNHjkBDQwMiIvR2QAipWj169ICPjw/S0tIwa9YsHDx4EG3atMGYMWNw9epVYccjtViTJk3QoEEDjBs3DkpKSjh37pywI5VJREQEbdq0weLFi6GiooI7d+5U275atmyJw4cP49SpU2VeMkXIn4ausSUEQGxsLPT19TF//nxs3Lix1HUyMzNhaGiIwsJCREVFoU2bNjWcsnKuXbsGNzc3BAYGQl5eHjNmzICtrS2UlZWFHY0Q8ofJy8vDoUOH4O7ujpSUFOjq6sLR0RFmZmYQExMTdjxSC3348AGWlpY4e/YsVq9ejSVLltTqH2I/fvyI/v374/nz57h06VK1Xqq0aNEibNmyBZcuXULv3r2rbT+E1BVU2JI/3ps3b6CpqQlVVVVERUVBXFy8xDrPnj2DoaEhioqKEB0dXevnkysuLsaJEyfg5uaGa9euQU1NDQ4ODrCysoKcnJyw4xFC/nA8Hg8hISFwc3NDZGQklJWVMXfuXEyfPh0NGjQQdjxSy/B4PKxevRrOzs4YOXIkDh06hHr16gk71g+9fv0a/fr1Q15eHi5fvvzDXmC/qqioCIaGhnj69CkSEhLQuHHjatkPIXUFFbbkj8bj8TB06FDExcUhISEBLVu2LLFORkYGDA0NweVyERUVVauL2g8fPsDDwwPbt2/n53Z0dMTQoUNr9S/chJA/1+3bt+Hu7g4fHx+Ii4tj8uTJsLe3r1OXepCacfr0aUycOBFKSkoICgpChw4dhB3ph54/fw49PT2IiYkhJiYGzZo1q5b9ZGZmQktLCz169MCZM2fos5780eivn/zRNm7ciNDQUBw5cuSHRa2BgQF4PF6tPlObmpoKe3t7KCsrY+nSpTA0NERCQgIiIyMxfPhw+qAjhNRa3bp1g6enJ54+fYr58+fDz88P7dq1w4gRIxAdHU2D4xC+4cOH4+bNm+BwOOjZsyeCg4OFHemHWrRogfDwcOTm5mLQoEF4//59teynZcuW8Pb2xrlz57Bp06Zq2QchdQWdsSV/rIsXL6J///5YsmQJ1qxZU+L+p0+fwtDQEAAQFRUFFRWVmo5YJsYYYmJi4ObmhuDgYDRq1Ag2NjaYPXs2mjdvLux4hBBSKV++fMHRo0fh5uaG+/fvQ1NTE46Ojhg7duwPR6snf5ZPnz7B2toaJ06cwIoVK+Ds7Fxrf8C9f/8+9PX1oaamhgsXLkBeXr5a9rNs2TJs2LABUVFR6NevX7Xsg5Dajgpb8kd69eoVtLS00K5dO4SHh5cYtCQ9PR2GhobgcDiIjo6ututjKqOwsBDHjx+Hm5sb4uPjoaGhAQcHB0ycOBHS0tLCjkcIIVWCMYYLFy7Azc0N58+fh6KiIubMmQMbGxu6lpCAMYYNGzZg2bJlGDx4MI4ePVprr8++desWDA0NoaOjg5CQEEhJSVX5PoqLizFgwAA8evQIiYmJaNq0aZXvg5Dajgpb8sfhcrkwMTHBnTt3kJiYWOLsZnp6OgwMDCAqKoro6OhaM3rw27dvsXfvXuzYsQNZWVkwNjaGo6MjBg0aRHPPEkJ+a0lJSXB3d8fhw4cBAJaWlnBwcICGhoaQkxFhO3/+PMaPH49GjRohKCgInTt3FnakUsXExMDY2BgDBgxAYGBgqQNV/qqsrCxoamqiW7duOHfuHERFRat8H4TUZrWz3wYh1Wjt2rWIiIiAj49PiaI2LS0N+vr6EBMTw8WLF2tFUfvw4UPY2NhAWVkZq1evxrBhw3D//n2cP38exsbGVNQSQn57HTt2xN69e/Hs2TMsX74cZ86cQadOnWBiYoLQ0FC6DvcPZmJigri4OMjIyOCvv/6Cv7+/sCOVSk9PDydOnMD58+dhZWUFLpdb5fto3rw5fHx8EB4ejnXr1lV5+4TUdlTYkj9KREQEnJ2d4eTkBCMjI4H7njx5An19fUhISCA6OrrUwaRqytcueEOGDEHHjh0RHByMpUuXIiMjA/v27aOzFISQP1Ljxo2xbNkypKen4/Dhw8jOzoaJiQk6d+4MDw8P5OfnCzsiEYI2bdrgypUrGD58OMaMGYPFixdXS+H4q0xMTODr6ws/Pz/MmjWrWn6QMTIywsqVK+Hs7IzIyMgqb5+Q2oy6IpM/xtcuOl27dsX58+cFuuikpqbCwMAA0tLSiIqKQosWLYSS8eugKe7u7rh37x5/0BQLCwtISkoKJRMhhNRWjDFcunQJbm5uOHXqFBo1aoRZs2Zh9uzZUFRUFHY8UsMYY3Bzc8PChQthZGQEX19fNGrUSNixSvDy8sLkyZMxf/58bN68ucp7XnG5XBgbG+PevXtITEykY4H8MaiwJX+E4uJiDBw4EMnJyUhISBCYT+7x48cwNDSEjIwMoqKioKSkVOP5Xr16hV27dmH37t148+YNhg8fDkdHR+jr61NXY0IIKYfHjx9j27ZtOHjwIIqKijBu3Dg4OjqiW7duwo5GalhkZCQsLCwgJyeHoKAgaGpqCjtSCdu3b4ednR1WrVqFFStWVHn7r169gqamJjp27IgLFy7Q9bbkj0BdkckfwcXFBZcuXYKvr69AUZuSkgIDAwPIysoKpai9c+cOJk+ejFatWsHV1RUWFhZITk5GcHAwDAwMqKglhJByUlNTw7Zt25CZmYm1a9ciMjISmpqa6N+/P06fPg0ejyfsiKSG9O/fH3FxcWjYsCF69+6No0ePCjtSCXPnzsWaNWuwcuVKbN26tcrbb9asGY4dO4aLFy/CxcWlytsnpFZihPzmzp8/zzgcDlu7dq3A8uTkZKakpMQ6dOjAXrx4UWN5uFwuO336NOvfvz8DwJSVldmmTZvYu3fvaiwDIYT87oqKitixY8eYrq4uA8DU1dXZjh07WG5urrCjkRry+fNnNmnSJAaAOTg4sMLCQmFHEsDj8djChQsZAHbgwIFq2ceaNWsYh8NhoaGh1dI+IbUJdUUmv7XMzExoaWlBR0cHZ8+e5U/gnpycDENDQzRo0ACRkZE1cv1JXl4eDh06hK1bt+LRo0fQ1dWFo6MjTE1Nq2XYf0IIIf+5du0a3NzcEBgYCHl5ecyYMQO2tra1YuR7Ur0YY9i5cyccHR3Rp08fHD9+vFbN8coYw+zZs7Fv3z4cO3YM5ubmVdo+j8fDkCFDcOvWLSQmJgptDBFCagIVtuS3VVxcDENDQ6SlpSExMRGNGzcG8N/0Of3794eCggIiIyMFuiZXh+fPn2PHjh3Yu3cvPn78CFNTU8ybNw+9evWq1v0SQggR9PTpU+zYsQP79+9Hbm4uzM3N4ejoiJ49ewo7GqlmMTExMDc3h7i4OE6cOIEePXoIOxIfj8eDpaUl/P39cfLkSQwZMqRK23/z5g00NTXRpk0bREZGQkxMrErbJ6S2oGtsyW9r+fLluHr1Kvz8/ASKWkNDQzRs2LDai9q4uDhMmDABqqqq2LVrFyZPnozU1FT4+/tTUUsIIUKgoqKCzZs349mzZ9iyZQtu3LgBXV1d9OnTBwEBASguLhZ2RFJN9PT0cOvWLbRo0QJ6eno4ePCgsCPxiYiIwMvLC4MHD4aZmRkuXrxYpe03btwYfn5+uHLlSrUMVEVIbUGFLfktnT17Fhs3bsT69evRp08fAEBSUhIMDAzQuHHjaitquVwuTpw4AT09PfTo0QNXr17Fv//+i8zMTLi6ukJVVbXK90kIIaRi5OXlYWdnh0ePHiEoKAhiYmIwNzeHuro6tmzZgo8fPwo7IqkGLVq0wMWLF2FlZYWpU6di9uzZKCwsFHYsAIC4uDj8/PzQp08fDB8+HDdv3qzS9vv06YN169Zhw4YNCAkJqdK2CaktqCsy+e1kZGRAS0sLvXv3RnBwMERERPDgwQMYGhqiWbNmiIiIQJMmTap0nzk5OTh48CC2bduGtLQ06OnpwdHREX///TcNsU8IIXVAfHw83NzccOzYMUhLS2PKlCmws7NDmzZthB2NVIP9+/fD1tYWOjo6CAgIQPPmzYUdCQCQm5uLQYMGITk5GRcvXkTnzp2rrG0ej4cRI0bgypUrSEhIQKtWraqsbUJqAypsyW+lsLAQ+vr6ePHiBRISEtCwYUPcv38fhoaGaN68OcLDw6u0qE1PT8e2bdvg4eGB/Px8WFhYwNHREdra2lW2D0IIITXnxYsX2LlzJ/bs2YMPHz5gxIgRcHR0RN++fWkKtt/MtWvXYGZmBsYYAgIC0Lt3b2FHAgC8f/8ehoaGePXqFWJiYqCmplZlbb979w5aWlr8s9c0eCX5nVBXZPJbWbJkCW7duoXjx4+jYcOGuHfvHgwNDaGkpFRlZ2oZY4iNjcXo0aPRtm1bHDp0CLa2tkhPT8eRI0eoqCWEkDpMSUkJa9euxbNnz7Br1y4kJSWhX79+6NmzJ3x8fFBUVCTsiKSK/PXXX7h16xbatm0LAwMD7NmzB7XhfI+CggLCwsJQr149DBgwAJmZmVXWdsOGDeHn54ebN29iyZIlVdYuIbUBFbbktxEcHIwtW7Zg06ZN0NXVxd27d2FoaIgWLVogIiKCP4BUZRUVFcHX1xe6urro27cv7t27h507d+LZs2dYt24dDaFPCCG/ERkZGcycORP3799HSEgIFBQUMGHCBLRu3RobNmzAu3fvhB2RVAFFRUVERERg5syZmDVrFqZNm4YvX74IOxaaNm2K8PBwMMYwcOBAZGdnV1nbf/31FzZt2gRXV1cEBwdXWbuECBt1RSa/hbS0NGhpaaF///4IDAzE3bt30b9/f7Rq1QoXLlxAo0aNKt32+/fvsW/fPuzYsQOZmZkYMGAAHB0dYWJiwp8XlxBCyO/v3r17cHd3x5EjRyAqKgorKyvY29ujffv2wo5GqsChQ4cwc+ZMdO3aFYGBgbVinuOUlBTo6emhefPmiIqKQoMGDaqkXcYYTE1NER0djfj4eLRu3bpK2iVEmKiwJXVeQUEB+vbti7dv3yI+Ph5Pnz6FkZERVFRUcOHCBTRs2LBS7aakpGDr1q3w9PREcXExJkyYAAcHB3Tt2rWKHwEhhJC6JDs7G3v27MHOnTuRnZ2NoUOHwtHREf3796frcOu4W7duwdTUFPn5+fD394e+vr6wI+Hu3bvQ19dHx44dERYWBllZ2Spp98OHD+jevTsaN26My5cvQ0JCokraJURY6HQTqfMWLlyIO3fuwN/fH+np6ejfvz9UVVUrVdQyxhAVFYW///4b7du3x/Hjx7Fw4UJkZGTg4MGDVNQSQghB06ZNsXLlSjx9+hQHDx5ERkYGBgwYAE1NTXh6eqKgoEDYEUklaWtrIy4uDp07d4aRkRG2bdsm9Otuu3TpgvPnz+POnTsYOXJklXWVbtCgAY4fP47bt29j4cKFVdImIcJEhS2p0wICArB9+3Zs2bIFIiIi6N+/P9q0aVPhoragoACHDh3id2dOT0+Hh4cHMjIy4OzsXC1z3hJCCKnbpKSkMHnyZNy+fRvh4eFQVlbGlClT0KpVK7i4uFTpdZGk5jRp0gRhYWFwcHCAvb09Jk2ahM+fPws1U8+ePXH69GlcvnwZ48aNQ3FxcZW0q6OjA1dXV2zbtg2BgYFV0iYhwkJdkUmtlJubi8ePH6OgoACSkpJQU1ODnJycwDqPHz9G9+7dYWJigsWLF2PgwIFQU1NDWFhYua9Bef36Nb872atXrzBkyBA4OjrCyMiIupMRQgipsPT0dBw7dgxnzpwBj8eDiYkJxo8fX6VTtpCa4+vri6lTp6JDhw44ceIEVFVVhZrn7NmzGDlyJMaOHYtDhw5VyVgfjDFYWFggNDQU8fHxaNu2bRUkJaTmUWFLao0HDx5gz549CDt/Ho8ePxbo+sPhcNBOTQ2DTExgY2ODNm3aoFevXsjNzYWHhwdGjhyJdu3aITQ0tFxF7f379/kDgHA4HP4AIB06dKjGR0gIIYSQuub27dsYNWoUcnJycOzYMQwYMECoefz8/DBu3DjY2Nhg586dVfJDfE5ODrS1tSEvL48rV65ASkqqCpISUrOosCVCl5aWhlk2NggNC4OCjAx6N28OdQUFtKpXD5JiYigoLkZGTg5S3r/HlawsvP/8GaqtWuHFy5c4ePAgbG1t0b59e4SGhqJ+/fo/3A9jDKGhoXBzc0NYWBiUlJRga2uLGTNm/NKoyYQQQgj5vb179w7jx4/HhQsXsH79eixcuFCoPbsOHDiAadOm4Z9//sH69eurpM3ExET89ddfmDJlCnbt2lUlbRJSk6iwJULl4eEBBzs7yImJYZKGBvq2bAnxMrrVFPF4uJyZCc+7d5FTVARRMTF069YNoaGhqFevXqnb5Ofnw9vbG+7u7khKSoK2tjYcHR1hbm5OIwASQgghpFy4XC5WrlyJdevWYcyYMThw4ECJy6RqkpubG+bNm4d169ZhyZIlVdLm3r17YWNjA19fX4wdO7ZK2iSkptDgUURo1q5di+nTp6OvoiJ2GhnBsFWrMotaABAXEYFhq1bYM2gQDFu2RH5+PgYMGFBqUZuVlYXly5dDWVkZNjY26NChAy5duoSbN29iwoQJJYpaVVVVWFtb829HR0eDw+EgOjq6Kh4un7OzM12/SwghhNQxoqKiWLt2LQIDAxESEoJevXrh8ePHQsvj6OgIZ2dnLF26FDt27KiSNmfMmIHx48dj+vTpSE5OrpI2CakpVNj+Jnx8fODu7i7sGOXm4eGB5cuXw7JTJ9jr6EBGXLxC28uIi8NeRwcTO3XCmjVrcODAAf59CQkJmDRpElRUVLB161ZMnDgRjx8/xokTJ6Cnp0dFJSGEEEIqzdTUFNevX0dBQQF69OiBkJAQoWVZuXIlHB0dMXfuXBw6dOiX2+NwONi7dy9atGiBMWPGID8/vwpSElIzqLD9TdSlwjYtLQ0OdnYwbt0a4zQ0fqmtcR07wrh1a9jNnYt9+/bBwMAA3bt3x6VLl7BhwwZkZmbC3d0dbdq0qaL0v2758uX0QUEIId8xMDAAh8MBh8NBYmKisOP8MVRVVQW+P3A4HJw8eVJoeWra17+58s6m8JWGhgZu3rwJPT09DBs2DGvWrAGPx6uekGXgcDhwdXXFtGnTMGXKFJw4ceKX25STk4O/vz9SUlJgZ2dXBSkJqRlU2JIaN8vGBnJiYpjerVultmeMoYDLBfDfG/r0bt0gDcBm5kwUFhbC398fjx8/xrx588ocTEpYxMTEaLRBQggpxfTp05GVlYXOnTsD+G/qnPL2svl6+ciHDx+qMeH/s7a2xsiRI2tkX8B/j6+iU81YW1vD2dm5QttkZWVh8ODBFdqmLlFVVRW4xCgrK6vSJwbq16+PkydPwsnJCStWrICZmRlycnKqJmgFcDgc7NmzB+bm5hg7dixCQ0N/uc0uXbpgx44d8PDwgLe3dxWkJKT6UWErRNHR0dDR0YGUlBTatm2LvXv3lnr95ZEjR6CtrQ1paWk0bNgQY8eOxbNnz/j3GxgY4OzZs3j69Cn/l8eKfPjFxcXB2NgYjRs3hrS0NFq3bo0pU6YI5CztWtOvXzi8vLz4y6ytrSEnJ4cnT57A2NgYsrKyUFJSwqpVq8AYw4MHDxAaFoZJGhqQEhPDyUePYBMaihGBgRh/6hS237qFT4WFAvuxPnsWTpcv49bLl7ALD8fIEydwLjUVAJBbWIgj9++jiMsFA/DkyROcOnVK4ItNQUEBnJycoKamBklJSSgrK2PRokUoKCgo93NUXkVFRXBxcYG6ujqkpKTQqFEj9O3bFxcuXOCv8/1rbG1tzX/dvv/37ReSmnwchBAiDDIyMlBUVISYmFi17aPwu88YYeNyuUI50/cjioqKkJSU/OH9RUVFNZim+ikqKv7Sj+AiIiJwcnLCqVOnEBkZCV1dXTx8+LAKE5aPqKgovL29MWjQIIwaNQqXL1/+5TYnT54MKysr2NjY4MGDB1WQkpDqRYWtkCQkJMDExARv376Fi4sLpk6dilWrVpXo/rN27VpMmjQJ6urq2LJlCxwcHBAREYF+/frxi7dly5ZBU1MTjRs3hre3N38E4PLIzs7GoEGDkJ6ejn/++Qfbt2/HhAkTcO3atUo/Ni6XCxMTEzRr1gybNm2CtrY2nJyc4OTkhD179kBBRgZ9W7bE9lu3cODOHWg0aoSZmpoYqKqKqKdPseLSJRR/9yH//NMnbLx2DVrNmmGmpibaNGiA/OJiLIyKwqnHj/GXkhJkxMXRunVrPHz4EPnp1ZEAAF6hSURBVJmZmQAAHo+Hv//+G//++y+GDx+O7du3Y+TIkXBzc4OFhUWlH+OPODs7w8XFBYaGhtixYweWLVuGVq1aIT4+/ofbzJw5k/+6ff03YcIEAEDTpk2F8jgIIaQ2evr0KYYPHw4FBQXIysqiU6dOCAkJQXp6OgwNDQEACgoK4HA4/MEADQwMYGtrCwcHBzRu3BjGxsb8H2a/7fL84cOHEj/i3r9/H8OGDUO9evUgLy8PPT09pKamwtnZGYcOHUJwcDD/h8jo6OhSzxonJiaCw+EgPT0dAODl5YUGDRrg1KlT0NDQgKSkJDIyMlBQUIAFCxagRYsWkJWVha6ubpUPXpidnY3hw4fzf8Q+evRoiXW+7Yr89Xny8/ODvr4+pKSkSt3mW18fX2hoKDp27Ag5OTmYmJggKyuLv87NmzcxcOBANG7cGPXr14e+vn6Jz8mv13oOGzYMMjIy6NixI65evYrHjx/DwMAAsrKy6N27N1L/90P3V8HBwejevTukpKTQpk0buLi4oLi4uJLPWPkNHz4cN2/eBIfDQc+ePREcHFzt+/yeuLg4/P39oauri6FDh5b53aM8OBwOdu7cCVVVVZibmyMvL6+KkhJSTRgRiuHDhzMZGRn2/Plz/rKUlBQmJibGvr4s6enpTFRUlK1du1Zg27t37zIxMTGB5UOHDmUqKioVzhEUFMQAsJs3b/5wnaioKAaARUVFCSxPS0tjAJinpyd/mZWVFQPA5s6dy1/G4/HY0KFDmYSEBGvbpg0b2rYt22xoyACwhbq6LMTcnP9vtZ5eieVNZWQYALZaT09g3fEaGgwAW967NwsxN2dD27ZlHdq14++TMca8vb2ZiIgIi4mJEci+Z88eBoDFxsbyl6moqDArK6ufPu6ydOvWjQ0dOrTMdZycnFhZh15KSgqrX78+GzhwICsuLq7w4yCEkLpIX1+f2dvbCyz7+jnz1dChQ9nAgQPZnTt3WGpqKjt9+jS7ePEiKy4uZoGBgQwAS05OZllZWezDhw/8duXk5NjChQvZw4cP2cOHD/ntJiQk8Nt+//69wHt+ZmYma9iwITM1NWU3b95kycnJ7ODBg+zhw4fs06dPbMyYMczExIRlZWWxrKwsVlBQwP/ceP/+Pb/dhIQEBoClpaUxxhjz9PRk4uLirHfv3iw2NpY9fPiQ5eXlsWnTprHevXuzS5cuscePH7PNmzczSUlJ9ujRI8bYf59JFf2ct7KyYk5OTvzbgwcPZt26dWNXr15lcXFxrHfv3kxaWpq5ubnx1wHAgoKCBJ5/VVVVFhgYyJ48ecJevHhR5j6/Pr4BAwawmzdvslu3brGOHTuy8ePH89eJiIhg3t7eLCkpiT148IBNnTqVNWvWjOXk5AjkaNGiBfPz82PJycls5MiRTFVVlfXv35+dP3+ePXjwgP3111/MxMSEv82lS5dYvXr1mJeXF0tNTWVhYWFMVVWVOTs789dRUVEp8bnu6enJ6tevX/4ntgw5OTnM1NSUAWArVqxgXC63StqtaIaePXuyxo0bswcPHvxye/fv32cyMjIC35EIqY2qr68P+SEul4vw8HCMGjUKSkpK/OVqamoYPHgwTp8+DQA4ceIEeDwexowZgzdv3vDXU1RUhLq6OqKiorB06dJfyvJ1sIQzZ86gW7duEK/g6MQ/Ymtry/8/h8OBra0tzp49i9QnTzBcRwcxz55BVlwc3Zs1w8dvutKqKShAWkwMd7KzYdiqFX+5oqwstBUVBfYRm5mJNvXro3eLFgAAdQUFhNy6hdzcXP68cv7+/ujYsSM6dOgg8Bz2798fABAVFYXevXtXyWMG/ns+79+/j5SUFKirq1d4+7y8PIwaNQoKCgrw9fWFqKgogJp/HIQQUhuoqqqCMca/nZGRATMzM3Tp0gUABAYGbNiwIYD/erp8PxCQuro6Nm3axL/99expWXbu3In69evj2LFj/M/Gdu3a8e+XlpZGQUEBFL/7bCqPoqIi7Nq1C93+N9ZERkYGPD09kZGRwf9esGDBApw/fx6enp5Yt24dDAwMypX7W99eKvTo0SOcO3cON27cQI8ePQAABw4cQMeOHX/ajoODA0xNTcu936KiIuzZswdt27YF8N93glWrVvHv//rZ9dW+ffvQoEEDXLx4EcOGDeMvnzx5MsaMGQMAWLx4MXr16oUVK1bA2NgYAGBvb4/Jkyfz13dxccE///wDKysrAP/9faxevRqLFi2Ck5MTgPK99r9CXl4eAQEB2LBhA5YtW4Zbt27h6NGjFR6c6lcznDt3Dvr6+hgwYAAuX76M1q1bV7o9DQ0N7NmzB5MmTYK+vr7Ac05IbUKFrRBkZ2cjPz8fampqJe77dllKSgoYYz8skKqiCNXX14eZmRlcXFzg5uYGAwMDjBw5EuPHjy/zGpuyiIiIlBiF+NsvA63q1cPlzEzkFRVh3KlTpbbx8bvrRpvJypZYJys3F31athRolzGGx48fQ1NTE4wx3Lt3D0+ePEGTJk1K3U92dna5H1d5rFq1CiNGjEC7du3QuXNnmJiYwNLSEl27di3X9tOnT0dqaiquXLmCRo0a8ZenpKQgKSmpxh4HIYTURnZ2dpg1axbCwsIwYMAAmJmZlev9VVtbu8L7SkxMhJ6eXpX94PstCQkJgdx3794Fl8sV+KwE/htb4dvPgl+RlJQEMTExgeeiQ4cO5Sq4dHR0KrQvGRkZflELAM2bNxf4nHr16hWWL1+O6OhoZGdng8vl4vPnz8jIyBBo59vnqFmzZgDA/1Hj67IvX74gJycH9erVw+3btxEbG4u1a9fy1+Fyufjy5Qs+f/4MGRmZCj2OyuJwOFiyZAm0tLQwfvx49OjRA0FBQfxB0WpCw4YNceHCBejp6cHIyAgxMTFo8b8TAZVhaWmJS5cuYfbs2dDR0RF4HQipLaiwrcV4PB44HA7OnTvHP3P3ra9nJX8Fh8NBQEAArl27htOnTyM0NBRTpkyBq6srrl27Bjk5uR+OSMn938jEFSUpJgbGGBpISmKhrm6p69T/rqiWKOXxl2j3f+vcuXMHx48fh6+vr8Avs5MmTYKlpaXANsrKyhVMX7Z+/fohNTUVwcHBCAsLg4eHB9zc3LBnzx5M+7/27jyupvz/A/irRbsokSjt2SnLjC2yFsOMIutYstZIywxmmJCxjTG5ZR9FhLEXYw0jZBnGNoyaNiUUQlRKqnt+f/h2f65uWlS3eD0fjx4P3fs5n/M5F3Ve53PO+zNp0nu3DQgIwI4dO7Bt2zZYW1tLvScWi9G6dWusWLFC5rYVfRxERNXRpEmTYG9vj8OHD+P48eNYunQp/Pz8MH369Pdup/nOxVFFxTclRt6eDX63KJK6unqZx1eafgv7fvt3a1ZWFpSUlHD16tUiv+8r4nf9h3r38yvJuxcDFBQUpD6TcePG4enTpwgICICxsTFUVVXRuXPnIoW93u6n8POS9Vph8a2srCwsWLBA5uyyPFYjcHBwwJUrV+Do6IhOnTph06ZNkhnoqtCwYUOcPHkS3bp1Q9++fXH27Fno6emVu7+VK1fi8uXLcHZ2xt9//43atWtX4GiJPhyDrRw0aNAAampqiI+PL/Le26+Zm5tDEASYmpoWuYr7rtIuh1CcTp06oVOnTli8eDF+//13jB49Gjt37sSkSZOgo6MDAEWWULh7967MvsRiMe7cuSM15tjYWMmfc/Pz0VBLC9cfP0YLPT1JIC0rAy0t3H3x4v/7/V/QLrwF6V09e/ZEnz59yrWvstDV1YWLiwtcXFyQlZWF7t27w9fX973BNjIyEjNmzICXl5ekcNTbzM3N8c8//6B3794f/HdNRFSTGRkZwdXVFa6urpg9ezYCAwMxffp0qKioACjdRdfCu19SU1NhY2MDAEXWzm3Tpg22bNmCvLw8mbO2KioqRfb1dr+FvztLsyavjY0NCgoK8PjxY9ja2pbYvjyaNWuG/Px8XL16VXIrckxMTJUtj/S28+fPY+3atRgwYAAA4N69e1KP2ZRXu3btEBMTI/OOOHkxMzPDhQsXMHnyZAwfPhxXr17F4sWLK7Xy99uMjY1x8uRJ2NrawsHBAX/++We5q0Crq6tjz549aN++PVxdXbFt2zaek1C1wqrIcqCkpIQ+ffpg//79SElJkbweHx+Po0ePSr53cnKCkpISFixYIHWlE3hzNfjp06eS7zU1NfHirZBXWunp6UX6LpwtLFxGxtjYGEpKSjh79qxUu7Vr1xbb7+rVq6XGunr1asmJQXJGBrobGUEsCNgho3x8gViMrFIsx9DV0BB3XrzAhQcPJP0qKCgU+wPbxcUFjRs3Rs+ePTF16lQsXboUu3fvxn///Vdhyz+8/XcCvLnSbmFh8d4leVJTUzFs2DB069YNy5cvl9lm2LBhePDgAQIDA4u8l5OTw0qFRPRJ8PLyQnh4OBITE3Ht2jVERERInhE1NjaGgoICDh06hLS0NGRlZRXbj7q6Ojp16oSff/4Z0dHROHPmDHx8fKTauLu7IyMjAyNGjMCVK1cQFxeHrVu3IiYmBsCb539v3ryJmJgYPHnyBHl5ebCwsICRkRF8fX0RFxeHw4cPw8/Pr8TjsrKywujRozF27FiEhoYiMTERly9fxtKlS3H48OEP+MT+X9OmTeHg4ICpU6fi0qVLuHr1KiZNmlSumekPZWlpia1btyI6OhqXLl3C6NGjK2Qc8+bNQ0hICBYsWIDbt28jOjoaO3fuLPJ3W9U0NTWxfft2rFixAn5+fujfv3+R84XK1LRpU5w4cQIJCQkYOHAgsrOzy92XlZUVAgMD8fvvv8s8JyGSJ87Yyomvry+OHz+Orl27ws3NDQUFBVi9ejVatWolubprbm6ORYsWYfbs2UhKSsLgwYNRu3ZtJCYmIiwsDFOmTMGMGTMAvHl+aNeuXfj222/RsWNHaGlpYdCgQSWOY8uWLVi7di0cHR1hbm6OzMxMBAYGQltbW3IltU6dOnB2dsaqVaugoKAAc3NzHDp0qNjnOtXU1HDs2DGMGzcOn3/+OY4ePYrDhw9jzpw52LdnD+LS09HP1BT9zcyw+7//cOf5c7TT14eSoiJSsrJw7t49TLWxQbe3np+VZUjTpjh3/z6WXLyIfiYmuJ+ZCb169WBiagpDQ0OEhYVJte/YsSPq1auHR48eITQ0VOrqsKKiIhQVFXHixAl4eHjAysoKOTk5AMp2y3WLFi1gZ2eH9u3bQ1dXF1euXMHevXulimm9y8PDA2lpaZg1axZ27twp9V6bNm3Qpk0bjBkzBrt374arqysiIiLQtWtXFBQU4L///sPu3bsRHh5e5megiIhqmoKCAkybNg3379+HtrY2HBwcIBKJAACNGzeWFA9ycXHB2LFjpYonvWvTpk2YOHEi2rdvj6ZNm+KXX35Bv379JO/Xq1cPp06dwsyZM9GjRw8oKSnB2toaXbt2BfCmJkLhevRZWVmIiIiAnZ0dduzYATc3N7Rp0wYdO3bEokWL4OzsXOKxBQcHY9GiRfjuu+/w4MED6OnpoVOnTlLFlN6WlJQEU1NTyX5LIzg4GJMmTUKPHj2gr6+PRYsWYe7cuaXatiJt3LgRU6ZMQbt27WBkZIQlS5ZIzmc+hL29PQ4dOoSffvoJy5YtQ61atdCsWbMSHwWqCgoKCvD29oa1tTWGDRuGDh06IDQ0VHLHQGVr27Ytjhw5gr59+8LJyQkHDhwody2VESNG4OzZs/Dw8MBnn31W5PEpIrmRSy1mEgThTbl7GxubN8vgmJsLQUFBwnfffSeoqalJtdu3b5/QrVs3QVNTU9DU1BSaNWsmTJs2TYiJiZG0ycrKEkaNGiXUrVtXAFDqJQGuXbsmjBw5UmjSpImgqqoqNGjQQBg4cKBw5coVqXZpaWnCkCFDBA0NDUFHR0eYOnWq8O+//8pc7kdTU1NISEgQ+vXrJ2hoaAj6+vrC/PnzhYKCAmH69OmCjoaGcGDIEOGIs7Pg0b69YKGjI6gqKQnqysqCSZ06wtCmTYVtAwdKLffT0cBAaqmfwq9dX30lDLKwEOqpqQkABC0tLWHcuHHCkydPhAMHDgg6OjoCAEFDQ0No2bKloKqqKujo6Ajt27cXfH19hZiYGOH06dNCYGCgoK2tLRgZGQktWrQQVFRUBAACAEFZWVlo0aKF8NVXXwkzZ84UAgMDhTNnzggpKSmSZYUKLVq0SPjss8+EunXrCurq6kKzZs2ExYsXC69fv5a0eXe5nx49ekj29e7X28s0vH79Wli2bFmR41iwYIHw4sWLUv19ExFVV7KW+6HinTp1Sqhbt67w7NkzeQ+lxqvI5X5K4+7du0L79u0FNTU1YevWrVW2X0EQhJMnTwoqKirCkCFDhLy8vHL3k5OTI9jY2AgWFhY8B6FqQ0EQ3rkPleRq8ODBkuViaqLx48dj7969xd4CFhUVhZYtW2Lm559LLefzoSKSk7H80iVERUVJLV3w+PFjuLq6ol69emW6ZaagoADJycmIi4tDbGwsYmNjJX9OSkqSFKrQ0tKClZUVLC0tYWVlJfXnwueriIioZHZ2drhw4QJUVFRw8eJFVl0twcyZM9GgQQPMnDlT3kOp0bS0tJCfnw81NbUqfd44JycHbm5u2LJlCzw9PbF8+fJKqcAtyx9//AEnJyeMGTMGGzdulBQ9K6uEhAS0a9cO9vb22LVrF5+3JbljsJWjnJwcqWdK4uLi0LJlS4wbN67GPrdQUrAFAAd7e9y4eBFreveGRgX8EM/Oy8O0P/+EdefOOBYe/sH9lSQ3NxeJiYlFAm9sbKzUM9N6enoyA6+FhUWZK0wSEX3sHjx4IHkEpEmTJpJiUFS99O/fH5GRkTLfmzNnDubMmVPFI/owhUU7lZSUPmit1/IQBAFr166Fl5cXunbtit27d6NBgwZVsu/ff/8dX3/9Ndzd3REQEFDuULpv3z4MHToUq1evxrRp0yp4lERlw2ArRwYGBhg/fjzMzMxw9+5drFu3Drm5ubh+/Xqxa9eWRVpa2nufD1VRUZEsaF9RShNsExMT0bplS3Rr2BCeH/hcqCAIWHn1Ks49fIhbt29X2i+lnJycEotz6erq4vXr14iPj5cZep89eyZp27hx4yKB18rKCqampjyZIyKiauvtCxDv0tXVrfDzik9BZGQknJ2dUatWLYSGhkqqVle23377Da6urvDx8cHChQvL3Y+npyfWr1+P8+fPs94HyRWDrRy5uLggIiICDx8+lKzhtmTJErRr165C+jcxMSl2SR4A6NGjB06fPl0h+ypUmmALAEFBQZg8eTLGtGyJkS1alGtfgiBgR3Q0tt2+jaCgIEycOLFc/ZTG5s2b4eLi8t42JRXwePr0qSTovnuLc2FVYyUlJZiYmMgMvUZGRuW+XYiIiIiqrwcPHmDIkCG4ceMG1q5diwkTJlTJfpcvX45Zs2Zh2bJlmDVrVrn6eP36Nbp164YnT57g2rVrqFu3bsUOkqiUGGw/YufPny/2qioA6OjooH379lU4ImmLFy+Gj48P7E1NMblt2zLdlpydl4fAf/5BeGIiFi9eXOm3PqWmpuL27dvvbdO+fftyPVcrCAJSU1NlBt74+Hjk5eUBAFRVVWFhYSHzmV59fX0+20JERFSD5ebmwsPDAxs2bICbmxv8/f2r5C6uuXPnYtGiRVi3bh1cXV3L1UdSUhJsbGzQs2dP7Nu3j+ckJBcMtiRXQUFB8PLwgJayMsa2aIFuhoao9Z5ZybyCApx78AAhUVHIys9HwKpVlTpTK2/5+flFilgVht6kpCTJGsS1a9cutogVr5wSEVF5CYIg+QLeLFtT+EWVIzAwEO7u7ujQoQP27t0LAwODSt2fIAjw9PTE6tWrsXXrVowePbpc/Rw4cACDBw+GSCSCl5dXxQ6SqBQYbEnuEhMT4ebqivDjx6GjoYEuBgaw1NFBE21tqCopIbegAMkZGYhLT8eF1FSkZ2fDvl8/rFu/vsoLPVQnr169wp07d2RWbk5NTZW0q1+/frFFrDQ0NOR4BEREVFPk5eUhMTERcXFxyMrKgp6eHqysrNC4cWM+JlMJ/vrrLwwZMgSCIGDv3r3o0qVLpe5PLBZj4sSJ2Lp1K/bt24evvvqqXP3MmDEDAQEBiIyMRKdOnSp4lETvx2BL1UZUVBTWr1+PE+HhiImLw9v/NBUUFNDU0hJ97e3h5uYmtaQPFZWZmSmziFVMTIzUcgZGRkYyQ6+pqWmVLTtAREQ1R0FBAQ4ePAiRSISzZ8/CxMQEHh4emDhxIrS1teU9vI/Kw4cP4ezsjEuXLiEgIACurq6VOlOen5+PkSNH4o8//sDhw4fRp0+fMveRl5eHHj164MGDB7h+/TqLiVGVYrClaikrKwvx8fHIzc2VPFuqpaUl72HVeIIgSBWxejv0xsXFITs7G8D/L3sgq4iVoaEhr84TERGuXr0Kf39/7Ny5E+rq6pg4cSI8PDw+6bupKtrr16/x3XffYfXq1ZgwYQLWrFkDNTW1St3fV199hbNnz+LkyZPo3Llzmfu4d+8erK2t0aVLFxw4cIDnDFRlGGyJCMCb0JuSkiJzqaI7d+5IilipqanJLGJlZWWF+vXr87krIqJPTEpKCtasWYP169fj+fPnGDx4MLy9vdG1a1f+TqggW7ZsgaurK1q3bo19+/bByMio0vaVnZ0NBwcH3Lp1CxEREbC2ti5zH0eOHMEXX3yBX375BTNnzqz4QRLJwGBLRCXKz8/H3bt3ZVZuvnv3ruS2cW1tbZmzvJaWlqhTp46cj4KIiCpTdnY2QkJC4O/vj5iYGHTo0AHe3t6SNVrpw1y9ehVOTk7IycnBnj170KNHj0rbV0ZGBnr16oXk5GRERkaiadOmZe5j9uzZWL58Oc6cOYOuXbtWwiiJpDHYEtEHefXqFRISEmRWbn748KGkXYMGDWQGXgsLC6irq8vxCIiIqCKJxWIcO3YMIpEIJ0+eROPGjeHu7o4pU6bwmcsPlJaWhhEjRuDMmTPw8/ODh4dHpc2KP3nyBD169EBGRgbOnTsHY2PjMm2fn5+PXr164c6dO7h+/Trq169fKeMkKsRgS0SVJiMjA3FxcTKf6X27iFWTJk1kFrEyMTHhVX4iohrs1q1b8Pf3x/bt26GkpIRx48bBy8sLVlZW8h5ajZWfn48ffvgBfn5+GD16NDZs2FBpqxykpKTA1tYWCgoKiIyMLPPSQw8ePICNjQ3atWuHI0eO8HlbqlQMtkRU5QRBwJMnT4otYpWTkwMAUFZWhpmZmczQyyUmiIhqjsePH2PdunVYu3YtHj9+jC+++ALe3t7o1asXn8Mtp507d2LChAlo2rQpwsLCYGJiUin7SUxMRLdu3aCrq4szZ86Uedb9+PHjcHBwwKJFizBnzpxKGSMRwGBLRNWMWCx+bxGr/Px8AIC6urqkiNW7tzjr6enxRImIqBp69eoVduzYAZFIhFu3bqFNmzbw9vbGyJEjoaqqKu/h1Tg3b96Eo6Mjnj9/jp07d6Jv376Vsp/o6Gh0794dpqam+PPPP1G7du0ybT9v3jwsXrwYp06dqtRng+nTxmBLRDVGfn4+kpKSZIbee/fuSYpY1alTp9giVlxnkYhI/gRBwKlTpyASiXD48GHo6+vjm2++gaurKxo0aCDv4dUoz549w6hRo3DixAksXboUM2fOrJSLu9euXUPPnj1hY2ODo0ePlqk+RkFBAfr27Yvo6GjcuHED+vr6FT4+IgZbIvoo5OTkICEhQWbl5kePHkna6evry1yqyNzcvFLXBiQiItliYmIQEBCAzZs3QywW4+uvv4aXlxdatWol76HVGAUFBZg3bx6WLFmCYcOGYePGjdDS0qrw/Zw/fx79+vVDz549ERoaChUVlVJv+/DhQ1hbW6NVq1YIDw+HkpJShY+PPm0MtkT00Xvx4oXMIlYxMTHIyMgAACgoKKBJkyYyZ3lNTEygrKws56MgIvq4PX36FIGBgVi1ahVSUlLQt29feHt7w97enjUVSik0NBTjxo2DiYkJwsLCYGFhUeH7OH78OAYNGoTBgwfj999/L1NAPXXqFPr27Yt58+Zh/vz5FT42+rQx2BLRJ0sQBKSlpRVbxOrVq1cA3hSxMjc3l1nEqlGjRjzhIiKqQHl5edizZw9EIhGuXLmCZs2awcvLC2PGjKm06r8fk6ioKAwePBhpaWnYvn07BgwYUOH7CAsLg7OzM8aPH4/AwMAy3fq8cOFCzJ8/HydOnEDv3r0rfGz06WKwJSKSQSwW4/79+zJD7507d1BQUAAA0NDQKLaIVb169VjEioionARBwPnz5yESibB//37o6Ohg6tSpmDZtGho1aiTv4VVrL168wJgxY3Do0CEsWLAAP/74Y4VfhA0JCZEs37RixYpS/74rKChA//798c8//+DGjRtlXkKIqDgMtkREZZSXl/feIlaF6tatK/Ucb2HotbS0LHNFSSKiT9mdO3ewcuVKbNy4Ebm5uRg+fDi8vb3Rrl07eQ+t2hKLxVi4cCF8fX3x1VdfISQkpMILKK5Zswbu7u6YP38+fH19S73d48ePYWNjA0tLS5w8eZKP+1CFYLAlIqpA2dnZkiJW74betLQ0SbuGDRvKnOU1NzfnkhdERMV48eIFNm7ciJUrV+Lu3bvo3r07vL29MWjQIBYjKsbBgwfx9ddfw8DAAPv370ezZs0qtP+lS5dizpw58PPzw7ffflvq7c6ePYuePXti9uzZWLRoUYWOiT5NDLZERFXk+fPnUs/wvh1+3y5iZWxsLLOIlbGxMa9qExHhzfJv+/fvh0gkwoULF2Bubg4PDw+4uLjwjhgZYmNjMXjwYNy/fx8hISEYPHhwhfY/e/Zs/PzzzwgMDMSkSZNKvd3PP/+M2bNn4+jRo3BwcKjQMdGnh8GWiEjOBEHA48ePZQbeuLg45ObmAgBq1aoFc3NzmaG3UaNGfJ6XiD5Jly9fhkgkwp49e6ClpYVJkyZh+vTpMDY2lvfQqpXMzEy4uLhg37598PHxga+vb4XNcguCAHd3d6xbtw47duzA8OHDS7WdWCzGoEGDcOnSJdy4cQOGhoYVMh76NDHYEhFVY2KxGPfu3ZNZxCoxMVFSxEpTUxOWlpYyKzfXq1dPzkdBRFT57t27h9WrV2PDhg3IzMyEk5MTvL290blzZ3kPrdoQBAHLli3DnDlz4ODggO3bt0NHR6dC+haLxRg/fjx27NiB/fv344svvijVdk+ePIGNjQ2MjY0RERGBWrVqVch46NPDYEtEVEO9fv0aiYmJMkPv/fv3Je10dXVlBl5LS0toaWnJ8QiIiCpeVlYWtmzZAn9/f8THx6NTp07w9vaGk5MTH+f4n/DwcIwcORK6urrYv38/WrVqVSH95ufnY9iwYThy5AiOHj2Knj17lmq7CxcuoEePHvj222+xbNmyChkLfXoYbImIPkLZ2dmIj4+XWcTqyZMnknYGBgYyi1iZmZmxiBUR1WhisRiHDx+GSCRCREQEmjRpgunTp2PSpEmoW7euvIcnd3fu3IGjoyPi4+MRHByMYcOGVUi/ubm5GDRoEC5evIiTJ0/i888/L9V2fn5+mDFjBg4ePIiBAwdWyFjo08JgS0T0iUlPTy+2iFVmZiYAQFFRUVLE6t3Q26RJE1YfJaIa5caNG/D398fvv/8OFRUVTJgwAZ6enjA3N5f30OTq5cuXmDx5Mnbs2IFZs2Zh8eLFFTKr/fLlS/Tr1w/R0dE4c+YMWrduXeI2giBg8ODBiIyMxPXr1/mMNJUZgy0REQF4c1Lx6NGjIoE3NjYWCQkJkiJWKioqxRaxMjAwYBErIqq2Hj58iLVr12LdunV4+vQpvvzyS3h7e6N79+6f7M8uQRDg7++PmTNnomfPnti5c2eF1GZ4/vw5evbsidTUVERGRsLS0rLEbdLT02FjY4OGDRvi7NmzUFFR+eBx0KeDwZaIiEpUUFCAe/fuyZzlTUxMhFgsBvCmiJWswGtlZQVdXV05HwUR0Rs5OTnYtm0b/P39ERUVBRsbG3h7e2P48OGfbJiKiIjAsGHDoKWlhdDQUNjY2Hxwn2lpaejevTuys7MRGRmJJk2alLjN5cuX0a1bN7i7u2PFihUfPAb6dDDYEhHRB3n9+jXu3Lkjs4jVgwcPJO3q1asnFXgLQ6+lpSU0NTXleARE9KkSBAHHjx+HSCRCeHg4DAwMMG3aNEydOhV6enryHl6VS05OhpOTE27fvo3AwEB8/fXXH9zn/fv30a1bN6iqquLs2bPQ19cvcZuVK1fC09MTYWFhFb7mLn28GGyJiKjSvHz5stgiVk+fPpW0a9y4scxZXjMzs0929oSIqlZUVBT8/f2xdetWAMDYsWPh5eWF5s2by3lkVSsnJwdubm7YsmULPD09sXz58g9egichIQG2traoX78+Tp8+XeISQ4IgwNnZGSdPnsS1a9dgZmb2QfunTwODLRERycWzZ89kzvLGxsbi5cuXAN4UsTIxMZFZxMrIyIhFrIiowqWlpeG3337DmjVr8PDhQzg4OMDb2xt9+/b9ZJ7DFQQBa9euhZeXF7p06YLdu3eXaqb1fW7fvo3u3bvDysoKJ06cKHG5uRcvXqBdu3bQ0dHB+fPnWamfSsRgS0RE1YogCHj48KHMwJuQkIDXr18DAFRVVSVFrN5dn7dhw4afzAkoEVWO3Nxc7Nq1CyKRCDdu3EDLli3h5eWFr7/+GmpqavIeXpWIjIyEs7MzlJWVERoais8+++yD+rty5Qp69eqFjh074vDhwyV+jteuXUPnzp0xZcoUrFq16oP2TR8/BlsiIqoxCgoKkJycLLNy8927dyVFrLS0tGTO8lpaWpZ4CxwR0dsEQcCZM2cgEolw8OBB6Onpwc3NDd98880Hz2LWBA8ePMCQIUNw/fp1rF27FhMnTvyg/s6ePQt7e3v069cPe/fuLfE253Xr1uGbb77B7t274ezs/EH7po8bgy0REX0UcnNzcefOHZmVm1NSUiTt9PT0ZAZeCwsLFrEioveKi4vDypUrERwcjLy8PIwaNQpeXl5o27atvIdWqXJzc+Hh4YENGzbA1dUVAQEBH1T/4OjRo/jqq6/g7OyMkJCQ9z5WIggCRo4ciSNHjuDatWuwsLAo937p48ZgS0REH73MzEzEx8cXCbwxMTFIT0+XtDM0NJRZudnU1JRFrIhIIj09HUFBQVi1ahXu3buHXr16wdvbGwMGDICioqK8h1dpAgMD4e7ujg4dOmDPnj1o1KhRufvas2cPRowYgUmTJmH9+vXvfXwkIyMDHTp0gKamJi5evPjJ3ApOZcNgS0REn7SnT58WW8QqOzsbAKCkpARTU1OZlZuNjIw+6hNZIipeXl4eQkNDIRKJcOnSJVhZWcHT0xPjxo37aO8AuXTpEoYMGYKCggLs3bsXXbt2LXdfwcHBmDBhAmbMmIFffvnlveH2n3/+QadOnTBu3DisX7++3PukjxeDLRERkQyCICA1NVXqOd7C0JuQkIC8vDwAgJqamswiVlZWVmjQoAGLWBF9Ii5evAiRSIR9+/ahTp06mDJlCtzd3WFoaCjvoVW4R48ewdnZGX/99RcCAgLg6upa7p91AQEB8PLywsKFC+Hj4/PetkFBQZg8eTJ+//13jBw5slz7o48Xgy0REVEZ5efnS4pYvTvLe/fuXRT+aq1du7ZU4PXx8fng9SCJqHpLSkrCqlWrEBQUhOzsbDg7O8Pb2xsdO3aU99AqVF5eHr777jusWrUKEyZMwJo1a8p9i/CiRYswd+5cBAQEwMPDo9h2giBg7NixCAsLw5UrV9CsWbPyDp8+Qgy2REREFejVq1fFFrGKiYlB7dq1y9Tf+PHj8fz5c+zfv79yBvwOBQUFhIWFYfDgwVWyP6KPVWZmJjZt2oSAgAAkJiaia9eu8Pb2xuDBgz+qNbhDQkIwdepUtG7dGvv27YORkVGZ+xAEAbNmzcKvv/6KTZs2wcXFpdi2WVlZ6NixI5SVlXHp0iVoaGh8yPDpI8JgS0REVEUEQSjz7XqlDbZ5eXkVMhtcUcH29evXLLhFhDfLlP3xxx8QiUSIjIyEiYkJPDw8MHHiRGhra8t7eBXi6tWrcHJyQk5ODvbs2YMePXqUuQ9BEODq6oqgoCDs2rULQ4cOLbbtv//+i88++wwjR47Exo0bP2To9BFhtQsiIqIq8r5Qu3fvXrRu3Rrq6uqoV68e+vTpg5kzZ2LLli04cOAAFBQUoKCggNOnTyMpKQkKCgrYtWsXevToATU1NWzfvh2+vr6wtraW6tff3x8mJiZSr23atAktW7aEqqoqDAwM4O7uDgCSdo6OjlBQUJB8P378+CJB18vLC3Z2dpLv7ezs4O7uDi8vL+jp6cHe3h7AmxPQ/v37Q0tLC/r6+hgzZgyePHlS5s+OqKZSUlKCo6Mjzp49iytXrqBr166YNWsWDA0N4e3tjcTERHkP8YO1b98eV65cQevWrdG7d28EBASgrHNnCgoKWLt2LUaMGIFRo0bh2LFjxbZt1aoV1q1bh02bNiEkJORDh08fCQZbIiIiOUtNTcXIkSMxYcIEREdH4/Tp03BycsL8+fMxbNgwODg4IDU1FampqejSpYtkux9++AGenp6Ijo6WBMmSrFu3DtOmTcOUKVNw69Yt/PHHH5J1If/++28AbyqVpqamSr4vrS1btkBFRQXnz5/H+vXr8fz5c/Tq1Qs2Nja4cuUKjh07hkePHmHYsGFl6pfoY9G+fXts27YNSUlJcHd3R0hICCwsLDBkyBCcO3euzGGwOqlfvz7Cw8Ph5eUFLy8vjBkzRlJZvrSUlJSwefNmODg4wMnJCWfPni227bhx4+Di4gI3Nzfcvn37Q4dPHwFleQ+AiIjoU5eamor8/Hw4OTnB2NgYANC6dWsAgLq6OnJzc9GwYcMi23l5ecHJyalM+1q0aBG+++47eHp6Sl4rLGpTv359AEDdunVl7q8klpaW+OWXX6T2ZWNjgyVLlkhe27RpE4yMjBAbGwsrK6sy74PoY9C4cWMsWbIEPj4+CAkJgb+/P2xtbdGhQwd4e3vD2dm5RhaaU1ZWxq+//ooOHTpgwoQJuH37NkJDQ2FqalrqPmrVqoXdu3fjiy++wMCBA3Hq1Cl06NBBZtvVq1fj77//hrOzMy5fvgwtLa2KOhSqgThjS0REJGdt27ZF79690bp1azg7OyMwMBDp6eklblfcyV5xHj9+jJSUFPTu3bu8Q32v9u3bS33/zz//ICIiAlpaWpKvwiqmCQkJlTIGoppEQ0MDrq6uiIqKwuHDh1G3bl2MHj0apqamWLZsWal+DlRHI0aMwF9//YWMjAx06NABJ06cKNP2ampqOHDgAFq2bAl7e/tiZ2Q1NDSwZ88eJCcn45tvvqnRM9704RhsiYiI5ExJSQknTpzA0aNH0aJFC6xatQpNmzYt8dk7TU1Nqe8VFRWLnNgVrrcLvJn9LY+S+i1uPFlZWRg0aBBu3Lgh9RUXF4fu3buXayxEHyNFRUUMGDAAJ06cwM2bN2Fvb4958+bB0NAQ06ZNQ2xsrLyHWGZt2rTB33//jY4dO8LBwQG//PJLmYKnlpYWjhw5AkNDQ/Tt27fYi2HNmjXDhg0bsHXrVmzatKmihk81EIMtERFRNaCgoICuXbtiwYIFuH79OlRUVBAWFgYVFRUUFBSUqo/69evj4cOHUiePN27ckPy5du3aMDExwZ9//llsH7Vq1Sqyv/r16yM1NVXqtbf7LU67du1w+/ZtmJiYwMLCQurr3RBMRG+0bt0aGzduRHJyMmbOnIk9e/agadOmGDRoEE6dOlWjZiV1dXVx+PBh/PDDD/j+++8xfPhwZGVllXp7HR0dHD9+HFpaWujTpw/u378vs92oUaMwZcoUuLu74+bNmxU1fKphGGyJiIjk7NKlS1iyZAmuXLmC5ORkhIaGIi0tDc2bN4eJiQlu3ryJmJgYPHnyROZMaSE7OzukpaXhl19+QUJCAtasWYOjR49KtfH19YWfnx9WrlyJuLg4XLt2DatWrZK8Xxh8Hz58KLkNslevXrhy5QpCQkIQFxeH+fPn499//y3xuKZNm4Znz55h5MiR+Pvvv5GQkIDw8HC4uLiUOqwTfar09fXh6+uL5ORkbNy4EXfv3kXv3r1hbW2NzZs3Izc3V95DLBUlJSUsXrwY+/btw9GjR9GpUyfEx8eXent9fX2cPHkSYrEYffv2RVpamsx2/v7+aNq0KZydnZGZmVlRw6cahMGWiIhIzrS1tXH27FkMGDAAVlZW8PHxgZ+fH/r374/JkyejadOm6NChA+rXr4/z588X20/z5s2xdu1arFmzBm3btsXly5cxY8YMqTbjxo2Dv78/1q5di5YtW2LgwIGIi4uTvO/n54cTJ07AyMgINjY2AAB7e3vMnTsXs2bNQseOHZGZmYmxY8eWeFyNGjXC+fPnUVBQgH79+qF169bw8vJC3bp1oajIUxCi0lBTU8OECRPwzz//4MSJEzA0NISLiwuMjY3x008/FRv0qhsnJydcunQJeXl56NChAw4fPlzqbZs0aYKTJ08iPT0d9vb2eP78eZE26urq2LNnD1JTUzFlypQaNbNNFUNB4N86ERERVQOxsbH45ZdfYGlpCSsrK1hZWcHc3BxqamryHhpRtfLff/8hICAAW7ZsgVgsxtdffw0vLy+0atVK3kMr0YsXLzBmzBgcOnQICxYswI8//ljqC103b96EnZ0dWrRogfDwcJmPNOzevRvDhw/HunXr4OrqWtHDp2qMwZaIiIiqhaSkJDg7OyM2NhYZGRkA3jx73KRJE0nQfTv0GhsbQ1mZKxfSp+vp06fYsGEDVq9ejZSUFPTt2xfe3t6wt7ev1ndFiMViLFq0CPPnz8dXX32FkJAQaGtrl2rbS5cuoXfv3ujSpQsOHjwIVVXVIm3c3d0RGBiIixcvol27dhU9fKqmGGyJiIioWhEEAWlpaYiNjUVsbCzi4uIkf46Pj8erV68AvCl0ZWZmViTwWlpaonHjxlBQUJDzkRBVjdevX2PPnj0QiUS4evUqmjVrBi8vL4wZMwYaGhryHl6xDh06hNGjR8PAwAD79++XLAdWkoiICPTv3x8DBgzA7t27i1zgys3NRdeuXZGeno5r166hTp06lTF8qmYYbImIiKjGEIvFuH//fpHAGxcXhzt37kiKUmloaMDS0rJI4LWyskK9evUYeumjJAgCzp07B5FIhP3790NXVxdTp07FtGnT0KhRI3kPT6bY2Fg4Ojri3r17CAkJweDBg0u13aFDh+Do6IiRI0di8+bNRWao79y5g3bt2qFPnz7Ys2cP/89/AhhsiYiI6KOQl5eHxMREqcBbGHrv3bsnaaejoyMz8FpaWqJ27dpyPAKiinPnzh2sXLkSGzduRG5uLoYPHw5vb+9qeWtuZmYmXFxcsG/fPvj4+MDX1xdKSkolbrdz506MGjUKbm5uWL16dZHwGhYWBicnJ6xcuRLTp0+vrOFTNcFgS0RERB+97OxsxMfHF5nljY2Nlaoqa2BgIDP0mpuby3yWj6i6e/HiBTZu3IiVK1fi7t276N69O7y9vTFo0KBShceqIggCli1bhjlz5sDBwQHbt2+Hjo5OidsFBQVh8uTJmD17NpYsWVLkfW9vb6xZswbnz59Hx44dK2PoVE0w2BIREdEn7fnz5zIDb2xsrGQ9TAUFBRgbGxdbxKo6BQQiWfLz87F//36IRCJcuHAB5ubm8PDwgIuLS7W6UyE8PBwjR46Erq4uwsLC0Lp16xK3WbFiBb777jssXboUP/zwg9R7r1+/hq2tLR49eoTr16+XKixTzcRgS0RERCSDIAh4/PhxsUWscnNzAbwpYmVubi6ziFWjRo34bB9VO5cvX4ZIJMKePXugpaWFyZMnY/r06WjSpIm8hwbgzW3Ujo6OiI+PR3BwMIYNG1biNr6+vliwYAHWrFmDb775Ruq9u3fvwsbGBt27d0dYWBj/T36kGGyJiIiIyqigoEBSxOrd0JuUlCQpYqWpqfneIlZE8nTv3j2sXr0av/32G7KysjBkyBB4e3ujU6dO8h4aXr58icmTJ2PHjh2YOXMmlixZ8t7lvQRBwLfffgt/f3+EhIRgzJgxUu8fOnQIgwYNgp+fH7799tvKHj7JAYMtERERUQV6/fo1EhMTZVZuvn//vqSdrq6uVOAtDL2WlpbQ0tKS4xHQpyYrKwubN29GQEAA4uPj0alTJ3h7e8PJyUmua0ULggB/f3/MnDkTdnZ22LlzJ/T09N7bfvLkydi8eTP27NkDR0dHqfdnzZoFkUiEs2fPonPnzpU9fKpiDLZEREREVeTly5dFilgVht4nT55I2jVq1EjmLK+ZmRmLWFGlEYvFOHToEEQiEU6fPo0mTZpg+vTpmDRpEurWrSu3cUVERGDYsGHQ1NREWFgYbGxsim1bUFCAUaNGYf/+/Th48CD69esneS8vLw92dna4d+8erl+/zrsmPjIMtkRERETVwLNnzxAXFyezkFVWVhYAQFFRsdgiVk2aNGERK6owN27cgEgkwo4dO6CiooIJEybA09MT5ubmchlPcnIynJyccPv2bWzYsKHIrcZve/36NRwdHREREYETJ06ga9eukvfu378Pa2trfP755zh48GCR9W+p5mKwJSIiIqrGBEHAw4cPZQbe+Ph4vH79GgCgoqJSbBErAwMDFsyhcklNTcXatWuxbt06PHv2DF9++SW8vb3RvXv3Kv83lZOTg2+++QabN2+Gh4cHfv31V9SqVavYtv3798f169cREREhtX7vsWPH0L9/f/z888/4/vvvq2r4VMkYbImIiIhqqIKCAty7d6/YIlZisRgAoKWlJQm774ZeXV1dOR8F1QQ5OTnYtm0b/P39ERUVBRsbG3h7e2P48OFQUVGpsnEIgoB169bB09MTXbp0we7du6Gvry+zbUZGBvr06YPExEScPXsWzZs3l7z3448/YtmyZYiIiICtrW1VDZ8qEYMtERER0UcoNzdXUsTq3dCbkpIiaVevXj2ZgdfS0hKamppyPAKqjgRBwPHjxyESiRAeHg4DAwNMmzYNU6dOfW9hp4p27tw5DB06FMrKyggNDcVnn30ms93Tp09hZ2eH9PR0REZGwtTUFMCbdX379OmDuLg4XL9+HQ0aNKiysVPlYLAlIiIi+sRkZWUhPj5eZuXmp0+fSto1bty42CJWVTlLR9VTVFQU/P39sXXrVgDA2LFj4eXlJTUzWpkePHiAoUOH4tq1a1i7di0mTpwos11qaipsbW0hCAIiIyPRqFEjAEBKSgqsra1hY2ODo0eP8nnbGo7BloiIiIgknj59WmwRq5cvXwJ4U8TKxMREZhErIyMjFrH6xKSlpeG3337DmjVr8PDhQ/Tv3x/e3t7o06dPpT+Hm5ubC09PT/z2229wdXVFQECAzIsuSUlJsLW1hba2Ns6cOSOZXT558iT69euHn376CT4+PpU6VqpcDLZEREREVCJBEJCamlpsEau8vDwAgKqqqqSI1buhV19fn0WsPmK5ubnYtWsXRCIRbty4gVatWsHLywujR4+Gmppape47KCgI06ZNQ/v27bF3717JrOzbYmJiYGtriyZNmuDUqVPQ1tYGAPj6+mLhwoU4efIkevbsWanjpMrDYEtEREREH6SgoAB3796VGXqTkpJQeLqppaUlM/BaWlpCR0dHzkdBFUUQBJw5cwYikQgHDx6Enp4e3Nzc8M033xRb6KkiXLp0CUOGDEFBQQH27t0rtcxPoRs3bsDOzg5t27bF0aNHoaGhgYKCAtjb2+P27du4fv06GjZsWGljpMrDYEtERERElSY3Nxd37tyRWcQqNTVV0k5PT09m4LWwsGARqxosLi4OAQEBCA4ORn5+PkaNGgVvb2+0adOmUvb36NEjODs74+LFiwgICICbm1uRuwQuXLiAvn37onv37jhw4ABUVFTw6NEjWFtbo3nz5jhx4gRvp6+BGGyJiIiISC4yMzNlFrGKjY1Fenq6pJ2hoaFU4C0MvaampixiVUOkp6cjMDAQq1atwv3799GrVy94e3tjwIABFV60KS8vD9999x1WrVoFFxcXrF27tsit0CdPnsQXX3yBL7/8Ejt27ICysjJOnz6N3r17w8fHBwsWLKjQMVHlY7AlIiIiomrn6dOnMmd54+LikJ2dDQBQUlKCqampzMrNRkZGrHJbDeXl5WHfvn0QiUS4fPkyrKys4OnpiXHjxlX4zHxISAimTp2KVq1aITQ0FEZGRlLvHzhwAEOGDMGYMWOwceNGKCoqYvHixZg7dy7Cw8PRt2/fCh0PVS4GWyIiIiKqMQRBQEpKiszAm5CQIFXEysLCQuYzvQ0aNGARKzkTBAEXL16ESCRCaGgo6tSpgylTpsDd3R2GhoYVtp+rV6/CyckJOTk52L17N+zs7KTe37ZtG8aOHYvp06fD398fgiBgwIABuHbtGm7cuCGzCBVVTwy2RERERPRRyM/PL7aI1d27dyVFrGrXrl1sEau6devK9yA+QUlJSVi1ahWCgoKQnZ0NZ2dneHt7o2PHjhXSf1paGkaMGIEzZ87g119/haenp9SFjfXr18PNzQ1z587FTz/9hLS0NNjY2MDMzAynTp2CsrJyhYyDKheDLRERERF99F69eiVVxOrt0Pvw4UNJu/r16xdbxEpDQ0OOR/Dxy8jIQHBwMAICApCYmIiuXbvC29sbgwcP/uBiTvn5+fjhhx/g5+eHUaNGITAwUOrv85dffsH333+P5cuXY8aMGTh37hzs7Owwa9YsLFmy5EMPjaoAgy0RERERfdIyMjIkRazevcX5+fPnknZGRkYyQ6+pqSlq1aolvwP4yBQUFOCPP/6ASCRCZGQkTExM4OHhgYkTJ0rWni2vnTt3YuLEibCyskJoaChMTU0l7/n4+GDx4sX47bffMGXKFEnYPXLkCPr37/+hh0WVjMGWiIiIiEgGQRDeW8QqJycHwJsiVmZmZjKLWBkaGrKI1Qe4evUqRCIRdu3aBXV1dUycOBEeHh5SgbSsbt68CUdHRzx//hw7d+6UFIkSBAGenp5YvXo1tm3bhhEjRuDLL7/EX3/9hevXrxcpPkXVC4MtEREREVEZicViSRGrd0PvnTt3kJ+fDwBQU1MrtohV/fr1WcSqlB48eIA1a9Zg/fr1ePHiBRwdHeHt7Y0uXbqU6zN89uwZRo8ejePHj2Pp0qWYOXMmFBQUIBaLMWHCBGzbtg2hoaHo2rUrbGxsYGhoiDNnznBmvhpjsCUiIiIiqkD5+flISkqSOcubnJwsKWKlra1dbBGrOnXqyPkoqqeXL18iJCQE/v7+iI2NRceOHeHt7Y2hQ4eWOXQWFBRg3rx5WLJkCZydnbFp0yZoaWkhPz8fI0aMwKFDh3D48GFoamrC1tYWXl5eWL58eSUdGX0oBlsiIiIioiqSk5ODhIQEmZWbHz16JGnXoEEDmaHX3Nwc6urqcjyC6kEsFuPo0aMQiUT4888/YWhoCHd3d0yZMgU6Ojpl6is0NBTjxo2DsbExwsLCYGlpidzcXHz11Vc4d+4cTpw4gb/++gvffvst/vjjDwwaNKiSjoo+BIMtEREREVE1kJGRIRV43w69L168AAAoKCgUW8TKxMTkk7xV9ubNm/D398f27duhrKyM8ePHw9PTE1ZWVqXuIyoqCo6Ojnj06BG2b9+OL774AtnZ2XBwcMCtW7cQERGBBQsW4MyZM7h27RpMTEwq74CoXBhsiYiIiIiqMUEQkJaWJnOWNy4uDq9evQIAKCsrF1vEqnHjxh99EatHjx5h3bp1WLt2LdLS0jBw4EB4e3ujZ8+epXoO98WLFxgzZgwOHToEX19f+Pj4IDMzE7169cL9+/dx6NAhDBs2DA0aNEBkZCRUVFSq4KiotBhsiYiIiIhqKLFYjAcPHsgsYpWYmCgpYqWuri5VxOrt0Kunp/dRFbF69eoVfv/9d4hEIvz7779o27YtvLy8MHLkSKiqqr53W7FYjEWLFmH+/Pn48ssvERISgry8PPTo0QOZmZlYs2YNhg4dCjc3N/j7+1fNAVGpMNgSEREREX2E8vLy3lvEqlDdunVlzvJaWlp+8Lqx8iQIAv7880+IRCIcOXIE+vr6+Oabb+Dm5ob69eu/d9tDhw5h9OjRMDAwQFhYGOrUqYNu3bpBSUkJLi4u+PHHH7Fv3z44OTlV0dFQSRhsq0BWVhbi4+ORm5sLVVVVWFhYQEtLS97DIiIiIqJPVHZ2NhISEmSG3sePH0va6evrF1vESk1NTY5HUDb//fcfAgICsGXLFojFYnz99dfw8vJCq1atit0mNjYWjo6OSE5ORkhICNq2bQtbW1vUq1cPZmZmOH36NK5evQpzc/Mi2/L8v+ox2FaSqKgorF+/HsePHUNsfDze/pgVFBRgZWGBfg4OcHV1RYsWLeQ4UiIiIiKi//f8+XPExcXJfKY3IyMDwJvz2SZNmhRbxEpZWVnORyHb06dPsWHDBqxevRopKSno27cvvL29YW9vL/MZ5MzMTLi4uGDfvn348ccfMXz4cPTs2RMmJiZ49uwZdHR0cP78eaipqfH8X84YbCtYYmIi3FxdEX78OHQ0NNDFwACWOjpooq0NVWVl5ObnIzkjA3Hp6biQmor07GzY9+uHdevXw9TUVN7DJyIiIiKSSRAEPH78WGbl5ri4OOTm5gIAatWqVWwRq0aNGlWLIlavX7/Gnj17IBKJcPXqVTRv3hyenp4YM2YMNDQ0pNoKgoBly5Zhzpw5cHBwwMyZMzF48GBYWVnh5s2bmDx5MuLj4nj+L2cMthUoKCgIXh4e0FJWxtgWLdDN0BC13vMfN08sxrn79xESFYWs/Hz4r1yJSZMmVeGIiYiIiIg+nFgsxr1792TO8iYmJqKgoAAAoKGhUWwRq3r16lV5EStBEHDu3DmIRCLs378furq6mDp1KqZNm4ZGjRpJtQ0PD8fIkSOhq6uLuXPnws3NDU2bNsV/0dGoo6LC8385q/HB1sTEBHZ2dti8eXO5tm3VqhUOHTr0weNYvHgxfHx8YG9qislt20KjDGuIZeflIfCffxCemIhFixbhxx9/hJ2dHQDg9OnTHzw2IiIiIiJ5ycvLQ2JioszKzffv35e009HRKbaIVe3ataX6rIwMcOfOHaxcuRIbN25Ebm4uhg8fDm9vb7Rr106qjaOjI+Lj49GvXz/s37+/ws7/ATADfIDqefP7Oy5cuIDjx4/Dy8sLdevWrfL9R0VFYffu3Rg/frzMxZiDgoLg4+ODMS1bYmQ57pfXqFULnh06oL6GBnx8fNCwYcMKGHX5HD9+HLt27cKlS5cQHR0NIyMjJCUlyW08RERERFSz1apVSxJW35WdnY34+PgigTc8PBxpaWmSdg0bNpQKvNnZ2Xj+/LmkOFNFMDMzg7+/PxYsWICgoCD4+flh27Zt+PzzzzF79mwMHDgQZmZmuHDhAhwdHbF///4KPf+fOHFihRxHWWVnZyM4OBgHDhzArVu3kJWVBQsLC0yZMgVTpkyBkpKSXMZVVjVixvbXX3/FzJkzkZiYWCRY5ubmQlFREbXKcIWkUGlnbPfu3QtnZ2dERERIrqIUSkxMROuWLdGtYUN4duhQ5jG8TRAErLx6FecePkSrNm2gpqZW5Vdrxo8fj127dqFdu3ZITk6GkpISgy3RJ87Ozg5nzpwBAFy/fh3W1tbyHdAnwsTEBF5eXvDy8gLwpvBIWFgYBg8eLNdxVZXC2xHr1KmD58+fy3cwRCQXP/30E+bPn48VK1bg+fPnUjO+mZmZAN78rDA2NpZZxMrY2FhmEavSZoBdu3ZhxIgRaNGiBaKiomBubg5PT0/07NkTnT77rMLP/2/dvg0XFxcAVTtj+++//6JNmzbo3bs3+vXrB21tbYSHhyMsLAxjx47Fli1bqmwsH6JGzNi+T0VdoSkvN1dXaCkrY3Lbtu9t9yo/H2olVIdTUFDA5LZtcePJE8TGxqJNmzYVOdRSWbJkCQIDA1GrVi0MHDgQ//77b5WPgYiqn8mTJ+Onn36Cnp4eACApKQmmpqYozbXR06dPo2fPnkhPT6+Su27Gjx+P58+fY//+/ZW+L+DN8Y0fP75MFwEL7wDy9fUt9TapqanQ0dEp+wBrCBMTE2zevFlyATk1NRW7du3C/Pnz5TswIpKbwiJOjo6OUpNbgiDg0aNHRWZ5T58+jaCgIKkiVubm5kVCb+HzviUpnKlcs2YN1NXVIRKJ4O3tDU0NjVKd/wMlZ4C3z//dXF1LNa6K1rBhQ9y6dQstW7aUvDZ16lRMmDABwcHBmDt3LiwsLOQytrKQf0myEvj6+mLmzJkAAFNTUygoKEBBQUFyAmFiYoLx48dLbXPz5k306NED6urqMDQ0xKJFixAcHCy13dvOnTuHzz77DGpqajAzM0NISIjkvc2bN8PZ2RkA0LNnT8n+T58+jaioKIQfP46xLVpI3VO/4vJlOIWGIjUrC/MiIzEkLAy/XLoEABALAvbHxsI1PBxf7duHUX/8gVVXryLz9WsAb25LGNuiBdLT05GdnS3p8/Xr15g3bx7at2+POnXqQFNTE7a2toiIiJA6lvnz50NRURF//vmn1OtTpkyBiooK/vnnn/d+3o0aNSrX7DcRfdw0NDTQsGHDSl2+4fX/fg5WFwUFBRCLxfIehkTDhg3fezE3Ly+vCkdT+Ro2bIg6derIexhEJCfvywAKCgro1KkTNm3ahIkTJ2LZsmUICwvD77//js8++wyqqqrQ09ODvb09DA0N8ccff2DPnj1wc3NDz549cf/+fRw7dgyWlpbQ1dWFsrIyGjRogHnz5uHp06cAimaATp06YdeuXVi2bBkyMjOLnP8D5c8Ahef/4cePS53/A1WTAfT09KRCbSFHR0cAQHR0dIl/X9VBtQ+2Tk5OGDlyJABAJBJh69at2Lp1K+rXry+z/YMHD9CzZ0/cvn0bs2fPhre3N7Zv346AgACZ7ePj4zF06FD07dsXfn5+0NHRwfjx43H79m0AQPfu3eHh4QEAmDNnjmT/zZs3x/r166GjoYFuhoZF+i0QBPicPYu6qqqY2KYNuv6vzaqrV7Hx5k20qFcPU62t0dfEBBF372Lu2bPI/98JVLfGjaGsqIiUlBRJfxkZGQgKCoKdnR2WLVsGX19fpKWlwd7eHjdu3JC08/HxgbW1NSZOnCi5RSM8PByBgYGYN28e2pbiyhIRUVndvXsXgwYNgo6ODjQ1NdGyZUscOXIESUlJ6NmzJ4A3RUEUFBQkFyPt7Ozg7u4OLy8vyQlI4QnL2z/Xnj9/LrmgWOj27dsYOHAgtLW1Ubt2bdja2iIhIQG+vr7YsmULDhw4IHUh8vTp01BQUJC6pfXGjRtSFzw3b96MunXr4o8//kCLFi2gqqqK5ORk5ObmYsaMGWjcuDE0NTXx+eefV/gtYo8fP8agQYOgrq4OU1NTbN++vUgbBQUFySx04ee0a9cu9OjRA2pqajK3eVvh8YWHh6N58+bQ0tKCg4MDUlNTJW3+/vtv9O3bF3p6eqhTpw569OiBa9euFRnHb7/9hoEDB0JDQwPNmzfHxYsXER8fDzs7O2hqaqJLly5ISEiQ2u7AgQNo166d5CLyggULkJ+fX85PjIg+duXNAFFRUZgzZw5++OEHxMfHS57TPXPmDHJychAdHY0GDRpAV1cXDx48gLa2NrS0tJCWloaFCxdCT08PysrK8PX1RdOmTQEAgwcPxsKFCxEYGIjo6Ohiz/+B8meAbo0bQ0dDQ+r8H5BvBnj48CEASO7Wqu6q/a3Ibdq0Qbt27bBjxw4MHjxYZvGmty1btgzp6em4du2a5DkwFxcXWFpaymwfExODs2fPwtbWFgAwbNgwGBkZITg4GL/++ivMzMxga2uLlStXom/fvlLP2B4/dgxdDAxklvTOE4vRzcgILq1bS167/eQJwhMTMfPzz9GzSZP/P8YGDTA3MhKR9++jZ5MmqKWkBG0VFTx79kzSRkdHB0lJSVBRUZG8NnnyZDRr1gyrVq3Cxo0bAby55SIkJATt27fHt99+i+XLl2PixIno0KEDfvjhh/d+dkRE5TVt2jS8fv0aZ8+ehaamJqKioqClpQUjIyPs27cPQ4YMQUxMDLS1taGuri7ZbsuWLXBzc8P58+dLva8HDx6ge/fusLOzw6lTp6CtrY3z588jPz8fM2bMQHR0NDIyMhAcHAwA0NXVxYULF0rVd3Z2NpYtW4agoCDUq1cPDRo0gLu7O6KiorBz5040atQIYWFhcHBwwK1bt4r93VJW48ePR0pKCiIiIlCrVi14eHjg8ePHJW73ww8/wM/PDzY2NlBTUyuxfXZ2Nn799Vds3boVioqK+PrrrzFjxgxJKM7MzMS4ceOwatUqCIIAPz8/DBgwAHFxcVIVSRcuXIgVK1ZgxYoV+P777zFq1CiYmZlh9uzZaNKkCSZMmAB3d3ccPXoUABAZGYmxY8di5cqVkosQU6ZMAQDeakxEMlVGBlBRUUGzZs2grq6Ou3fvSmWApKQkNG3aFEZGRkhISMDdu3cldzHu379fcmFRQ00NPRs3LnZJnw/JAF0MDHDqnWArrwzw+vVr+Pv7w9TUFB07dizz9vJQ7YNtWR07dgydO3eWKm6iq6uL0aNHY9WqVUXat2jRQvIPGgDq16+Ppk2b4s6dO+/dT2ZmJmLj49G/ffti23xhbi71feS9e9CsVQvt9PXx4n/3/gOAhY4O1JWVcfPxY8k/dnVlZTzLysL169ehqakp1Y9YLEZGRgbEYrHkSnlsbKzkfRUVFUyfPh1+fn64fPky0tLSsGHDhhKP6V0vX75Efn6+VN9E9PGqW7cu6tevX6o1BE1MTKSer01OTsaQIUPQ+n+/yM3MzCTv6erqAgAaNGhQ5BlbS0tL/PLLL5LvS/Oc6po1a1CnTh3s3LlTctLxdqVNdXV15ObmlqvCfF5eHtauXSu5sp2cnIzg4GAkJydL1jOcMWMGjh07huDgYCxZsgR2dnZlLrL39vIUsbGxOHr0KC5fviw5edi4cSOaN29eYj9eXl5wcnIq9X7z8vKwfv16mP/v95O7uzt++uknyfu9evWSar9hwwbUrVsXZ86cwcCBAyWvu7i4YNiwYQCA77//Hp07d8bcuXNhb28PAPD09JQUQAGABQsW4IcffsC4ceMAvPn3sXDhQsyaNUsSbMv6GQqCgLS0NBaWIvqIFc623rlzp8jjKvn5+cjIyJCcpx48eBDW1tbQ0NCQOnf94osvsHXrVqk+8vPzYWFhAX19fam2pqamyM3NRa1atZCXlyd5xKN+/foYM2YMrKys4ObmBssS6h2UNwNY6ujgcEKC1DPASkpKkmd9xWIxnj9/DrFYjA4dOhS5o6ZVq1ZYsGABZs+ejZs3b+LJkyc4fvx4uR4jKryoe/jw4Up9DKki1YxRlsHdu3fRuXPnIq8X98Bzk7eumhTS0dFBenr6e/eTkJAAQRDQRFtb5vtKCgrQe2tWAgBSsrLwMi8PI//4Q+Y2b/9DL3zI/O11s96n8FaJd928eRPAm//U5VVc30T0cXF3d4e/v3+5yvp7eHjAzc0Nx48fR58+fTBkyJBSFcBr/56Lg8W5ceMGbG1tK6UegIqKitS4b926hYKCgiJLVOTm5qJevXoVss/o6GgoKytLfRbNmjUrVaGtDmWsxqmhoSEJtQBgYGAgNTP86NEj+Pj44PTp03j8+DEKCgqQnZ2N5ORkqX7e/oz09fUBQHJRo/C1V69eISMjA9ra2vjnn39w/vx5LF68WNKmoKAAr169QnZ2tqRATFmIxWL89NNPWLNmTZm3JaKapXfv3jJfDwsLQ1hYmOT7wlnX0vZR2nPctLQ0rFixAh4eHu89/wc+LAMU9puTkyP1/pYtW+Dn54f//vtPqp6Cqalpkb5mzpyJnTt34vLly1iyZAlalGMpouXLlyMwMBALFy7EgAEDyry9vHx0wbasijuBK6nSZ2G1NdVirmDUUlSE4juzHoIgoK6qKmZ+/rnMbeq8VRSkcNv169ejRYsWOH78OBYtWgRbW1t0794dOjo6UFRUxLZt25CSkoLdu3dL9XXv3j1MnDgRr169Qp8+fTBv3rz3Ho8s33//PRITE4v0TUQfp0aNGkGxmFurSjJp0iTY29vj8OHDOH78OJYuXQo/Pz9Mnz79vdu9e0dK4f7f/hn8blEk9XdOGEqjNP0W9v32jHVWVhaUlJRw9erVIr8vtLS0yjyOivbu51eSdy8GKCgoSH0m48aNw9OnTxEQEABjY2Ooqqqic+fORWZK3u6n8POS9Vph8a2srCwsWLBA5uxyaW6hlkVRURFeXl4YPnx4ubYnoupvx44dWLduHXbt2gUDAwOp94YNGwZra2vMmTMHwJvg2qtXL/z4449S7fbu3YuVK1dK9TFs2DCYmppi2bJlUm09PDyQkpKCZ8+eSdUA0NTUxJQpU+Dk5ISVK1cWe/4PfFgGUH1rZrbQtm3bMH78eAwePBgzZ85EgwYNoKSkhKVLlxapZQC8md2Oi4sD8ObibFlt3rwZ33//PVxdXeHj41Pm7eWpRgTb0twWV8jY2Bjx8fFFXpf12ofsv7AyZW4ZCl801NLC9ceP0UJPT/IPtzji/51ofP7557C2toafnx/MzMxw5swZqfHs3bsXqqqqUrdTi8Vi2Nraom7dupgwYQKWLFkCNze3Mt2uBry5fTAlJUWqbyKi4hgZGcHV1RWurq6YPXs2AgMDMX36dMlzQaVZXqGwKEhqaipsbGwAQKo4BvBmtnDLli3Iy8uTOWuroqJSZF9v91u4ZM67/cpiY2ODgoICPH78uNJ+FjZr1gz5+fm4evWq5FbkmJgYudxie/78eaxdu1Zyhf7evXt48uTJB/fbrl07xMTEVOhyEQoKCrCwsKgRS1AQUflcvnwZAPDZZ58VecZWVVUV+vr6kp/NJiYmyMzMLPKzes+ePUX6UFVVha6ubpG2derUwdOnT5Gfnw8FBQXUrl0bGRkZ2Ldvn1SxprKc/wOlzwC5//vd9fZF5r1798LMzAyhoaFSGUBWfQKxWIzx48dDW1sbXl5eWLJkCYYOHVrqDHDgwAFMmjQJTk5ONfJumGpfFRn4/yvSpfklb29vj4sXL0qdsDx79qzEapFl3b+FhQUUFBSQnJFR6n66GxlBLAjYERVV5L0CsRhZb10Rf/W//zCFv7ALZwrevrJ+6dIlXLx4sUhfK1aswIULF7BhwwYsXLgQXbp0gZubW4WcnBARyeLl5YXw8HAkJibi2rVriIiIkDwjamxsDAUFBRw6dAhpaWnIysoqth91dXV06tQJP//8M6Kjo3HmzJkiV4zd3d2RkZGBESNG4MqVK4iLi8PWrVsRExMD4M3Jzc2bNxETE4MnT54gLy8PFhYWMDIygq+vL+Li4nD48GH4+fmVeFxWVlYYPXo0xo4di9DQUCQmJuLy5ctYunQpDh8+/AGf2P9r2rQpHBwcMHXqVFy6dAlXr17FpEmTyjUz/aEsLS2xdetWREdH49KlSxg9enSFjGPevHkICQnBggULcPv2bURHR2Pnzp01bjaAiKqWPDKAuro6jIyM4O/vL1kCtPDW4PKc/wOlzwCF/b79c7eqMsDZs2cxYsQIdO/eHdu3by/3HVzyVCNGXPjc0Y8//oitW7di586dePnypcy2s2bNQp06ddC3b1/89NNP8PPzQ9euXSXP0pZl9reQtbU1lJSUsGzZMmzZsgU7d+5EdnY2rCwsEFfCs7hva12/PvqbmWH3f/9hXmQk9sfG4mB8PH67cQPjDx/Gjbeec8rJz4e6urrkVreBAwfizp07cHR0xIYNGzB79mw4ODgUuW8+Ojoac+fOxfjx4zFo0CAoKipi8+bNyMrKwjfffFPiGG/evIlFixZh0aJFiI+Px4sXLyTfHzx4sNTHSkSfloKCAkybNg3NmzeHg4MDrKyssHbtWgBA48aNJcWD9PX14e7u/t6+Nm3ahPz8fLRv3x5eXl5YtGiR1Pv16tXDqVOnkJWVhR49eqB9+/YIDAyUzN5OnjwZTZs2RYcOHVC/fn2cP38etWrVwo4dO/Dff/+hTZs2WLZsWZF+ixMcHIyxY8fiu+++Q9OmTTF48GD8/fffMms0AP+/FE9ZlgQKDg5Go0aN0KNHDzg5OWHKlClo0KBBqbevKBs3bkR6ejratWuHMWPGwMPDo0LGYW9vj0OHDuH48ePo2LEjOnXqBJFIBGNj4woYNRF9rOSRAQwMDJCcnAwPDw98/vnnUhng0KFDMDMxKdP5P1D6DBCXng51dXWpR1+qIgPcvXsXX375JRQUFDB06FDs2bMH27Ztk3wV1uyp9oQaYuHChULjxo0FRUVFAYCQmJgoCIIgGBsbC+PGjZNqe/36dcHW1lZQVVUVDA0NhaVLlworV64UAAgPHz6UtDM2Nha++OKLIvvq0aOH0KNHD6nXAgMDBTMzM0FJSUkAIERERAjTp08XdDQ0hANDhghHnJ0lX32MjQU1JSWp197+8mjfXrDQ0RFUlZQEdWVlwaROHWFo06bCtoEDhSPOzsIBJydBWVFRaNy4sWT/YrFYWLJkiWBsbCyoqqoKNjY2wqFDh4Rx48YJxsbGgiAIQn5+vtCxY0fB0NBQeP78udT4AwICBADCrl273vs5BwcHCwBkfr37ORPRp6FHjx6Cp6envIdRY5w6dUqoW7eu8OzZM3kPpcYLDg4W6tSpI+9hEJEcVbcM4OjoKPP8/0MzwAEnJ0FHQ0No3Lix1BiqIgNEREQUe/4PQJg/f36Jf0/VgYIglFAl6SPh5eWF3377TVIIpCJERUWhZcuWRdak+lARyclYfukSoqKiSrXcAxFRZbKzs8OFCxegoqKCixcvSlW/paIKi3vMnDlT3kOp0bS0tJCfnw81NTUu6UNE5VbRGYDn/9XXRxlsc3JypO5Nf/r0KaysrNCuXTucOHGiQvflYG+PGxcvYk3v3tCogKUnsvPyMO3PP2HduTOOhYdXwAiJiD7MgwcPJM8XNWnSRGqReKo++vfvj8jISJnvzZkzR1I5tKYoLPqopKQkc0kLIqJ3VVUG4Pl/9fRRBltra2vY2dmhefPmePToETZu3IiUlBT8+eef6N69e4XuKzExEa1btkS3hg3hWcb1BN8lCAJWXr2Kcw8f4tbt2/xFTkREpfb2BYh36erqQldXt4pHRERUtaoqA/D8v3qqEcv9lNWAAQOwd+9ebNiwAQoKCmjXrh02btxY4aEWeLMwsv/KlZg8eTIaaGhgZDkWQQbe/KPeER2N8MREBAUF8R81ERGVSePGjeU9BCIiuaqqDMDz/+rpo5yxlYfFixfDx8cH9qammNy2bZluS8jOy0PgP/8gPDERixcvrnG3ixERERERfWp4/l+9MNhWoKCgIHh5eEBLWRljW7RAN0ND1HrPGlB5BQU49+ABQqKikJWfj4BVqzBx4sQqHDEREREREZUXz/+rDwbbCpaYmAg3V1eEHz8OHQ0NdDEwgKWODppoa0NVSQm5BQVIzshAXHo6LqSmIj07G/b9+mHd+vW8/YCIiIiIqIbh+X/1wGBbSaKiorB+/XqcCA9HTFwc3v6YFRQU0NTSEn3t7eHm5saS3kRERERENRzP/+WLwbYKZGVlIT4+Hrm5uVBVVYWFhQW0tLTkPSwiIiIiIqoEPP+vegy2REREREREVKMV/2QzERERERERUQ3AYEtEREREREQ1GoMtERERERER1WgMtkRERERERFSjMdgSERERERFRjcZgS0RERERERDUagy0RERERERHVaAy2REREREREVKMx2BIREREREVGNxmBLRERERERENRqDLREREREREdVoDLZERERERERUozHYEhERERERUY3GYEtEREREREQ1GoMtERERERER1WgMtkRERERERFSjMdgSERERERFRjcZgS0RERERERDUagy0RERERERHVaAy2REREREREVKMx2BIREREREVGNxmBLRERERERENRqDLREREREREdVoDLZERERERERUozHYEhERERERUY3GYEtEREREREQ1GoMtERERERER1WgMtkRERERERFSjMdgSERERERFRjcZgS0RERERERDUagy0RERERERHVaAy2REREREREVKMx2BIREREREVGNxmBLRERERERENRqDLREREREREdVoDLZERERERERUozHYEhERERERUY3GYEtEREREREQ1GoMtERERERER1WgMtkRERERERFSjMdgSERERERFRjcZgS0RERERERDUagy0RERERERHVaAy2REREREREVKMx2BIREREREVGNxmBLRERERERENRqDLREREREREdVoDLZERERERERUo/0fyr8OFfxcgk8AAAAASUVORK5CYII=" 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NTEnB19SVlTP0k+7BgwffPC9fv65vGTZsGAIDA9G2bVuUK1cO9vb2cHZ2hoODQ6Z9v/586ujooEyZMvLLmr/H19cXixcvxs2bN5Gamirf/uVn7u7duyhbtixKlCjx3XZy4+ekePHiqFChQqZtwP9/Ln7lc/uzz9qP/PXXXwgLC0Pjxo1hYmICe3t79O7dG82aNfvpsURE9HMsbImI8olMJoNEIsHBgweh/F/x8iUdHZ0Mj7+1DwD5fN30NuvWrYslS5Z8c9+v/7j/cmQ23YkTJ9CxY0e0aNECq1evRpkyZaCqqgpvb+8s3dhGJpMBAFxcXNCvX79v7lOvXr2ftvOrsnKecrO99LsNZ6f9rwtgeZvf2JYslWboJzcYGhoiKioKhw4dwsGDB3Hw4EF4e3ujb9++8PX1zXH7mzdvhqurKzp37owJEybA0NAQysrKmDt3rnyEO6ty6+fkZ+/jr3xuc/JZq1WrFm7duoV9+/YhJCQE27dvx+rVqzF16lR4eHj89HgiIvoxFrZERDlkaGgIDQ2NTJeOAsiwzdjYGIIgoEqVKvLRx5wyNjbG5cuX0apVqyyNrn7L9u3boaGhgUOHDmUopry9vTPt+60+DAwMUKxYMUilUtjZ2f1Shq/duXMHgiBk6O/27dsAkOluvz9TqVIlAMCtW7dQtWpV+faUlBTExsZmO3P6KPzzpCTIBCFD0fooIQFA5hHa7HgYHw+JRJLpCoBKlSrh2rVrmc5LVterVVNTg6OjIxwdHSGTyTBs2DCsWbMGU6ZMyXRlga2trfxxYmIinj17hnbt2n237eDgYFStWhU7duzIkO3rS46NjY1x6NAhvH379rujtnnxc/ItefG5BX58lYO2tjZ69OiBHj16ICUlBV27dsXs2bMxceJEaGho5FoGIqKiiHNsiYhySFlZGXZ2dti1axeePn0q337nzh0cPHhQ/rhr165QVlaGh4dHphEeQRDw5s2bbPft7OyMJ0+eYN26dZme+/jxIz58Yw7nt/JLJBJI/xspBD5f7vvlHZXTaWtrZ1oiR1lZGd26dcP27dtx7dq1TMd8a+mdn3n69Cl27twpfxwfHw8/Pz+YmZlluAw5K+zs7KCmpobly5dnOO8bNmzA+/fv5Xd9ziodHR0YliqFpNRUHP9i+RmpTIa9MTHQVFFB3e9cGp4VMXFxqFGtWqaRyXbt2uHp06cZlmFKSkr67mW0X/r6s6WkpCQfjfz6kue1a9dmuJTY09MTaWlpGeaLfy19JPPL83v27Fn8+++/Gfbr1q0bBEH45ghl+rF58XPyvcy5/bkFPv+MvH//PtP2r3OrqanB1NQUgiBkON9ERPRrOGJLRJQLpk+fjtDQUDRr1gxDhw6FVCrFypUrUadOHURFRQH4PBI1a9YsTJw4Effv30fnzp1RrFgxxMbGYufOnRg8eDDGjx+frX779OmDwMBAuLu7Izw8HM2aNYNUKsXNmzcRGBiIQ4cOwcLC4odttG/fHkuWLIGDgwN69+6Nly9fYtWqVTAxMckwnxcAGjZsiLCwMCxZsgRly5ZFlSpVYGlpiXnz5iE8PByWlpZwc3ODqakp3r59i0uXLiEsLAxvv7Fm649Ur14dAwcOxPnz51G6dGls3LgRL168+OYo8s8YGBhg4sSJ8PDwgIODAzp27Ihbt25h9erVaNSoUYYbRWVVNycnrPHywpLz53EnLg6ltbVx8vFjRL95g8FmZtD6ah5oVqVKpTj97BlcBg7M9JybmxtWrlyJvn374uLFiyhTpgw2bdoErSyMDg8aNAhv375Fy5YtUb58eTx48AArVqyAmZkZatWqlWHflJQUtGrVCs7OzvLz1Lx5c3Ts2PG77Xfo0AE7duxAly5d0L59e8TGxsLLywumpqZITEyU72dra4s+ffpg+fLliImJgYODA2QyGU6cOAFbW1uMGDEiT35Ovie3P7fA55+RgIAAjB07Fo0aNYKOjg4cHR1hb28PIyMjNGvWDKVLl8aNGzewcuVKtG/fXr78EBER5UB+3X6ZiKiwO3LkiGBubi6oqakJxsbGwvr164Vx48YJGhoaGfbbvn270Lx5c0FbW1vQ1tYWatasKQwfPly4deuWfB9ra+tvLg3y9TIugvB5aZ758+cLtWvXFtTV1QV9fX2hYcOGgoeHh/D+/Xv5fgCE4cOHfzP7hg0bhGrVqgnq6upCzZo1BW9vb2HatGnC178mbt68KbRo0ULQ1NQUAGRY+ufFixfC8OHDhQoVKgiqqqqCkZGR0KpVK2Ht2rVZPYWCIHxe7qd9+/bCoUOHhHr16skzBQUFZdgvfXma8+fPZ9ievjTLl8vfCMLn5X1q1qwpqKqqCqVLlxaGDh0qxMXFZdgnq+f9+vXrAgChjoGBoKumJqgoKQmVixcX/mjUKMPSPenL/QysVy/Tsj4AhN6mphm2TbC0FAAI0dHR3zw3Dx48EDp27ChoaWkJpUqVEkaPHi2EhIT8dLmf4OBgwd7eXjA0NBTU1NSEihUrCkOGDBGePXuW6XweO3ZMGDx4sKCvry/o6OgIv//+u/DmzZtM5+nL5X5kMpkwZ84coVKlSoK6urpgbm4u7Nu375uf17S0NGHhwoVCzZo1BTU1NcHAwEBo27atcPHixQz75eTnJP0z9LVv/Qxk5XOb/pn6+jMYGxubaQmhxMREoXfv3oKenp4AQP7616xZI7Ro0UIoWbKkoK6uLhgbGwsTJkzI8DNKRES/TiIIv3h3DSIi+qnOnTvj+vXriImJETuKwqhcuTLq1KmDffv2iR3lhxzatEHUv/9iVatWvzxC+6Wk1FQMP3IEZk2aIOTQoVxImD0+Pj7o378/zp8//9NRfiIiooKGc2yJiHLJx48fMzyOiYnBgQMHYGNjI04gylOeXl5ITEvDusuXc9yWIAhYd/kyEtPS4OnllQvpiIiIihbOsSUiyiVVq1aFq6urfG1UT09PqKmp4c8//xQ7WoHw6tWrDDeo+pqamtoP1zctaKpUqYJly5fDzc0Nhlpa6GVq+kvtpEil2HTtGg7FxmLx4sXQ1NTE8+fPM+xTvHjxby7VRERERJ+xsCUiyiUODg7w9/fH8+fPoa6ujiZNmmDOnDmoVq2a2NEKhEaNGuHBgwfffd7a2hoRERH5FygXDBo0CC9evMDkyZPxMikJbvXrZ+uy5KTUVMw/cwbn/ytkx40bh3HjxmXaz9vbG66urrkVm4iIqNDhHFsiIsoXp06dynS59pf09fXRsGHDfEyUczKZDGvWrMHbt28xd/Zs6KiooK+pKZqXLw9Vpe/P9kmVSnHyyRP4RUfjfUoKmjZrhr/++gtK3zmmdu3aKFOmTF69DCIiIoXHwpaIiCibZDIZgoOD8eeff+LBgwfo1asXZs+ejaHu7jgUGgp9LS00LVMG1fT1UVFXF+rKykiWSvEwPh4xcXE4/ewZ4pKS0MbeHmrq6ti7dy8qVaqEhQsXolu3bt8tcImIiOjbWNgSERFlkVQqRVBQEKZNm4bbt2/Lt+/cuROdO3cGAERHR8PLywuHDx3CrZgYfPlrViKRoEa1amjdpg2GDh2KWrVqYceOHejWrZt8nxo1asDDwwPdu3eHsrJyvr02IiIiRcbCloiIKAtiYmLQvn17xMTEQElJCTKZTP7cw4cPUaFChUzHJCYmon379pDJZFixYgVMTEygo6OTYZ+HDx+iUqVK8sfpbVerVg379+/nHG0iIqIs4M2jiIiIsiA+Ph6PHj3KVNQWK1YM5cuX/+YxOjo6KF68OADAzMzsm/tUqFABxYoVQ0JCAoDPlzkrKSnh0aNHSExMzN0XQUREVEhxEg8REVEWNGzYEBcvXkTZsmUzbDc3N4dEIvnldiUSCerXr59hW7ly5RAZGQlzc/NfbpeIiKgoYWFLRESURSVLlkRqaipKly4t39agQYMct/vl3aCNjIyQmpqqUGv6EhERiY2FLRERURZIpVK4uLgAACIjI7Fx40ZoamrC1tY2x223bNkSmpqa8Pb2xqVLlyAIAlxcXCCVSnPcNhERUVHAm0cRERFlgYeHBzw8PHD48GG0atUKwOdi92d3Lu7YsSMAYM+ePT/c78u2jhw5gtatW8PDwwNTpkzJhfRERESFG0dsiYiIfuLIkSPw8PDA9OnT5UUtgFxdjufLtlq1aoVp06Zh2rRpOHr0aK71QUREVFhxxJaIiOgHnj17BjMzM9SrVw8hISHZLmazOmL7NalUCgcHB1y9ehVRUVEwMjLK1vFERERFCUdsiYiIviMtLQ29evWCsrIytmzZkqsjtD+T3qeSkhJ69+7N+bZEREQ/wMKWiIjoO6ZPn44TJ07A398fhoaG+d6/oaEh/P39cezYMXh4eOR7/0RERIqChS0REdE3HDp0CHPmzMGsWbNgbW0tWg5ra2vMnDkTs2bNQmhoqGg5iIiICjLOsSUiIvrK48ePYW5ujkaNGmHfvn1QUvr174F/dY7tl2QyGdq3b48LFy4gKioK5cqV++W2iIiICiOO2BIREX0hNTUVvXr1goaGBvz8/HJU1OYWJSUlbNq0CRoaGujVqxfS0tLEjkRERFSgiP/bmoiIqACZPHky/v33X2zbtg2lSpUSO45cqVKlsG3bNpw+fZpr2xIREX2FhS0REdF/9u3bhwULFmDevHlo1qyZ2HEyadasGebOnYt58+Zh//79YschIiIqMDjHloiICMDDhw9hbm6OZs2aYffu3ZBIJLnSbm7Msf2STCZDp06dcPr0aURGRqJixYq50i4REZEi44gtEREVeSkpKejRoweKFSsGHx+fXCtq84KSkhJ8fX2ho6ODnj17IjU1VexIREREomNhS0RERd7//vc/XLx4EQEBAShRooTYcX6qRIkSCAwMxIULFzBx4kSx4xAREYmOhS0RERVpu3btwtKlS7Fw4UJYWlqKHSfLLC0tsWDBAixevBi7d+8WOw4REZGoOMeWiIiKrNjYWJibm6NVq1YIDg7Ok0uQc3uO7ZcEQUC3bt0QHh6OS5cuoUqVKrneBxERkSLgiC0RERVJycnJcHZ2RokSJbBhw4YCPa/2eyQSCTZu3Ah9fX306NEDKSkpYkciIiISBQtbIiIqksaPH48rV64gKCgIenp6Ysf5ZXp6eggKCsLly5cxYcIEseMQERGJgoUtEREVOUFBQVi5ciWWLl2Khg0bih0nxxo2bIglS5Zg+fLlCA4OFjsOERFRvuMcWyIiKlLu3LmDBg0aoF27dvD398/zS5Dzco7tlwRBQI8ePXDo0CFcunQJxsbGedofERFRQcIRWyIiKjI+ffoEJycnGBkZYe3atQo5r/Z7JBIJ1q9fD0NDQzg5OeHTp09iRyIiIso3LGyJiKjIGDNmDG7cuIHAwEDo6uqKHSfX6erqIigoCNHR0Rg7dqzYcYiIiPKNitgBiIiI8oO/vz/WrFmDtWvXwszMLN/67dWrV771BQBmZmZYvnw5hgwZAisrq3zvn4iISAycY0tERIXerVu3YGFhgU6dOmHTpk35eglyWloaAEBFJf++SxYEAS4uLtizZw8uXLiAGjVq5FvfREREYmBhS0REhVpSUhJ+++03pKam4vz589DR0RE7Ur5ITExEo0aNoKamhjNnzkBTU1PsSERERHmGc2yJiKhQGzlyJO7cuYOgoKAiU9QCgI6ODoKCghATE4NRo0aJHYeIiChPsbAlIqJCy8/PDxs3bsTq1atRp04dsePkuzp16mDVqlVYv349Nm3aJHYcIiKiPMNLkYmIqFCKjo5Go0aN4OzsDG9vb7HjiMrV1RVBQUE4f/48TE1NxY5DRESU61jYEhFRofPhwwc0atQISkpKOHfuHLS0tPKt7+nTp2PXrl2IiorK9bZ9fHwwZswYvHv3LlvHffjwAZaWlhAEAefOnYO2tnauZyMiIhITL0UmIqJCRRAEDB06FA8fPkRQUFC+FrUFlba2NoKCgvDgwQMMGzYM/E6biIgKGxa2RERUqGzcuBGbNm2Cl5cXatWqle3jk5OTMWrUKBgaGkJDQwPNmzfH+fPnAXweMdXT08uw/65du+TLB/n4+MDDwwOXL1+GRCKBRCKBj48PAEAikcDT0xNt27aFpqYmqlatiuDgYHk7ERERkEgkGUZjo6KiIJFIcP/+fURERKB///54//69vO3p06dn+XXVqlULXl5e8PPzK/KXZhMRUeHDwpaIiAqNK1euYMSIEXBzc4OLi8svtfHnn39i+/bt8PX1xaVLl2BiYoI2bdrg7du3Pz22R48eGDduHGrXro1nz57h2bNn6NGjh/z5KVOmoFu3brh8+TJ+//139OzZEzdu3MhSrqZNm2LZsmXQ1dWVtz1+/PhsvTYXFxcMGjQIw4cPx9WrV7N1LBERUUHGwpaIiAqFhIQEODk5oUaNGvjnn39+qY0PHz7A09MTCxcuRNu2bWFqaop169ZBU1MTGzZs+Onxmpqa0NHRgYqKCoyMjGBkZJRh/VgnJycMGjQI1atXx8yZM2FhYYEVK1ZkKZuamhqKFy8OiUQib/tXli9avnw5qlevDicnJyQkJGT7eCIiooKIhS0RESk8QRAwePBgPH36FIGBgRmKyey4e/cuUlNT0axZM/k2VVVVNG7cOMsjqz/SpEmTTI9zo93s0NTURFBQEJ48eQJ3d3fOtyUiokKBhS0RESm8NWvWYNu2bVi/fj2qV6+eZ/0oKSllKgRTU1NzrW0AGdrPrba/Vr16daxfvx5bt27F2rVr86QPIiKi/MTCloiIFFpkZCTGjBmDYcOGZZjP+iuMjY2hpqaGU6dOybelpqbK1381MDBAQkICPnz4IH/+62V91NTUIJVKv9n+mTNnMj1Ov8GVgYEBAODZs2e/1HZ29ejRA0OHDsXo0aMRGRmZK20SERGJhYUtEREprPfv38PJyQm1a9fGkiVLctyetrY2hg4digkTJiAkJATR0dFwc3NDUlISBg4cCEtLS2hpaWHSpEm4e/cutm7dKr/rcbrKlSsjNjYWUVFReP36NZKTk+XPBQUFYePGjbh9+zamTZuGc+fOYcSIEQAAExMTVKhQAdOnT0dMTAz279+PxYsXZ2o7MTERR44cwevXr5GUlJSj17tkyRLUrl0bzs7OiI+Pz1FbREREYmJhS0RECkkQBAwcOBCvXr1CYGAg1NXVc6XdefPmoVu3bujTpw8aNGiAO3fu4NChQ9DX10eJEiWwefNmHDhwAHXr1oW/v3+mJXe6desGBwcH2NrawsDAAP7+/vLnPDw8sG3bNtSrVw9+fn7w9/eHqakpgM9zef39/XHz5k3Uq1cP8+fPx6xZszK03bRpU7i7u6NHjx4wMDDAggULcvRaNTQ0EBgYiJcvX2LQoEGcb0tERApLIvC3GBERKaAVK1Zg1KhRCA4ORrdu3cSO81MSiQQ7d+5E586dxY6Syfbt29G9e3esWLFCPoJMRESkSDhiS0RECuf8+fMYN24cRo8erRBFbUHXrVs3jBo1CmPHjsWFCxfEjkNERJRtHLElIiKFEhcXhwYNGsDQ0BAnTpyAmpqa2JGypCCP2AJASkoKrKys8OrVK1y6dAl6enpiRyIiIsoyFrZERKQwBEFAly5dcOzYMURGRqJy5cpiRypU7t+/D3Nzc9jY2GDHjh2QSCRiRyIiIsoSXopMREQKY+nSpdi9ezd8fX1Z1OaBypUrw9fXF7t27cKyZcvEjkNERJRlHLElIiKFcObMGVhZWWHMmDFYuHCh2HEKtfHjx+Off/7BiRMn8Ntvv4kdh4iI6KdY2BIRUYH35s0bmJubo0KFCoiIiICqqqrYkQq11NRUWFtb48mTJ4iMjESJEiXEjkRERPRDvBSZiIgKNJlMhr59+yIpKQnbtm1jUZsPVFVVERAQgA8fPqBfv36QyWRiRyIiIvohFrZERFSgLVy4EAcOHMCmTZtQoUIFseNk24QJEzBhwgSxY2RbhQoVsGnTJuzbtw+LFi0SOw4REdEPsbAlIqIC6+TJk/j7778xceJEtG3bVuw4v+TWrVu4deuW2DF+Sdu2bfG///0PkyZNwsmTJ8WOQ0RE9F2cY0tERAXSq1evYGZmBhMTExw5cgQqKipiR/olHTt2BADs2bNH5CS/Ji0tDS1btsS9e/cQGRkJAwMDsSMRERFlwhFbIiIqcGQyGVxcXJCamgp/f3+FLWoLAxUVFfj7+yMlJQV9+vThfFsiIiqQWNgSEVGBM2fOHBw+fBhbtmxB2bJlxY5T5JUrVw5btmxBaGgo5s6dK3YcIiKiTFjYEhFRgRIeHo5p06ZhypQpaN26tdhx6D+tW7fG5MmTMXXqVERERIgdh4iIKAPOsSUiogLjxYsXMDMzg6mpKUJDQ6GsrCx2pBxT9Dm2X5JKpWjdujVu3LiBqKgolC5dWuxIREREADhiS0REBYRUKkXv3r0hCAK2bNlSKIrawkZZWRlbt26FIAj4/fffIZVKxY5EREQEgIUtEREVEDNmzEBERAT8/f1hZGQkdhz6DiMjI/j7+yM8PBwzZ84UOw4REREAFrZERFQAHD58GDNnzoSHhwdsbW3FjkM/YWtri+nTp2PGjBkICwsTOw4RERHn2BIRkbiePn0KMzMzmJub4+DBg1BSKlzfuRamObZfkkqlaNu2LS5fvoyoqCiUKVNG7EhERFSEFa6/HoiISKGkpaWhV69eUFVVxebNmwtdUVuYKSsrY/PmzVBRUUGvXr2QlpYmdiQiIirC+BcEERGJZurUqTh16hS2bdsGAwMDseNQNhkaGmLbtm04efIkpk2bJnYcIiIqwljYEhGRKA4ePIi5c+di9uzZsLKyEjsO/SIrKyvMmjULc+bMQUhIiNhxiIioiOIcWyIiynePHj2Cubk5LC0tsXfv3kJ9CXJhnWP7JZlMBkdHR5w9exZRUVEoX7682JGIiKiIKbx/SRARUYGUmpqKnj17QktLC35+foW6qC0qlJSU4OfnBy0tLfTs2ROpqaliRyIioiKGf00QEVG+mjRpEs6dO4eAgACULFlS7DiUS0qWLImAgACcPXsWf//9t9hxiIioiGFhS0RE+Wbv3r1YtGgR5s+fjyZNmogdh3JZkyZNMG/ePCxcuBD79u0TOw4RERUhnGNLRET54sGDBzA3N0eLFi2wc+dOSCQSsSPli6Iwx/ZLgiCgc+fOOHHiBCIjI1GpUiWxIxERURHAwpaIiPJcSkoKrKys8PLlS1y6dAn6+vpiR8pT9+/fx8qVKyGVSrF7924AQKdOnaCsrIwRI0agcuXK4gbMY3FxcWjQoAFKly6N48ePQ01NTexIRERUyLGwJSKiPDdmzBisXr0ap06dQqNGjcSOk+d27NiBbt26QVlZGTKZDMDnGyxJpVJs374dXbt2FTlh3jt//jyaNWuG4cOHY+nSpWLHISKiQo6FLRER5an0Im/58uUYOXKk2HHyRVpaGkxMTPDw4UOk/5qVSCSoWLEi7ty5AxUVFZET5o/ly5dj9OjR2LFjB7p06SJ2HCIiKsRY2BIRUZ65d+8eGjRogNatWyMwMLDIzKsFAB8fH/Tv3z/Ttn79+omUKP8JggAnJyeEhYXh0qVLqFq1qtiRiIiokGJhS0REeeLTp09o1qwZ3r9/j4sXL6J48eJiR8pX6aO2Dx48AABUqlSpSI3Wpnv//j0aNGgAfX19nDp1Curq6mJHIiKiQojL/RARUZ4YN24crl27hsDAwCJX1AKAiooKpk+fLn/s4eFR5IpaAChevDiCgoJw9epVjBs3Tuw4RERUSHHEloiIcl1AQAB69uwJT09PuLu7ix1HNGlpafKi/v3790WysE3n6emJYcOGISAgAM7OzmLHISKiQoaFLRER5aqYmBg0bNgQ7du3x9atW4vUvNqvJSYmIjAwEKmpqbC0tISJiQl0dHTEjiUKQRDQq1cvHDhwABcvXkS1atXEjkRERIUIC1siIso1Hz9+RJMmTfDx40dcuHABxYoVEztSvouOjoaXlxdCQ0Jw+84dfPlrViKRoLqJCewdHODu7g5TU1MRk+a/hIQEWFhYQEtLC6dPn4ampqbYkYiIqJBgYUtERLlm8ODB2LRpE86ePYt69eqJHSdfxcbGYqi7Ow6FhkJfSwtNy5RBNX19VNTVhbqKCpLT0vAwPh4xcXE4/ewZ4pKS0MbeHp5eXqhSpYrY8fPNlStXYGlpib59+2LNmjVixyEiokKChS0REeWKLVu2wMXFBevXr8fAgQPFjpOv1q9fjzGjRkFHRQV9TU3RvHx5qCp9//6MqTIZTj5+DL/oaCSmpWHZ8uUYNGhQPiYW1/r16+Hm5oYtW7agd+/eYschIqJCgHdFJiKiHLt58yaGDBmCPn36YMCAAfLtNjY2qFOnjojJ8t7s2bPh5uaG5kZGWNWqFWwrVsxU1C45dw6u+/fLH6sqKWHh2bNoUbYsmhsZwc3NDbNnz861TPfv34dEIoGPj0+utZmbBg4cCBcXFwwePBg3b94UOw4RERUCLGyJiChHkpKS0L17d1SsWBGenp5F6mZR69evx+TJk9Gndm2MtrCAlqpqto5XVVbGaAsLuNSujcmTJ2PDhg15lLRgkUgk8PT0RMWKFeHk5ISkpKR86zspKQnTp09HREREvvVJRER5j4UtERHlyPDhwxEbG4ugoCBoa2uLHSffxMbGYsyoUWhTpQp6/cJNoHZ17YpetWoBAHrVqoU2Vapg9MiRiI2Nze2oBZKOjg6CgoJw7949jBgxIt/6TUpKgoeHBwtbIqJChoUtEZGC+vDhg9gR4OPjAx8fH6xevRq1a9cWO06+GuruDh0VFbjVr/9Lx6spK0P5v0uWJRIJ3OrXh46KCoYWoXV/a9eujdWrV8Pb2xu+vr5ixyEiIgXGwpaISAFMnz4dEokE0dHR6N27N/T19dG8eXPY2NjAxsYm0/6urq6oXLmy/HH6nMtFixZh7dq1MDY2hrq6Oho1aoTz589nK0tERIS8LTc3N2hqamLYsGHo2LEjHj169M1joqOjYWtrCy0tLZQrVw4LFizItM/Lly8xcOBAlC5dGhoaGqhfv36mYie7r+Po0aOwsrKCtrY29PT00KlTJ9y4cSPDPunn9s6dO3B1dYWenh6KFy+O/v37f/cS2ejoaBwKDUVfU9NMlx+ffvIEQw8dQqft2zH00CGcfvLkm220CwrC5uvXM2wrr62NQ6GhUFNTg6GhIVq3bo1Lly7Jn0+fs3zx4kU0bdoUmpqaqFKlCry8vL7Zx5euXLkCV1dXVK1aFRoaGjAyMsKAAQPw5s2bTPs+efIEAwcORNmyZaGuro4qVapg6NChSElJke/z7t07jBkzBhUqVIC6ujpMTEwwf/58yGQy+T5fvl+rVq1C1apVoaWlBXt7ezx69AiCIODhw4fQ0tKCq6srWrZsibdv32bKc/DgQfn7WKxYMbRv3x7Xvzp3rq6u0NHRwZMnT9C5c2fo6OjAwMAA48ePh1QqlecxMDAAAHh4eEAikUAikWD69OkAgOfPn6N///4oX7481NXVUaZMGXTq1An379//6fklIiJxqYgdgIiIss7JyQnVqlXDnDlzIAgCAgMDs3X81q1bkZCQgCFDhkAikWDBggXo2rUr7t27B9Vszg+dPHkyVFRUMG3aNLx79w7Lli2DnZ0doqKiMqxPGhcXBwcHB3Tt2hXOzs4IDg7GX3/9hbp166Jt27YAPq9/a2Njgzt37mDEiBGoUqUKgoKC4Orqinfv3mH06NHZfh1hYWFo27YtqlatiunTp+Pjx49YsWIFmjVrhkuXLmUo/AHA2dkZVapUwdy5c3Hp0iWsX78ehoaGmD9/fqbX7uXlBX0tLTQvXz7D9kvPn2PO6dOooKsL17p1EZ+SgqXnz6NUFtZrXXnpEi6/fAl1FRU0s7JCmzZtcPLkSdy4cQMNGjTIcD7btWsHZ2dn9OrVC4GBgRg6dCjU1NQy3Ljra4cPH8a9e/fQv39/GBkZ4fr161i7di2uX7+OM2fOyOdGP336FI0bN8a7d+8wePBg1KxZE0+ePEFwcDCSkpKgpqaGpKQkWFtb48mTJxgyZAgqVqyI06dPY+LEiXj27BmWLVuWoe8tW7YgJSUFI0eOxNu3b7FgwQI4OzujZcuWiIiIwIwZMzB37lyEh4dj9OjR2LRpk/zYTZs2oV+/fmjTpg3mz5+PpKQkeHp6onnz5oiMjMzwPkqlUrRp0waWlpZYtGgRwsLCsHjxYhgbG2Po0KEwMDCAp6cnhg4dii5duqBr164AIF+aqlu3brh+/TpGjhyJypUr4+XLlzh8+DAePnyY6fNCREQFjEBERAXetGnTBABCr169Mmy3trYWrK2tM+3fr18/oVKlSvLHsbGxAgChZMmSwtu3b+Xbd+/eLQAQ9u7dm+UsR48eFQAIEolEuHDhgnx7YGCgAED4559/MuQDIPj5+cm3JScnC0ZGRkK3bt3k25YtWyYAEDZv3izflpKSIjRp0kTQ0dER4uPjs/06zMzMBENDQ+HNmzfybZcvXxaUlJSEvn37yreln9sBAwZkeJ1dunQRSpYs+c1zUKNaNaG9sbFwwMkpw7+qenpCCQ0NIahzZ/m2WS1aCAAEQy2tDPsCEHqbmsofa6uqCh2MjYX2xsZCzerVv9lv+vlcvHhxhvOZ/lpTUlIynCdvb2/5fklJSZna8/f3FwAIx48fl2/r27evoKSkJJw/fz7T/jKZTBAEQZg5c6agra0t3L59O8Pz//vf/wRlZWXh4cOHGXIYGBgI7969k+83ceJEAYBQv359ITU1VRAEQbhx44agrKwsKCkpCR8/fhQEQRASEhIEPT09wc3NLUM/z58/F4oXL55he79+/QQAwowZMzLsa25uLjRs2FD++NWrVwIAYdq0aRn2i4uLEwAICxcuzPS6iYio4OOlyERECsQ9h/Mve/ToAX19ffljKysrAMC9e/ey3Mb+/5atcXR0RMOGDeXbu3fvjjJlyuDAgQMZ9tfR0YGLi4v8sZqaGho3bpyhzwMHDsDIyAi9evWSb1NVVcWoUaOQmJiIY8eOZet1PHv2DFFRUXB1dUWJEiXk+9WrVw+tW7fOlBHIfG6trKzw5s0bxMfHZ9iekJCA23fuoNoX/QPA248fce/dO7SqXBnaX4x+NyhdGhV1dTP19zVtVVXcevsWZbS1cSsmBomJid/cT0VFBUOGDJE/VlNTw5AhQ/Dy5UtcvHjxu+1/OYr+6dMnvH79Gr/99hsAyC93lslk2LVrFxwdHWFhYZGpjfRR3aCgIFhZWUFfXx+vX7+W/7Ozs4NUKsXx48czHOfk5ITixYvLH1taWgIAXFxcoKLy+eKxmjVronfv3pDJZFi6dCmAz6PM7969Q69evTL0o6ysDEtLS4SHh2fK+K33MSufb01NTaipqSEiIgJxcXE/3Z+IiAoWFrZERAqkSpUqOTq+YsWKGR6nF4dZ/UP+8uXLWL58OQCgc+fOGZ6TSCQwMTHJNB+xfPnymZYA0tfXz9DngwcPUK1aNSh9tf5rrf/uGvzgwYNsvY70/WvUqJHpNdSqVQuvX7/OdPOtrJ6bu3fvQhCETMXqy//m45bT0cnUZ/lixTJt+9qAevXw4P17bLhyBYIgYPz48d8syMqWLZvp7tPVq1cHgB/OBX379i1Gjx6N0qVLQ1NTEwYGBvLP0/v37wEAr169Qnx8/E/XHo6JiUFISAgMDAwy/LOzswPweb70l74+t+lFboUKFTJsb9myJYDP856vXLmCmJgY+fav+woNDc3Uj4aGhnwObbqvP2vfo66ujvnz5+PgwYMoXbo0WrRogQULFuD58+c/PZaIiMTHObZERApE86u5mhKJBIIgZNov/WY5X1NWVv7m9m+18bX4+Hg4OTmhYsWKuHv3bhbS5rzPgthmcnIyAEBdJXd/hbaoUAF1SpXC3jt3EHDzJnx9feHr64sdO3bI5yLnhLOzM06fPo0JEybAzMwMOjo6kMlkcHBwyHDDp6yQyWRo3bo1/vzzz28+n15op/veuf3e9kqVKqF79+7o3bs3gM/zbI2MjDLtp/LVe/C99rJqzJgxcHR0xK5du3Do0CFMmTIFc+fOxdGjR2Fubp6jtomIKG+xsCUiUmD6+vrfHNX7eoQzpwRBgJubG54/fw5PT0+4uLjIR9O+3OfOnTvyG/FkR6VKlXDlyhXIZLIMo7Y3b96UP5/d9gDg1q1bmZ67efMmSpUq9ctr7qqrqwMAktPSMmw31NICADz5xiXEjxMSstR2CU1NWJYti4CbN7F//3707dsXs2fPzlDYPn36FB8+fMiQ//bt2wDw3RscxcXF4ciRI/Dw8MDUqVPl279+Dw0MDKCrq4tr1679MKexsTESExPlI7S5bd68eXB1dcXhw4cBAIaGhrnW19dXD3zN2NgY48aNw7hx4xATEwMzMzMsXrwYmzdvzpX+iYgob/BSZCIiBWZsbIybN2/i1atX8m2XL1/GqVOncrUfT09PBAYGYsOGDShXrhwAwM/PDwlfFGzBwcF49uzZL40utmvXDs+fP0dAQIB8W1paGlasWAEdHR1YW1tnq70yZcrAzMwMvr6+ePfunXz7tWvXEBoainbt2mU7YzoTExNIJBI8/GrubQlNTVTV08OR+/fxITVVvv3SixeZ9v2aVBDkxzyMj4dEIkHjxo1RtmxZ+QhxurS0NKxZs0b+OCUlBWvWrIGBgUGGOc9fSh/J/Hr0+eu7FyspKaFz587Yu3cvLly4kKmd9OOdnZ3x77//4tChQ5n2effuHdK+Kvqzq2LFitiwYQNOnz4NDQ0NzJkzB6lfnNN0X37us0rrvy8gvvxcAEBSUhI+ffqUYZuxsTGKFSuW6T0gIqKChyO2REQKbMCAAViyZAnatGmDgQMH4uXLl/Dy8kLt2rUz3fToV128eBF//PEHRowYAScnJ0RERAAASpQogebNm6N///548eIFli1bBhMTE7i5uWW7j8GDB2PNmjVwdXXFxYsXUblyZQQHB+PUqVNYtmwZimVhjurXFi5ciLZt26JJkyYYOHCgfLmf4sWLy9ct/RU6OjqobmKCmLg42H8159m1bl1MP3EC448ehX2VKkhIScHeO3dQSVcXH39Q7H1MTUXfffvQvHx5PElMRGkDAwwcOBDnz5/H4sWLM+xbtmxZzJ8/H/fv30f16tUREBCAqKgorF279rtLNunq6srnjKampqJcuXIIDQ1FbGxspn3nzJmD0NBQWFtbY/DgwahVqxaePXuGoKAgnDx5Enp6epgwYQL27NmDDh06wNXVFQ0bNsSHDx9w9epVBAcH4/79+yhVqtQvnN3/5+TkhOHDh8PLywsnTpxAgwYN0LNnTxgYGODhw4fYv38/mjVrhpUrV2arXU1NTZiamiIgIADVq1dHiRIlUKdOHaSlpaFVq1ZwdnaGqakpVFRUsHPnTrx48QI9e/bM0WshIqK8x8KWiEiB1apVC35+fpg6dSrGjh0LU1NTbNq0CVu3bpUXoDnx7t07ODs7o169eli0aFGG5yZNmoQrV65g7ty5SEhIQKtWrbB69Wr5iFh2aGpqIiIiAv/73//g6+uL+Ph41KhRA97e3nB1df2l7HZ2dggJCcG0adMwdepUqKqqwtraGvPnz8/xTbjsHRywecMGDJbJoPrFpdMWRkaY2KQJ/K5dg8/Vqyijo4M/GjXCmSdPcOUHo4vqKipob2KCS8+fI/b9e6iqquLWrVtYvXo1hg4dmmFffX19+Pr6YuTIkVi3bh1Kly6NlStX/vQLha1bt2LkyJFYtWoVBEGAvb09Dh48iLJly2bYr1y5cjh79iymTJmCLVu2ID4+HuXKlUPbtm3l762WlhaOHTuGOXPmICgoCH5+ftDV1UX16tXh4eGR4Q7IObF48WKcOXMGT58+RenSpbFw4UIkJyejXLlysLKyQv/+/X+p3fXr12PkyJH4448/kJKSgmnTpmHkyJHo1asXjhw5gk2bNkFFRQU1a9ZEYGAgunXrliuvh4iI8o5EyMmdNoiIqNASBAHdunXD0aNHERkZKS8GIyIiYGtri6CgIHTv3l3klOKIjo5G7dq1McHSErZf3fE3J8IfPsTCs2cRHR0tvyP0l2xsbPD69eufzoEtTO7du4cGDRqgVatWCA4O/ukcWSIiKpo4x5aIiL5p+fLl2LlzJ3x8fHI8wlnYmJqaoo29Pfyio5H0jbmfvyIpNRV+0dFoY2//zaK2qKpatSq8vb2xY8cOrFixQuw4RERUQLGwJSIiAJ9vQvT8+XM8f/4cBw8exPjx4zF48GD89ttv8u0fP34UO6Yo3r9/Lz8H6f9mzJyJ+NRUrLx4EW+/uulQdgmCgHWXLyMxLQ2eXl65lLrw6NKlC8aMGYPx48fj3LlzYschIqICiIUtEREBAE6fPo0yZcqgTJkyaNeuHdLS0rB27Vr5tjJlymS4a3FRMnr06AznoUyZMrC0tMTHT58Q8egRXPbu/eW2BUGA/40bOBQbi39WrODo+HfMnz8f5ubmcHZ2RlxcnNhxiIiogOEcWyIiAvB5rdMLFy5g6tSpuHbtGry8vFC6dOkM+9SuXRtlypQRKaF4oqOj8fTpUwCfl9uJjIzE9u3bcffuXXTu3Bk+Pj5oU6UK3OrXh9Z37kz8LUmpqVh3+TIOxcaiTp062Lp1K+rWrZtXL0PhPXjwAObm5rCyssKuXbs435aIiORY2BIRkdyiRYswYcIE7N27Fx06dBA7ToGRkpKCI0eOIDAwEDt27JAvpaSpqYkPHz5gw4YNGDNqFHRUVNDX1BTNy5fPcLfkr6VKpTj55An8oqORmJaGrt27Y9OmTQAAMzMzDBgwQL60DWW0b98+ODo6YtGiRRg3bpzYcYiIqIBgYUtERAA+X4rcokULjBs3DvPnzxc7ToHx6tUr1KpVC2/evIGKigrS/luPViKR4I8//pCvMxsbG4vBbm4IO3IEehoaaFauHKrp66Oiri7UlZWRLJXiYXw8YuLicPrZM8QlJaGNvT08/xsZL168eIa2lZSU4ODggIkTJ6JZs2aivf6C6M8//8TSpUtx/PhxNGnSROw4RERUALCwJSIivH79Gubm5qhUqRLCw8Ohmo3LaQu71NRUWFtb4+zZs5DJZBmeO3/+PCwsLOSPN2/ejD59+qBv3744d+YMbsXE4MtfsxKJBDWqVUPrNm0wdOjQDHc/bt++PQ4ePIivfy07Ojpiz549efTqFFNqaipsbGzw8OFDREZGolSpUmJHIiIikbGwJSIq4mQyGdq3b48LFy4gMjIS5cuXFztSgZOQkICGDRsiJiZGvq1ChQp48OBBhnmerVq1glQqRUREBAAgMTERjo6OkEqlWL58OUxMTKCjo/PNPtauXQt3d3d5YSuRSFCvXj2EhYWxcPuGx48fw8zMDI0bN8a+ffug9INLv4mIqPDjbwEioiJu/vz5OHToEDZt2sSi9jsOHTqEu3fvomTJklBWVoaysjJ69+6doah98OABjh49iv79+8u3vX79GhEREThx4gT09fW/W9QCQIcOHTIUtYIgYODAgSxqv6N8+fLYvHkzDh48iAULFogdh4iIRMbCloioCDt+/DgmT56MSZMmwcHBQew4BVJQUBB69uyJnj174vbt22jQoAGkUimcnZ0z7Ofr6wsdHR10795dvm327NmQSCSQSCSYPXv2D/spW7YszMzMAHwe+R00aBBGjx6NwMDAXH9NhYWDgwMmTZqEyZMn48SJE2LHISIiEfFSZCKiIurly5cwMzND9erVERYWBhUVFbEjFTgBAQH4/fff0atXL/j4+EBZWRkJCQk4c+YMWrduLd9PJpPBxMQENjY22LhxIwDg/v37MDExgVQqBQAoKyvj7t27qFSp0nf72759O44dO4aFCxdCVVUV/fr1w7Zt27B79260a9cub1+sgkpLS4OdnR1iYmIQGRkJQ0NDsSMREZEIWNgSERVBUqkUDg4OuHLlCqKioork2rQ/s23bNri4uKB3797w9vaGsrLyd/c9duwYbGxscPz4cVhZWQEA3Nzc4O3tnaGwHTBgANauXZvlDGlpaXByckJISAhCQkJgbW2dsxdVSD19+hTm5uaoX78+Dh48+MP3ioiICideikxEVATNnj0bR44cwdatW1nUfsPWrVvx+++/4/fff/9pUQsA3t7eMDY2RvPmzQF8Hq39sqgFPn+ZsHHjRjx48CDLOVRUVLBt2zZYWVmhQ4cOOHfu3K+9oEKubNmy2Lp1K8LCwjBnzhyx4xARkQhY2BIRFTFHjx7F9OnTMW3aNLRq1UrsOAXOli1b5Ev2bNy48adFbWJiIoKDg+Hq6iq/mdS5c+cyFLXppFJptotTdXV17Ny5E/Xq1YODgwOuXbuWreOLilatWmHq1KmYPn06wsPDxY5DRET5jJciExEVIc+ePYO5uTnq1q2LkJAQXrL5lc2bN6Nfv37o168f1q9fn6UlZLy9vTFw4EDcv38fFStWlG9/8eIFpFIpXF1dAUA+R7d06dK/lO3du3ewtbXF8+fPceLECZiYmPxSO4WZVCpFmzZtcO3aNURFRcHIyEjsSERElE9Y2BIRFRFpaWlo3bo1bt68iaioqF8usAorPz8/uLq6yufBZnVd1BYtWkBdXR2HDx/+5vMdO3YEAOzZsyfHGV++fIkWLVrg06dPOHHiBCpUqJDjNgubFy9ewMzMDLVq1cLhw4f55Q0RURHBS5GJiIoIDw8PHD9+HNu2bWNR+xVfX1+4urpi4MCB2Spq79y5gxMnTmRYuzYvGRoaIiwsDADQunVrvHz5Ml/6VSSlS5fGtm3bcOzYMXh4eIgdh4iI8gkLWyKiIiA0NBSzZ8/GzJkzeWfdr/j4+KB///5wc3PDmjVrslzUAp8LYl1dXXTp0iUPE2ZUvnx5hIWF4f3797C3t0dcXFy+9a0orK2tMWPGDMyaNQuhoaFixyEionzAS5GJiAq5J0+ewMzMDBYWFti/f3+2CrfCbuPGjRg0aBAGDx6M1atXZ+vcyGQyVK5cGW3btsWaNWu+u19uXor8pWvXrsHa2ho1atRAaGgodHR0crV9RSeTydCuXTtcunQJkZGRKFeunNiRiIgoD/GvGyKiQiwtLQ09e/aEuro6Nm3axKL2Cxs2bMDAgQPh7u6e7aIW+Hx36UePHuXbZchfq1OnDkJCQnDt2jV07twZnz59EiVHQaWkpIRNmzZBTU0NvXr1QlpamtiRiIgoD/EvHCKiQmzy5Mn4999/ERAQgFKlSokdp8BYt24dBg0ahGHDhmHVqlW/VPB7e3ujZs2asLS0zIOEWdOoUSPs3bsXp06dQs+ePZGamipaloLIwMAAAQEBOH36NKZMmSJ2HCIiykMsbImICqn9+/dj/vz5mDt3Lpo1ayZ2nAJj7dq1GDx4MEaMGIGVK1fK157Njvfv32PHjh0Z1q4Vi7W1NXbs2IEDBw6gf//+kMlkouYpaJo1a4Y5c+Zg3rx5OHDggNhxiIgoj7CwJSIqhB4+fIi+ffuiQ4cOGDdunNhxCgwvLy8MGTIEI0eOxPLly3+5KA0ICEBKSgr69OmTywl/Tdu2bbFlyxb4+/tj+PDh4O0zMho/fjzat2+PPn364NGjR2LHISKiPMDCloiokElJSUGPHj2go6MDX19fzqv9j6enJ4YOHYrRo0fjn3/+ydFIq7e3N9q0aYOyZcvmYsKccXJywrp16+Dl5YX//e9/LG6/oKSkBF9fX2hra6NHjx68ZJuIqBDiXztERIXMxIkTceHCBQQEBKBEiRJixykQVq1ahWHDhmHMmDFYunRpjoramzdv4syZM6LdNOpHBgwYgGXLlmHBggWYO3eu2HEKlJIlSyIwMBDnz5/HxIkTxY5DRES5TEXsAERElHt2796NJUuWYOnSpfjtt9/EjlMgrFy5EiNHjsTYsWOxaNGiHM+J9fHxgb6+vnwZn4Jm9OjRiI+Px99//41ixYph5MiRYkcqMH777TcsWLAAY8eORYsWLQrse0hERNnHwpaIqJCIjY2Fq6srunTpgtGjR4sdp0BYvnw5Ro8ejXHjxmHhwoU5LmqlUik2bdqE3r17Q11dPZdS5r7Jkyfj/fv3GDVqFHR1ddGvXz+xIxUYY8aMwfHjx9GvXz9ERkaicuXKYkciIqJcwEuRiYgKgeTkZPTo0QP6+vrYuHGj6HfqLQiWLVuG0aNHY8KECblS1AJAaGgonj59WiAvQ/6SRCLBwoUL4ebmhgEDBmD79u1iRyowJBIJNm7cCD09PTg7OyMlJUXsSERElAtY2BIRFQITJkzA5cuXERgYCD09PbHjiG7p0qX4448/8Ndff2H+/Pm5Vuh7e3ujbt26aNCgQa60l5ckEgk8PT3h7OyMXr164dChQ2JHKjD09fURFBSEy5cvY8KECWLHISKiXMDClohIwQUHB2PFihVYsmQJLCwsxI4juiVLlmDs2LH43//+h7lz5+ZaUfv27Vvs3r27QKxdm1XKysrw8/ODg4MDunTpghMnTogdqcCwsLDA4sWLsXz5co5oExEVAixsiYgU2N27dzFw4EA4OTlh2LBhYscR3aJFizBu3DhMmjQJc+bMydUC1N/fHzKZDC4uLrnWZn5QVVVFYGAgmjRpgg4dOuDixYtiRyowhg8fDicnJwwYMAB3794VOw4REeWAROBCd0RECunTp09o2rQpEhIScPHiRejq6oodSVQLFizAX3/9hcmTJ2PGjBm5PqpqYWGBcuXKYffu3dk6Lv3Ou3v27MnVPNmVmJgIOzs73LlzB8ePH4epqamoeQqK9+/fo2HDhtDV1cXp06ehoaEhdiQiIvoFHLElIlJQf/zxB6KjoxEUFFTki9r58+fjr7/+wpQpU/KkqL169SouXrxY4G8a9SM6Ojo4cOAAypUrh9atW+PevXtiRyoQihcvjqCgIERHR2Ps2LFixyEiol/EEVsiIgXk7++P3r17Y82aNRg8eLDYcUQ1d+5cTJo0CdOmTcP06dPzpI979+7h5cuXaNy4MZSUsved8PXr1wEAtWvXzoto2fby5Us0b94caWlpOHHiBMqVKyd2pAJhzZo1cHd3x7Zt29CjRw+x4xARUTaxsCUiUjC3bt2ChYUFOnbsiM2bNyvMjYzywuzZszF58mRMnz4d06ZNEzuOwnj8+DGaNm0KHR0dHDt2DAYGBmJHEp0gCPj999+xd+9eXLx4EdWrVxc7EhERZQMLWyIiBfLx40dYWloiJSUFFy5cgI6OjtiRRDNr1ixMmTIFHh4emDp1qthxFM7t27dhZWWF8uXL4+jRoyhevLjYkUSXkJAACwsLaGho4MyZM9DU1BQ7EhERZRHn2BIRKZCRI0fizp07CAoKKtJF7YwZMzBlyhTMnDmTRe0vql69Og4fPox79+6hQ4cOSEpKEjuS6IoVK4bg4GDcvn0bo0aNEjsOERFlAwtbIiIFsWnTJmzYsAGrVq1C3bp1xY4jmvTLjtMvQ6ZfV69ePRw8eBCRkZHo0qULkpOTxY4kurp162LVqlVYv349Nm/eLHYcIiLKIha2REQKIDo6Gu7u7ujXr59C35k3JwRBwLRp0+Dh4YE5c+Zg0qRJOW7TxsYGY8aMAQBUrlwZy5Yty3Gbiua3337Dnj17cOzYMfTu3RtpaWliRxJd//790bdvXwwZMgTR0dFixyEioizgHFsiogLuw4cPaNy4MQDg3Llz0NbWFjlR/ksvamfOnIl58+bhr7/+ypV2bWxsYGZmhmXLluHVq1fQ1taGlpZWrrStaPbu3YuuXbvi999/x8aNG7N99+fChj93RESKpWj/1iIiKuAEQcCwYcNw//59BAUFFck/rgVBkM+nXbBgQa4VtV8zMDD4YVGbmpqaJ/0WFI6OjvDz84Ofnx9Gjx6Nov69t7a2NoKCgnD//n0MHz5c7DhERPQTLGyJiAowb29v+Pn5Yc2aNTA1NRU7Tr4TBAGTJ0/G7NmzsXDhQkyYMOGX2/rw4QP69u0LHR0dlClTBosXL87w/NeXIkskEnh6eqJjx47Q1tbG7Nmzf9h+REQEJBIJjhw5AgsLC2hpaaFp06a4deuWfJ+7d++iU6dOKF26NHR0dNCoUSOEhYVlyjFr1ix51kqVKmHPnj149eoVOnXqBB0dHdSrVw8XLlzIcNzJkydhZWUFTU1NVKhQAaNGjcKHDx+ydY569eqFNWvWYOXKlZgyZUq2ji2MTE1N4eXlBV9fX3h7e4sdh4iIfoCFLRFRAXX16lUMHz4cgwYNgouLi9hx8p0gCJg0aRLmzJmDxYsXY/z48Tlqb8KECTh27Bh2796N0NBQRERE4NKlSz88Zvr06ejSpQuuXr2KAQMGZKmfv//+G4sXL8aFCxegoqKS4bjExES0a9cOR44cQWRkJBwcHODo6IiHDx9maGPp0qVo1qwZIiMj0b59e/Tp0wd9+/aFi4sLLl26BGNjY/Tt21c+qnr37l04ODigW7duuHLlCgICAnDy5EmMGDEim2cJcHNzw6JFizB79mzMnz8/28cXNn369MHAgQMxfPhwXL16Vew4RET0PQIRERU48fHxQo0aNYR69eoJSUlJYsfJdzKZTPjzzz8FAMKSJUty3F5CQoKgpqYmBAYGyre9efNG0NTUFEaPHi0IgiBUqlRJWLp0qfx5AMKYMWOy3Ed4eLgAQAgLC5Nv279/vwBA+Pjx43ePq127trBixQr540qVKgkuLi7yx8+ePRMACFOmTJFv+/fffwUAwrNnzwRBEISBAwcKgwcPztDuiRMnBCUlpR/2/SNTp04VAAirV6/+peMLk6SkJKFu3bpCjRo1hPj4eLHjEBHRN3DEloiogBEEAUOGDMGTJ08QFBQETU1NsSPlK0EQ8Ndff2HBggVYtmwZ/vjjjxy3effuXaSkpMDS0lK+rUSJEqhRo8YPj7OwsMh2X/Xq1ZP/d5kyZQAAL1++BPB5xHb8+PGoVasW9PT0oKOjgxs3bmQasf2yjdKlSwNAhiWe0relt3v58mX4+PhAR0dH/q9NmzaQyWSIjY3N9msAPo9Wjx49GsOHDy/yy95oamoiKCgIT548gbu7e5Gff0xEVBCpiB2AiIgyWrt2Lfz9/bFt2zZUr15d7Dj5ShAETJgwAYsXL8by5csxcuRIUfP8ys26VFVV5f8tkUgAADKZDAAwfvx4HD58GIsWLYKJiQk0NTXRvXt3pKSk/LSNH7WbmJiIIUOGYNSoUZnyVKxYMduvIb2PJUuWICEhAa6urtDR0UHnzp1/qa3CoEaNGli3bh169eoFa2trDB48WOxIRET0BRa2REQFSGRkJEaPHo2hQ4eiR48eYsfJV4IgYNy4cVi6dClWrFjxS/NDv8fY2Biqqqo4e/asvNCLi4vD7du3YW1tnWv9/MypU6fg6uqKLl26APhckN6/fz/H7TZo0ADR0dEwMTHJcVtfUlJSwtq1a5GQkIAePXpg//79sLOzy9U+FEnPnj1x7NgxjBo1Co0bN4aZmZnYkYiI6D+8FJmIqIB4//49nJ2dYWpqiiVLlogdJ18JgoCxY8di6dKlWLlyZa4WtQCgo6ODgQMHYsKECTh69CiuXbsGV1fXfF+rtVq1atixYweioqJw+fJl9O7dWz7qmhN//fUXTp8+jREjRiAqKgoxMTHYvXt3rpxHZWVlbN68Ga1atUKnTp1w+vTpHLepyJYuXQpTU1M4OTkhPj5e7DhERPQfFrZERAWAIAgYNGgQXr58iaCgIGhoaIgdKd8IgoAxY8Zg2bJlWL16dZ6tGbpw4UJYWVnB0dERdnZ2aN68ORo2bJgnfX3PkiVLoK+vj6ZNm8LR0RFt2rRBgwYNctxuvXr1cOzYMdy+fRtWVlYwNzfH1KlTUbZs2VxIDaipqSE4OBgWFhZo164doqKicqVdRaShoYHAwEC8fPkSgwYN4nxbIqICQiLw/8hERKJbuXIlRo4cieDgYHTr1k3sOPlGEASMHj0aK1asgJeXF4YMGSJ2JPqB+Ph4tGrVCg8ePMDx48dRs2ZNsSOJZvv27ejevTtWrlyZZ1/GEBFR1rGwJSIS2YULF9C0aVMMHToU//zzj9hx8o0gCBg5ciRWrVqFNWvW8GY8CuLNmzewtrbGu3fvcPLkSVSuXFnsSKIZNWoU1qxZg1OnTv3SHbSJiCj3sLAlIhJRXFwcGjRoAAMDA5w8eRJqampiR8oXMpkMI0aMgKenJ9atW4dBgwaJHemn3N3dv7vsjYuLC7y8vPI5kXiePXsGKysrAMCJEyfkyxoVNcnJybCyssLr169x6dIl6OnpiR2JiKjIYmFLRCQSQRDQpUsXHDt2DJGRkUVm5Esmk2H48OFYs2YN1q1bh4EDB4odKUtevnz53ZsF6erqwtDQMJ8Tiev+/fto3rw59PT0cOzYMZQsWVLsSKKIjY1FgwYNYGtri+3bt8uXYiIiovzFwpaISCRLly7F2LFjsXv3bnTs2FHsOPlCJpNh2LBhWLt2LdavX48BAwaIHYly4ObNm2jRogUqV66MsLAw6Orqih1JFLt370bnzp2xdOlSjBkzRuw4RERFEgtbIiIRnDlzBlZWVhg9ejQWLVokdpx8IZPJ4O7ujvXr12Pjxo1wdXUVOxLlgsjISNja2qJ+/foICQmBpqam2JFEMW7cOCxfvhwnTpzAb7/9JnYcIqIih4UtEVE+e/PmDRo0aIBy5crh2LFjUFVVFTtSnpPJZBgyZAg2bNgAb29v9OvXT+xIlItOnz6N1q1bw8bGBjt37iwyc8W/lJqaihYtWuDp06eIjIxEiRIlxI5ERFSkcB1bIqJ8JJPJ0K9fPyQmJiIgIKDIFLVubm7YsGEDfH19WdQWQk2bNsWuXbsQFhYGFxcXSKVSsSPlO1VVVQQEBCAxMRH9+vWDTCYTOxIRUZHCwpaIKB8tWrQI+/fvx6ZNm1ChQgWx4+Q5qVSKgQMHwsfHB35+fujTp4/YkTKZMGECmjZtmmeFyIABA4rEXOLWrVsjICAAO3bswODBg4tkYVexYkX4+flh3759WLx4sdhxiIiKFBa2RET55OTJk5g0aRL+97//oV27dmLHyXPpRa2fnx82bdoEFxcXsSNl8uHDB3h5ecHe3h5KSnnzK/H169d4/fp1nrRd0HTu3Bk+Pj7YuHEjxo4di6I426l9+/b466+/MHHiRJw6dUrsOERERYaK2AGIiIqCV69eoWfPnmjatClmzpwpdpw8J5VKMWDAAGzevBmbN29Gr169xI70TTt27JBfOkq5w8XFBQkJCRg2bBiKFy8ODw8PsSPlu1mzZuHUqVPo0aMHIiMjYWBgIHYkIqJCjyO2RER5TCaToU+fPkhJSYG/vz9UVAr3d4pSqRSurq7YsmULtm7dWmCLWgDw9vaGra0tqlSpInaUQmXo0KGYN28eZsyYUSQvyVVRUcG2bduQnJyMPn36FMnLsomI8hsLWyKiPDZ37lyEhoZi8+bNKFeunNhx8pRUKkW/fv3g7++PrVu3okePHmJH+q779+8jPDycyw7lkb/++guTJk3C+PHjsW7dOrHj5Lty5cphy5YtCA0Nxbx588SOQ0RU6BXuYQMiIpFFRERg6tSpmDx5Muzt7cWOk6fS0tLQr18/BAQEwN/fH05OTmJH+iFfX1/o6OigW7duYkcptGbNmoX4+HgMGTIExYoVQ8+ePcWOlK/s7e3x999/Y8qUKWjWrBmsra3FjkREVGhxHVsiojzy4sULmJmZoVatWjh8+DCUlZXFjpRn0tLS0KdPHwQHB2Pbtm0FvliUyWQwNjZGy5YtsWHDhjztq2PHjgCAPXv25Gk/BZVMJkP//v2xdetW7Ny5Ex06dBA7Ur6SSqWws7PDzZs3ERUVhdKlS4sdiYioUOKlyEREeUAqlaJ3794QBAFbt24t9EWti4sLgoODERAQUOCLWgA4fvw47t+/j/79+4sdpdBTUlLChg0b0LFjR3Tv3h3h4eFiR8pXysrK2Lp1KwRBwO+//14k1/glIsoPLGyJiPLAzJkzERERAX9/fxgZGYkdJ8+kpqaid+/e2L59OwIDA9G1a1exI2WJt7c3TExM0KxZM7GjFAkqKirYunUrrK2t4ejoiLNnz4odKV+VKVMGW7duxdGjRzFr1iyx4xARFUosbImIcllYWBhmzJiB6dOnw9bWVuw4eSa9qN25cyeCgoLQpUsXsSNlSUJCAoKDg+Hq6gqJRCJ2nCJDXV0dO3bsgJmZGdq2bYsrV66IHSlftWzZEtOnT4eHhweOHDkidhwiokKHc2yJiHLR06dPYW5ujvr16+PgwYOF9hLk1NRU9OrVC3v27EFQUBA6deokdqQs27hxIwYNGoQHDx6gQoUKed5fUZ9j+7X379+jZcuWePLkCY4fP47q1auLHSnfSKVSODg44MqVK4iKikKZMmXEjkREVGhwxJaIKJekpaWhV69eUFFRwebNmwttUZuSkoIePXpgz5492L59u0IVtcDny5Bbt26dL0UtZVa8eHGEhISgRIkSsLOzw8OHD8WOlG+UlZWxZcsWKCsro1evXkhLSxM7EhFRocHClogol0ybNg2nTp2Cv78/DA0NxY6TJ9KL2v3792PHjh1wdHQUO1K23LlzBydPnuTatSIzMDDA4cOHoaKiAjs7O7x48ULsSPnG0NAQ27Ztw4kTJzB9+nSx4xARFRosbImIckFISAjmzJmDWbNmoUWLFmLHyRMpKSlwdnbGgQMHsGPHDoVctsXHxwfFixdH586dxY5S5JUrVw5hYWFITEyEvb094uLixI6Ub1q0aIFZs2Zh9uzZCAkJETsOEVGhwDm2REQ59OjRI5ibm8PS0hJ79+6FklLh+84wOTkZTk5OCA0Nxc6dO9G2bVuxI2WbVCpF5cqV0b59e3h5eeVbv5xj+2PR0dFo0aIFTExMcPjwYRQrVkzsSPlCJpOhQ4cOOHfuHKKiolC+fHmxIxERKbTC99cXEVE+Sk1NRc+ePaGpqQlfX99CW9R2794doaGh2LVrl0IWtQBw9OhRPH78mGvXFjCmpqY4dOgQoqOj0blzZ3z69EnsSPlCSUkJfn5+0NTURM+ePZGamip2JCIihVb4/gIjIspHf//9N86dO4eAgACUKlVK7Di5Ljk5Gd26dcPhw4exe/duODg4iB3pl3l7e6NWrVpo3Lix2FHoKw0bNsT+/fvx77//wtnZucgUeaVKlUJAQADOnj2LyZMnix2HiEihsbAlIvpF+/btw8KFCzFv3jw0bdpU7Di57tOnT+jatSuOHDmCPXv2oE2bNmJH+mXv3r3Dzp07uXZtAWZlZYUdO3YgJCQE/fr1g1QqFTtSvmjatCnmzp2LBQsWYN++fWLHISJSWCxsiYh+wYMHD9C3b1907NgRY8eOFTtOrvv06RO6dOmCo0ePYu/evbC3txc7Uo4EBAQgNTUVffr0ETsK/YCDgwP8/f0REBCAoUOHoqjcBmTcuHFwdHRE37598eDBA7HjEBEpJBa2RETZlH53YF1dXfj4+BS6EcBPnz6hc+fOOHbsGPbt2wc7OzuxI+WYt7c3HBwcUKZMGbGj0E9069YNGzZswLp16zBhwoQiUdxKJBL4+PhAV1cXPXr0QEpKitiRiIgUDgtbIqJs+uuvvxAZGYnAwEDo6+uLHSdXffz4EZ06dcLx48exb98+tGrVSuxIOXbjxg2cPXuWa9cqEFdXVyxfvhyLFy/GrFmzxI6TL0qUKIGAgABcunQJ//vf/8SOQ0SkcFTEDkBEpEh27tyJZcuW4Z9//il0NyFKSkpCp06dcOrUKezfvx+2trZiR8oVPj4+KFGiBBwdHcWOQtkwcuRIxMfHY/LkydDV1cXo0aPFjpTnLC0tsXDhQowZMwZWVlbo0qWL2JGIiBQGC1sioiy6d+8e+vfvj27dumHkyJFix8lVSUlJ6NixI/79918cOHAANjY2YkfKFWlpafDz80Pv3r2hrq4udhzKpkmTJiE+Ph5jxoxBsWLFMGDAALEj5blRo0bh+PHj6N+/P+rXr4+qVauKHYmISCHwUmQioixITk6Gs7MzSpYsiQ0bNhSqebVJSUlwdHTEmTNncPDgwUJT1AJAaGgonj9/zrVrFZREIsG8efPg7u4ONzc3BAUFiR0pz0kkEmzYsAElSpSAs7MzkpOTxY5ERKQQWNgSEWXBuHHjcPXqVQQFBaF48eJix8k1Hz58QIcOHXD27FkcPHgQLVq0EDtSrvL29ka9evVgbm4udhT6RRKJBKtWrUKvXr3w+++/4+DBg2JHynN6enoICgrC1atXMX78eLHjEBEpBBa2REQ/ERgYiFWrVmHZsmVo0KCB2HFyTXpRe/78eYSEhMDKykrsSLnqzZs32LNnD9euLQSUlJTg7e2Ndu3aoWvXrjh27JjYkfJcw4YNsXTpUqxcubJIjFQTEeUUC1sioh+IiYnBoEGD0KNHD7i7u4sdJ9ckJiaiXbt2uHDhAkJCQtC8eXOxI+U6f39/yGQyuLi4iJYhMTER79+/R1xcHKKiopCYmChaFkWnqqqKbdu2oVmzZnB0dMT58+fFjpTnhg4dih49emDgwIGIiYkROw4RUYEmEYrCAnFERL9AKpUiOjoaMpkMderUgbKystiRckV6URsVFYWQkBA0bdpU7Eh54tq1a1BTU0P16tXztd/o6Gh4eXkhNCQEt+/cybAOq0QiQXUTE9g7OMDd3R2mpqb5mq0wSExMhL29PW7duoVjx46hTp06YkfKU/Hx8bCwsIC2tjb+/fdfaGhoiB2JiKhAYmFLRFSEJCQkoF27drh8+TIOHTqEJk2aiB2p0IiNjcVQd3ccCg2FvpYWmpYpg2r6+qioqwt1FRUkp6XhYXw8YuLicPrZM8QlJaGNvT08vbxQpUoVseMrlLi4ONja2uLFixc4ceIETExMxI6Upy5fvgxLS0u4urrCy8tL7DhERAUSC1sioiIiISEBbdu2xdWrV3Ho0CH89ttvYkcqNNavX48xo0ZBR0UFfU1N0bx8eagqfX+2T6pMhpOPH8MvOhqJaWlYtnw5Bg0alI+JFd/Lly/RokULfPr0CSdPnkT58uXFjpSn1q1bh8GDB2PLli3o3bu32HGIiAocFrZEREVAfHw82rZti2vXriE0NBSWlpZiRyo0Zs+ejcmTJ6NNlSpwq18fWqqqWT42KTUV6y5fxqHYWMyaNQupqanw8PAAfzVnzaNHj9C8eXNoaWnh2LFjMDQ0FDtSnhEEAX369MGuXbtw4cIF1KxZU+xIREQFCgtbIqJCLj4+Hg4ODoiOjkZoaCgaN24sdiSFtnXrVrx8+RJjxozB+vXr4ebmhj61a6NXFufLfkpLQ/CtW6hnYIB6/xViW6Ojsfn6dTg6OmLv3r0sbLPhzp07sLKygpGREcLDw6Gnpyd2pDyTmJiIRo0aQUVFBWfPnoWWlpbYkYiICgzeFZmIqBB7//492rRpgxs3biAsLIxFbS7YunUrli1bhtjYWIwZNQptqlTJclELAMlSKbZGR+PKq1fybb1q1UKbKlVw5PBh3LhxIy9iF1omJiYIDQ3FgwcP0L59e3z48EHsSHlGR0cHQUFBuHv3LkaOHCl2HCKiAoWFLREVGdOnT4eZmVmetO3j41PgRorevXsHe3t73Lx5E2FhYbCwsBA7Uq4pCO/lUHd36KiowK1+/Rz3KZFI4Fa/PoqpqmLM6NE5bq+oqVu3LkJCQnDlyhV06dIFycnJYkfKM3Xq1MHq1auxceNG+Pr6ih2HiKjAYGFLRFQIpRe1MTExOHLkCBo2bCh2JIXx8eNHfPz4EZUrV4a6ujoMDQ3RunVrXLp0CTY2Nti/fz8ePHiAQ6GheJGQgGGhoQA+3xBq07VrGHX4MLrv3IkuO3ZgQng4Lr98KW/7xYcP6LVnD4DPlx+3CwpCu6AgbL5+HVqqqjDW1cWh0NBMo7abN29G48aNoaWlBX19fbRo0QKh//VLnzVu3Bh79+7FiRMn0LNnT6SlpYkdKc+4urrC1dUVQ4cOxfXr18WOQ0RUILCwJSKFkZycjFGjRsHQ0BAaGhpo3rw5zp8/D+Dbo2y7du2CRCKRP+/h4YHLly9DIpFAIpHAx8cHwOfRMk9PT7Rt2xaampqoWrUqgoOD5e1ERERAIpHg3bt38m1RUVGQSCS4f/8+IiIi0L9/f7x//17e9vTp0/PyVPxQXFwcWrdujbt37+LIkSNo0KCBaFm+pyC/l8OGDUNKSgoePHiAlJQU1K9fH5qamrhx4wb+/vtvmJmZQUNDA1pqavijUSMM/m/kOCk1FYdiY1HX0BD969XD77Vr431yMqYcP467//VXXF0dw/97P5qWK4fxjRtjfOPGaFauHACgYrFiAABPT095Pg8PD/Tp0weqqqqYMWMGPDw8UKFCBRw9ejRX3ovCxMbGBsHBwdi3bx/69+8PmUwmdqQ8s2rVKlStWhVOTk5ITEwUOw4RkehUxA5ARJRVf/75J7Zv3w5fX19UqlQJCxYsQJs2bXDnzp2fHtujRw9cu3YNISEhCAsLAwAUL15c/vyUKVMwb948/PPPP9i0aRN69uyJq1evolatWj9tu2nTpli2bBmmTp2KW7duAfg8F04M6UVtbGwsjhw5kmeX6+ZUQX4vNTQ0IJVK8fDhQwCf38sv389y5crh2rVraFOpElpXrizfrqOmBu/27TMs8+NQpQqGhIRgb0wMxjRqBA0VFTQvXx6rLl1C5eLF0bJSpQz9K/137OFDhwB8vjHSjBkz0KVLFwQHB8ufB8AbTH1H+/btsXnzZvTq1QvFihXDqlWr5F+KFCZaWloICgpCo0aNMGzYMPj6+hbK10lElFUcsSUihfDhwwd4enpi4cKFaNu2LUxNTbFu3Tpoampiw4YNPz1eU1MTOjo6UFFRgZGREYyMjKCpqSl/3snJCYMGDUL16tUxc+ZMWFhYYMWKFVnKpqamhuLFi0MikcjbFqOwffv2Lezs7HD//n0cPXq0wBa1Bf291NbWhkwmg0wm++Z7mZaWhrS0NFTT18+wXVkikRe1MkFAQkoKpIIAkxIlcOeLEeKsuBUTg8TEROzatQsymQxTp07NUNQCYBHzAz169MC6devg6emJSZMmiR0nz9SqVQteXl7YtGkTNm7cKHYcIiJRccSWiBTC3bt3kZqaimbNmsm3qaqqonHjxrhx4wYMDAxy1H6TJk0yPY6KispRm/kpvah99OgRjh49inr16okd6bsK+nvp7OwMT09PVKhQAQ0bNkS7du3Qt29fVK1aFQCQlJQEAKioq5vp2LD797Hj9m08jo9H2hcjqkba2tl6DYIg4M6dO7h79y6UlJRgmo27LtNnAwcOREJCAv744w/o6upi4sSJYkfKEy4uLjh+/DhGjBiBRo0aFeiffSKivMQRWyIqFJSUlDJdmpmampprbQMZL/3MrbZzw5s3b9CqVSuFKGqzQuz3snHjxihWrBhWrFiBsmXLYuHChahduzYOHjwIAPJ5m+oqGb8bPvrgAZacP48y2toY3agRZlpZYXaLFqhvaAjZL1w2XJjv7JtfxowZg+nTp2PSpElYtWqV2HHyzD///IMaNWrAyckJCQkJYschIhIFC1siUgjGxsZQU1PDqVOn5NtSU1Nx/vx5mJqawsDAAAkJCRnWsPx6lE5NTQ1SqfSb7Z85cybT4/Q5mekjiM+ePfultvPS69ev0apVKzx58gTh4eGoW7duvmfILkV4LwVBwLBhw7Br1y7ExsaiZMmSmD17NgBAWVkZAJD81V13Tz5+DCNtbUxu2hStKlVCQyMjmJcujZSvcmb1AmJ1dXUYGxtDJpMhOjo6i0fR16ZOnYqxY8dixIgR8PPzEztOntDU1ERgYCCePn2KwYMHc/41ERVJLGyJSCFoa2tj6NChmDBhAkJCQhAdHQ03NzckJSVh4MCBsLS0hJaWFiZNmoS7d+9i69at8jvlpqtcuTJiY2MRFRWF169fZxgRCwoKwsaNG3H79m1MmzYN586dw4gRIwAAJiYmqFChAqZPn46YmBjs378fixcvztR2YmIijhw5gtevX8svV81L6UXts2fPEB4ejjp16uR5n7mhIL+XUqkUpUqVyvBe6ujooGzZsvI+0ovjh/HxGTIp/Tfn9cuS4uabN7j55k2G/dJHej/8YBRaIpHAxMQEnTt3hpKSEmbMmJHpDr8sXrJGIpFg0aJFGDRoEPr3748dO3aIHSlPVK9eHevXr8e2bduwZs0aseMQEeU/gYhIQXz8+FEYOXKkUKpUKUFdXV1o1qyZcO7cOfnzO3fuFExMTARNTU2hQ4cOwtq1a4Uv/zf36dMnoVu3boKenp4AQPD29hYEQRAACKtWrRJat24tqKurC5UrVxYCAgIy9H3y5Emhbt26goaGhmBlZSUEBQUJAITY2Fj5Pu7u7kLJkiUFAMK0adPy8lQIL1++FOrWrSsYGhoK169fz9O+8kJBfS/j4uIEbW1toXr16oKWlpYAQKhdu7YAQFi8eLEgCIKwYMECAYBQpXhx4U9LS2Fas2bCAScnYYyFhQBA+K1sWWFkw4aCU82ago6qqlBJV1cw1NISDjg5yf9V1NUV9DU0hGHm5sJflpbCant74YCTk9Db1FQAINSsXl2ed8qUKQIAoWnTpsKiRYuEFStWCH379hX+97//5eE7VPikpaUJPXr0EFRVVYWQkBCx4+SZYcOGCWpqasLFixfFjkJElK8kgsCvfImoaJNIJNi5cyc6d+4sdpQsefnyJVq1aoVXr17h6NGjvLHQF3L6XqakpGDy5MkIDQ3FvXv3IJPJYGJigiFDhmDo0KEAPt/VuUGDBoi5fRsCAEMtLfi0bw9BEBB48yYO3ruHuE+fUFFXF33q1MHJR49w5dUr+LRvL+/nxps38IyMxP3375Emk6G3qSlcateG39Wr2HbzJkaOHInly5fL9/f29saKFSsQHR0NLS0t1KtXD5MnT4adnV1OTleRk5qaii5duuDo0aMIDQ1F8+bNxY6U65KTk9G0aVO8e/cOly5dyrAUFhFRYcbCloiKPEUqbF++fImWLVvizZs3CA8PR82aNcWOVKDk13sZHR2N2rVrY4KlJWwrVsy1dsMfPsTCs2cRHR2dpXV3Kfs+fvyIdu3a4dKlSwgPD0eDBg3EjpTr7t69iwYNGqB169YICgri0lBEVCRwji0RkYJ48eIFbG1tWdQWAKampmhjbw+/6Ggk5dIdm5NSU+EXHY029vYsavOQpqYm9uzZg5o1a6JNmza4ceOG2JFynbGxMby9vbF9+3asXLlS7DhERPmCI7ZERArg+fPnaNmyJd69e4fw8HDUqFFD7EhFXmxsLOrWro3mRkYYbWGRo7YEQcDyixdx4vlzXIqM5PubD96+fQtra2u8ffsWJ0+eRJUqVcSOlOvGjBmD1atX4+TJk2jcuLHYcYiI8hQLWyKiAu7Zs2do2bIl4uPjER4ejurVq4sdif6zfv16uLm5oU/t2uj1i3OdBUGA/40b2Hz9OoDPl1Obm5ujefPmaNy4MRo3bgwTExNeTpoHnj9/DisrK8hkMpw4cQJly5YVO1KuSklJgZWVFV68eIHIyEjo6+uLHYmIKM+wsCUiKsCePXsGW1tbJCYmIjw8HNWqVRM7En1l4sSJmDdvHtpUqQK3+vWhpaqa5WOTUlOx7vJlHIqNxezZs7Fjxw5cvHgRAKCiooK0/9bK1dPTw5EjRwrlfFCxPXjwAFZWVihWrBiOHTuGUqVKiR0pV92/fx/m5uawtrbGzp07+QUJERVanGNLRFRAPX36FDY2Nvjw4QMiIiJY1BZAUqkUFy9ehI6ODk48f47hR44g/OFDpH615uzXUqVShD98iOFHjuDk8+dYv349Jk2alOFOyOlFrUQigUwmk6+fS7mrUqVKCAsLw+vXr+Hg4ID379+LHSlXVa5cGb6+vti9ezeWLl0qdhwiojzDEVsiKpSkUilSUlKgoqIC1WyMoBUUT548ga2tLT59+oTw8HAYGxuLHalASU1NRVpaGjQ0NEQdgZo5cyamTZuG0NBQGBsbY6i7Ow6FhkJfSwtNy5RBNX19VNTVhbqyMpKlUjyMj0dMXBxOP3uGuKQktLG3h6eXV4b5nU2bNsXZs2ch+6I4PnjwIBwcHMR4iUXG5cuXYWNjg7p16yIkJARaWlpiR8pVEyZMwLJly3D8+HE0adJE7DhERLmOhS0RFTqvX7+Gubk5KlasiIiICIUrbB8/fgxbW1skJycjIiICVatWFTtSgSIIAqpXr45mzZrBx8dHtBzh4eGws7PD5MmT4eHhId8eHR0NLy8vHD50CLdiYvDlr1mJRIIa1aqhdZs2GDp06Dfvfrx//3506NAhw7YuXbrA19cXxYoVy7sXRPj333/RunVrNG/eHLt374a6urrYkXJNamoqbGxs8OjRI0RGRqJkyZJiRyIiylUsbImoUJHJZOjQoQPOnTuHqKgolC9fXuxI2fL48WPY2NggNTUV4eHhLGq/4eTJk7CyskJ4eDhsbGxEyfD8+XOYmZmhdu3aCA0NhbKy8jf3S0xMxJ07d5CcnAx1dXWYmJhAR0fnh20LggBTU1PcvHkTXbp0Qb9+/dCnTx+UL18eO3fu5B2T89iRI0fQrl07ODo6Ytu2bVBRURE7Uq559OgRzM3NYWlpib1790JJiTPSiKjw4P/RiKhQWbBgAQ4ePIjNmzcrXFH76NEj2NjYIC0tjSO1P+Dt7Y0qVaqgRYsWovQvlUrRu3dvSCQSbNmy5btFLQDo6OjAzMwMlpaWMDMz+2lRC3we1V26dCnat28PX19fdOrUCefPn4cgCGjcuDH27NmTmy+HvtKqVSsEBQVh165dcHNzy3BJuKKrUKECNm3ahAMHDmDhwoVixyEiylUsbImo0Dhx4gQmT56MSZMmKdx8xIcPH2Yoagvjmpq54cOHDwgMDES/fv1EG23y8PDAsWPH4O/vDyMjozzpw8HBAfv27ZNfelyjRg2cPXsWrVq1QqdOnTBt2rRCVXAVNB07doSfnx98fX0xZswYFKaL29q2bYuJEyfi77//xokTJ8SOQ0SUa3gpMhEVCi9fvoS5uTmqVauGsLAwhbp88MGDB7C1tYUgCIiIiEClSpXEjlRg+fn5oV+/foiNjUXlypXzvf/Q0FA4ODhgxowZmDx5cr73L5PJMG/ePEyePBnt27fHpk2boKenl+85ioo1a9bA3d0dkydPxsyZM8WOk2vS0tLQqlUr3LlzB5GRkTA0NBQ7EhFRjrGwJSKFJ5VK0bZtW0RFRSEqKgply5YVO1KW3b9/H7a2tpBIJIiIiEDFihXFjlSg2draQklJCUeOHMn3vp88eQJzc3OYm5vj4MGDos5PPHjwIHr37g0DAwPs3LkTtWvXFi1LYbdo0SJMmDABCxYswIQJE8SOk2uePn0KMzOzAvF5JiLKDfy/GBEpvDlz5iAsLAxbt25VuKLWxsYGSkpKLGqz4N69e4iIiICrq2u+952WloZevXpBTU0NmzdvFr0IaNu2LS5cuAANDQ1YWloiODhY1DyF2fjx4zF58mT8+eefWLNmjdhxck3ZsmWxZcsWHD58GHPmzBE7DhFRjrGwJSKFFh4ejunTp2Pq1Kmws7MTO06WxcbGwtraGioqKixqs8jPzw/FihVD165d873vKVOm4PTp09i2bRsMDAzyvf9vMTY2xr///gtHR0c4OTlh4sSJkEqlYscqlGbMmIFRo0Zh6NCh2Lp1q9hxck3r1q0xZcoUTJs2DeHh4WLHISLKEV6KTEQKK33JlTp16uDQoUM/vDttQXLv3j3Y2tpCTU0N4eHhCnf3ZjHIZDJUrVoVdnZ2WL9+fb72feDAAbRv3x7z5s3DX3/9la99Z4UgCFiyZAn+/PNP2NnZwd/fHyVKlBA7VqEjk8kwcOBAbNq0Cdu3b0enTp3EjpQrpFIp7O3tcf36dURFReXZDdGIiPIaC1siUkhSqRStW7fGjRs3EBUVhdKlS4sdKUvu3r0LW1tbaGhoIDw8HOXKlRM7kkIIDw9Hy5YtcfLkSTRr1izf+n306BHMzMzw22+/Ffh1P48cOYIePXpAV1cXO3fuRP369cWOVOikX5K+Z88e7N+/X6GuEvmR9C8JTU1NcfjwYYX5kpCI6EsF9zc0EdEPfLnkiqIUtXfu3IGNjQ00NTVZ1GaTt7c3qlWrhqZNm+Zbn6mpqejRowe0tbXh5+dXoIta4PP6qxcvXoSenh6aNGlSqC6ZLShUVFSwZcsWtGzZEp06dcK///4rdqRcYWRkBH9/fxw7dgwzZswQOw4R0S8p2L+liYi+ITQ0FLNmzcKMGTNgY2MjdpwsSS9qtbS0WNRmU3x8PIKDg+Hq6gqJRJJv/U6cOBHnz59HYGAgSpYsmW/95kSlSpVw6tQpODk54ffff8fYsWORlpYmdqxCRU1NDdu3b0fDhg3Rrl07REVFiR0pV9ja2sLDwwMzZ85EaGio2HGIiLKNlyITkUJJX3KlQYMGOHDgQIEfRQOAmJgY2NjYoFixYggPD0eZMmXEjqRQNmzYgMGDB+PBgwf5Nh95z5496NSpExYvXoyxY8fmS5+5SRAErFy5EmPHjkXz5s0REBDAtUpzWXx8PFq2bImHDx/ixIkTqFGjhtiRckwmk6Ft27aIjIxUuKXTiIhY2BKRwkhLS0PLli1x7949REZGFpi70/7I7du3YWNjg+LFiyM8PJw3ZvkFzZs3h46ODkJCQvKlv/v378Pc3BzW1tbYuXNnvo4S57bjx4/DyckJ6urq2LFjBywsLMSOVKi8fv0a1tbWiI+Px8mTJ1GpUiWxI+XYq1evYGZmBmNjYxw9ehQqKipiRyIiypKCP9RBRPSfgrjkyo/cunULNjY20NPTQ0REBIvaX3D79m2cOnUq39auTUlJgbOzM/T09ODt7a3QRS0AtGjRAhcvXkSZMmXQvHlz+Pj4iB2pUClVqhQOHz4MNTU12NnZ4fnz52JHyjEDAwNs27YNp0+fxtSpU8WOQ0SUZSxsiUghHDhwAPPmzcOcOXPQvHlzseP81M2bN2FjYwN9fX2Eh4crzA2uChpfX18UL14cnTt3zpf+JkyYgKioKAQGBkJfXz9f+sxr5cuXx/Hjx9GnTx/0798fw4cPR0pKitixCo2yZcsiLCwMHz9+ROvWrfH27VuxI+WYlZUVZs+ejblz5+LAgQNixyEiyhJeikxEBV76kitNmjTBnj17Cvy82hs3bsDW1halSpXC0aNHObfxF0mlUlSqVAmOjo7w9PTM8/62b9+O7t27Y/ny5Rg5cmSe9yeGtWvXYsSIEWjcuDGCg4N5FUEuunHjBlq0aIEqVargyJEjKFasmNiRckQmk8HR0RFnzpxBVFQUKlSoIHYkIqIfYmFLRAVaamoqrK2t8fjxY0RGRhb4u9NGR0ejZcuWMDAwwNGjRxXikumCKjQ0FG3atMHZs2fRuHHjPO3r7t27aNCgAezt7REYGKjwlyD/yL///otu3boB+FzMN2nSROREhcelS5dga2sLc3NzHDx4EJqammJHypE3b97A3Nwc5cuXx7Fjx6Cqqip2JCKi7yrYwx5EVOQp0pIr169fh62tLQwNDVnU5gJvb2+YmpqiUaNGedrPp0+f4OTkBAMDA6xfv75QF7UA0KRJE1y8eBFVq1aFtbU11q5dK3akQiP9bu3nz5+Hk5OTwl/yXbJkSQQEBOD8+fOYNGmS2HGIiH6IhS0RFVh79uzB4sWLsWDBAvz2229ix/mha9euwdbWFkZGRixqc0FcXBx27tyZL2vXjh07FtHR0QgMDETx4sXztK+CokyZMjh69CgGDx6MIUOGwM3NDcnJyWLHKhSaNWuGnTt34vDhw+jTpw+kUqnYkXKkSZMmmD9/PhYtWoQ9e/aIHYeI6Lt4KTIRFUjpS67Y2Nhgx44dBXoU7erVq2jVqhXKli2LI0eOFPiRZUXg5eWFESNG4PHjx3k6DzQgIAA9e/aEp6cn3N3d86yfgszHxwfu7u4wMzNDcHBwvq0VXNjt3LkTTk5OcHV1xbp16wr0/8N+RhAEdOnSBceOHUNkZCQqV64sdiQiokxY2BJRgZOSkoLmzZvj1atXuHTpUoG+O+2VK1fQqlUrlC9fHmFhYSxqc4mlpSUMDQ2xd+/ePOvj9u3baNiwITp06ICtW7cqdOGRUxcuXEDXrl2RnJyMoKAgtGjRQuxIhYKfnx/69euHP/74A4sXL1boz1hcXBwaNGgAQ0NDnDhxAmpqamJHIiLKgJciE1GBM2HCBFy+fBlBQUEFuqi9fPkyWrZsiQoVKnCkNhdFR0fj3Llzebp27cePH+Hk5ISyZcti7dq1Cl1w5AYLCwtcvHgRpqamaNWqFZYvXw5+751zffv2xcqVK7F06VLMmDFD7Dg5oq+vj8DAQERGRuLPP/8UOw4RUSYqYgcgIvrS9u3bsXz5cqxYsQIWFhZix/muqKgo2NnZoVKlSjh8+DBKlCghdqRCw8fHByVLloSjo2Oe9TFq1Cjcvn0bZ8+eVfhlWXKLgYEBDh8+jL/++gujR4/GhQsXsGbNGoW/s6/Yhg8fjoSEBEycOBG6urr4448/xI70yxo1aoTFixdj1KhRsLKykt9dm4ioIOClyERUYKQvudKmTRsEBAQU2FG0yMhI2NnZoUqVKjh8+HCBHlVWNGlpaahQoQKcnJywfPnyPOlj8+bN6NOnD9avX4+BAwfmSR+KbuvWrRg0aBBq1qyJHTt2cE5lLpg4cSLmzZuHdevWYdCgQWLH+WWCIMDZ2RmhoaG4dOkSjI2NxY5ERASAhS0RFRCfPn1C06ZNER8fj4sXLxbYu9NeunQJdnZ2MDExQWhoKPT09MSOVKjs378fHTp0wKVLl2Bubp7r7d+4cQMWFhbo1q0bfH19C+yXJwXB5cuX0aVLF8THxyMgIACtWrUSO5JCEwQBI0aMgKenJ/z9/dGjRw+xI/2y9+/fo2HDhihevDhOnToFDQ0NsSMREXGOLREVDOlLrgQFBRXYovbixYuws7NDtWrVWNTmEW9vb9SvXz9PitoPHz7AyckJlSpVgqenJ4van6hfvz4uXLiAhg0bwt7eHosWLeK82xyQSCRYsWIFXFxc4OLigv3794sd6ZcVL14cgYGBuHbtGsaNGyd2HCIiAByxJaICIH3JFS8vLwwZMkTsON+UnJyMly9fQlVVFYaGhlBS4veCuU0mkyExMRHq6upQV1fP9fY/fvyI1NRUaGtrQ1lZOdfbL6ykUikmT56MefPmoUePHtiwYQO0tbXFjqWw0tLS4OzsjAMHDuDgwYOwtbUVO9IvS0hIQFxcHEqWLMnPBBGJjoUtEYkqfckVR0dHbNmyhaNoRAVUcHAwXF1dUbVqVezcuZNzK3MgOTkZjo6O+PfffxEWFgZLS0uxIxERKTwWtkQkmo8fP+K3337Dp0+fcOHCBd6dlqiAu379Orp06YJXr17B398fDg4OYkdSWB8+fIC9vT1u3LiBY8eOoW7dumJHIiJSaLyWjohEk77kSnBwMItaIgVQu3ZtnDt3Ds2aNUO7du0wZ84czrv9Rdra2ti/fz8qVaqE1q1bIyYmRuxIREQKjYUtEYli8+bNWL9+PVatWsWRCiIFoqenhz179mDKlCn4+++/0a1bNyQkJIgdSyHp6ekhNDQU+vr6sLOzw6NHj8SORESksFjYElG+i46OxpAhQ9C3b1/0799f7DgKwcbGBhKJBBKJBFFRUWLHUSjTp0+HmZmZ/LGrqys6d+4sWp785urqKv/s7Nq1K1faVFJSgoeHB3bv3i2fI3rr1q1cabuoMTAwwOHDh6GkpAQ7Ozu8ePFC7EhERAqJhS0R5av0JVcqV66M1atX5+vNor4ucHKTj49Pni//4+bmhmfPnqFOnToAgPv37xe6m21VrlwZERERWd4/IiIClStXzlYf//zzD3x8fLJ1jCKZPn06XF1d5Y//+ecfPHv2LE/66tixI86fPw8AaNy4Mfbs2ZMn/RR25cuXR1hYGBISEtCmTRvExcXla//5/WVPbn7JQkSUjoUtEeWr4cOH4/7/sXffcTX2/x/AX6epZUeiIWVkteg20qSsWyRZiawQlXmblT3ulBARZZWWbJUW2VLZkpRE9ixpnPP5/XF/nZ+jUKlO8X4+Hh4P5xqfz/s6V9c5531dn5GVhdDQUJoeooKkpaWhoKAAMTExYYcioKioSNghVEiDBg1+eBOirh3PzzRo0AAKCgrVVn67du1w+fJlmJmZYciQIXB1dQWPx6u2+n5Xbdq0wenTp/H48WMMGDAAeXl5wg6plOLiYmGHIOB3u1YJIb+GEltCSI3x9/fHnj17sH37dmhqalZ4/8LCQsyaNQvNmjVDvXr10Lt3b/7TorKemB4+fJj/RDMgIADu7u64fv06v1nml6d2HA4H27ZtQ//+/SElJQU1NTWEhYXxy0lISACHw8G7d+/4y1JTU8HhcJCVlYWEhARMmDAB79+/55ft5uZW4eOrCufOnYOBgQGkpKSgpKSEWbNmIT8/n79eVVUVq1evhr29PeTk5KCsrIwdO3YIlPH48WOMGDECDRs2ROPGjTFkyBBkZWXx1395urNq1SooKiqiXbt2AIALFy5AS0sL9erVg56eHv/9T01NBWMM6urq+PfffwXq+vI+PnjwoMreg7Vr16J58+aQk5PDxIkT8fnzZ4H13z6dMjIygqOjI5ydndG0aVOYm5v/tA4OhwM/Pz8MHToU0tLS0NDQEHhayeVyMXHiRLRu3RpSUlJo164dNm3aVGYcq1evRvPmzdGwYUMsX74cJSUlmDdvHho3boxWrVrB399fYL+fnR9hkJOTQ1hYGFavXo0VK1ZgyJAhAtcLKZ+OHTsiKioKt2/fxpAhQ0r97f6qsLAwdO7cGVJSUmjSpAnMzMwwb9487NmzB0eOHOF/fiUkJPBbhAQHB8PQ0BD16tXDgQMHymz54uXlVarlxO7du9GxY0dISkqiRYsWcHR0BAD+dkOHDgWHw+G/LuupsbOzM4yMjPivv3et3rp1C/3794esrCyaN28OW1tbvHr1qqreNkJIHUGJLSGkRty8eRMzZszAxIkTYWtrW6ky5s+fj/DwcOzZswfJyclQV1eHubk53rx589N9bWxsMGfOHHTs2BG5ubnIzc2FjY0Nf/3SpUthZWWF69evY8yYMRg5ciTu3r1brrh69uwJLy8v1K9fn1/23LlzK3WMvyIjIwMWFhawsrLCjRs3EBwcjHPnzvF/UH7h4eEBPT09pKSkYPr06Zg2bRq/f2RxcTHMzc0hJyeHxMREnD9/HrKysrCwsBB4OhIbG4u0tDScPn0ax48fx4cPHzB48GB07twZycnJWLFiBRYsWMDfnsPhwN7evlSS5u/vjz59+kBdXb1K3oOQkBC4ublh9erVSEpKQosWLeDj4/PT/fbs2QMJCQmcP38e27dvL1dd7u7uGDFiBG7cuIEBAwZgzJgx/L9FHo+HVq1aITQ0FHfu3MGyZcuwaNEihISECJQRFxeHp0+f4uzZs9i4cSNcXV0xaNAgNGrUCJcvX4aDgwOmTp2KnJwcAOU/P8LA4XCwcOFCnDx5EufOnUP37t1x+/ZtocZUF+np6eH48eO4cOECbGxsquwpaW5uLkaNGgV7e3vcvXsXCQkJGDZsGFxdXTFixAhYWFjwP7969uzJ3++ff/6Bk5MT7t69W66bPgCwbds2zJgxA1OmTMHNmzdx9OhR/jX+5Wakv78/cnNz+a/L69tr9d27dzAxMYG2tjaSkpIQGRmJ58+fY8SIERUqlxDyG2CEEFLNPnz4wNq1a8c6d+7MPn36VKky8vLymLi4ODtw4AB/WVFREVNUVGTr169n/v7+rEGDBgL7REREsK8/5lxdXVnXrl1LlQ2AOTg4CCzT19dn06ZNY4wxFh8fzwCwt2/f8tenpKQwACwzM5MxxsqsvyoZGhoyJyenH24zceJENmXKFIFliYmJTEREhBUUFDDGGFNRUWFjx47lr+fxeKxZs2Zs27ZtjDHG9u3bx9q1a8d4PB5/m8LCQiYlJcWioqIYY4zZ2dmx5s2bs8LCQv4227ZtY02aNOHXwxhjO3fuZABYSkoKY4yxJ0+eMFFRUXb58mXG2H/nr2nTpiwgIKCC78b39ejRg02fPl1gmb6+vsB5t7OzY0OGDOG/NjQ0ZNra2hWqBwBbsmQJ/3VeXh4DwE6dOvXdfWbMmMGsrKwE4lBRUWFcLpe/rF27dszAwID/uqSkhMnIyLCgoCDGWPnOz49ijoiIKPcx/ooHDx6wzp07MxkZGRYaGlojdf5uTp48ycTFxdno0aMF/kYq69q1awwAy8rKKrXu22uCMcYyMzMZAObl5SWwvKzPUU9PT6aiosJ/raioyBYvXvzdWMr6WywrBicnJ2ZoaMh/Xda1umLFCtavXz+BZY8fP2YAWFpa2ndjIIT8fuiJLSGkWjHG4ODggCdPniA0NBRSUlKVKicjIwPFxcXo1asXf5m4uDi6d+9e7ierP9KjR49Sr6ui3Jp0/fp1BAQEQFZWlv/P3NwcPB4PmZmZ/O26dOnC/z+Hw4GCggJevHjBL+PBgweQk5Pjl9G4cWN8/vwZGRkZ/P06d+4MCQkJ/uu0tDR06dIF9erV4y/r3r27QHyKiooYOHAgdu/eDQA4duwYCgsLYW1tXWXvwd27d6Gvry+w7NtzWxZdXd0K1/X1+ygjI4P69evz30cA2Lp1K3R1dSEvLw9ZWVns2LED2dnZAmV07NgRIiL//1XcvHlzgemvREVF0aRJkwqfH2Fr06YNLl68iMGDB8Pa2hr//PMPuFyusMOqU/r3748DBw7g4MGDmD59+i/PF9y1a1eYmpqic+fOsLa2xs6dO8s1SJWenl6F6nnx4gWePn0KU1PTyob6Q99eq9evX0d8fLzA51779u0BoFZdE4SQ6le7RiAhhPx2du7cicDAQAQFBfH7YlYHERGRUj/8qqoJ35fE4+vya9sgKgCQl5eHqVOnYtasWaXWKSsr8/8vLi4usI7D4fAH+8nLy4Ouri4OHDhQqgx5eXn+/ys78NekSZNga2sLT09P+Pv7w8bGBtLS0pUqqypV5nh+9D4ePHgQc+fOhYeHB3r06AE5OTls2LABly9f/mkZVXF+agMZGRkEBgZCT08P8+fPR0pKCgIDA9GkSRNhh1ZnWFtbIy8vD/b29qhfvz7WrVtX6ZHQRUVFcfr0aVy4cAHR0dHYvHkzFi9eXOpv8lvfXhs/+6yt7M3L8n6GfxtPXl4eBg8ejHXr1pXatkWLFpWKhRBSN1FiSwipNqmpqZg1axYcHBwwcuTIXyqrTZs2/H5VKioqAP770XP16lU4OztDXl4eHz9+RH5+Pv+Hz7fzvUpISHz3qdGlS5cwbtw4gdfa2toA/j9hyM3NRaNGjSpcdk3R0dHBnTt3fqm/qo6ODoKDg9GsWTPUr1+/3Pu1a9cO+/fvR2FhISQlJQGgzL5zAwYMgIyMDLZt24bIyEicPXu20rGWpUOHDrh8+XKpc1nTzp8/j549e2L69On8ZVXx9Kiy50dYOBwO5syZAy0tLdjY2EBPTw8RERHVNu3W72jChAn48OEDnJ2d0aBBAyxevLjSZXE4HPTq1Qu9evXCsmXLoKKigoiIiAp9fsnLy+PZs2dgjPGT7K8/D+Xk5KCqqorY2FgYGxuXWYa4uHip+uTl5XHr1i2BZampqaVu9HxLR0cH4eHhUFVVrXUjxhNCahY1RSaEVIsPHz7A2toampqa8PT0/OXyZGRkMG3aNMybNw+RkZG4c+cOJk+ejE+fPmHixInQ19eHtLQ0Fi1ahIyMDAQGBpaaq1RVVRWZmZlITU3Fq1evUFhYyF8XGhqK3bt34/79+3B1dcWVK1f4gy6pq6tDSUkJbm5uSE9Px4kTJ+Dh4VGq7Ly8PMTGxuLVq1f49OnTLx9zRS1YsAAXLlyAo6MjUlNTkZ6ejiNHjpQaPOpHxowZg6ZNm2LIkCFITExEZmYmEhISMGvWLP4ARmUZPXo0eDwepkyZgrt37yIqKoo/AvLXT5hERUUxfvx4LFy4EBoaGuVqJlwRTk5O2L17N/z9/fnnUhgDGGloaCApKQlRUVG4f/8+li5dWuFBcspS2fMjbKamprh27RoaNWqEnj17IjAwUNgh1SlOTk5YsWIFlixZAm9v70qVcfnyZf6gatnZ2Th06BBevnyJDh06QFVVFTdu3EBaWhpevXr1wxYpRkZGePnyJdavX4+MjAxs3boVp06dEtjGzc0NHh4e8Pb2Rnp6OpKTk7F582b++i+J77Nnz/jNoU1MTJCUlIS9e/ciPT0drq6upRLdssyYMQNv3rzBqFGjcPXqVWRkZCAqKgoTJkwQ+s1GQkjNosSWEFLlGGOYNGkSXrx4gZCQEIF+l79i7dq1sLKygq2tLXR0dPDgwQNERUWhUaNGaNy4Mfbv34+TJ0+ic+fOCAoKKjXljpWVFSwsLGBsbAx5eXkEBQXx17m7u+PgwYPo0qUL9u7di6CgIP6UROLi4ggKCsK9e/fQpUsXrFu3DitXrhQou2fPnnBwcICNjQ3k5eWxfv36KjnmiujSpQvOnDmD+/fvw8DAANra2li2bBkUFRXLXYa0tDTOnj0LZWVlDBs2DB06dOBPmfOjJ4T169fHsWPHkJqaCi0tLSxevBjLli0DgFLnf+LEiSgqKsKECRN+Go+RkRHGjx9f7vhtbGywdOlSzJ8/H7q6unj06BGmTZtW7v2rytSpUzFs2DDY2NhAX18fr1+/Fnh6W1mVPT+1gYqKCs6fPw9ra2uMGTMGs2fPRklJibDDqjMWL16MuXPnwsnJqdTo4uVRv359nD17FgMGDEDbtm2xZMkSeHh4oH///pg8eTLatWsHPT09yMvL4/z5898tp0OHDvDx8cHWrVvRtWtXXLlypdQo8HZ2dvDy8oKPjw86duyIQYMGIT09nb/ew8MDp0+fhpKSEr9ljLm5Of/a7datGz5+/CjQ8uJ7FBUVcf78eXC5XPTr1w+dO3eGs7MzGjZsKNB/nRDy++OwXx2NgBBCvrF161Y4OjoiLCwMVlZWwg7npzgcDiIiIkrNoVibGBkZQUtLC15eXsIOpdwOHDjAn9/36353iYmJMDU1xePHj9G8efMflqGiogJ3d/cKJbekbLXl75wxhi1btmD27Nno3bs3v2k1+bkvg/H5+fkhODgYw4cPF3ZIhBBSa9CtLEJIlUpKSsLs2bMxc+bMOpHU1iU+Pj6QlZXFzZs3hR1Kmfbu3Ytz584hMzMThw8fxoIFCzBixAh+UltYWIicnBy4ubnB2tr6p0nt7du30aBBg3I9tSHf5+DgAFlZWWGHwcfhcDBz5kzExsbizp070NPTQ1JSkrDDqhM4HA58fHwwcuRIjB49GpGRkcIOiRBCag16YksIqTLv3r2Djo4OmjZtisTERP4gQrVdbXmS9SNPnjxBQUEBgP9GOP56qp3aYv369fDx8cGzZ8/QokULWFpaYtWqVfxRjwMCAjBx4kRoaWnh6NGjaNmypZAjLtuBAwcwderUMtepqKgIpc/ur3jx4gU+fPgA4L9RYis7onV1yMnJgZWVFa5fv47t27fTk/lyKi4uhpWVFWJiYhAZGYk+ffoIOyRCCBE6SmwJIVWCMQYrKyvEx8cjOTkZrVu3FnZIhFTKx48f8fz58zLXiYuL80flJlWjsLAQjo6O8PPzw/Tp0+Hp6Vkrb9zUNp8/f8bAgQNx9epVxMXFVXi+WUII+d1QYksIqRJeXl5wcXHB4cOHMWTIEGGHQwipY3bs2AFHR0d0794dYWFhUFBQEHZItV5eXh769u2L9PR0nDlzBh07dhR2SIQQIjSU2BJCftmlS5dgYGCAWbNmlZoGhxBCyuvixYsYPnw4GGMIDw+v8umgfkdv377lT8Fz7tw5qKmpCTskQggRCkpsCSG/5M2bN9DW1oaioiLOnj0LcXFxYYdECKnDnj17Bmtra1y+fBmbN2/GlClTBOZCJqU9f/4cBgYGKC4uxrlz52pt/3VCCKlONCoyIaTSeDwe7OzskJeXh+DgYKEntQUFBXj8+DFevXoFumdX++Tn5yM/P7/C+/F4PHz8+BH5+fl0Xv8ACgoKiI2NxZQpU+Dg4IDJkyfj8+fPwg6rVmvevDliYmLA4/FgZmaGly9fCjukcispKcGTJ0/w8uVLur4JIb+EEltCSKV5eHjg+PHj2LdvH5SVlYUaS0REBOrXr4/Zs2ejQYMG9ISnlsnOzoacnBxCQkIqtB+Xy4W5uTk0NDTw4cMHOq9/CAkJCWzZsgX+/v7Yv38/DA0NkZOTI+ywajVlZWXExMTg7du3MDc3x7t374QdUrmIiYkhJSUFzZo1w7///ivscAghdRgltoSQSjl//jwWLlyIBQsWYMCAAUKN5dChQxgxYgSGDRuGwMBAoT85JqXt3bsX0tLSsLa2rtB+K1euRGxsLA4cOIAWLVpUU3Sktho/fjzOnTuH3Nxc6Orq4uzZs8IOqVbT0NBAdHQ0srKyMGjQoEq1kBCGQYMGYf78+Vi4cCHOnz8v7HAIIXUU9bElhFTYy5cvoa2tjdatWyM+Ph5iYmJCiyU8PBwjR46ElZUV9u/fL9RYSNkYY9DQ0EDv3r0REBBQ7v1iY2PRt29fuLq6wtXVtfoCJLXey5cvMWLECJw7dw4eHh6YOXMmPb3/gcuXL8PU1BQ9e/bEsWPH6sSc4sXFxTA2NkZWVhZSU1PRtGlTYYdECKlj6IktIaRCeDwebG1tUVhYiIMHDwo1kQwNDYWNjQ2GDx9OSW0tlpiYiIyMDEyYMKHc++Tm5mL06NEwNTXFkiVLqjE6UhfIy8vj9OnTmDVrFpycnGBnZ4eCggJhh1Vr6evr49ixYzh79ixGjRqFkpISYYf0U+Li4jh48CAKCwtha2sLHo8n7JAIIXUMJbaEkApZu3YtoqOjceDAAaGOvBkSEoJRo0bBxsYG+/bto6S2FgsICICamhoMDAzKtX1JSQlGjx4NUVFRHDhwAKKiotUcIakLxMTE4OHhgQMHDiAsLAy9evVCVlaWsMOqtYyNjREWFoZjx47B3t6+TiSKrVq1wr59+xAVFYW1a9cKOxxCSB1DiS0hpNzOnDmDpUuXYvHixejXr5/Q4ggODsbo0aMxcuRI7N27l5LaWiwvLw8hISGws7ODiEj5vnLc3d1x9uxZBAUFoVmzZtUcIalrRo8ejYsXL+Ldu3fQ09NDbGyssEOqtQYNGoR9+/Zh//79mDVrVp0YddjCwgKLFi3C0qVLcebMGWGHQwipQ6iPLSGkXJ4/fw4tLS20b98eMTExQnuKFhQUhLFjx2LMmDHw9/enp3m13J49ezB+/HhkZWVBRUXlp9tHRUWhf//+WLlyJRYtWlQDEZK66s2bNxg1ahRiYmKwbt06zJkzh/rdfsfOnTsxZcoULFy4EKtXrxZ2OD9VUlICMzMz3L9/HykpKWjevLmwQyKE1AGU2BJCfurLlCu3bt1CSkqK0EanDQwMhK2tLcaOHYvdu3dTUlsHGBkZQVRUtFxP1XJycqCtrQ09PT2cOHGi3E94yZ+Ly+ViyZIlWLt2LWxsbLBr1y7IyMgIO6xaaePGjZgzZw7WrFmDf/75R9jh/FRubi60tLTQpUsXREZG0uc9IeSn6FcDIeSnVq5cibi4OAQGBgotqT1w4ABsbW0xbtw4SmrriIcPH+LMmTPlGjSqpKQEo0aNQr169bBv3z5Kakm5iIqKYs2aNQgNDcXx48fRo0cPZGRkCDusWmn27NlwdXXFwoUL4ePjI+xwfqpFixYIDAxEbGwsVq1aJexwCCF1AP1yIIT8UGxsLNzd3eHm5gYTExOhxLBv3z6MGzcOdnZ28PPzo6S2jtizZw/k5OQwbNiwn267ZMkSXLx4EQcPHqRpPkiFDR8+HJcvX8bnz5+hp6eHyMhIYYdUK7m6usLZ2RkzZszAvn37hB3OT5mamsLV1RVubm7Ul5oQ8lPUFJkQ8l21oSnY3r17MX78eNjb22PHjh30JK+O4PF4UFNTQ9++fbFz584fbnv8+HEMHjwYGzZswNy5c2soQvI7evfuHWxtbXHixAmsXLkSCxcupH6332CMYfLkyQgICEBoaCiGDh0q7JB+iMvlwsLCAjdu3EBqaqrQWg0RQmo/SmwJIWX6evCO1NRUoYxOu2fPHkyYMAETJ06Er68vJbV1SGxsLMzMzHD+/Hn07Nnzu9tlZ2dDW1sbvXr1wpEjRygJIb+Mx+Nh+fLlcHd3x9ChQ/ktB8j/43K5GD16NA4fPozjx4+jb9++wg7ph74evPD06dM0Ej4hpEz0K5EQUiY3NzckJibi4MGDQklq/f39MWHCBEyaNImS2jooICAAbdu2RY8ePb67TVFREWxsbCAnJ4eAgABKakmVEBERgZubG44cOYLY2Fh0794daWlpwg6rVhEVFcW+fftgZmYGS0tLnD9/Xtgh/VDz5s1x8OBBnD17Fu7u7sIOhxBSS9EvRUJIKZGRkVi1ahVWrlyJPn361Hj9u3fvxsSJEzFlyhRs376dkto65v379wgPD8f48eN/mKwuXLgQ165dQ3BwMBo3blyDEZI/wd9//40rV66Aw+GgW7duOHLkiLBDqlUkJCQQFhaGbt26YcCAAUhJSRF2SD9kaGiIFStWYNWqVYiKihJ2OISQWoiaIhNCBOTk5EBLSwvdu3fH8ePHazyp9PPzw+TJkzFt2jRs2bKFkto6yM/PD1OnTkV2djZatmxZ5jaHDx/G0KFD4eXlBScnpxqOkPxJPn78iPHjx+PQoUNYunQp3Nzc6HPlKx8+fICZmRkyMzNx9uxZdOjQQdghfRePx8PAgQORlJSElJQUtGrVStghEUJqEUpsCSF8xcXFMDY2xqNHj5CSklLjo9Pu2LEDU6dOxfTp07FlyxZqmlpH9erVC3Jyct8dmTYzMxPa2towNTVFWFgYnWdS7RhjWLt2LRYvXowBAwZg//79aNiwobDDqjVev34NIyMjvH37FomJiWjdurWwQ/quV69eQUtLC61bt0Z8fDz1tyWE8NEtS0II35IlS3D58mUEBwfXeFLr6+uLqVOnwtHRkZLaOiwtLQ0XLlz47ty1hYWFGDFiBBo3boxdu3bReSY1gsPhYOHChTh58iTOnz+Pbt264fbt28IOq9Zo0qQJoqOjUa9ePZiZmeHp06fCDum7mjZtiuDgYFy8eBFLliwRdjiEkFqEEltCCID/plxZv3491qxZ88NRbKvD9u3b4eDggJkzZ8Lb25uSnTpsz549aNiwIYYMGVLm+nnz5uHGjRsIDQ2lJ2akxllYWCApKQlSUlLQ19dHWFiYsEOqNVq0aIGYmBgUFRWhb9++ePXqlbBD+q5evXphzZo1WLduHY4fPy7scAghtQQ1RSaE4NGjR9DW1kbv3r1rfMoVHx8fzJgxA05OTvD09KSktg7jcrlQUVHB33//DR8fn1Lrw8LCYG1tja1bt2L69OlCiJCQ/+Tn52PSpEk4ePAgFixYgFWrVgllnu7a6N69e+jTpw+UlZURFxeH+vXrCzukMvF4PAwZMgQXLlxASkoKlJWVhR0SIUTIKLEl5A9XVFSEPn364NmzZ0hOTq7R0Wm3bt0KR0dHODs7Y+PGjZTU1kGZmZnw8vLC4MGDUVRUhIEDB+LKlSvo1q2bwHYPHjyAjo4OBgwYgKCgIDrXROgYY9i4cSPmz58PMzMzBAYGokmTJsIOq1ZITU2FkZERunbtilOnTkFaWlrYIZXpzZs30NbWhqKiIs6cOQMJCQlhh0QIESJKbAn5w82ePRtbtmxBYmIi9PX1a6zezZs3Y9asWZg9ezb+/fdfSnTqqMDAQIwZMwYAUK9ePcjKyuLixYtQV1fnb/P582f06NED+fn5SEpKqrVPgMifKTY2lj+fckREBLS0tIQdUq1w4cIF9O3bF3369MGRI0dqbdJ4+fJlGBgYYObMmfDw8BB2OIQQIaI+toT8wQ4fPgxPT09s2LChRpPaTZs2YdasWZgzZw4ltXWcoqIi//+fP3/G69evoaGhgRYtWiApKQkA4OLigrt37yI0NJSSWlLrmJqa4tq1a2jUqBF69uyJwMBAYYdUK/Ts2RNHjhxBXFwcxowZg5KSEmGHVCZ9fX2sX78eGzduxOHDh4UdDiFEiOiJLSF/qIcPH0JHR6fGp1zx8vKCi4sL5s2bh3Xr1lFSW8elp6ejbdu2Za6TlJTEhAkTsH37duzYsQOTJ0+u4egIKb+CggI4ODhg7969cHFxwfr162kqGQBHjhyBlZUVxo0bBz8/v1o5BzBjDFZWVoiLi0NKSkqtnq6IEFJ9KLEl5A9UWFiIXr164c2bN0hOTq6x0Wk9PT0xe/ZsLFiwAGvWrKGk9jeQl5cHOTk5/mtRUVE0btwYb968AZfLBQCoqanh5s2btbafHiFfMMawdetWuLi4oHfv3ggODkazZs2EHZbQ7d+/H+PGjcPMmTPh5eVVKz+73717Bx0dHTRp0gTnzp2DpKSksEMihNSw2nfbjRBS7ebOnYubN2/W6JQrHh4emD17Nv755x9Kan8jsrKykJKSAvDfXKE9e/aEpqYmP6kFgKysLOjp6SErK0tIURJSPhwOB46OjoiLi8OdO3egp6fHb1L/Jxs7dix8fHzg7e0NV1dXYYdTpoYNGyIkJAQ3btzAvHnzhB0OIUQIKLEl5A8TGhqKLVu2wNPTE7q6ujVS54YNGzB37lwsWrQIq1evpqT2N/PlyciAAQMQHR2Nmzdvltrm7t27OHXqVE2HRkilGBgYIDk5GYqKiujduzf8/f2FHZLQOTg4YN26dVixYgX+/fdfYYdTJj09PWzcuBGbN29GaGiosMMhhNQwaopMyB9EGFOurF+/HgsWLMCSJUuwfPlySmrrsLy8PDx48ACFhYWQlJSEuro6ZGVl0adPHzDGEB8fj1evXqFFixYAABEREfB4PHTr1g0rVqxAv3796PyTOqWwsBAzZ87Ezp07MX36dHh6etba0YFryuLFi7F69Wr4+vpiypQpwg6nFMYYbGxsEBkZieTkZIER2gkhvzdKbAn5QwhjypW1a9di4cKFWLp0Kdzd3SmpqYPu3LmD7du3IzoyEvcfPMDXXxkcDgdt1dXRz8ICDg4O0NTUxJ49ezB+/HgAwF9//YXly5fDzMyMzj2p03bs2AFHR0d0794doaGh/Js3fyLGGIqLi8HlciEuLl4rB9hijKGwsBDAfy1K6POHkD8DJbaE/CEcHBwQEBCAy5cvo2vXrtVe3+rVq7F48WK4urrCzc2t2usjVSszMxPTHBwQFR2NRtLS6NmiBTQaNYJy/fqQFBNDYUkJsj98QPrbt7iQm4u3nz7BvF8/jLW1hZubGzZt2oQBAwbQD0ry27h48SKGDx8OxhjCw8PRo0cPYYdECCHkK5TYEvIHCAoKwujRo2tsypVVq1ZhyZIlcHNzq7UDjZDv8/Pzg/OsWZAVE8M4TU30btUK4j+Y4qOYx8O5nBzsvXMHeSUl8PL2xqRJk2owYkJqxrNnz2BtbY3Lly9j8+bNmDJlCt28IYSQWoIGjyLkN3fv3j1MnjwZY8aMqZFkY8WKFViyZAnc3d1/+6R2w4YNUFNTg6ioKLS0tIQdTpVYtWoVJk+ejN4KCthqagpjZeUfJrUAIC4iAmNlZWw1NUVvBQVMnjwZq1atQkJCAjgcDhISEmom+F/A4XCoZQH5KQUFBcTGxmLKlClwcHDA5MmT8fnzZ2GHRQghBJTYEvJb+/TpE6ytraGkpITt27dX+5MFd3d3LFu2DCtWrMCyZcuqta4fWb16NQ4fPlytdURHR2P+/Pno1asX/P39sXr16mqtryb4+flhyZIlsO3YEU56epAWF6/Q/tLi4nDS08PYjh2xZMkSnDhxopoiJdWhJq6b34GEhAS2bNkCf39/7N+/H4aGhsjJyRF2WIQQ8sejpsiE/MYmTpyIoKAgXLlyBZ06darWutzc3ODu7o5Vq1Zh0aJF1VrXz8jKymL48OEICAiotjr++ecfbNiwAQUFBb/FKKmZmZno3LEjeisowElP75fKYozB+9o1JObm4lpKCtq2bQuRnzz1FbbPnz9DTEysVg6EU1Nq4rr53SQlJWHYsGEoLCxEaGgo+vTpI+yQCCHkj1W7f2kQQipt79692L17N3x8fKo1qWWMwdXVFe7u7li9enWVJ7U8Hq9WNvV78eIFpKSkfprU1tb4vzXNwQGyYmKYXAUDi3E4HEzu2hVy4uJwdnKq9UktANSrV++PTmqrS35+vrBDqFZ6enq4du0aNDU1YWpqCm9vb9TF5wXjx4+HpaVljdXH4XCE1jqAy+UKpV5CSPWr/b82CCEVdvv2bUybNg3jx4/nT73yRUJCAvT09FCvXj20adMGvr6+cHNzK9VMef/+/dDV1YWUlBQaN26MkSNH4vHjxwLbGBkZoXnz5li+fDnU1NSwYsUKtGzZEuvXry8VU2FhIVxdXaGurg5JSUkoKSlh/vz5/CkZvuBwOHB0dMSBAwfQsWNHSEpKIjIyEgDw77//omfPnmjSpAmkpKSgq6uLsLCwUvvn5+djz5494HA44HA4Au/BkydPYG9vj+bNm0NSUhIdO3bE7t27K/T+cjgc+Pv7Iz8/n1/Hl6dcP4q/vHXn5OTA0tISMjIyaNasGVxcXBAVFVWqv6qqqmqp8wv8d16MjIwElv3o/b9z5w6ioqMxTlMTww8fhk9yMi48eYJpUVH4OzwcDlFRSHr2rFQ9rwoK4HX1KsYeO4a/w8Mx4cQJbLl2DcU8HqTFxWHUqhWioqOxZ88egf0uX74MCwsLNGjQANLS0jA0NMT58+cFtvn48SOcnZ2hqqoKSUlJNGvWDH379kVycnI5ztD/e/fuHZydnaGkpMSfe3fdunXg8XgC25XVx7a81wpQ/uulU6dOuHPnDoyNjSEtLV3qenn+/DnExMTg7u5eqo60tDRwOBxs2bKlwsfH4/GwadMmdO7cGfXq1YO8vDwsLCyQlJTEP/4fXTcpKSno378/6tevD1lZWZiamuLSpUsCdQQEBIDD4eDMmTOYPn06mjVrhlatWgGouvNZG8nLy+P06dOYNWsWnJycYGdnh4KCAmGHVS2Ki4uFHYKAoqKiCu/D5XLx8uXLaoiGECJ0jBDyW/n48SPr0KED69ixI8vPzxdYl5yczCQlJZmqqipbu3YtW7VqFVNUVGRdu3ZlX38crFy5knE4HGZjY8N8fHyYu7s7a9q0KVNVVWVv375ljDHG4/GYsrIyA8AaNmzInJycmI+PDzMxMWEA2MmTJ/nlcblc1q9fPyYtLc2cnZ2Zr68vc3R0ZGJiYmzIkCECMQJgHTp0YPLy8szd3Z1t3bqVpaSkMMYYa9WqFZs+fTrbsmUL27hxI+vevTsDwI4fP87ff9++fUxSUpIZGBiwffv2sX379rELFy4wxhh79uwZa9WqFVNSUmLLly9n27ZtY3///TcDwDw9Pcv9Hu/bt48ZGBgwSUlJfh0ZGRk/jL+8dX/69Im1bduW1atXj82fP595eXkxXV1d1qVLFwaAxcfH87dVUVFhdnZ2peIzNDRkhoaG5X7/Z86cyRpJS7MjVlYMAFNr0IA1rleP2XbsyKZoaTEFGRkmKSrKDv79Nztpbc1OWluzfYMGsSb16jFJUVFmqaHBHHV02KgOHZiSnBwLGTKEnbS2ZisNDBgANnToUH4ssbGxTEJCgvXo0YN5eHgwT09P1qVLFyYhIcEuX77M32706NFMQkKCzZ49m/n5+bF169axwYMHs/3795f7POXn57MuXbqwJk2asEWLFrHt27ezcePGMQ6Hw5ycnAS2BcBcXV35r8t7rTBWvuvly3lRVFRkSkpKP7xeTExMmKamZqnjcXd3Z6KiouzZs2cVPr7x48czAKx///7My8uL/fvvv2zIkCFs8+bNjLEfXze3bt1iMjIyrEWLFmzFihVs7dq1rHXr1kxSUpJdunSJX4e/vz8DwDQ1NZmhoSHbvHkzW7t2LWOsas5nXXDgwAEmJSXFtLW1WWZmprDDKSU0NJR16tSJ1atXjzVu3JiZmpqyuXPnMgAC/+Lj41lmZiYDwA4ePMj69OnDJCUlmb+/P3N1dWVdu3YVKNfT05OpqKgILNu1axfT1NRkEhISTEFBgc2YMYMx9t/n1td1fdnPzs6u1PeBk5OTwGeZoaEhmzFjBnNycmJNmjRhRkZGjDHGbt68ySwsLJiMjAxr1qwZGzt2LHv58mWZ70FRURHbtm0by8vLq/T7SAipnSixJeQ3wuPxmK2tLZORkWF37twptX7w4MFMWlqaPXnyhL8sPT2diYmJ8X+sZ2VlMVFRUbZq1SqBfW/evMnExMTYqlWrGI/HYwsXLuT/MNm7dy9/u8LCQqagoMCsrKz4y/bt28dERERYYmKiQJnbt29nANj58+f5ywAwERERdvv27VLxf/r0SeB1UVER69SpEzMxMRFYLiMjU2bCN3HiRNaiRQv26tUrgeUjR45kDRo0KFX+j9jZ2TEZGZlSy78Xf3nr9vLyYgBYSEgIf5v8/Hymrq5e6cT2Z++/cqtWbGCbNuyktTUDwMRERNiu/v35SezWvn0ZADZNW5u/zFRFhYkAzMvUlL/sy78Tw4ezk9bWbK2hIQPAlFq1Yoz99/epoaHBzM3NGY/H48fx6dMn1rp1a9a3b1/+sgYNGvB/CFfWihUrmIyMDLt//77A8n/++YeJioqy7Oxs/rJvE9vyXCuMle96+cLwf+/Hz64XX19fBoDdvHlToExNTU2Bv/XyHl9cXBwDwGbNmlXqPfr6PHzvurG0tGQSEhL8mzeMMfb06VMmJyfH+vTpw1/2JbHt3bs3KykpESijKs5nXZGamspat27NmjRpwmJiYoQdDt/Tp0+ZmJgY27hxI8vMzGQ3btxgW7duZR8/fmQjRoxgFhYWLDc3l+Xm5rLCwkJ+YquqqsrCw8PZw4cP2dOnT8uV2Pr4+LB69eoxLy8vlpaWxq5cucK/gffixQsGgPn7+7Pc3Fz24sULxlj5E1tZWVk2b948du/ePXbv3j329u1bJi8vzxYuXMju3r3LkpOTWd++fZmxsXGZ7wOPx2Nr1qxhtra2An//hJC6j5oiE/Ib2b17N/bt2wdfX1906NBBYB2Xy0VMTAwsLS2hqKjIX66uro7+/fvzXx86dAg8Hg8jRozAq1ev+P8UFBSgoaGB+Ph4LFy4EGvWrEGbNm0gKyuLsWPH8veXkJBA9+7d8fDhQ/6y0NBQdOjQAe3btxco08TEBAAQHx8vEKuhoSE0NTVLHZ+UlBT//2/fvsX79+9hYGBQruaMjDGEh4dj8ODBYIwJxGFubo73799XWbPIb+OvSN0nT55EixYtMHz4cP7+0tLSmDJlSqXj+dn7n52TA41GjfjbazdrhhaysvzXrRs2hLSYGHLz8gAAPMZw8ckTdFdURNvGjUvV921T3cc5OcjLy0NqairS09MxevRovH79mh9Hfn4+TE1NcfbsWX4T2oYNG+Ly5ct4+vTpLx23gYEBGjVqJHDcZmZm4HK5OHv2bJn7lfdaAcp3vXytPNfLsGHDICYmhuDgYP6yW7du4c6dO7Cxsanw8YWHh4PD4ZQ5/dbPRkrncrmIjo6GpaUl1NTU+MtbtGiB0aNH49y5c/jw4YPAPpMnT4aoqKjAsqo4n3VF165dkZSUBF1dXfTr1w///vtvreh3m5ubi5KSEgwbNgyqqqro3Lkzpk+fDllZWUhJSUFSUhIKCgpQUFAQGDvA2dkZw4YNQ+vWrdGiRYty1bVy5UrMmTMHTk5OaNu2Lbp16wZnZ2cA/zXdBv77m1BQUOC/Li8NDQ2sX78e7dq1Q7t27bBlyxZoa2tj9erVaN++PbS1tbF7927Ex8fj/v37pfbncDgYOHAg9u3bV+FuKISQ2o1GyiDkN3Hjxg04Ojry56z91osXL1BQUAB1dfVS675elp6eDsYYNDQ0yqzn1atXiImJwcaNG3HkyBGIi4uX+nHcqFEj3LhxQ6DMu3fvfvcHzIsXLwRet27dusztjh8/jpUrVyI1NVWgb255pjF6+fIl3r17hx07dmDHjh3liqOyvo2/InU/evQI6urqpY6pXbt2lY7nZ+8/ACjXr8//v7y0dKn1shISyPtf/7r3hYX4VFIClQYNyh3DgwcPkJ6eDgCws7P77nbv379Ho0aNsH79etjZ2UFJSQm6uroYMGAAxo0bJ5Bc/Ux6ejpu3LhR7r+7r5eX51r5UsePrhfxb6ZMatWq1U+vl6ZNm8LU1BQhISFYsWIFACA4OBhiYmIYNmxYhY8vIyMDioqKaFzGTYifefnyJT59+lTm31+HDh3A4/Hw+PFjdOzYkb+8rOu3Ks5nXdK4cWOcPHkSS5cuxbx585CUlIRdu3ZBRkZGaDF17doVpqam6Ny5M8zNzdGvXz8MHz4cjb66qVUWvQqOkv7ixQs8ffoUpqamvxLud+nq6gq8vn79OuLj4yH71c24LzIyMtC2bdtSyzt37oxJkybB0dER3bp1Q5cuXaolVkJIzaLElpDfwMePH2FtbY127dph06ZNv1QWj8cDh8PBqVOnBJ66MMawc+dOhIaGwsvLC05OTjhy5EipJzNfb/91mZ07d8bGjRvL3FZJSUng9ddPZr9ITEzE33//jT59+sDHxwctWrSAuLg4/P39ERgYWK7jAoCxY8d+N7Gqqh8338ZfXXV/L6HncrkC5+VH7//du3cxa9YsSH41IrDI924U/MJTp8LCQv77sGHDBmhpaZW53ZcfpyNGjICBgQEiIiIQHR2NDRs2YN26dTh06FCpp6bfw+Px0LdvX8yfP7/M9WX94K2o710vX3z7Y7s81wsAjBw5EhMmTEBqaiq0tLQQEhICU1NTNG3aVKDu6j6+yijr+q2K81nXiIqKYvXq1dDV1YWdnR169OiBiIgItGnTRmjxnD59GhcuXEB0dDQ2b96MxYsX4/Llyz/c79tkXEREpNTf69eDSpV1/svjZ+V+L568vDwMHjwY69atK7Xtj54we3t748qVK7C2tkZSUhLk5OQqFTchpPagxJaQOo4xhilTpiA3NxdJSUnf/VHRrFkz1KtXDw8ePCi17utlbdq0AWMMrVu35v8wZoxh7ty5CA0Nhbe3N2bOnFmhGNu0aYPr16/D1NS0XE9XyxIeHo569eohKioKkpKS/OX+/v6lti2rDnl5ecjJyYHL5cLMzKxSMVRWRepWUVHBrVu3wBgTOI60tLRS2zZq1Ajv3r0rtfzRo0cCT8J+9P5/SZQKS0rKfTwNJCUhLSaGR+/fl3sfSUlJ/g/6+vXrl+sctGjRAtOnT8f06dPx4sUL6OjoYNWqVeVOhNq0aYO8vLwKn+/yXitf6vj2eqkKlpaWmDp1Kr858v3797Fw4cJSdZfn+Nq0aYOoqCi8efPmh09tv3fdSEtLl/n3d+/ePYiIiJS6MfU9v3o+6yorKyu0b98eQ4cOhZ6eHgIDA4V2zBwOB7169UKvXr2wbNkyqKioICIiAhISEuWeBkdeXh7Pnj0T+IxKTU3lr5eTk4OqqipiY2NhbGxcZhni4uKl6pOXl8etW7cElqWmppZq9fAtHR0dhIeHQ1VVtUJTdklJSSE0NBS6urqYOnUqDhw4UOnvJ0JI7UB9bAmp43x9fXHw4EH4+fn98Ie1qKgozMzMcPjwYYF+bg8ePMCpU6f4r4cNGwZRUVG4u7uD/TfAHObMmYONGzfC29sbo0ePrnCMI0aMwJMnT7Bz585S6woKCso116WoqCg4HI7Aj6GsrKwy50KUkZEplfCJiorCysoK4eHhpX48AajW6R8qUveAAQPw9OlTgWmMPn36VGYT5jZt2uDSpUsCU14cP3681DQzP3r/W7ZsCQDI/qaf5I+IcDjo0bIlrjx9ivtv3pRaX1Z/QnV1dejq6qJNmzb4999/kfe//rpf+/I+cLlcvP8maW7WrBkUFRVLTQ/1IyNGjMDFixcRFRVVat27d+9Q8p1kvrzXClD6evkaYwyvX78ud7xfa9iwIczNzRESEoKDBw9CQkKi1Dyj5T0+KysrMMbKnELo65i/d93069cPR44cQVZWFn/58+fPERgYiN69e6P+V83Yy1JV57Mu69ixI65cuYLevXtj4MCBWLVqVakpmarb5cuXsXr1aiQlJSE7OxuHDh3Cy5cv0aFDB6iqquLGjRtIS0vDq1evfjitj5GREV6+fIn169cjIyMDW7duLXVduLm5wcPDA97e3khPT0dycjI2b97MX/8l8X327Bnevn0LADAxMUFSUhL27t2L9PR0uLq6lvl5+a0ZM2bgzZs3GDVqFK5evYqMjAxERUVhwoQJP03W27ZtCz8/PwQFBX23mwghpO6gJ7aE1GHJyclwcnLC9OnTMWLEiJ9u7+bmhujoaPTq1QvTpk0Dl8vFli1b0KlTJ/4d9zZt2mDlypVYuHAhsrKyICIignPnzsHMzAybN29GYWEh5s6dW6E4bW1tERISAgcHB8THx6NXr17gcrm4d+8eQkJCEBUV9dN+XAMHDsTGjRthYWGB0aNH48WLF9i6dSvU1dUF+icC//XB+tIPWFFREa1bt4a+vj7Wrl2L+Ph46OvrY/LkydDU1MSbN2+QnJyMmJgYvCkjSasq5a178uTJ2LJlC8aNG4dr166hRYsW2LdvH6TL6Pc6adIkhIWFwcLCAiNGjEBGRgb2799fqqnjz95/FSUlpL99i37f6dtcFrvOnZH8/DkWJCSgv5oalOrXx5uCApzLycEGY2PIfjX4jFKrVvwmuX5+fujfvz86duyICRMmoGXLlnjy5Ani4+NRv359HDt2DB8/fkSrVq0wfPhwdO3aFbKysoiJicHVq1fh4eFR7hjnzZuHo0ePYtCgQRg/fjx0dXWRn5+PmzdvIiwsDFlZWQJNe79WnmsFKH29WFpaQk5ODpmZmYiIiMCUKVMqfL18YWNjg7Fjx8LHxwfm5uZo2LBhpY7P2NgYtra2/CTDwsICPB4PiYmJMDY2hqOjI4DvXzcrV67E6dOn0bt3b0yfPh1iYmLw9fVFYWFhmXNWf6uqzmdd17BhQxw5cgTLly/HkiVLkJSUhD179vz0xkBVqV+/Ps6ePQsvLy98+PABKioq8PDwQP/+/aGnp8eftzkvLw/x8fFQVVUts5wOHTrAx8cHq1evxooVK2BlZYW5c+cKJIZ2dnb4/PkzPD09MXfuXDRt2lRgQDwPDw/Mnj0bO3fuRMuWLZGVlQVzc3MsXboU8+fPx+fPn2Fvb49x48bh5s2bPzwuRUVFnD9/HgsWLEC/fv1QWFgIFRUVWFhYQETk589vbGxscObMGTg5OaF79+7Q1tYu3xtKCKl9amz8ZUJIlXr37h1TU1NjOjo67PPnz+XeLzY2lmlrazMJCQnWpk0b5ufnx+bMmcPq1asnsF1YWBhTVFRkAJikpCRr3749mzFjBktLS+NvY2hoyDp27FiqDjs7u1JzGhYVFbF169axjh07MklJSdaoUSOmq6vL3N3d2fv37/nbAfjutCC7du1iGhoa/Hi+zKn47UfZvXv3WJ8+fZiUlBQDIDCFyfPnz9mMGTOYkpISExcXZwoKCszU1JTt2LGjvG8h/xi/N93P9+Ivb92PHj1if//9N5OWlmZNmzZlTk5OLDIystR0P4wx5uHhwVq2bMkkJSVZr169WFJSUqnpfgoLC9mCBQuYgYEBk5eXZ2JiYkxaWpqpqqqy/v37s4kTJwrMYzvof1P/fP2vmbQ0M1NREVgWMHAgM1VRYQ0kJZm4iAhTkJFhg9q0YUesrATmsdXV1WX+/v7M19eXeXh4MAcHB6ahocHq1avHJCQkmIqKChsxYgSLjY3lxztv3jzWtWtXJicnx2RkZFjXrl2Zj49Phc4RY//N67xw4UKmrq7OJCQkWNOmTVnPnj3Zv//+y4qKigTO29fT/TBW/muFMcbCw8NZ7969mYyMDJORkfnl64Uxxj58+MD/G/7efK/lPb6SkhK2YcMG1r59eyYhIcHk5eVZ//792bVr1/jb/Oi6SU5OZubm5kxWVpZJS0szY2Nj/jy3X3yZ7ufq1asCy6vyfP4ujhw5wurXr8/at2/P7t27J+xw/ngFBQVMW1ubtWnThr17907Y4RBCKonDWC0Yg54QUiGMMVhbWyMmJgbJycm/PLKopaUlbt++zR+1ljGGWbNmYcuWLdi+fTumTp1aFWGTX5CQkABjY2PEx8fDyMioQvu+f/8eTZs2BZfLhZiYGHg8nkATvdmzZ2Pjxo2Yp68PY2XlKos5PjsbG74ZmEZUVBQiIiL8GCIiIko1sa3Nvr1WCKmstLQ0DB06FDk5Odi3bx+GDBki7JD+aBkZGdDR0UG/fv0QEhJC/W0JqYOojy0hddCWLVsQHh4Of3//Cie1BQUFAq/T09Nx8uRJfrLEGIOjoyO2bNkCX19fSmp/Aw0aNMDIkSMhIiKC4uJigaT2r7/+wrp162Derx/23rmDTz/oW1cRn4qLsffOHZj364dly5bxl3O5XH4MCgoKGDhwYJXUVx1+dq0Q8ivatWuHy5cvo2/fvrC0tMSyZctqvN8t+X9t2rTB7t27ERYWhq1btwo7HEJIJVAfW0LqmKtXr/Invh86dGiF91dTU8P48eOhpqaGR48eYdu2bZCQkMD8+fPB4/Hg6OiIbdu2YefOnZg0aVI1HEHt9vLlyx8OOCIhIVGp+UCF6fr163j//r3AcYmKikJDQwPR0dEQExPDtu3b0bljR+y8fh1OFZy38luMMey8fh15JSXYtn07VFVVkZubCz8/P4HBiqSlpXHq1CkMGjSoXH3hvigoKCg1GNG3GjduDImv+vlWxo+uFUKqgpycHMLCwrB27VosXrwYycnJ2L9/f6n+1KRmWFlZYdasWZg9ezb09fXRrVs3YYdECKkAaopMSB3y9u1baGtro3nz5khMTKzUD/cJEyYgPj4ez549g6SkJHr06IHVq1dDS0sLM2bMgK+vL3bu3ImJEydWwxHUfqqqqnj06NF31xsaGiIhIaHmAvqfijZF5vF4OHnyJDw9PREXFwclJSWIiooiOzsbwH8DySQnJ6P1VwNG+fn5YfLkybDt2BGjNDUrFSdjDEF372L/7dvw8/Pj/x0VFRXB1NQUFy9eBJfLhbi4OLS0tHD16lWoq6vDyckJ48ePLzXva1kCAgIwYcKEH25TmSbb3/retaKjo/NL5RJSlsjISIwaNQpNmzbF4cOH0bFjR2GH9EcqKipC79698fLlSyQnJ6NRo0bCDokQUk6U2BJSRzDGMHToUJw9exbJycnfHbGyMng8HqZNm4adO3fCz88P9vb2VVZ2XXP+/PlSTVC/1qhRI+jq6tZgRBWTn5+PPXv2YNOmTbh//z709fXh4uKCYcOG4cSJExg6dChERUURFxeHPn36lNp/1apVWLJkCcxbt8bkrl0h/ZM5JL/2qbgYO69fR1RmJlatWoVFixYJrH/16hV0dHTw+PFjTJs2DT4+Prh8+TI8PT0RFhYGOTk5TJ48GTNnzvzh3Ki5ubm4ffv2D2PR1dWlH6SkzsnIyMDQoUPx8OFDBAQECIwkTGpOVlYWtLW1YWhoiIiICOpvS0gdQYktIXXExo0bMWfOHBw9ehSDBw+usnJ5PB6mTp2KXbt2Yffu3Rg/fnyVlU1qTk5ODrZs2YIdO3bg/fv3sLKygouLC3r06MHfhsvlYvjw4bCyssLYsWO/W5afnx+cZ82CrJgYxmlqonerVhD/QVPhYi4X5548wd47d5BXUoJNmzd/94n/7du34ejoiN27dws8Lc7OzubHn5eXh+HDh8PFxQX6+vqVeDcIqbvy8/MxadIkHDx4EAsWLMCqVasgKioq7LD+OEePHsWQIUOwceNGuLi4CDscQkg5UGJLSB1w8eJF9OnTB87OztiwYUOVlcvj8TBlyhTs3r0b/v7+sLOzq7KySc24evUqPD09ERoaCmlpaf4TTxUVlV8qNzMzE9McHBAVHY1G0tLo2aIFNBo1gnL9+pAUFUUhl4vsDx+Q/vYtLuTm4u2nTzDv1w/btm8XSFgrKi8vD/7+/ti0aRMyMjLQo0cPuLi4YOjQoRATo2EhyJ+BMQZPT0/MmzcPZmZmCAwMRJMmTYQdVrUqKSnB3bt3UVxcDE1NTdSrV0/YIWHu3LnYtGkTEhMT8ddffwk7HELIT1BiS0gt9/r1a2hra0NJSQkJCQkQr0DT0B/h8XiYNGkSAgICsGfPHtja2lZJuaT6cblcHD58GJ6enjh//jzU1NTg5OSECRMmQE5OrkrrunPnDrZv347TUVFIS08XGPyJw+GgnYYG+pqbY9q0aejQoUOV1cvlcnH8+HF4enrizJkzUFFRwcyZMzFp0iQ0aNCgyuohpDaLi4uDjY0NZGVlERERAS0tLWGHVK1evHiBPn364PPnz0hMTPxhl4SaUFxcDENDQ+Tk5CAlJeW3v7lASF1HiS0htRiPx8PgwYNx+fJlpKSkVNmXPJfLxaRJk7B3717s2bPnh81SSe3x4cMH7Nq1C97e3sjKykKfPn3g4uKCwYMH10hTxby8PDx48ACFhYWQlJSEurp6uQZ7+lUpKSnw9PTEwYMHISkpCXt7ezg5Of3y/M2E1AWPHj3CsGHDcPfuXezcuRNjxowRdkjV6vHjxzAwMICkpCQSExPRrFkzocejra2Nv/76C0ePHq3QCO6EkBrGCCG11tq1axkAdvLkySors6SkhNnZ2TERERF24MCBKiuXVJ+HDx8yZ2dnJicnx8TExNjYsWNZUlKSsMOqcU+ePGGLFy9mTZo0YRwOhw0dOpSdPXuW8Xg8YYdGSLX69OkTGzduHAPAnJ2dWVFRkbBDqlbp6elMQUGBde3alb1580bY4bCTJ08yAGzdunXCDoUQ8gP0xJaQWioxMRHGxsaYP38+Vq9eXSVlcrlcTJgwAQcOHMD+/fsxatSoKimXVD3GGM6fPw9PT08cPnwYDRs2xNSpUzFjxgy0bNlS2OEJ1adPn7B//354eXnh7t270NXVhYuLC6ytrX957lpCaivGGLZu3QoXFxf06tULISEhQn+aWZ1u3boFQ0NDtGvXDtHR0TXSOuRHFi5ciA0bNiAhIQG9e/cWaiyEkLJRYktILfTixQtoa2tDXV0dsbGxVTJoDpfLxfjx4xEUFIQDBw7AxsamCiIlVa24uBihoaHw9PREUlIS2rVrB2dnZ4wbNw7S0tLCDq9W4fF4iI6OhqenJ6Kjo6GoqAhHR0dMmTKF+sKR31ZiYiKGDx8OCQkJHDp0CN26dRN2SNXm6tWrMDExgb6+Po4fPy7UAaVKSkpgYmKCjIwMpKamQl5eXmixEELKRh0FCKlleDwebG1tUVxcjKCgoCpJaktKSjBu3DgEBQUhMDCQktpa6O3bt1i3bh1at26NMWPGoGHDhjhx4gTu3LkDBwcHSmrLICIiAgsLC0RFReHWrVsYMGAA3N3doaSkhGnTpiEtLU3YIRJS5QwMDJCcnIyWLVvCwMAA/v7+wg6p2nTr1g3Hjx/H+fPnMXLkSBQXFwstFjExMQQFBaG4uBi2trbg8XhCi4UQUjZKbAmpZVavXo3Tp08jMDAQioqKv1zel6Q2ODgYQUFBGDFiRBVESarK/fv3MWPGDLRq1Qqurq4wNzfHjRs3cPr0aQwYMIAGKimnjh07YufOnXj8+DEWLlyIiIgItG/fHgMHDkRMTAyocRL5nbRs2RJnzpzBuHHjYG9vjxkzZqCoqEjYYVULQ0NDhIeH48SJE5gwYYJQE8qWLVviwIEDiI6Oxpo1a4QWByHkO4TYv5cQ8o24uDgmIiLCli1bViXlFRcXs5EjRzIxMTEWFhZWJWWSX8fj8VhsbCwbNGgQA8CaNWvG3Nzc2PPnz4Ud2m/j8+fPLCAggHXt2pUBYJ07d2a7du1iBQUFwg6NkCrl6+vLxMXFWa9evdjTp0+FHU61CQ4OZiIiIszBwUHoA8YtXbqUiYiIsLi4OKHGQQgRRH1sCaklnj17Bi0tLXTs2BHR0dG/PH1LSUkJxowZg0OHDiE4OBjDhg2rokhJZRUWFiIoKAienp64ceMGOnfuDBcXF4waNUqofcd+Z4wxJCQkwNPTE8ePH4e8vDymTZuGadOmoXnz5sIOj5AqcfHiRQwfPhyMMYSHh6NHjx7CDqla7N69GxMnTsT8+fOxdu1acDgcocTB5XLRt29f3L17F6mpqfRZQkgtQW3cCKkFuFwuRo8eDQ6HgwMHDvxyUltcXIzRo0fj0KFDCAkJoaRWyF6+fInly5dDRUUFEyZMgJKSEmJiYnD9+nVMmDCBktpqxOFwYGxsjKNHjyItLQ3W1tbYsGEDlJWVYW9vj5s3bwo7REJ+WY8ePXDt2jW0adMGhoaG8PX1/S2b39vb28PT0xPr168XalNgUVFRBAYGgjGG0aNHg8vlCi0WQshXhPm4mBDyn2XLljEREREWHx//y2UVFRUxKysrJi4uzg4fPvzrwZFKu3nzJps4cSKTlJRkUlJSbNq0aezevXvCDuuP9/r1a7Z27VrWsmVLBoCZmpqy48ePMy6XK+zQCPklhYWFbMaMGQwAmzhx4m/b9N7d3Z0BYN7e3kKN40v3IVdXV6HGQQj5DyW2hAhZdHQ043A4bMWKFb9cVlFRERs2bBgTFxdnR44cqYLoSEVxuVx28uRJ1rdvXwaAKSoqsjVr1rDXr18LOzTyjaKiIhYYGMi6devGALB27doxHx8flpeXJ+zQCPkl/v7+TFJSknXv3p09fvxY2OFUOR6Px+bMmcMAsICAAKHGsnz5csbhcFh0dLRQ4yCEUB9bQoTq6dOn0NLSgra2Nk6dOvVLI+AWFRVh5MiROH78OMLDwzF48OAqjJT8zKdPn7Bv3z54eXnh3r170NPTg4uLC6ytrSEuLi7s8MgPMMZw4cIFeHp6IiIiAg0aNICDgwNmzJiBli1bCjs8QiolKSkJw4YNQ2FhIUJDQ9GnTx9hh1SlGGOYOnUqdu3ahZCQEFhZWQklDi6Xi/79+yM1NRWpqalVMpsBIaRyKLElREi+TPb+8OFDpKSk/NJk70VFRRgxYgROnTqF8PBwDBo0qAojJT/y9OlTbN26Fb6+vnj79i0sLS3h4uKCXr16CW1gE1J5mZmZ2Lx5M/z8/FBQUAAbGxu4uLhAV1dX2KERUmEvX76EjY0NEhMT4eHhgZkzZ/5Wn0tcLhdjx45FeHg4jh07BnNzc6HE8eLFC2hra0NdXR2xsbFVMv88IaTiaPAoQoRk2bJluHDhAg4ePPjLSa21tTVOnTqFiIgISmprSHJyMmxtbaGqqorNmzfD1tYW6enpCA8PR+/evX+rH49/ktatW2Pjxo3IycnBhg0bcOHCBejp6aFPnz6IiIigQWJInSIvL4/o6Gg4OTnByckJdnZ2KCgoEHZYVUZUVBR79+6Fubk5hg4disTERKHE0axZMxw8eBDnz5+Hq6urUGIghNATW0KE4tSpUxgwYADWrl2LBQsWVLqcwsJCWFtbIzo6GhEREejfv38VRkm+xeVycezYMXh6euLs2bNQUVHBrFmzMHHiRDRo0EDY4ZFqwOVyceTIEXh6euLcuXNQU1PDrFmzYG9vDzk5OWGHR0i5BQUFYeLEiWjfvj0OHToEVVVVYYdUZQoKCjBw4EAkJSUhPj5eaC0s1q5di4ULF+LkyZP0fUyIEFBiS0gNe/z4MbS0tPDXX3/h2LFjle5XW1hYCCsrK8TExODw4cOwsLCo4kjJFx8/foS/vz+8vb2RkZGBnj17wsXFBZaWltTk7A+SlJQET09PhISEQFpaGpMmTcLMmTN/qwSB/N6uX7+OoUOH4sOHDzh48CDMzMyEHVKV+fjxI/r27YsHDx7g7Nmz0NTUrPEYeDweBg8ejMuXLyMlJQVKSko1HgMhfzJKbAmpQcXFxTA0NEROTg5SUlLQpEmTSpXz+fNnWFlZITY2FkeOHBFav6LfXXZ2NjZv3oydO3ciLy8P1tbWcHFxQffu3YUdGhGinJwcbNmyBTt27MD79+8xbNgwuLi4oEePHtQEndR6b968wahRoxATE4O1a9di7ty5v83f7Zs3b2BkZIRXr17xW1jUtNevX0NbWxtKSkpISEigwQMJqUHUx5aQGrRo0SJcvXoVISEhv5TUDhs2DHFxcTh69CgltdXg0qVLsLGxgZqaGvz8/DB16lRkZmYiKCiIklqCVq1aYe3atXj8+DG2bNmCGzduoFevXvjrr79w8OBBFBcXCztEQr6rcePGOHnyJBYsWID58+dj5MiRyM/PF3ZYVaJx48aIjo6GrKwszMzM8OTJkxqPoUmTJggODsaVK1ewePHiGq+fkD8ZPbElpIYcPXoUQ4YMgYeHB2bPnl2pMj5//oyhQ4ciISEBx44d+62akQlbSUkJDh06BE9PT1y6dAnq6upwdnaGnZ0dZGVlhR0eqcV4PB5OnToFT09PxMbGolWrVpg5cyYmT56MRo0aCTs8Qr4rPDwcdnZ2aN26NSIiIqCuri7skKpEdnY2evfuDVlZWZw5c+aXBmisLA8PD8ydOxdHjx6l6fcIqSGU2BJSA7KysqCtrQ1DQ0NERERUqtlXQUEBLC0tkZiYiGPHjsHU1LQaIv3zvHv3Dn5+fti8eTOys7NhbGwMFxcXDBw48JfmFSZ/puvXr8PLywuBgYEQFxfH+PHj4eTkBA0NDWGHRkiZbt++jaFDh+Lly5cIDAz8bQY9un//PgwMDNCqVSvExcXV+AB/jDH+d3ZKSgpUVFRqtH5C/kSU2BJSzYqKimBgYIAXL14gOTm5Uk9wCgoKMGTIEJw7dw7Hjx+HiYlJNUT6Z8nIyMCmTZuwe/duFBUVYfTo0XB2doaWlpawQyO/gWfPnmHbtm3Ytm0bXr16hUGDBsHFxQVGRka/TX9G8vt49+4dbG1tceLECaxYsQILFy78LW7s3bhxA4aGhujUqROioqIgLS1do/W/ffsWOjo6aNasGRITEyEhIVGj9RPyp6n7n1qE1HLz589HSkoKQkJCKpXUfvr0CX///TfOnz+PkydPUlL7CxhjOHPmDCwtLaGhoYGgoCDMnj0b2dnZCAgIoKSWVBkFBQW4u7sjOzsbO3fuxMOHD2FiYgIdHR3s2bMHhYWFwg6REL6GDRviyJEjWLZsGZYsWQIrKyt8+PBB2GH9si5duuDUqVNISUnB0KFDa/y6a9SoEUJCQpCSkvJLU/sRQsqHntgSUo0OHToEKysreHt7Y+bMmRXe/0tSe/HiRZw8eRKGhobVEOXvr6ioCMHBwfD09ERKSgo0NTXh4uKCMWPGQEpKStjhkT8AYwwxMTHw9PTEqVOnoKCggBkzZsDBwQFNmzYVdniE8B09ehS2trZQVFTE4cOH0a5dO2GH9Mvi4uIwYMAADBw4EMHBwTU+TZu3tzecnJwQHh6OYcOG1WjdhPxJKLElpJpkZGRAR0cH/fr1Q0hISIWbH+bn52Pw4MG4cuUKTp48iT59+lRTpL+vV69ewdfXF1u3bkVubi4sLCzg4uKCvn37UnNQIjR3797Fpk2bsHfvXjDGYGtrC2dnZ6HMu0lIWdLS0jB06FDk5ORg3759GDJkiLBD+mXHjh3D0KFDMWbMGPj7+9doU2vGGKytrRETE4Pk5GShTENEyJ+AEltCqsHnz5/Rq1cvvH//HteuXavwoBX5+fkYNGgQrl69ilOnTsHAwKCaIv093b17F15eXti7dy8AUOJAaqXXr1/D19cXW7ZsQW5uLszNzeHi4oJ+/frRjRcidB8/fsT48eNx6NAhLF26FG5ubnW+321QUBDGjBmDGTNmwNvbu0avs/fv30NHRweNGjXC+fPnISkpWWN1E/KnqNufUITUUnPmzMHt27cRGhpaqaR24MCBSEpKQmRkJCW15cQYw+nTpzFgwABoamri6NGjWLx4MR4/fowdO3ZQUktqnSZNmmDRokXIysrCvn378PLlS1hYWKBTp07YuXMnCgoKhB0i+YPJyckhLCwMmzdvRkBAAOzt7fH+/Xthh/VLRo0ahX379uHw4cPYsGFDjdbdoEEDHD58GG/fvsWKFStqtG5C/hT0xJaQKhYcHIyRI0di27ZtcHBwqNC+eXl5GDhwIJKTkxEZGYlevXpVU5S/F8YYbt26BR8fH1y6dAkuLi6wsbGhO+KkTmGMITExEZ6enjhy5AiaNGkCBwcHzJgxAwoKCsIOjxBCCKnVKLElpArdv38furq6GDRoEAIDAyvUzCkvLw8DBgxAamoqIiMj0bNnz2qM9PfD4/EgIiICxhg14yR13oMHD+Dt7c2fjmrUqFFwcXGhkbsJIYSQ76DElpAqUlBQgL/++gufP39GUlIS5OTkyr3vx48fMWDAAFy/fh1RUVHo0aNHNUZKCKkr3r17Bz8/P2zevBnZ2dkwMjKCi4sLBg0aVOf7OxJCCCFVib4VCakiTk5OuH//PkJDQyuU1H748AEWFha4ceMGoqOjKaklhPA1bNgQc+fORUZGBoKDg/H582cMGTIE7dq1w9atW5GXlyfsEAkhhJBagZ7YElIF9u/fD1tbW/j5+WHixInl3u9LUnvnzh1ER0eje/fu1RglIeR3cOnSJXh6eiI8PBxycnKYMmUKHB0doaSkJOzQCCGEEKGhJ7aE/KK7d+9i6tSpsLW1hb29fbn3e//+PczNzXHnzh2cPn2aklpCSLn89ddfCA4OxsOHDzFp0iT4+vqidevWGDVqFK5cuSLs8H5LRkZG4HA44HA4SE1NLfd+AQEBaNiwIf+1m5sb9ZMm5fblb+7rvyFCyPdRYkvIL8jPz4e1tTVUVFSwbdu2cg9a9CWpvXfvHmJiYtCtW7dqjrRmVeePt29/KBLyp1JWVsaGDRvw+PFjeHp64urVq9DX10evXr0QFhaGkpISYYf4W5k8eTJyc3PRqVMnAEBWVlaFB6qbO3cuYmNjqyO8WiEgIABGRkYV2sfIyAgBAQHl2lZVVRVeXl4VjqsyEhISwOFw8O7duxqpD/jv+BISEvivc3Nza+x4CfkdUGJLyC9wdHREZmYmwsLCICMjU6593r17h379+uH+/fuIiYmBnp5eNUdJCPmdycnJYebMmUhLS8Phw4chLi4Oa2trqKurY+PGjXV+7tHaQlpaGgoKChATE6t0GbKysmjSpMl31xcVFVW6bPIfLpcLHo8n7DAEFBcXV2o/BQUFNGjQoIqjIeT3RYktIZXk7++PgIAAbNu2DZqamuXa50tSm56ejpiYGOjq6lZzlJVTWFiIWbNmoVmzZqhXrx569+6Nq1evAij7ienhw4f5Ty4CAgLg7u6O69ev85tRfbkbz+FwsG3bNvTv3x9SUlJQU1NDWFgYv5yy7pCnpqaCw+EgKysLCQkJmDBhAt6/f88v283NrTrfCkLqDFFRUQwZMgQJCQm4du0a+vTpg3/++QdKSkpwdnbGw4cPhR3iby8gIADKysqQlpbG0KFD8fr1a4H137ZmGT9+PCwtLbFq1SooKiqiXbt2P61DVVUVq1evhr29PeTk5KCsrIwdO3YIbLNgwQK0bdsW0tLSUFNTw9KlSwWSqy9x7N69G8rKypCVlcX06dPB5XKxfv16KCgooFmzZli1apVAue/evcOkSZMgLy+P+vXrw8TEBNevX6/EO1U2xhjc3NygrKwMSUlJKCoqYtasWQD+e7L76NEjuLi48D//gf//Tjp69Cg0NTUhKSnJH0Hc2dlZoHxLS0uMHz+e/7qwsBALFiyAkpISJCUloa6ujl27diErKwvGxsYAgEaNGoHD4fD3K+upsZaWlsB30Zfvur///hsyMjL89/HIkSPQ0dFBvXr1oKamBnd3d2pZQUgVosSWkEq4desWZsyYAXt7e4wbN65c+7x9+xZ9+/ZFRkYGYmNjoaOjU81RVt78+fMRHh6OPXv2IDk5Gerq6jA3N8ebN29+uq+NjQ3mzJmDjh07Ijc3F7m5ubCxseGvX7p0KaysrHD9+nWMGTMGI0eOxN27d8sVV8+ePeHl5YX69evzy547d26lj5OQ35WOjg727t2LrKwszJo1C/v374eGhgaGDRuGxMRE0LiRVe/y5cuYOHEiHB0dkZqaCmNjY6xcufKn+8XGxiItLQ2nT5/G8ePHy1WXh4cH9PT0kJKSgunTp2PatGlIS0vjr5eTk0NAQADu3LmDTZs2YefOnfD09BQoIyMjA6dOnUJkZCSCgoKwa9cuDBw4EDk5OThz5gzWrVuHJUuW4PLly/x9rK2t8eLFC5w6dQrXrl2Djo4OTE1Ny/XdUB7h4eHw9PSEr68v0tPTcfjwYXTu3BkAcOjQIbRq1QrLly/nf/5/8enTJ6xbtw5+fn64ffs2mjVrVq76xo0bh6CgIHh7e+Pu3bvw9fWFrKwslJSUEB4eDgBIS0tDbm4uNm3aVKFjcXNzw9ChQ3Hz5k3Y29sjMTER48aNg5OTE+7cuQNfX18EBASUunlACPkFjBBSIR8/fmTt27dnnTp1Yvn5+eXa582bN0xXV5c1btyYpaSkVG+AvygvL4+Ji4uzAwcO8JcVFRUxRUVFtn79eubv788aNGggsE9ERAT7+uPE1dWVde3atVTZAJiDg4PAMn19fTZt2jTGGGPx8fEMAHv79i1/fUpKCgPAMjMzGWOszPoJIT+Wn5/PfH19WYcOHRgApqury/bv388KCwuFHVqdYGhoyJycnH64zahRo9iAAQMEltnY2Ah8Xn372WhnZ8eaN29eofOgoqLCxo4dy3/N4/FYs2bN2LZt2767z4YNG5iurq5AHNLS0uzDhw/8Zebm5kxVVZVxuVz+snbt2rE1a9YwxhhLTExk9evXZ58/fxYou02bNszX17fc8f+Ih4cHa9u2LSsqKipzvYqKCvP09BRY5u/vzwCw1NRUgeVlnbMhQ4YwOzs7xhhjaWlpDAA7ffp0mXWV9X30vRi6du3KXF1d+a8BMGdnZ4FtTE1N2erVqwWW7du3j7Vo0aLM+r8+PvrOI6R86IktIRXAGIODgwNycnIQFhYGaWnpn+7z5s0bmJmZISsrC3FxcbV+RMyMjAwUFxejV69e/GXi4uLo3r17uZ+s/si38/T26NGjSsolhHyftLQ0pkyZglu3buHUqVNo3Lgxxo4di9atW2PNmjVV9sTtT3b37l3o6+sLLCvPvOSdO3eGhIREherq0qUL//8cDgcKCgp48eIFf1lwcDB69eoFBQUFyMrKYsmSJcjOzhYoQ1VVVWDO9ebNm0NTUxMiIiICy76Ue/36deTl5aFJkyaQlZXl/8vMzERGRkaF4v8ea2trFBQUQE1NDZMnT0ZERES5mupKSEgIvCflkZqaClFRURgaGlY23B/6dvyM69evY/ny5QLv3ZcByT59+lQtMRDyp6HElpAK8PPzw4EDB7Bjx45y9YV6/fo1TE1NkZ2djbi4OHTt2rUGoqxeIiIipZoxVnZgjLLKBiBQflWVTQj57xqzsLBAdHQ0bt68if79+8Pd3R2tWrUq1ZyV1IzyDjz4NXFxcYHXHA6HP2DSxYsXMWbMGAwYMADHjx9HSkoKFi9eXGpgqrLK+FG5eXl5aNGiBVJTUwX+paWlYd68eRU+hrIoKSkhLS0NPj4+kJKSwvTp09GnT5+ffg9ISUmVGqH6Z99VUlJSlYqxvN+B357XvLw8uLu7C7x3N2/eRHp6OurVq1epWAghgiixJaScUlNTMXPmTEydOhWjRo366favX7+GmZkZcnJyEBcXV+G7ycLSpk0bSEhI4Pz58/xlxcXFuHr1KjQ1NSEvL4+PHz8iPz+fv/7beR0lJCTA5XLLLP/SpUulXnfo0AEAIC8vDwACfacqUjYhpPw6deoEPz8/ZGdn459//kFERATat2+PgQMHIiYmhvrhVlCHDh0E+qMCpT/vasKFCxegoqKCxYsXQ09PDxoaGnj06NEvl6ujo4Nnz55BTEwM6urqAv+aNm1aBZH/R0pKCoMHD4a3tzcSEhJw8eJF3Lx5E0DFPv/l5eUFvku4XC5u3brFf925c2fweDycOXOmzP2/PEX/tr5vy/3w4QMyMzN/Go+Ojg7S0tJKvXfq6uoCT8kJIZVHVxIh5fDhwwdYW1ujQ4cO5ZpT7tWrVzA1NcWTJ08QHx/PH/yiLpCRkcG0adMwb948REZG4s6dO5g8eTI+ffqEiRMnQl9fH9LS0li0aBEyMjIQGBhYag5CVVVVZGZmIjU1Fa9evUJhYSF/XWhoKHbv3o379+/D1dUVV65cgaOjIwBAXV0dSkpKcHNzQ3p6Ok6cOAEPD49SZefl5SE2NhavXr2iJlyE/KJmzZph2bJlePToEfz9/ZGTk4O+ffuia9eu8Pf3x+fPn4UdYp0wa9YsREZG4t9//0V6ejq2bNmCyMjIGo9DQ0MD2dnZOHjwIDIyMuDt7Y2IiIhfLtfMzAw9evSApaUloqOjkZWVhQsXLmDx4sVISkqqgsj/G+F4165duHXrFh4+fIj9+/dDSkoKKioqAP77/D979iyePHmCV69e/bAsExMTnDhxAidOnMC9e/cwbdo0gRH3VVVVYWdnB3t7exw+fBiZmZlISEhASEgIAEBFRQUcDgfHjx/Hy5cvkZeXxy933759SExMxM2bN2FnZwdRUdGfHtuyZcuwd+9euLu74/bt27h79y4OHjyIJUuWVPLdIoR8ixJbQn6CMYbJkyfj+fPnCA0N/WmToZcvX8LExAS5ubmIj49Hp06daijSqrN27VpYWVnB1tYWOjo6ePDgAaKiotCoUSM0btwY+/fvx8mTJ9G5c2cEBQWVmnLHysoKFhYWMDY2hry8PIKCgvjr3N3dcfDgQXTp0gV79+5FUFAQf7okcXFxBAUF4d69e+jSpQvWrVtXalTRnj17wsHBATY2NpCXl8f69eur/f0g5E8gKSmJ8ePHIzU1FbGxsVBRUYG9vT1UVFTg7u4u0IeTlPbXX39h586d2LRpE7p27Yro6GihJC1///03XFxc4OjoCC0tLVy4cAFLly795XI5HA5OnjyJPn36YMKECWjbti1GjhyJR48eoXnz5mXu82UKt6ysrHLV0bBhQ+zcuRO9evVCly5dEBMTg2PHjvHn/l2+fDmysrLQpk0bfguf77G3t4ednR3GjRsHQ0NDqKmp8afw+WLbtm0YPnw4pk+fjvbt22Py5Mn81kgtW7aEu7s7/vnnHzRv3px/A3bhwoUwNDTEoEGDMHDgQFhaWqJNmzY/PTZzc3McP34c0dHR6NatG/766y94enryk3ZCyK/jMGprRMgP+fj4YMaMGQgNDcXw4cN/uO3Lly9hamqKFy9eIC4urtzz2/4pOBwOIiIiYGlpKexQCCHlcP/+fWzatAkBAQHgcrkYM2YMnJ2d61QrlKpgZGQELS2tcrXYIf/P398fq1evxp07d0r13yXlExAQAGdnZ4GnzYSQstETW0J+4Nq1a/w73z9Lal+8eAETExO8ePEC8fHxlNQSQuq8tm3bYuvWrXj8+DHc3d0RFRWFLl26oG/fvjh58iR/YKE/gY+PD2RlZfn9PcnPnTx5EqtXr6aktpJkZWXh4OAg7DAIqTPoiS0h3/Hu3Tvo6OigSZMmOHfuHCQlJb+77fPnz2FiYoI3b94gPj4e7du3r8FI6w56YktI3VZcXIywsDBs3LgRSUlJaNeuHZydnTFu3LhyTX9WVz158gQFBQUAAGVl5QpPz1NeiYmJ6N+//3fXf+nnSf4MDx48AACIioqidevWQo6GkNqPEltCysAYg5WVFeLi4pCSkvLDL5QvSe3bt28RHx9frmmACCGkLmOM4fz58/D09MThw4fRsGFDTJ06FTNmzEDLli2FHV6dVVBQgCdPnnx3vbq6eg1GQwghdQsltoSUYdOmTXB2dv7p08Vnz57BxMQE7969o6SWEPJHyszMhLe3N3bt2oWCggLY2NjAxcUFurq6wg6NEELIH4QSW0K+cfnyZRgYGMDR0REbN2787na5ubkwMTHBhw8fEB8fj7Zt29ZglIQQUrt8+PABu3btgre3N7KysmBgYAAXFxf8/fff5ZoOhRBSPl/m1qXrihBBNHgUIV958+YNRowYAR0dHaxdu/a72+Xm5sLY2BgfP35EQkJCnUxquVwu7t27BwAoKSkBl8sFYwx0r4sQUhn169eHi4sLHjx4gLCwMPB4PAwbNgxt27bFpk2b8PHjR2GHSEidxxhDSUkJRo8ejTdv3gg7HEJqFUpsCfkfxhjGjx+PvLw8hISEfHdwkKdPn8LIyAj5+flISEiAhoZGDUf6a/Ly8rB582a0a9cOHTp0wNixY3H//n1wOBz+P0IIqSxRUVFYWVnh3LlzuHLlCvT19TF37ly0atUKc+fOxaNHj4QdIqkDXr58ifPnz+PatWv8gbtqMx6Ph5s3b+L8+fP48OFDtdXD4XDw8uVLnD59GhMmTKCb0YR8hRJbQv7Hw8MDx44dw969e6GsrFzmNk+ePIGRkREKCgqQkJBQpwbyyM7Oxrx589CqVSu4uLigW7duuHz5Mvbv3w9NTU2IiNDHASGkanXr1g2BgYHIzMzEtGnTsHv3bqipqWHEiBG4ePGisMMjtZi8vDwaNmyIUaNGQVFREadOnRJ2SD8kIiICNTU1LFiwACoqKrhx40a11dWqVSvs3bsXR48e/WGXKUL+NNTHlhAA58+fh6GhIebMmYN169aVuU1OTg6MjY1RVFSE+Ph4qKmp1XCUlXPp0iV4enoiPDwccnJymDJlChwdHaGkpCTs0Aghf5j8/Hzs2bMHXl5eSE9Ph76+PlxcXGBlZQUxMTFhh0dqoXfv3sHW1hYnTpzAihUrsHDhwlp9I/b9+/cwMTHBkydPcPbs2WrtqjR//nxs3LgRZ8+eRc+ePautHkLqCkpsyR/v1atX0NLSgqqqKuLj48ucSP7x48cwNjZGcXExEhISav18ciUlJTh06BA8PT1x6dIlqKurw9nZGXZ2dpCVlRV2eISQPxyPx8PJkyfh6emJuLg4KCkpYebMmZg8eTIaNmwo7PBILcPj8bBixQq4ubnB0tISe/bsQf369YUd1ne9fPkSffr0QX5+Ps6dO/fdVmC/qri4GMbGxnj06BFSUlLQtGnTaqmHkLqCElvyR+PxeBg4cCCSkpKQkpKCVq1aldomOzsbxsbG4HK5iI+Pr9VJ7bt37+Dn54fNmzfz43ZxccHAgQNr9R1uQsif6/r16/Dy8kJgYCDExcUxYcIEODk51amuHqRmHDt2DGPHjoWioiIiIiLQvn17YYf0XU+ePIGBgQHExMSQmJiI5s2bV0s9OTk50NbWRrdu3XD8+HH6rid/NPrrJ3+0devWISoqCvv37/9uUmtkZAQej1ern9RmZGTAyckJSkpKWLRoEYyNjZGSkoK4uDgMHjyYvugIIbVW165d4e/vj0ePHmHOnDkIDg5G27ZtMWTIECQkJNDgOIRv8ODBuHr1KjgcDrp3744jR44IO6TvatmyJWJiYpCXl4d+/frh7du31VJPq1atsG/fPpw6dQrr16+vljoIqSvoiS35Y505cwYmJiZYuHAhVq5cWWr9o0ePYGxsDACIj4+HiopKTYf4Q4wxJCYmwtPTE0eOHEGTJk3g4OCA6dOno0WLFsIOjxBCKuXz5884cOAAPD09cfv2bWhpacHFxQUjR4787mj15M/y8eNHjB8/HocOHcLSpUvh5uZWa2/g3r59G4aGhlBXV8fp06chJydXLfUsXrwYa9euRXx8PPr06VMtdRBS21FiS/5Iz58/h7a2Ntq2bYuYmJhSg5ZkZWXB2NgYHA4HCQkJ1dY/pjKKiooQEhICT09PJCcnQ1NTE87Ozhg7diykpKSEHR4hhFQJxhhOnz4NT09PREZGQkFBATNmzICDgwP1JSRgjGHt2rVYvHgx+vfvjwMHDtTa/tnXrl2DsbEx9PT0cPLkSdSrV6/K6ygpKYGZmRnu37+P1NRUNGvWrMrrIKS2o8SW/HG4XC4sLCxw48YNpKamlnq6mZWVBSMjI4iKiiIhIaHWjB78+vVr+Pr6YsuWLcjNzYW5uTlcXFzQr18/mnuWEPJbu3v3Lry8vLB3714AgK2tLZydnaGpqSnkyIiwRUZGYvTo0WjSpAkiIiLQqVMnYYdUpsTERJibm8PMzAzh4eFlDlT5q3Jzc6GlpYWuXbvi1KlTEBUVrfI6CKnName7DUKq0apVqxAbG4vAwMBSSW1mZiYMDQ0hJiaGM2fO1Iqk9t69e3BwcICSkhJWrFiBQYMG4fbt24iMjIS5uTkltYSQ316HDh3g6+uLx48fY8mSJTh+/Dg6duwICwsLREVFUT/cP5iFhQWSkpIgLS2Nv/76C6GhocIOqUwGBgY4dOgQIiMjYWdnBy6XW+V1tGjRAoGBgYiJicHq1aurvHxCajtKbMkfJTY2Fm5ubnB1dYWpqanAuocPH8LQ0BASEhJISEgoczCpmvKlCd6AAQPQoUMHHDlyBIsWLUJ2djZ27NhBTykIIX+kpk2bYvHixcjKysLevXvx4sULWFhYoFOnTvDz80NBQYGwQyRCoKamhgsXLmDw4MEYMWIEFixYUC2J46+ysLBAUFAQgoODMW3atGq5IWNqaoply5bBzc0NcXFxVV4+IbUZNUUmf4wvTXS6dOmCyMhIgSY6GRkZMDIygpSUFOLj49GyZUuhxPhl0BQvLy/cunWLP2iKjY0NJCUlhRITIYTUVowxnD17Fp6enjh69CiaNGmCadOmYfr06VBQUBB2eKSGMcbg6emJefPmwdTUFEFBQWjSpImwwyolICAAEyZMwJw5c7Bhw4Yqb3nF5XJhbm6OW7duITU1la4F8segxJb8EUpKStC3b1+kpaUhJSVFYD65Bw8ewNjYGNLS0oiPj4eiomKNx/f8+XP4+Phg27ZtePXqFQYPHgwXFxcYGhpSU2NCCCmHBw8ewNvbG7t370ZxcTFGjRoFFxcXdO3aVdihkRoWFxcHGxsbyMrKIiIiAlpaWsIOqZTNmzdj1qxZWL58OZYuXVrl5T9//hxaWlro0KEDTp8+Tf1tyR+BmiKTP4K7uzvOnj2LoKAggaQ2PT0dRkZGkJGREUpSe+PGDUyYMAHKysrw8PCAjY0N0tLScOTIERgZGVFSSwgh5aSurg5vb2/k5ORg1apViIuLg5aWFkxMTHDs2DHweDxhh0hqiImJCZKSktC4cWP07NkTBw4cEHZIpcycORMrV67EsmXLsGnTpiovv3nz5jh48CDOnDkDd3f3Ki+fkFqJEfKbi4yMZBwOh61atUpgeVpaGlNUVGTt27dnT58+rbF4uFwuO3bsGDMxMWEAmJKSElu/fj178+ZNjcVACCG/u+LiYnbw4EGmr6/PADANDQ22ZcsWlpeXJ+zQSA359OkTGzduHAPAnJ2dWVFRkbBDEsDj8di8efMYALZr165qqWPlypWMw+GwqKioaimfkNqEmiKT31pOTg60tbWhp6eHEydO8CdwT0tLg7GxMRo2bIi4uLga6X+Sn5+PPXv2YNOmTbh//z709fXh4uKCYcOGVcuw/4QQQv5z6dIleHp6Ijw8HHJycpgyZQocHR1rxcj3pHoxxrB161a4uLigV69eCAkJqVVzvDLGMH36dOzYsQMHDx6EtbV1lZbP4/EwYMAAXLt2DampqUIbQ4SQmkCJLfltlZSUwNjYGJmZmUhNTUXTpk0B/Dd9jomJCRo1aoS4uDiBpsnV4cmTJ9iyZQt8fX3x/v17DBs2DLNnz0aPHj2qtV5CCCGCHj16hC1btmDnzp3Iy8uDtbU1XFxc0L17d2GHRqpZYmIirK2tIS4ujkOHDqFbt27CDomPx+PB1tYWoaGhOHz4MAYMGFCl5b969QpaWlpQU1NDXFwcxMTEqrR8QmoL6mNLfltLlizBxYsXERwcLJDUGhsbo3HjxtWe1CYlJWHMmDFQVVWFj48PJkyYgIyMDISGhlJSSwghQqCiooINGzbg8ePH2LhxI65cuQJ9fX306tULYWFhKCkpEXaIpJoYGBjg2rVraNmyJQwMDLB7925hh8QnIiKCgIAA9O/fH1ZWVjhz5kyVlt+0aVMEBwfjwoUL1TJQFSG1BSW25Ld04sQJrFu3DmvWrEGvXr0AAHfv3oWRkRGaNm1abUktl8vFoUOHYGBggG7duuHixYv4999/kZOTAw8PD6iqqlZ5nYQQQipGTk4Os2bNwv379xEREQExMTFYW1tDQ0MDGzduxPv374UdIqkGLVu2xJkzZ2BnZ4eJEydi+vTpKCoqEnZYAABxcXEEBwejV69eGDx4MK5evVql5ffq1QurV6/G2rVrcfLkySotm5Dagpoik99OdnY2tLW10bNnTxw5cgQiIiK4c+cOjI2N0bx5c8TGxkJeXr5K6/zw4QN2794Nb29vZGZmwsDAAC4uLvj7779piH1CCKkDkpOT4enpiYMHD0JKSgr29vaYNWsW1NTUhB0aqQY7d+6Eo6Mj9PT0EBYWhhYtWgg7JABAXl4e+vXrh7S0NJw5cwadOnWqsrJ5PB6GDBmCCxcuICUlBcrKylVWNiG1ASW25LdSVFQEQ0NDPH36FCkpKWjcuDFu374NY2NjtGjRAjExMVWa1GZlZcHb2xt+fn4oKCiAjY0NXFxcoKurW2V1EEIIqTlPnz7F1q1bsX37drx79w5DhgyBi4sLevfuTVOw/WYuXboEKysrMMYQFhaGnj17CjskAMDbt29hbGyM58+fIzExEerq6lVW9ps3b6Ctrc1/ek2DV5LfCTVFJr+VhQsX4tq1awgJCUHjxo1x69YtGBsbQ1FRscqe1DLGcP78eQwfPhxt2rTBnj174OjoiKysLOzfv5+SWkIIqcMUFRWxatUqPH78GD4+Prh79y769OmD7t27IzAwEMXFxcIOkVSRv/76C9euXUObNm1gZGSE7du3ozY872nUqBGio6NRv359mJmZIScnp8rKbty4MYKDg3H16lUsXLiwysolpDagxJb8No4cOYKNGzdi/fr10NfXx82bN2FsbIyWLVsiNjaWP4BUZRUXFyMoKAj6+vro3bs3bt26ha1bt+Lx48dYvXo1DaFPCCG/EWlpaUydOhW3b9/GyZMn0ahRI4wZMwatW7fG2rVr8ebNG2GHSKqAgoICYmNjMXXqVEybNg2TJk3C58+fhR0WmjVrhpiYGDDG0LdvX7x48aLKyv7rr7+wfv16eHh44MiRI1VWLiHCRk2RyW8hMzMT2traMDExQXh4OG7evAkTExMoKyvj9OnTaNKkSaXLfvv2LXbs2IEtW7YgJycHZmZmcHFxgYWFBX9eXEIIIb+/W7duwcvLC/v374eoqCjs7Ozg5OSEdu3aCTs0UgX27NmDqVOnokuXLggPD68V8xynp6fDwMAALVq0QHx8PBo2bFgl5TLGMGzYMCQkJCA5ORmtW7euknIJESZKbEmdV1hYiN69e+P169dITk7Go0ePYGpqChUVFZw+fRqNGzeuVLnp6enYtGkT/P39UVJSgjFjxsDZ2RldunSp4iMghBBSl7x48QLbt2/H1q1b8eLFCwwcOBAuLi4wMTGhfrh13LVr1zBs2DAUFBQgNDQUhoaGwg4JN2/ehKGhITp06IDo6GjIyMhUSbnv3r2Djo4OmjZtinPnzkFCQqJKyiVEWOhxE6nz5s2bhxs3biA0NBRZWVkwMTGBqqpqpZJaxhji4+Px999/o127dggJCcG8efOQnZ2N3bt3U1JLCCEEzZo1w7Jly/Do0SPs3r0b2dnZMDMzg5aWFvz9/VFYWCjsEEkl6erqIikpCZ06dYKpqSm8vb2F3u+2c+fOiIyMxI0bN2BpaVllTaUbNmyIkJAQXL9+HfPmzauSMgkRJkpsSZ0WFhaGzZs3Y+PGjRAREYGJiQnU1NQqnNQWFhZiz549/ObMWVlZ8PPzQ3Z2Ntzc3KplzltCCCF1W7169TBhwgRcv34dMTExUFJSgr29PZSVleHu7l6l/SJJzZGXl0d0dDScnZ3h5OSEcePG4dOnT0KNqXv37jh27BjOnTuHUaNGoaSkpErK1dPTg4eHB7y9vREeHl4lZRIiLNQUmdRKeXl5ePDgAQoLCyEpKQl1dXXIysoKbPPgwQPo6OjAwsICCxYsQN++faGuro7o6Ohy90F5+fIlvznZ8+fPMWDAALi4uMDU1JSakxFCCKmwrKwsHDx4EMePHwePx4OFhQVGjx5dpVO2kJoTFBSEiRMnon379jh06BBUVVWFGs+JEydgaWmJkSNHYs+ePVUy1gdjDDY2NoiKikJycjLatGlTBZESUvMosSW1xp07d7B9+3ZER0bi/oMHAk1/OBwO2qqro5+FBRwcHKCmpoYePXogLy8Pfn5+sLS0RNu2bREVFVWupPb27dv8AUA4HA5/AJD27dtX4xESQgghpK65fv06hg4dig8fPuDgwYMwMzMTajzBwcEYNWoUHBwcsHXr1iq5Ef/hwwfo6upCTk4OFy5cQL169aogUkJqFiW2ROgyMzMxzcEBUdHRaCQtjZ4tWkCjUSMo168PSTExFJaUIPvDB6S/fYsLubl4++kTVJWV8fTZM+zevRuOjo5o164doqKi0KBBg+/WwxhDVFQUPD09ER0dDUVFRTg6OmLKlCm/NGoyIYQQQn5vb968wejRo3H69GmsWbMG8+bNE2rLrl27dmHSpEn4559/sGbNmiopMzU1FX/99Rfs7e3h4+NTJWUSUpMosSVC5efnB+dZsyArJoZxmpro3aoVxH/QrKaYx8O5nBz437yJD8XFEBUTQ9euXREVFYX69euXuU9BQQH27dsHLy8v3L17F7q6unBxcYG1tTWNAEgIIYSQcuFyuVi2bBlWr16NESNGYNeuXaW6SdUkT09PzJ49G6tXr8bChQurpExfX184ODggKCgII0eOrJIyCakpNHgUEZpVq1Zh8uTJ6K2ggK2mpjBWVv5hUgsA4iIiMFZWxvZ+/WDcqhUKCgpgZmZWZlKbm5uLJUuWQElJCQ4ODmjfvj3Onj2Lq1evYsyYMaWSWlVVVYwfP57/OiEhARwOBwkJCVVxuHxubm7Uf5cQQgipY0RFRbFq1SqEh4fj5MmT6NGjBx48eCC0eFxcXODm5oZFixZhy5YtVVLmlClTMHr0aEyePBlpaWlVUiYhNYUS299EYGAgvLy8hB1Gufn5+WHJkiWw7dgRTnp6kBYXr9D+0uLicNLTw9iOHbFy5Urs2rWLvy4lJQXjxo2DiooKNm3ahLFjx+LBgwc4dOgQDAwMKKkkhBBCSKUNGzYMly9fRmFhIbp164aTJ08KLZZly5bBxcUFM2fOxJ49e365PA6HA19fX7Rs2RIjRoxAQUFBFURJSM2gxPY3UZcS28zMTDjPmgXz1q0xSlPzl8oa1aEDzFu3xqyZM7Fjxw4YGRlBR0cHZ8+exdq1a5GTkwMvLy+oqalVUfS/bsmSJfRFQQgh3zAyMgKHwwGHw0Fqamq59wsICBAYNNDNzQ1aWlpVHh/5PX35myvvbApfaGpq4urVqzAwMMCgQYOwcuVK8Hi86gnyBzgcDjw8PDBp0iTY29vj0KFDv1ymrKwsQkNDkZ6ejlmzZlVBlITUDEpsSY2b5uAAWTExTO7atVL7M8ZQyOUC+O8DfXLXrpAC4DB1KoqKihAaGooHDx5g9uzZPxxMSljExMRotEFCCCnD5MmTkZubi06dOgH4b+qciraymTt3LmJjY6sjvFohICAARkZGFdrHyMgIAQEB5dpWVVW1xm6Uf+ny8+7duxqpD/jv+L7uYpSbm1vp423QoAEOHz4MV1dXLF26FFZWVvjw4UPVBFoBHA4H27dvh7W1NUaOHImoqKhfLrNz587YsmUL/Pz8sG/fviqIkpDqR4mtECUkJEBPTw/16tVDmzZt4OvrW2b/y/3790NXVxdSUlJo3LgxRo4cicePH/PXGxkZ4cSJE3j06BH/zmNF5llLSkqCubk5mjZtCikpKbRu3Rr29vYCcZbV1/TLD46vvyzHjx8PWVlZPHz4EObm5pCRkYGioiKWL18Oxhju3LmDqOhojNPURD0xMRy+fx8OUVEYEh6O0UePYvO1a/hYVCRQz/gTJ+B67hyuPXuGWTExsDx0CKcyMgAAeUVF2H/7Noq5XDAADx8+xNGjRwW+JAsLC+Hq6gp1dXVISkpCSUkJ8+fPR2FhYbnfo/IqLi6Gu7s7NDQ0UK9ePTRp0gS9e/fG6dOn+dt8e47Hjx/PP2/f/nNzcxPKcRBCiDBIS0tDQUEBYmJilS5DVlb2hyPdF33zHUMqjsvlCuXp5I8UFxdXaj8FBYVfugkuIiICV1dXHD16FHFxcdDX18e9e/cqXV5liYqKYt++fejXrx+GDh2Kc+fO/XKZEyZMgJ2dHRwcHHDnzp0qiJKQ6kWJrZCkpKTAwsICr1+/hru7OyZOnIjly5fj8OHDAtutWrUK48aNg4aGBjZu3AhnZ2fExsaiT58+/ORt8eLF0NLSQtOmTbFv3z7+CMDl8eLFC/Tr1w9ZWVn4559/sHnzZowZMwaXLl2q9LFxuVxYWFigefPmWL9+PXR1deHq6gpXV1ds374djaSl0btVK2y+dg27btyAZpMmmKqlhb6qqoh/9AhLz55FyTdfmE8+fsS6S5eg3bw5pmppQa1hQxSUlGBefDyOPniAvxQVISb/HJgAAF5zSURBVC0ujtatW+PevXvIyckBAPB4PPz999/4999/MXjwYGzevBmWlpbw9PSEjY1NpY/xe9zc3ODu7g5jY2Ns2bIFixcvhrKyMpKTk7+7z9SpU/nn7cu/MWPGAACaNWsmlOMghJDaKiAgAMrKypCWlsbQoUPx+vVrgfXfNkUeP348LC0tsWrVKigqKqJdu3Y/rUNVVRWrV6+Gvb095OTkoKysjB07dghss2DBArRt2xbS0tJQU1PD0qVLBZKrL3Hs3r0bysrKkJWVxfTp08HlcrF+/XooKCigWbNmWLVqlUC57969w6RJkyAvL4/69evDxMQE169fr8Q7VTbGGNzc3KCsrAxJSUkoKirym5saGRnh0aNHcHFx4d9gBf6/uffRo0ehqakJSUlJZGdnw8jICM7OzgLlW1paCgzEWFhYiAULFkBJSQmSkpJQV1fHrl27kJWVBWNjYwBAo0aNwOFw+PuV9dRYS0tL4GYvh8PBtm3b8Pfff0NGRob/Ph45cgQ6OjqoV68e1NTU4O7ujpKSkip7/75n8ODBuHr1KjgcDrp3744jR45Ue53fEhcXR2hoKPT19TFw4MAf/vYoDw6Hg61bt0JVVRXW1tbIz8+vokgJqR6VvyVKfomrqytERUVx/vx5KCoqAgBGjBiBDh068Ld59OgRXF1dsXLlSixatIi/fNiwYdDW1oaPjw8WLVqEvn37omXLlnj79i3Gjh1boTguXLiAt2/fIjo6Gnp6evzlK1eurPSxff78GRYWFvD29gYATJ8+HYMHD8a6deug1KoVerZogftv3iAqMxPz9PVhrKzM37dLs2ZYmpiIxJwcgeVP8/KwwsAAugoK/GX7b9/Gow8fsKRnT/Rs2RLioqLIePMGd9PS8GUWq8DAQMTExODMmTPo3bs3f99OnTrBwcEBFy5cQM+ePSt9rN86ceIEBgwYUOoH0I/06NEDPXr04L9+8OABHB0d0bdvX0ydOlUox0EIIbXR5cuXMXHiRKxZswaWlpaIjIyEq6vrT/eLjY1F/fr1BVrP/IyHhwdWrFiBRYsWISwsDNOmTYOhoSE/MZaTk0NAQAAUFRVx8+ZNTJ48GXJycpg/fz6/jIyMDJw6dQqRkZHIyMjA8OHD8fDhQ7Rt2xZnzpzBhQsXYG9vDzMzM+jr6wMArK2tISUlhVOnTqFBgwbw9fWFqakp7t+/j8aNG1fwHSstPDwcnp6eOHjwIDp27Ihnz57xE+dDhw6ha9eumDJlCiZPniyw36dPn7Bu3Tr4+fmhSZMm/BuvPzNu3DhcvHgR3t7e6Nq1KzIzM/Hq1SsoKSkhPDwcVlZWSEtLQ/369SElJVWhY3Fzc8PatWvh5eUFMTExJCYmYty4cfD29oaBgQEyMjIwZcoUACjX38mvatu2LS5fvsy/mbJ06VK4ublB5CczPlQlKSkpHD16FGZmZjA3N8fZs2cFfltWlIyMDEJDQ9GtWzfMmDGj3E3aCREGemIrBFwuFzExMbC0tOQntQCgrq6O/v37818fOnQIPB4PI0aMwKtXr/j/FBQUoKGhgfj4+F+O5ctgCcePH690M56yODo68v/P4XDg6OiIoqIiZDx8CI1GjZD4+DFkxMWh07w53hcW8v+pN2oEKTEx3HjxQqA8BRkZgaQWAM7n5ECtQQP0bNkSAKDRqBHS0tORl5fHv8scGhqKDh06oH379gLvoYmJCQBUyXv4tYYNG+L27dtIT0+v1P75+fkYOnQoGjVqhKCgIIiKigKo+eMghJDaQFVVlX+jEgA2bdoECwsLzJ8/H23btsWsWbNgbm7+03JkZGTg5+eHjh07omPHjuWqe8CAAZg+fTrU1dWxYMECNG3aVOCzdsmSJejZsydUVVUxePBgzJ07FyEhIQJl8Hg87N69G5qamhg8eDCMjY2RlpYGLy8vtGvXDhMmTEC7du345Z47dw5XrlxBaGgo9PT0oKGhgX///RcNGzZEWFgYgP+eQFd0GrqEhAT+09Ds7GwoKCjAzMwMysrK6N69Oz+Jbdy4MURFRSEnJwcFBQUofPW9W1xcDB8fH/Ts2RPt2rWDtLT0T+u9f/8+QkJCsHv3bgwdOhRqamowNTWFjY0NREVF+Yl6s2bNKtUkePTo0ZgwYQLU1NSgrKwMd3d3/PPPP7Czs4Oamhr69u2LFStWwNfXl79PVlZWhfsoV4ScnBzCwsKwevVqrFy5EoMHD67RPsRfYjh16hT/PGdmZv5SeZqamti+fTv27NkDf3//KoqSkKpHT2yF4MWLFygoKIC6unqpdV8vS09PB2MMGhoaZZYjXsEpcspiaGgIKysruLu7w9PTE0ZGRrC0tMTo0aMhKSlZqTJFRERKjULctm1b/v+V69fHuZwc5BcXY9TRo2WW8f6bfqPNZWRKbZObl4derVoJlMsYw4MHD6ClpQXGGG7duoWHDx9CXl6+zHpefJNA/6rly5djyJAhaNu2LTp16gQLCwvY2tqiS5cu5dp/8uTJyMjIwIULFwT6iKWnp+Pu3bs1dhyEEFIb3b17F0OHDhVY1qNHD0RGRv5wv86dO5eau/xnvv7c5nA4UFBQEPisDQ4Ohre3NzIyMpCXl4eSkpJSc6qrqqpCTk6O/7p58+YQFRUVeILXvHlzfrnXr19HXl5eqT7CBQUFyPjf2BK/ytramj9bgIWFBQYMGIDBgwf/tF+zhIREub/LvkhNTYWoqCgMDQ1/JeTv+rqlGfDf+3f+/HmB5t1cLhefP3/Gp0+fypWMVwUOh4OFCxdCW1sbo0ePRrdu3RAREcEfFK0mNG7cGKdPn4aBgQFMTU2RmJiIlv97EFAZtra2OHv2LKZPnw49PT107ty5CqMlpGpQYluL8Xg8cDgcnDp1iv/k7muysrK/XAeHw0FYWBguXbqEY8eOISoqCvb29vDw8MClS5cgKyv73REpuf8bmbiiJMXEwBhDQ0lJzPtf06tvNfgmqZYo4/hLlfu/bW7cuIGQkBAEBQUhKyuLv37cuHGwtbUV2EdJSamC0f9Ynz59kJGRgSNHjiA6Ohp+fn7w9PTE/7V353E15f8fwF8t2pWyJNo3+1KYsStb2WYqsg7JWqS6M5hByDpjTCr7yBrGLsYaRpRlGLuR0aKEQohKSXXP7w/f7s/VjUp1i9fz8ejx0L2f8zmfc1HndT7nvD+rV6/GmDFjPrhtcHAwtm3bhi1bthRaqkIsFqNZs2ZYsmSJzG3L+jiIiD4nmjIujn7M+xePFRQUJAWTzp8/j2HDhmHOnDlwcHCAjo4Otm/fjoCAgI/28aF+MzMzYWBgIHNGtqTL0RTFyMgId+7cwYkTJ3D8+HFMmDABixcvxunTpz94wVxdXb3Q+YCioqLUjDogXcSppLcWF7ffAu//vWZmZmLOnDlwcXEp1FYeqxE4Ojri0qVLcHZ2Rtu2bbF+/XoMHDiwwvZft25dnDhxAh07dkSPHj0QGRmJWrVqlbq/pUuX4uLFi3B1dcU///wjddGGqDJgsJWDOnXqQE1NDXFxcYXee/c1CwsLCIIAMzMzqRlPWUq6HML72rZti7Zt22LBggX4448/MGzYMGzfvh1jxoyBrq4uABS6lebevXsy+xKLxZJniArExMRI/pyTl4e6Wlq4+uQJGteqJQmkJWWgpYV7L1/+f7//C9pubm4y29vb26N79+6l2ldJ6Onpwd3dHe7u7sjMzETnzp3h7+//wWAbFRWFyZMnw9fXV1I46l0WFha4fv06unXr9sl/10REVVWjRo1w4cIFqdc+pdhhaZ07dw4mJiaYMWOG5LWifieWhK2tLR49egRlZeUSrW5QUurq6ujXrx/69euHiRMnomHDhrh58yZsbW2hoqJS7AvXtWvXRkpKiuT7/Px8/Pvvv5KiUM2aNYNYLMbp06dl/v4tmEV/f3/v95uenl6s22ltbW1x584dmXfEyYu5uTnOnTuHsWPHYtCgQbh8+TIWLFjwSZW/S8LExAQnTpxAp06d4OjoiL/++qvUVaDV1dWxa9cutGrVCh4eHtiyZQvPSahS4TO2cqCkpITu3btj3759SE5OlrweFxeHI0eOSL53cXGBkpIS5syZU+jKpSAIUpUgNTU18fKdkFdcaWlphfoumC0sWEbGxMQESkpKiIyMlGq3cuXKIvtdvny51FiXL18uuRKclJ6OzkZGEAsCtskoH58vFiOzGMsxdDA0xN2XL3Hu4UNJvwoKCkX+wHZ3d0f9+vVhb2+P8ePH4+eff8bOnTvx33//ldnyD+9X59TS0oKlpeUHl+RJSUnBwIED0bFjRyxevFhmm4EDB+Lhw4cICQkp9F52djYrFRLRF8Hb2xtHjx7Fb7/9htjYWCxfvvyjtyGXBysrKyQlJWH79u2Ij4/H0qVLERYW9sn9du/eHe3atYOTkxOOHTuGxMREnDt3DjNmzMClS5fKYORvKxyvW7dO8qjOli1boK6uDhMTEwBvb5+OjIzEw4cP8fTp0w/21bVrVxw6dAiHDh3Cf//9B09PT6mL4KampnBzc8OoUaOwb98+JCQk4NSpU5JnkU1MTKCgoICDBw8iNTUVmZmZkn43b96MqKgo3Lx5E25ubjLvXHvfrFmzEBoaijlz5uDWrVu4ffs2tm/fDj8/v1J+WmVDU1MTW7duxZIlSxAQEIBevXoVOl8oTw0aNMDx48cRHx+Pvn37Iisrq9R9WVtbIyQkBH/88YfMcxIieeKMrZz4+/vj2LFj6NChAzw9PZGfn4/ly5ejadOmuHbtGoC3s3Tz58/HtGnTkJiYCCcnJ1SvXh0JCQkICwvDuHHjMHnyZABAq1atsGPHDnz//fdo06YNtLS00K9fv4+OY9OmTVi5ciWcnZ1hYWGBjIwMhISEQFtbG7179wbwdgFyV1dXLFu2DAoKCrCwsMDBgweLfK5TTU0NR48ehZubG77++mscOXIEhw4dwvTp07Fn1y7EpqWhp5kZepmbY+d//+Huixew1deHkqIikjMzceb+fYy3sUHHd56flaV/gwY48+ABFp4/j56mpniQkYFaNWvC1MwMhoaGhU4y2rRpg5o1a+Lx48fYu3ev1C9sRUVFKCoq4vjx4/D29oa1tTWys7MBlOyW68aNG8POzg6tWrWCnp4eLl26hN27d0sV03qft7c3UlNTMXXqVGzfvl3qvebNm6N58+YYPnw4du7cCQ8PD0RERKBDhw7Iz8/Hf//9h507dyI8PLzQs0ZERJ+btm3bIiQkBLNnz8asWbPQvXt3+Pn5Yd68eRU6jm+++QYikQheXl7IyclBnz59JBVwP4WCggIOHz6MGTNmwN3dHampqahbty46d+4MfX19mducOnUK9vb2SEhIKNYsb40aNfDLL7/g+++/R35+Ppo1a4YDBw5InuudO3cuxo8fDwsLC+Tk5BS6+P2uUaNG4fr16xgxYgSUlZUhEokks7UFVq1ahenTp2PChAl49uwZjI2NJSs91K9fX1Lwyd3dHSNGjMDGjRsxbdo0JCQkoG/fvtDR0cG8efOKNWPr4OCAgwcPYu7cuVi0aBGqVauGhg0bfvRRoIqgoKAAkUiEli1bYuDAgWjdujX27t0LGxubCtl/ixYtcPjwYfTo0QMuLi7Yv39/qWupDB48GJGRkfD29sZXX31V6PEpIrkRSG7++usvwcbGRlBRUREsLCyEtWvXCj/88IOgpqYm1W7Pnj1Cx44dBU1NTUFTU1No2LChMHHiROHOnTuSNpmZmcLQoUOFGjVqCAAEExOTYo3hypUrwpAhQwRjY2NBVVVVqFOnjtC3b1/h0qVLUu1SU1OF/v37CxoaGoKurq4wfvx44d9//xUACBs2bJC0c3NzEzQ1NYX4+HihZ8+egoaGhqCvry/Mnj1byM/PFyZNmiToamgI+/v3Fw67ugrerVoJlrq6gqqSkqCurCyY6ugIAxo0ELb07SscdnUVDru6CnU0NIQ2BgaS79/92vHtt0I/S0uhppqaAEDQ0tIS3NzchKdPnwr79+8XdHV1BQCChoaG0KRJE0FVVVXQ1dUVWrVqJfj7+wt37twRTp06JYSEhAja2tqCkZGR0LhxY0FFRUUAIAAQlJWVhcaNGwvffvutMGXKFCEkJEQ4ffq0kJycLIjFYqnPaf78+cJXX30l1KhRQ1BXVxcaNmwoLFiwQHjz5o2kzezZs4V3/+t16dJFsq/3v2bPni1p9+bNG2HRokWFjmPOnDnCy5cvi/X3TURUWXXp0kXw8fGR9zCqnPXr1wuWlpZSv2eoZDZs2CDo6OhU2P7u3bsntGrVSlBTUxM2b95cYfsVBEE4ceKEoKKiIvTv31/Izc0tdT/Z2dmCjY2NYGlpyXMQqjQUBOEDl+Kowjk5OX3ScjHyNnLkSOzevVtyO9H7oqOj0aRJk0Lr136qiKQkLL5wAdHR0VLrtT158gQeHh6oWbNmiW6Zyc/PR1JSEmJjYxETE4OYmBjJnxMTEyWFPrS0tGBtbQ0rKytYW1tL/bng2WQiIvo4Ozs7nDt3DioqKjh//jyrrhaTq6srBg4cCFdXV3kPpUrS0tJCXl4e1NTUKnRZnuzsbHh6emLTpk3w8fHB4sWLy2S1i+L4888/4eLiguHDh2PdunWlXmc3Pj4etra2cHBwwI4dO/i8Lckdg60cZWdnS1UMjI2NRZMmTeDm5lZln1v4WLAFAEcHB1w7fx4runWDRhn8EM/KzcXEv/5Cy3btcDQ8/JP7+5icnBwkJCQUCrwxMTFSz0zXqlVLZuC1tLQsVYVOIqLP2cOHDyWPgBgbG5d4eZ7iioqKkloz/n0f+v1Fn5+Cop1KSkowMzOr0H0LgoCVK1fC19cXHTp0wM6dO1GnTp0K2fcff/yB7777Dl5eXggODi51KN2zZw8GDBiA5cuXY+LEiWU8SqKSYbCVIwMDA4wcORLm5ua4d+8eVq1ahZycHFy9erXItWtLIjU19YPPh6qoqEgWRy8rxQm2CQkJaNakCTrWrQufT3wuVBAELL18GWcePcLNW7fK7ZdSdnb2R4tz6enp4c2bN4iLi5MZep8/fy5pW79+/UKB19raGmZmZuV2MkdERG9/nj/8X9FBWSpTRV36MkRFRcHV1RXVqlXD3r170aZNmwrZ7++//w4PD49Pfk7dx8cHq1evxtmzZ1nvg+SKwVaO3N3dERERgUePHkFVVRXt2rXDwoULYWtrWyb9m5qafnD5gS5dushcK+9TFCfYAsDatWsxduxYDG/SBEMaNy7VvgRBwLbbt7Hl1i2sXbsWo0ePLlU/xbFx40a4u7t/sE1ERATs7OyKfP/Zs2eSoPv+Lc4FVY2VlJRgamoqM/QaGRmV+nYhIiIiqrwePnyI/v3749q1a1i5ciVGjRpVIftdvHgxpk6dikWLFmHq1Kml6uPNmzfo2LEjnj59iitXrpTZmstEJcVg+xk7e/as5LYuWXR1ddGqVasKHJG0BQsWwM/PDw5mZhjbokWJbkvOys1FyPXrCE9IwIIFCyQVFstLSkoKbt269cE2rVq1KtVztYIgICUlRWbgjYuLkyxKr6qqCktLS5nP9Orr6/PZFiIioiosJycH3t7eWLNmDTw9PREUFFQhd3HNnDkT8+fPx6pVq+Dh4VGqPhITE2FjYwN7e3vs2bOH5yQkFwy2JFdr166Fr7c3tJSVMaJxY3Q0NES1D8xK5ubn48zDhwiNjkZmXh6Cly0r15laecvLyytUxKog9CYmJkqWYahevXqRRax45ZSIiEpLEATJF/B22ZqCLyofISEh8PLyQuvWrbF7924YGBiU6/4EQYCPjw+WL1+OzZs3Y9iwYaXqZ//+/XByckJgYCB8fX3LdpBExcBgS3KXkJAATw8PhB87Bl0NDbQ3MICVri6MtbWhqqSEnPx8JKWnIzYtDedSUpCWlQWHnj2xavXqCi/0UJm8fv0ad+/elVm5OSUlRdKudu3aRRax0tDQkOMREBFRVZGbm4uEhATExsYiMzMTtWrVgrW1NerXr8/HZMrB33//jf79+0MQBOzevRvt27cv1/2JxWKMHj0amzdvxp49e/Dtt9+Wqp/JkycjODgYUVFRaNu2bRmPkujDGGyp0oiOjsbq1atxPDwcd2JjpRaFV1BQQAMrK/RwcICnp6fUkj5UWEZGhswiVnfu3JFazsDIyEhm6DUzM6uwZQeIiKjqyM/Px4EDBxAYGIjIyEiYmprC29sbo0ePhra2tryH91l59OgRXF1dceHCBQQHB8PDw6NcZ8rz8vIwZMgQ/Pnnnzh06BC6d+9e4j5yc3PRpUsXPHz4EFevXi3zIqVEH8JgS5VSZmYm4uLikJOTI3m2VEtLS97DqvIEQZAqYvVu6I2NjUVWVhaA/1/2QFYRK0NDQ16dJyIiXL58GUFBQdi+fTvU1dUxevRoeHt7f9F3U5W1N2/e4IcffsDy5csxatQorFixAmpqauW6v2+//RaRkZE4ceIE2rVrV+I+7t+/j5YtW6J9+/bYv38/zxmowjDYEhGAt6E3OTlZ5lJFd+/elRSxUlNTk1nEytraGrVr1+ZzV0REX5jk5GSsWLECq1evxosXL+Dk5ASRSIQOHTrwd0IZ2bRpEzw8PNCsWTPs2bMHRkZG5bavrKwsODo64ubNm4iIiEDLli1L3Mfhw4fRp08f/Prrr5gyZUrZD5JIBgZbIvqovLw83Lt3T2bl5nv37kluG9fW1pY5y2tlZQUdHR05HwUREZWnrKwshIaGIigoCHfu3EHr1q0hEokka7TSp7l8+TJcXFyQnZ2NXbt2oUuXLuW2r/T0dHTt2hVJSUmIiopCgwYNStzHtGnTsHjxYpw+fRodOnQoh1ESSWOwJaJP8vr1a8THx8us3Pzo0SNJuzp16sgMvJaWllBXV5fjERARUVkSi8U4evQoAgMDceLECdSvXx9eXl4YN24cn7n8RKmpqRg8eDBOnz6NgIAAeHt7l9us+NOnT9GlSxekp6fjzJkzMDExKdH2eXl56Nq1K+7evYurV6+idu3a5TJOogIMtkRUbtLT0xEbGyvzmd53i1gZGxvLLGJlamrKq/xERFXYzZs3ERQUhK1bt0JJSQlubm7w9fWFtbW1vIdWZeXl5eGnn35CQEAAhg0bhjVr1pTbKgfJycno1KkTFBQUEBUVVeKlhx4+fAgbGxvY2tri8OHDfN6WyhWDLRFVOEEQ8PTp0yKLWGVnZwMAlJWVYW5uLjP0cokJIqKq48mTJ1i1ahVWrlyJJ0+eoE+fPhCJROjatSufwy2l7du3Y9SoUWjQoAHCwsJgampaLvtJSEhAx44doaenh9OnT5d41v3YsWNwdHTE/PnzMX369HIZIxHAYEtElYxYLP5gEau8vDwAgLq6uqSI1fu3ONeqVYsnSkREldDr16+xbds2BAYG4ubNm2jevDlEIhGGDBkCVVVVeQ+vyrlx4wacnZ3x4sULbN++HT169CiX/dy+fRudO3eGmZkZ/vrrL1SvXr1E28+aNQsLFizAyZMny/XZYPqyMdgSUZWRl5eHxMREmaH3/v37kiJWOjo6RRax4jqLRETyJwgCTp48icDAQBw6dAj6+vqYMGECPDw8UKdOHXkPr0p5/vw5hg4diuPHj+Pnn3/GlClTyuXi7pUrV2Bvbw8bGxscOXKkRPUx8vPz0aNHD9y+fRvXrl2Dvr5+mY+PiMGWiD4L2dnZiI+Pl1m5+fHjx5J2+vr6MpcqsrCwKNe1AYmISLY7d+4gODgYGzduhFgsxnfffQdfX180bdpU3kOrMvLz8zFr1iwsXLgQAwcOxLp166ClpVXm+zl79ix69uwJe3t77N27FyoqKsXe9tGjR2jZsiWaNm2K8PBwKCkplfn46MvGYEtEn72XL1/KLGJ1584dpKenAwAUFBRgbGwsc5bX1NQUysrKcj4KIqLP27NnzxASEoJly5YhOTkZPXr0gEgkgoODA2sqFNPevXvh5uYGU1NThIWFwdLSssz3cezYMfTr1w9OTk74448/ShRQT548iR49emDWrFmYPXt2mY+NvmwMtkT0xRIEAampqUUWsXr9+jWAt0WsLCwsZBaxqlevHk+4iIjKUG5uLnbt2oXAwEBcunQJDRs2hK+vL4YPH15u1X8/J9HR0XByckJqaiq2bt2K3r17l/k+wsLC4OrqipEjRyIkJKREtz7PmzcPs2fPxvHjx9GtW7cyHxt9uRhsiYhkEIvFePDggczQe/fuXeTn5wMANDQ0iixiVbNmTRaxIiIqJUEQcPbsWQQGBmLfvn3Q1dXF+PHjMXHiRNSrV0/ew6vUXr58ieHDh+PgwYOYM2cOZsyYUeYXYUNDQyXLNy1ZsqTYv+/y8/PRq1cvXL9+HdeuXSvxEkJERWGwJSIqodzc3A8WsSpQo0YNqed4C0KvlZVViStKEhF9ye7evYulS5di3bp1yMnJwaBBgyASiWBrayvvoVVaYrEY8+bNg7+/P7799luEhoaWeQHFFStWwMvLC7Nnz4a/v3+xt3vy5AlsbGxgZWWFEydO8HEfKhMMtkREZSgrK0tSxOr90JuamippV7duXZmzvBYWFlzygoioCC9fvsS6deuwdOlS3Lt3D507d4ZIJEK/fv1YjKgIBw4cwHfffQcDAwPs27cPDRs2LNP+f/75Z0yfPh0BAQH4/vvvi71dZGQk7O3tMW3aNMyfP79Mx0RfJgZbIqIK8uLFC6lneN8Nv+8WsTIxMZFZxMrExIRXtYmI8Hb5t3379iEwMBDnzp2DhYUFvL294e7uzjtiZIiJiYGTkxMePHiA0NBQODk5lWn/06ZNwy+//IKQkBCMGTOm2Nv98ssvmDZtGo4cOQJHR8cyHRN9eRhsiYjkTBAEPHnyRGbgjY2NRU5ODgCgWrVqsLCwkBl669Wrx+d5ieiLdPHiRQQGBmLXrl3Q0tLCmDFjMGnSJJiYmMh7aJVKRkYG3N3dsWfPHvj5+cHf37/MZrkFQYCXlxdWrVqFbdu2YdCgQcXaTiwWo1+/frhw4QKuXbsGQ0PDMhkPfZkYbImIKjGxWIz79+/LLGKVkJAgKWKlqakJKysrmZWba9asKeejICIqf/fv38fy5cuxZs0aZGRkwMXFBSKRCO3atZP30CoNQRCwaNEiTJ8+HY6Ojti6dSt0dXXLpG+xWIyRI0di27Zt2LdvH/r06VOs7Z4+fQobGxuYmJggIiIC1apVK5Px0JeHwZaIqIp68+YNEhISZIbeBw8eSNrp6enJDLxWVlbQ0tKS4xEQEZW9zMxMbNq0CUFBQYiLi0Pbtm0hEong4uLCxzn+Jzw8HEOGDIGenh727duHpk2blkm/eXl5GDhwIA4fPowjR47A3t6+WNudO3cOXbp0wffff49FixaVyVjoy8NgS0T0GcrKykJcXJzMIlZPnz6VtDMwMJBZxMrc3JxFrIioShOLxTh06BACAwMREREBY2NjTJo0CWPGjEGNGjXkPTy5u3v3LpydnREXF4cNGzZg4MCBZdJvTk4O+vXrh/Pnz+PEiRP4+uuvi7VdQEAAJk+ejAMHDqBv375lMhb6sjDYEhF9YdLS0oosYpWRkQEAUFRUlBSxej/0Ghsbs/ooEVUp165dQ1BQEP744w+oqKhg1KhR8PHxgYWFhbyHJlevXr3C2LFjsW3bNkydOhULFiwok1ntV69eoWfPnrh9+zZOnz6NZs2afXQbQRDg5OSEqKgoXL16lc9IU4kx2BIREYC3JxWPHz8uFHhjYmIQHx8vKWKloqJSZBErAwMDFrEiokrr0aNHWLlyJVatWoVnz57hm2++gUgkQufOnb/Yn12CICAoKAhTpkyBvb09tm/fXia1GV68eAF7e3ukpKQgKioKVlZWH90mLS0NNjY2qFu3LiIjI6GiovLJ46AvB4MtERF9VH5+Pu7fvy9zljchIQFisRjA2yJWsgKvtbU19PT05HwURERvZWdnY8uWLQgKCkJ0dDRsbGwgEokwaNCgLzZMRUREYODAgdDS0sLevXthY2PzyX2mpqaic+fOyMrKQlRUFIyNjT+6zcWLF9GxY0d4eXlhyZIlnzwG+nIw2BIR0Sd58+YN7t69K7OI1cOHDyXtatasKRV4C0KvlZUVNDU15XgERPSlEgQBx44dQ2BgIMLDw2FgYICJEydi/PjxqFWrlryHV+GSkpLg4uKCW7duISQkBN99990n9/ngwQN07NgRqqqqiIyMhL6+/ke3Wbp0KXx8fBAWFlbma+7S54vBloiIys2rV6+KLGL17NkzSbv69evLnOU1Nzf/YmdPiKhiRUdHIygoCJs3bwYAjBgxAr6+vmjUqJGcR1axsrOz4enpiU2bNsHHxweLFy/+5CV44uPj0alTJ9SuXRunTp366BJDgiDA1dUVJ06cwJUrV2Bubv5J+6cvA4MtERHJxfPnz2XO8sbExODVq1cA3haxMjU1lVnEysjIiEWsiKjMpaam4vfff8eKFSvw6NEjODo6QiQSoUePHl/Mc7iCIGDlypXw9fVF+/btsXPnzmLNtH7IrVu30LlzZ1hbW+P48eMfXW7u5cuXsLW1ha6uLs6ePctK/fRRDLZERFSpCIKAR48eyQy88fHxePPmDQBAVVVVUsTq/fV569at+8WcgBJR+cjJycGOHTsQGBiIa9euoUmTJvD19cV3330HNTU1eQ+vQkRFRcHV1RXKysrYu3cvvvrqq0/q79KlS+jatSvatGmDQ4cOffRzvHLlCtq1a4dx48Zh2bJln7Rv+vwx2BIRUZWRn5+PpKQkmZWb7927JylipaWlJXOW18rK6qO3wBERvUsQBJw+fRqBgYE4cOAAatWqBU9PT0yYMOGTZzGrgocPH6J///64evUqVq5cidGjR39Sf5GRkXBwcEDPnj2xe/fuj97mvGrVKkyYMAE7d+6Eq6vrJ+2bPm8MtkRE9FnIycnB3bt3ZVZuTk5OlrSrVauWzMBraWnJIlZE9EGxsbFYunQpNmzYgNzcXAwdOhS+vr5o0aKFvIdWrnJycuDt7Y01a9bAw8MDwcHBn1T/4MiRI/j222/h6uqK0NDQDz5WIggChgwZgsOHD+PKlSuwtLQs9X7p88ZgS0REn72MjAzExcUVCrx37txBWlqapJ2hoaHMys1mZmYsYkVEEmlpaVi7di2WLVuG+/fvo2vXrhCJROjduzcUFRXlPbxyExISAi8vL7Ru3Rq7du1CvXr1St3Xrl27MHjwYIwZMwarV6/+4OMj6enpaN26NTQ1NXH+/Pkv5lZwKhkGWyIi+qI9e/asyCJWWVlZAAAlJSWYmZnJrNxsZGT0WZ/IElHRcnNzsXfvXgQGBuLChQuwtraGj48P3NzcPts7QC5cuID+/fsjPz8fu3fvRocOHUrd14YNGzBq1ChMnjwZv/766wfD7fXr19G2bVu4ublh9erVpd4nfb4YbImIiGQQBAEpKSlSz/EWhN74+Hjk5uYCANTU1GQWsbK2tkadOnVYxIroC3H+/HkEBgZiz5490NHRwbhx4+Dl5QVDQ0N5D63MPX78GK6urvj7778RHBwMDw+PUv+sCw4Ohq+vL+bNmwc/P78Ptl27di3Gjh2LP/74A0OGDCnV/ujzxWBLRERUQnl5eZIiVu/P8t67dw8Fv1qrV68uFXj9/Pw+eT1IIqrcEhMTsWzZMqxduxZZWVlwdXWFSCRCmzZt5D20MpWbm4sffvgBy5Ytw6hRo7BixYpS3yI8f/58zJw5E8HBwfD29i6ynSAIGDFiBMLCwnDp0iU0bNiwtMOnzxCDLRERURl6/fp1kUWs7ty5g+rVq5eov5EjR+LFixfYt29f+Qz4PQoKCggLC4OTk1OF7I/oc5WRkYH169cjODgYCQkJ6NChA0QiEZycnD6rNbhDQ0Mxfvx4NGvWDHv27IGRkVGJ+xAEAVOnTsVvv/2G9evXw93dvci2mZmZaNOmDZSVlXHhwgVoaGh8yvDpM8JgS0REVEEEQSjx7XrFDba5ubllMhtcVsH2zZs3LLhFhLfLlP35558IDAxEVFQUTE1N4e3tjdGjR0NbW1vewysTly9fhouLC7Kzs7Fr1y506dKlxH0IggAPDw+sXbsWO3bswIABA4ps+++//+Krr77CkCFDsG7duk8ZOn1GWO2CiIiognwo1O7evRvNmjWDuro6atasie7du2PKlCnYtGkT9u/fDwUFBSgoKODUqVNITEyEgoICduzYgS5dukBNTQ1bt26Fv78/WrZsKdVvUFAQTE1NpV5bv349mjRpAlVVVRgYGMDLywsAJO2cnZ2hoKAg+X7kyJGFgq6vry/s7Owk39vZ2cHLywu+vr6oVasWHBwcALw9Ae3Vqxe0tLSgr6+P4cOH4+nTpyX+7IiqKiUlJTg7OyMyMhKXLl1Chw4dMHXqVBgaGkIkEiEhIUHeQ/xkrVq1wqVLl9CsWTN069YNwcHBKOncmYKCAlauXInBgwdj6NChOHr0aJFtmzZtilWrVmH9+vUIDQ391OHTZ4LBloiISM5SUlIwZMgQjBo1Crdv38apU6fg4uKC2bNnY+DAgXB0dERKSgpSUlLQvn17yXY//fQTfHx8cPv2bUmQ/JhVq1Zh4sSJGDduHG7evIk///xTsi7kP//8A+BtpdKUlBTJ98W1adMmqKio4OzZs1i9ejVevHiBrl27wsbGBpcuXcLRo0fx+PFjDBw4sET9En0uWrVqhS1btiAxMRFeXl4IDQ2FpaUl+vfvjzNnzpQ4DFYmtWvXRnh4OHx9feHr64vhw4dLKssXl5KSEjZu3AhHR0e4uLggMjKyyLZubm5wd3eHp6cnbt269anDp8+AsrwHQERE9KVLSUlBXl4eXFxcYGJiAgBo1qwZAEBdXR05OTmoW7duoe18fX3h4uJSon3Nnz8fP/zwA3x8fCSvFRS1qV27NgCgRo0aMvf3MVZWVvj111+l9mVjY4OFCxdKXlu/fj2MjIwQExMDa2vrEu+D6HNQv359LFy4EH5+fggNDUVQUBA6deqE1q1bQyQSwdXVtUoWmlNWVsZvv/2G1q1bY9SoUbh16xb27t0LMzOzYvdRrVo17Ny5E3369EHfvn1x8uRJtG7dWmbb5cuX459//oGrqysuXrwILS2tsjoUqoI4Y0tERCRnLVq0QLdu3dCsWTO4uroiJCQEaWlpH92uqJO9ojx58gTJycno1q1baYf6Qa1atZL6/vr164iIiICWlpbkq6CKaXx8fLmMgagq0dDQgIeHB6Kjo3Ho0CHUqFEDw4YNg5mZGRYtWlSsnwOV0eDBg/H3338jPT0drVu3xvHjx0u0vZqaGvbv348mTZrAwcGhyBlZDQ0N7Nq1C0lJSZgwYUKVnvGmT8dgS0REJGdKSko4fvw4jhw5gsaNG2PZsmVo0KDBR5+909TUlPpeUVGx0IldwXq7wNvZ39L4WL9FjSczMxP9+vXDtWvXpL5iY2PRuXPnUo2F6HOkqKiI3r174/jx47hx4wYcHBwwa9YsGBoaYuLEiYiJiZH3EEusefPm+Oeff9CmTRs4Ojri119/LVHw1NLSwuHDh2FoaIgePXoUeTGsYcOGWLNmDTZv3oz169eX1fCpCmKwJSIiqgQUFBTQoUMHzJkzB1evXoWKigrCwsKgoqKC/Pz8YvVRu3ZtPHr0SOrk8dq1a5I/V69eHaampvjrr7+K7KNatWqF9le7dm2kpKRIvfZuv0WxtbXFrVu3YGpqCktLS6mv90MwEb3VrFkzrFu3DklJSZgyZQp27dqFBg0aoF+/fjh58mSVmpXU09PDoUOH8NNPP+HHH3/EoEGDkJmZWeztdXV1cezYMWhpaaF79+548OCBzHZDhw7FuHHj4OXlhRs3bpTV8KmKYbAlIiKSswsXLmDhwoW4dOkSkpKSsHfvXqSmpqJRo0YwNTXFjRs3cOfOHTx9+lTmTGkBOzs7pKam4tdff0V8fDxWrFiBI0eOSLXx9/dHQEAAli5ditjYWFy5cgXLli2TvF8QfB89eiS5DbJr1664dOkSQkNDERsbi9mzZ+Pff//96HFNnDgRz58/x5AhQ/DPP/8gPj4e4eHhcHd3L3ZYJ/pS6evrw9/fH0lJSVi3bh3u3buHbt26oWXLlti4cSNycnLkPcRiUVJSwoIFC7Bnzx4cOXIEbdu2RVxcXLG319fXx4kTJyAWi9GjRw+kpqbKbBcUFIQGDRrA1dUVGRkZZTV8qkIYbImIiORMW1sbkZGR6N27N6ytreHn54eAgAD06tULY8eORYMGDdC6dWvUrl0bZ8+eLbKfRo0aYeXKlVixYgVatGiBixcvYvLkyVJt3NzcEBQUhJUrV6JJkybo27cvYmNjJe8HBATg+PHjMDIygo2NDQDAwcEBM2fOxNSpU9GmTRtkZGRgxIgRHz2uevXq4ezZs8jPz0fPnj3RrFkz+Pr6okaNGlBU5CkIUXGoqalh1KhRuH79Oo4fPw5DQ0O4u7vDxMQEc+fOLTLoVTYuLi64cOECcnNz0bp1axw6dKjY2xobG+PEiRNIS0uDg4MDXrx4UaiNuro6du3ahZSUFIwbN65KzWxT2VAQ+LdORERElUBMTAx+/fVXWFlZwdraGtbW1rCwsICampq8h0ZUqfz3338IDg7Gpk2bIBaL8d1338HX1xdNmzaV99A+6uXLlxg+fDgOHjyIOXPmYMaMGcW+0HXjxg3Y2dmhcePGCA8Pl/lIw86dOzFo0CCsWrUKHh4eZT18qsQYbImIiKhSSExMhKurK2JiYpCeng7g7bPHxsbGkqD7bug1MTGBsjJXLqQv17Nnz7BmzRosX74cycnJ6NGjB0QiERwcHCr1XRFisRjz58/H7Nmz8e233yI0NBTa2trF2vbChQvo1q0b2rdvjwMHDkBVVbVQGy8vL4SEhOD8+fOwtbUt6+FTJcVgS0RERJWKIAhITU1FTEwMYmJiEBsbK/lzXFwcXr9+DeBtoStzc/NCgdfKygr169eHgoKCnI+EqGK8efMGu3btQmBgIC5fvoyGDRvC19cXw4cPh4aGhryHV6SDBw9i2LBhMDAwwL59+yTLgX1MREQEevXqhd69e2Pnzp2FLnDl5OSgQ4cOSEtLw5UrV6Cjo1Mew6dKhsGWiIiIqgyxWIwHDx4UCryxsbG4e/eupCiVhoYGrKysCgVea2tr1KxZk6GXPkuCIODMmTMIDAzEvn37oKenh/Hjx2PixImoV6+evIcnU0xMDJydnXH//n2EhobCycmpWNsdPHgQzs7OGDJkCDZu3Fhohvru3buwtbVF9+7dsWvXLv6f/wIw2BIREdFnITc3FwkJCVKBtyD03r9/X9JOV1dXZuC1srJC9erV5XgERGXn7t27WLp0KdatW4ecnBwMGjQIIpGoUt6am5GRAXd3d+zZswd+fn7w9/eHkpLSR7fbvn07hg4dCk9PTyxfvrxQeA0LC4OLiwuWLl2KSZMmldfwqZJgsCUiIqLPXlZWFuLi4grN8sbExEhVlTUwMJAZei0sLGQ+y0dU2b18+RLr1q3D0qVLce/ePXTu3BkikQj9+vUrVnisKIIgYNGiRZg+fTocHR2xdetW6OrqfnS7tWvXYuzYsZg2bRoWLlxY6H2RSIQVK1bg7NmzaNOmTXkMnSoJBlsiIiL6or148UJm4I2JiZGsh6mgoAATE5Mii1hVpoBAJEteXh727duHwMBAnDt3DhYWFvD29oa7u3ululMhPDwcQ4YMgZ6eHsLCwtCsWbOPbrNkyRL88MMP+Pnnn/HTTz9JvffmzRt06tQJjx8/xtWrV4sVlqlqYrAlIiIikkEQBDx58qTIIlY5OTkA3haxsrCwkFnEql69eny2jyqdixcvIjAwELt27YKWlhbGjh2LSZMmwdjYWN5DA/D2NmpnZ2fExcVhw4YNGDhw4Ee38ff3x5w5c7BixQpMmDBB6r179+7BxsYGnTt3RlhYGP9PfqYYbImIiIhKKD8/X1LE6v3Qm5iYKClipamp+cEiVkTydP/+fSxfvhy///47MjMz0b9/f4hEIrRt21beQ8OrV68wduxYbNu2DVOmTMHChQs/uLyXIAj4/vvvERQUhNDQUAwfPlzq/YMHD6Jfv34ICAjA999/X97DJzlgsCUiIiIqQ2/evEFCQoLMys0PHjyQtNPT05MKvAWh18rKClpaWnI8AvrSZGZmYuPGjQgODkZcXBzatm0LkUgEFxcXua4VLQgCgoKCMGXKFNjZ2WH79u2oVavWB9uPHTsWGzduxK5du+Ds7Cz1/tSpUxEYGIjIyEi0a9euvIdPFYzBloiIiKiCvHr1qlARq4LQ+/TpU0m7evXqyZzlNTc3ZxErKjdisRgHDx5EYGAgTp06BWNjY0yaNAljxoxBjRo15DauiIgIDBw4EJqamggLC4ONjU2RbfPz8zF06FDs27cPBw4cQM+ePSXv5ebmws7ODvfv38fVq1d518RnhsGWiIiIqBJ4/vw5YmNjZRayyszMBAAoKioWWcTK2NiYRayozFy7dg2BgYHYtm0bVFRUMGrUKPj4+MDCwkIu40lKSoKLiwtu3bqFNWvWFLrV+F1v3ryBs7MzIiIicPz4cXTo0EHy3oMHD9CyZUt8/fXXOHDgQKH1b6nqYrAlIiIiqsQEQcCjR49kBt64uDi8efMGAKCiolJkESsDAwMWzKFSSUlJwcqVK7Fq1So8f/4c33zzDUQiETp37lzh/6ays7MxYcIEbNy4Ed7e3vjtt99QrVq1Itv26tULV69eRUREhNT6vUePHkWvXr3wyy+/4Mcff6yo4VM5Y7AlIiIiqqLy8/Nx//79IotYicViAICWlpYk7L4fevX09OR8FFQVZGdnY8uWLQgKCkJ0dDRsbGwgEokwaNAgqKioVNg4BEHAqlWr4OPjg/bt22Pnzp3Q19eX2TY9PR3du3dHQkICIiMj0ahRI8l7M2bMwKJFixAREYFOnTpV1PCpHDHYEhEREX2GcnJyJEWs3g+9ycnJknY1a9aUGXitrKygqakpxyOgykgQBBw7dgyBgYEIDw+HgYEBJk6ciPHjx3+wsFNZO3PmDAYMGABlZWXs3bsXX331lcx2z549g52dHdLS0hAVFQUzMzMAb9f17d69O2JjY3H16lXUqVOnwsZO5YPBloiIiOgLk5mZibi4OJmVm589eyZpV79+/SKLWFXkLB1VTtHR0QgKCsLmzZsBACNGjICvr6/UzGh5evjwIQYMGIArV65g5cqVGD16tMx2KSkp6NSpEwRBQFRUFOrVqwcASE5ORsuWLWFjY4MjR47wedsqjsGWiIiIiCSePXtWZBGrV69eAXhbxMrU1FRmESsjIyMWsfrCpKam4vfff8eKFSvw6NEj9OrVCyKRCN27dy/353BzcnLg4+OD33//HR4eHggODpZ50SUxMRGdOnWCtrY2Tp8+LZldPnHiBHr27Im5c+fCz8+vXMdK5YvBloiIiIg+ShAEpKSkFFnEKjc3FwCgqqoqKWL1fujV19dnEavPWE5ODnbs2IHAwEBcu3YNTZs2ha+vL4YNGwY1NbVy3ffatWsxceJEtGrVCrt375bMyr7rzp076NSpE4yNjXHy5Eloa2sDAPz9/TFv3jycOHEC9vb25TpOKj8MtkRERET0SfLz83Hv3j2ZoTcxMREFp5taWloyA6+VlRV0dXXlfBRUVgRBwOnTpxEYGIgDBw6gVq1a8PT0xIQJE4os9FQWLly4gP79+yM/Px+7d++WWuanwLVr12BnZ4cWLVrgyJEj0NDQQH5+PhwcHHDr1i1cvXoVdevWLbcxUvlhsCUiIiKicpOTk4O7d+/KLGKVkpIiaVerVi2ZgdfS0pJFrKqw2NhYBAcHY8OGDcjLy8PQoUMhEonQvHnzctnf48eP4erqivPnzyM4OBienp6F7hI4d+4cevTogc6dO2P//v1QUVHB48eP0bJlSzRq1AjHjx/n7fRVEIMtEREREclFRkaGzCJWMTExSEtLk7QzNDSUCrwFodfMzIxFrKqItLQ0hISEYNmyZXjw4AG6du0KkUiE3r17l3nRptzcXPzwww9YtmwZ3N3dsXLlykK3Qp84cQJ9+vTBN998g23btkFZWRmnTp1Ct27d4Ofnhzlz5pTpmKj8MdgSERERUaXz7NkzmbO8sbGxyMrKAgAoKSnBzMxMZuVmIyMjVrmthHJzc7Fnzx4EBgbi4sWLsLa2ho+PD9zc3Mp8Zj40NBTjx49H06ZNsXfvXhgZGUm9v3//fvTv3x/Dhw/HunXroKioiAULFmDmzJkIDw9Hjx49ynQ8VL4YbImIiIioyhAEAcnJyTIDb3x8vFQRK0tLS5nP9NapU4dFrORMEAScP38egYGB2Lt3L3R0dDBu3Dh4eXnB0NCwzPZz+fJluLi4IDs7Gzt37oSdnZ3U+1u2bMGIESMwadIkBAUFQRAE9O7dG1euXMG1a9dkFqGiyonBloiIiIg+C3l5eUUWsbp3756kiFX16tWLLGJVo0YN+R7EFygxMRHLli3D2rVrkZWVBVdXV4hEIrRp06ZM+k9NTcXgwYNx+vRp/Pbbb/Dx8ZG6sLF69Wp4enpi5syZmDt3LlJTU2FjYwNzc3OcPHkSysrKZTIOKl8MtkRERET02Xv9+rVUEat3Q++jR48k7WrXrl1kESsNDQ05HsHnLz09HRs2bEBwcDASEhLQoUMHiEQiODk5fXIxp7y8PPz0008ICAjA0KFDERISIvX3+euvv+LHH3/E4sWLMXnyZJw5cwZ2dnaYOnUqFi5c+KmHRhWAwZaIiIiIvmjp6emSIlbv3+L84sULSTsjIyOZodfMzAzVqlWT3wF8ZvLz8/Hnn38iMDAQUVFRMDU1hbe3N0aPHi1Ze7a0tm/fjtGjR8Pa2hp79+6FmZmZ5D0/Pz8sWLAAv//+O8aNGycJu4cPH0avXr0+9bConDHYEhERERHJIAjCB4tYZWdnA3hbxMrc3FxmEStDQ0MWsfoEly9fRmBgIHbs2AF1dXWMHj0a3t7eUoG0pG7cuAFnZ2e8ePEC27dvlxSJEgQBPj4+WL58ObZs2YLBgwfjm2++wd9//42rV68WKj5FlQuDLRERERFRCYnFYkkRq/dD7927d5GXlwcAUFNTK7KIVe3atVnEqpgePnyIFStWYPXq1Xj58iWcnZ0hEonQvn37Un2Gz58/x7Bhw3Ds2DH8/PPPmDJlChQUFCAWizFq1Chs2bIFe/fuRYcOHWBjYwNDQ0OcPn2aM/OVGIMtEREREVEZysvLQ2JiosxZ3qSkJEkRK21t7SKLWOno6Mj5KCqnV69eITQ0FEFBQYiJiUGbNm0gEokwYMCAEofO/Px8zJo1CwsXLoSrqyvWr18PLS0t5OXlYfDgwTh48CAOHToETU1NdOrUCb6+vli8eHE5HRl9KgZbIiIiIqIKkp2djfj4eJmVmx8/fixpV6dOHZmh18LCAurq6nI8gspBLBbjyJEjCAwMxF9//QVDQ0N4eXlh3Lhx0NXVLVFfe/fuhZubG0xMTBAWFgYrKyvk5OTg22+/xZkzZ3D8+HH8/fff+P777/Hnn3+iX79+5XRU9CkYbImIiIiIKoH09HSpwPtu6H358iUAQEFBocgiVqampl/krbI3btxAUFAQtm7dCmVlZYwcORI+Pj6wtrYudh/R0dFwdnbG48ePsXXrVvTp0wdZWVlwdHTEzZs3ERERgTlz5uD06dO4cuUKTE1Ny++AqFQYbImIiIiIKjFBEJCamipzljc2NhavX78GACgrKxdZxKp+/fqffRGrx48fY9WqVVi5ciVSU1PRt29fiEQi2NvbF+s53JcvX2L48OE4ePAg/P394efnh4yMDHTt2hUPHjzAwYMHMXDgQNSpUwdRUVFQUVGpgKOi4mKwJSIiIiKqosRiMR4+fCiziFVCQoKkiJW6urpUEat3Q2+tWrU+qyJWr1+/xh9//IHAwED8+++/aNGiBXx9fTFkyBCoqqp+cFuxWIz58+dj9uzZ+OabbxAaGorc3Fx06dIFGRkZWLFiBQYMGABPT08EBQVVzAFRsTDYEhERERF9hnJzcz9YxKpAjRo1ZM7yWllZffK6sfIkCAL++usvBAYG4vDhw9DX18eECRPg6emJ2rVrf3DbgwcPYtiwYTAwMEBYWBh0dHTQsWNHKCkpwd3dHTNmzMCePXvg4uJSQUdDH8NgWwEyMzMRFxeHnJwcqKqqwtLSElpaWvIeFhERERF9obKyshAfHy8z9D558kTSTl9fv8giVmpqanI8gpL577//EBwcjE2bNkEsFuO7776Dr68vmjZtWuQ2MTExcHZ2RlJSEkJDQ9GiRQt06tQJNWvWhLm5OU6dOoXLly/DwsKi0LY8/694DLblJDo6GqtXr8axo0cRExeHdz9mBQUFWFtaoqejIzw8PNC4cWM5jpSIiIiI6P+9ePECsbGxMp/pTU9PB/D2fNbY2LjIIlbKyspyPgrZnj17hjVr1mD58uVITk5Gjx49IBKJ4ODgIPMZ5IyMDLi7u2PPnj2YMWMGBg0aBHt7e5iamuL58+fQ1dXF2bNnoaamxvN/OWOwLWMJCQnw9PBA+LFj0NXQQHsDA1jp6sJYWxuqysrIyctDUno6YtPScC4lBWlZWXDo2ROrVq+GmZmZvIdPRERERCSTIAh48uSJzMrNsbGxyMnJAQBUq1atyCJW9erVqxRFrN68eYNdu3YhMDAQly9fRqNGjeDj44Phw4dDQ0NDqq0gCFi0aBGmT58OR0dHTJkyBU5OTrC2tsaNGzcwduxYxMXG8vxfzhhsy9DatWvh6+0NLWVljGjcGB0NDVHtA/9xc8VinHnwAKHR0cjMy0PQ0qUYM2ZMBY6YiIiIiOjTicVi3L9/X+Ysb0JCAvLz8wEAGhoaRRaxqlmzZoUXsRIEAWfOnEFgYCD27dsHPT09jB8/HhMnTkS9evWk2oaHh2PIkCHQ09PDzJkz4enpiQYNGuC/27eho6LC8385q/LB1tTUFHZ2dti4cWOptm3atCkOHjz4yeNYsGAB/Pz84GBmhrEtWkCjBGuIZeXmIuT6dYQnJGD+/PmYMWMG7OzsAACnTp365LEREREREclLbm4uEhISZFZufvDggaSdrq5ukUWsqlevLtVneWSAu3fvYunSpVi3bh1ycnIwaNAgiEQi2NraSrVxdnZGXFwcevbsiX379pXZ+T8AZoBPUDlvfn/PuXPncOzYMfj6+qJGjRoVvv/o6Gjs3LkTI0eOlLkY89q1a+Hn54fhTZpgSCnul9eoVg0+rVujtoYG/Pz8ULdu3TIYdekcO3YMO3bswIULF3D79m0YGRkhMTFRbuMhIiIioqqtWrVqkrD6vqysLMTFxRUKvOHh4UhNTZW0q1u3rlTgzcrKwosXLyTFmcqCubk5goKCMGfOHKxduxYBAQHYsmULvv76a0ybNg19+/aFubk5zp07B2dnZ+zbt69Mz/9Hjx5dJsdRUllZWdiwYQP279+PmzdvIjMzE5aWlhg3bhzGjRsHJSUluYyrpKrEjO1vv/2GKVOmICEhoVCwzMnJgaKiIqqV4ApJgeLO2O7evRuurq6IiIiQXEUpkJCQgGZNmqBj3brwad26xGN4lyAIWHr5Ms48eoSmzZtDTU2twq/WjBw5Ejt27ICtrS2SkpKgpKTEYEv0hbOzs8Pp06cBAFevXkXLli3lO6AvhKmpKXx9feHr6wvgbeGRsLAwODk5yXVcFaXgdkQdHR28ePFCvoMhIrmYO3cuZs+ejSVLluDFixdSM74ZGRkA3v6sMDExkVnEysTERGYRq+JmgB07dmDw4MFo3LgxoqOjYWFhAR8fH9jb26PtV1+V+fn/zVu34O7uDqBiZ2z//fdfNG/eHN26dUPPnj2hra2N8PBwhIWFYcSIEdi0aVOFjeVTVIkZ2w8pqys0peXp4QEtZWWMbdHig+1e5+VB7SPV4RQUFDC2RQtce/oUMTExaN68eVkOtVgWLlyIkJAQVKtWDX379sW///5b4WMgospn7NixmDt3LmrVqgUASExMhJmZGYpzbfTUqVOwt7dHWlpahdx1M3LkSLx48QL79u0r930Bb49v5MiRJboIWHAHkL+/f7G3SUlJga6ubskHWEWYmppi48aNkgvIKSkp2LFjB2bPni3fgRGR3BQUcXJ2dpaa3BIEAY8fPy40y3vq1CmsXbtWqoiVhYVFodBb8LzvxxTMVK5YsQLq6uoIDAyESCSCpoZGsc7/gY9ngHfP/z09PIo1rrJWt25d3Lx5E02aNJG8Nn78eIwaNQobNmzAzJkzYWlpKZexlYT8S5J9hL+/P6ZMmQIAMDMzg4KCAhQUFCQnEKamphg5cqTUNjdu3ECXLl2grq4OQ0NDzJ8/Hxs2bJDa7l1nzpzBV199BTU1NZibmyM0NFTy3saNG+Hq6goAsLe3l+z/1KlTiI6ORvixYxjRuLHUPfVLLl6Ey969SMnMxKyoKPQPC8OvFy4AAMSCgH0xMfAID8e3e/Zg6J9/Ytnly8h48wbA29sSRjRujLS0NGRlZUn6fPPmDWbNmoVWrVpBR0cHmpqa6NSpEyIiIqSOZfbs2VBUVMRff/0l9fq4ceOgoqKC69evf/DzrlevXqlmv4no86ahoYG6deuW6/INb/73c7CyyM/Ph1gslvcwJOrWrfvBi7m5ubkVOJryV7duXejo6Mh7GEQkJx/KAAoKCmjbti3Wr1+P0aNHY9GiRQgLC8Mff/yBr776CqqqqqhVqxYcHBxgaGiIP//8E7t27YKnpyfs7e3x4MEDHD16FFZWVtDT04OysjLq1KmDWbNm4dmzZwAKZ4C2bdtix44dWLRoEdIzMgqd/wOlzwAF5//hx45Jnf8DFZMBatWqJRVqCzg7OwMAbt++/dG/r8qg0gdbFxcXDBkyBAAQGBiIzZs3Y/Pmzahdu7bM9g8fPoS9vT1u3bqFadOmQSQSYevWrQgODpbZPi4uDgMGDECPHj0QEBAAXV1djBw5Erdu3QIAdO7cGd7e3gCA6dOnS/bfqFEjrF69GroaGuhoaFio33xBgF9kJGqoqmJ08+bo8L82yy5fxrobN9C4Zk2Mb9kSPUxNEXHvHmZGRiLvfydQHevXh7KiIpKTkyX9paenY+3atbCzs8OiRYvg7++P1NRUODg44Nq1a5J2fn5+aNmyJUaPHi25RSM8PBwhISGYNWsWWhTjyhIRUUndu3cP/fr1g66uLjQ1NdGkSRMcPnwYiYmJsLe3B/C2KIiCgoLkYqSdnR28vLzg6+srOQEpOGF59+faixcvJBcUC9y6dQt9+/aFtrY2qlevjk6dOiE+Ph7+/v7YtGkT9u/fL3Uh8tSpU1BQUJC6pfXatWtSFzw3btyIGjVq4M8//0Tjxo2hqqqKpKQk5OTkYPLkyahfvz40NTXx9ddfl/ktYk+ePEG/fv2grq4OMzMzbN26tVAbBQUFySx0wee0Y8cOdOnSBWpqajK3eVfB8YWHh6NRo0bQ0tKCo6MjUlJSJG3++ecf9OjRA7Vq1YKOjg66dOmCK1euFBrH77//jr59+0JDQwONGjXC+fPnERcXBzs7O2hqaqJ9+/aIj4+X2m7//v2wtbWVXESeM2cO8vLySvmJEdHnrrQZIDo6GtOnT8dPP/2EuLg4yXO6p0+fRnZ2Nm7fvo06depAT08PDx8+hLa2NrS0tJCamop58+ahVq1aUFZWhr+/Pxo0aAAAcHJywrx58xASEoLbt28Xef4PlD4DdKxfH7oaGlLn/4B8M8CjR48AQHK3VmVX6W9Fbt68OWxtbbFt2zY4OTnJLN70rkWLFiEtLQ1XrlyRPAfm7u4OKysrme3v3LmDyMhIdOrUCQAwcOBAGBkZYcOGDfjtt99gbm6OTp06YenSpejRo4fUM7bHjh5FewMDmSW9c8VidDQygnuzZpLXbj19ivCEBEz5+mvYGxv//zHWqYOZUVGIevAA9sbGqKakBG0VFTx//lzSRldXF4mJiVBRUZG8NnbsWDRs2BDLli3DunXrALy95SI0NBStWrXC999/j8WLF2P06NFo3bo1fvrppw9+dkREpTVx4kS8efMGkZGR0NTURHR0NLS0tGBkZIQ9e/agf//+uHPnDrS1taGuri7ZbtOmTfD09MTZs2eLva+HDx+ic+fOsLOzw8mTJ6GtrY2zZ88iLy8PkydPxu3bt5Geno4NGzYAAPT09HDu3Lli9Z2VlYVFixZh7dq1qFmzJurUqQMvLy9ER0dj+/btqFevHsLCwuDo6IibN28W+bulpEaOHInk5GRERESgWrVq8Pb2xpMnTz663U8//YSAgADY2NhATU3to+2zsrLw22+/YfPmzVBUVMR3332HyZMnS0JxRkYG3NzcsGzZMgiCgICAAPTu3RuxsbFSFUnnzZuHJUuWYMmSJfjxxx8xdOhQmJubY9q0aTA2NsaoUaPg5eWFI0eOAACioqIwYsQILF26VHIRYty4cQDAW42JSKbyyAAqKipo2LAh1NXVce/ePakMkJiYiAYNGsDIyAjx8fG4d++e5C7Gffv2SS4saqipwb5+/SKX9PmUDNDewAAn3wu28soAb968QVBQEMzMzNCmTZsSby8PlT7YltTRo0fRrl07qeImenp6GDZsGJYtW1aofePGjSX/oAGgdu3aaNCgAe7evfvB/WRkZCAmLg69WrUqsk0fCwup76Pu34dmtWqw1dfHy//d+w8Alrq6UFdWxo0nTyT/2NWVlfE8MxNXr16FpqamVD9isRjp6ekQi8WSK+UxMTGS91VUVDBp0iQEBATg4sWLSE1NxZo1az56TO979eoV8vLypPomos9XjRo1ULt27WKtIWhqair1fG1SUhL69++PZv/7RW5ubi55T09PDwBQp06dQs/YWllZ4ddff5V8X5znVFesWAEdHR1s375dctLxbqVNdXV15OTklKrCfG5uLlauXCm5sp2UlIQNGzYgKSlJsp7h5MmTcfToUWzYsAELFy6EnZ1diYvsvbs8RUxMDI4cOYKLFy9KTh7WrVuHRo0afbQfX19fuLi4FHu/ubm5WL16NSz+9/vJy8sLc+fOlbzftWtXqfZr1qxBjRo1cPr0afTt21fyuru7OwYOHAgA+PHHH9GuXTvMnDkTDg4OAAAfHx9JARQAmDNnDn766Se4ubkBePvvY968eZg6daok2Jb0MxQEAampqSwsRfQZK5htvXv3bqHHVfLy8pCeni45Tz1w4ABatmwJDQ0NqXPXPn36YPPmzVJ95OXlwdLSEvr6+lJtzczMkJOTg2rVqiE3N1fyiEft2rUxfPhwWFtbw9PTE1YfqXdQ2gxgpauLQ/HxUs8AKykpSZ71FYvFePHiBcRiMVq3bl3ojpqmTZtizpw5mDZtGm7cuIGnT5/i2LFjpXqMqOCi7qFDh8r1MaSyVDVGWQL37t1Du3btCr1e1APPxu9cNSmgq6uLtLS0D+4nPj4egiDAWFtb5vtKCgqo9c6sBAAkZ2biVW4uhvz5p8xt3v2HXvCQ+bvrZn1Iwa0S77tx4waAt/+pS6uovono8+Ll5YWgoKBSlfX39vaGp6cnjh07hu7du6N///7FKoDX6gMXB4ty7do1dOrUqVzqAaioqEiN++bNm8jPzy+0REVOTg5q1qxZJvu8ffs2lJWVpT6Lhg0bFqvQVusSVuPU0NCQhFoAMDAwkJoZfvz4Mfz8/HDq1Ck8efIE+fn5yMrKQlJSklQ/735G+vr6ACC5qFHw2uvXr5Geng5tbW1cv34dZ8+exYIFCyRt8vPz8fr1a2RlZUkKxJSEWCzG3LlzsWLFihJvS0RVS7du3WS+HhYWhrCwMMn3BbOuxe2juOe4qampWLJkCby9vT94/g98WgYo6Dc7O1vq/U2bNiEgIAD//fefVD0FMzOzQn1NmTIF27dvx8WLF7Fw4UI0LsVSRIsXL0ZISAjmzZuH3r17l3h7efnsgm1JFXUC97FKnwXV1lSLuIJRTVERiu/NegiCgBqqqpjy9dcyt9F5pyhIwbarV69G48aNcezYMcyfPx+dOnVC586doaurC0VFRWzZsgXJycnYuXOnVF/379/H6NGj8fr1a3Tv3h2zZs364PHI8uOPPyIhIaFQ30T0eapXrx4Ui7i16mPGjBkDBwcHHDp0CMeOHcPPP/+MgIAATJo06YPbvX9HSsH+3/0Z/H5RJPX3ThiKozj9FvT97ox1ZmYmlJSUcPny5UK/L7S0tEo8jrL2/uf3Me9fDFBQUJD6TNzc3PDs2TMEBwfDxMQEqqqqaNeuXaGZknf7Kfi8ZL1WUHwrMzMTc+bMkTm7XJxbqGVRVFSEr68vBg0aVKrtiajy27ZtG1atWoUdO3bAwMBA6r2BAweiZcuWmD59OoC3wbVr166YMWOGVLvdu3dj6dKlUn0MHDgQZmZmWLRokVRbb29vJCcn4/nz51I1ADQ1NTFu3Di4uLhg6dKlRZ7/A5+WAVTfmZktsGXLFowcORJOTk6YMmUK6tSpAyUlJfz888+FahkAb2e3Y2NjAby9OFtSGzduxI8//ggPDw/4+fmVeHt5qhLBtji3xRUwMTFBXFxcoddlvfYp+y+oTJlTgsIXdbW0cPXJEzSuVUvyD7co4v+daHz99ddo2bIlAgICYG5ujtOnT0uNZ/fu3VBVVZW6nVosFqNTp06oUaMGRo0ahYULF8LT07NEt6sBb28fTE5OluqbiKgoRkZG8PDwgIeHB6ZNm4aQkBBMmjRJ8lxQcZZXKCgKkpKSAhsbGwCQKo4BvJ0t3LRpE3Jzc2XO2qqoqBTa17v9FiyZ836/stjY2CA/Px9Pnjwpt5+FDRs2RF5eHi5fviy5FfnOnTtyucX27NmzWLlypeQK/f379/H06dNP7tfW1hZ37twp0+UiFBQUYGlpWSWWoCCi0rl48SIA4Kuvvir0jK2qqir09fUlP5tNTU2RkZFR6Gf1rl27CvWhqqoKPT29Qm11dHTw7Nkz5OXlQUFBAdWrV0d6ejr27NkjVaypJOf/QPEzQM7/fne9e5F59+7dMDc3x969e6UygKz6BGKxGCNHjoS2tjZ8fX2xcOFCDBgwoNgZYP/+/RgzZgxcXFyq5N0wlb4qMvD/V6SL80vewcEB58+flzphef78+UerRZZ0/5aWllBQUEBSenqx++lsZASxIGBbdHSh9/LFYmS+c0X89f/+wxT8wi6YKXj3yvqFCxdw/vz5Qn0tWbIE586dw5o1azBv3jy0b98enp6eZXJyQkQki6+vL8LDw5GQkIArV64gIiJC8oyoiYkJFBQUcPDgQaSmpiIzM7PIftTV1dG2bVv88ssvuH37Nk6fPl3oirGXlxfS09MxePBgXLp0CbGxsdi8eTPu3LkD4O3JzY0bN3Dnzh08ffoUubm5sLS0hJGREfz9/REbG4tDhw4hICDgo8dlbW2NYcOGYcSIEdi7dy8SEhJw8eJF/Pzzzzh06NAnfGL/r0GDBnB0dMT48eNx4cIFXL58GWPGjCnVzPSnsrKywubNm3H79m1cuHABw4YNK5NxzJo1C6GhoZgzZw5u3bqF27dvY/v27VVuNoCIKpY8MoC6ujqMjIwQFBQkWQK04Nbg0pz/A8XPAAX9vvtzt6IyQGRkJAYPHozOnTtj69atpb6DS56qxIgLnjuaMWMGNm/ejO3bt+PVq1cy206dOhU6Ojro0aMH5s6di4CAAHTo0EHyLG1JZn8LtGzZEkpKSli0aBE2bdqE7du3IysrC9aWloj9yLO472pWuzZ6mZtj53//YVZUFPbFxOBAXBx+v3YNIw8dwrV3nnPKzsuDurq65Fa3vn374u7du3B2dsaaNWswbdo0ODo6Frpv/vbt25g5cyZGjhyJfv36QVFRERs3bkRmZiYmTJjw0THeuHED8+fPx/z58xEXF4eXL19Kvj9w4ECxj5WIviz5+fmYOHEiGjVqBEdHR1hbW2PlypUAgPr160uKB+nr68PLy+uDfa1fvx55eXlo1aoVfH19MX/+fKn3a9asiZMnTyIzMxNdunRBq1atEBISIpm9HTt2LBo0aIDWrVujdu3aOHv2LKpVq4Zt27bhv//+Q/PmzbFo0aJC/RZlw4YNGDFiBH744Qc0aNAATk5O+Oeff2TWaAD+fymekiwJtGHDBtSrVw9dunSBi4sLxo0bhzp16hR7+7Kybt06pKWlwdbWFsOHD4e3t3eZjMPBwQEHDx7EsWPH0KZNG7Rt2xaBgYEwMTEpg1ET0edKHhnAwMAASUlJ8Pb2xtdffy2VAQ4ePAhzU9MSnf8Dxc8AsWlpUFdXl3r0pSIywL179/DNN99AQUEBAwYMwK5du7BlyxbJV0HNnkpPqCLmzZsn1K9fX1BUVBQACAkJCYIgCIKJiYng5uYm1fbq1atCp06dBFVVVcHQ0FD4+eefhaVLlwoAhEePHknamZiYCH369Cm0ry5dughdunSRei0kJEQwNzcXlJSUBABCRESEMGnSJEFXQ0PY37+/cNjVVfLV3cREUFNSknrt3S/vVq0ES11dQVVJSVBXVhZMdXSEAQ0aCFv69hUOu7oK+11cBGVFRaF+/fqS/YvFYmHhwoWCiYmJoKqqKtjY2AgHDx4U3NzcBBMTE0EQBCEvL09o06aNYGhoKLx48UJq/MHBwQIAYceOHR/8nDds2CAAkPn1/udMRF+GLl26CD4+PvIeRpVx8uRJoUaNGsLz58/lPZQqb8OGDYKOjo68h0FEclTZMoCzs7PM8/9PzQD7XVwEXQ0NoX79+lJjqIgMEBERUeT5PwBh9uzZH/17qgwUBOEjVZI+E76+vvj9998lhUDKQnR0NJo0aVJoTapPFZGUhMUXLiA6OrpYyz0QEZUnOzs7nDt3DioqKjh//rxU9VsqrKC4x5QpU+Q9lCpNS0sLeXl5UFNT45I+RFRqZZ0BeP5feX2WwTY7O1vq3vRnz57B2toatra2OH78eJnuy9HBAdfOn8eKbt2gUQZLT2Tl5mLiX3+hZbt2OBoeXgYjJCL6NA8fPpQ8X2RsbCy1SDxVHr169UJUVJTM96ZPny6pHFpVFBR9VFJSkrmkBRHR+yoqA/D8v3L6LINty5YtYWdnh0aNGuHx48dYt24dkpOT8ddff6Fz585luq+EhAQ0a9IEHevWhU8J1xN8nyAIWHr5Ms48eoSbt27xFzkRERXbuxcg3qenpwc9Pb0KHhERUcWqqAzA8//KqUos91NSvXv3xu7du7FmzRooKCjA1tYW69atK/NQC7xdGDlo6VKMHTsWdTQ0MKQUiyADb/9Rb7t9G+EJCVi7di3/URMRUYnUr19f3kMgIpKrisoAPP+vnD7LGVt5WLBgAfz8/OBgZoaxLVqU6LaErNxchFy/jvCEBCxYsKDK3S5GRERERPSl4fl/5cJgW4bWrl0LX29vaCkrY0TjxuhoaIhqH1gDKjc/H2cePkRodDQy8/IQvGwZRo8eXYEjJiIiIiKi0uL5f+XBYFvGEhIS4OnhgfBjx6CroYH2Bgaw0tWFsbY2VJWUkJOfj6T0dMSmpeFcSgrSsrLg0LMnVq1ezdsPiIiIiIiqGJ7/Vw4MtuUkOjoaq1evxvHwcNyJjcW7H7OCggIaWFmhh4MDPD09WdKbiIiIiKiK4/m/fDHYVoDMzEzExcUhJycHqqqqsLS0hJaWlryHRURERERE5YDn/xWPwZaIiIiIiIiqtKKfbCYiIiIiIiKqAhhsiYiIiIiIqEpjsCUiIiIiIqIqjcGWiIiIiIiIqjQGWyIiIiIiIqrSGGyJiIiIiIioSmOwJSIiIiIioiqNwZaIiIiIiIiqNAZbIiIiIiIiqtIYbImIiIiIiKhKY7AlIiIiIiKiKo3BloiIiIiIiKo0BlsiIiIiIiKq0hhsiYiIiIiIqEpjsCUiIiIiIqIqjcGWiIiIiIiIqjQGWyIiIiIiIqrSGGyJiIiIiIioSmOwJSIiIiIioiqNwZaIiIiIiIiqNAZbIiIiIiIiqtIYbImIiIiIiKhKY7AlIiIiIiKiKo3BloiIiIiIiKo0BlsiIiIiIiKq0hhsiYiIiIiIqEpjsCUiIiIiIqIqjcGWiIiIiIiIqjQGWyIiIiIiIqrSGGyJiIiIiIioSmOwJSIiIiIioiqNwZaIiIiIiIiqNAZbIiIiIiIiqtIYbImIiIiIiKhKY7AlIiIiIiKiKo3BloiIiIiIiKo0BlsiIiIiIiKq0hhsiYiIiIiIqEpjsCUiIiIiIqIqjcGWiIiIiIiIqjQGWyIiIiIiIqrSGGyJiIiIiIioSmOwJSIiIiIioiqNwZaIiIiIiIiqNAZbIiIiIiIiqtIYbImIiIiIiKhKY7AlIiIiIiKiKo3BloiIiIiIiKq0/wOTdUs8FC555QAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" @@ -158,8 +158,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T22:08:05.806535Z", - "start_time": "2025-02-07T22:07:54.154496Z" + "end_time": "2025-02-07T22:16:51.864590Z", + "start_time": "2025-02-07T22:16:40.797793Z" } }, "cell_type": "code", @@ -176,8 +176,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 23:07:54,165 INFO Started executing jobs locally\n", - "2025-02-07 23:07:54,170 INFO Starting job - tight relax 1 (10bbe269-f138-4ec5-ac16-c8b36c49c2c0)\n" + "2025-02-07 23:16:40,808 INFO Started executing jobs locally\n", + "2025-02-07 23:16:40,814 INFO Starting job - tight relax 1 (a2da27b5-def6-41e6-b5c5-6a9552fe839c)\n" ] }, { @@ -193,16 +193,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 23:07:55,243 INFO Finished job - tight relax 1 (10bbe269-f138-4ec5-ac16-c8b36c49c2c0)\n", - "2025-02-07 23:07:55,244 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 23:07:55,245 INFO Starting job - tight relax 2 (9e4af4a2-84aa-44e6-bf6f-65ccab84dc9b)\n" + "2025-02-07 23:16:41,672 INFO Finished job - tight relax 1 (a2da27b5-def6-41e6-b5c5-6a9552fe839c)\n", + "2025-02-07 23:16:41,672 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 23:16:41,673 INFO Starting job - tight relax 2 (8cb9dcbd-a140-449a-a3c8-1bfc0e6a82a1)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmp3s_tphxi/job_2025-02-07-22-07-55-245159-41630/POTCAR.spec is not gzipped, skipping...\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmpduvvl0_0/job_2025-02-07-22-16-41-673155-76431/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -210,18 +210,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 23:07:56,685 INFO Finished job - tight relax 2 (9e4af4a2-84aa-44e6-bf6f-65ccab84dc9b)\n", - "2025-02-07 23:07:56,685 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 23:07:56,686 INFO Starting job - get_supercell_size (44572fcd-84e9-47f1-a53e-2fa434369f15)\n", - "2025-02-07 23:07:56,698 INFO Finished job - get_supercell_size (44572fcd-84e9-47f1-a53e-2fa434369f15)\n", - "2025-02-07 23:07:56,699 INFO Starting job - static (204ab95a-b7ae-4990-9499-3d66bfc5037e)\n" + "2025-02-07 23:16:42,983 INFO Finished job - tight relax 2 (8cb9dcbd-a140-449a-a3c8-1bfc0e6a82a1)\n", + "2025-02-07 23:16:42,983 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 23:16:42,984 INFO Starting job - get_supercell_size (34bedd82-f290-4404-9b1f-074b4321526c)\n", + "2025-02-07 23:16:42,997 INFO Finished job - get_supercell_size (34bedd82-f290-4404-9b1f-074b4321526c)\n", + "2025-02-07 23:16:42,998 INFO Starting job - static (c239b33e-cfc3-457b-858a-e28029d45430)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmp3s_tphxi/job_2025-02-07-22-07-56-699384-12259/POTCAR.spec is not gzipped, skipping...\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmpduvvl0_0/job_2025-02-07-22-16-42-997738-44040/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -229,13 +229,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 23:07:57,446 INFO Finished job - static (204ab95a-b7ae-4990-9499-3d66bfc5037e)\n", - "2025-02-07 23:07:57,447 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 23:07:57,447 INFO Starting job - generate_phonon_displacements (588f56ac-2b21-481c-89e2-ad0fdd26a1cf)\n", - "2025-02-07 23:07:57,505 INFO Finished job - generate_phonon_displacements (588f56ac-2b21-481c-89e2-ad0fdd26a1cf)\n", - "2025-02-07 23:07:57,506 INFO Starting job - run_phonon_displacements (beeac6d5-8734-4dcf-a103-ab091dd1177c)\n", - "2025-02-07 23:07:57,618 INFO Finished job - run_phonon_displacements (beeac6d5-8734-4dcf-a103-ab091dd1177c)\n", - "2025-02-07 23:07:57,621 INFO Starting job - phonon static 1/1 (b673cf1a-5754-42e6-8471-d1c292300ece)\n" + "2025-02-07 23:16:43,649 INFO Finished job - static (c239b33e-cfc3-457b-858a-e28029d45430)\n", + "2025-02-07 23:16:43,649 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 23:16:43,650 INFO Starting job - generate_phonon_displacements (c07ffa41-28c9-4309-b86f-bc3848f59936)\n", + "2025-02-07 23:16:43,707 INFO Finished job - generate_phonon_displacements (c07ffa41-28c9-4309-b86f-bc3848f59936)\n", + "2025-02-07 23:16:43,708 INFO Starting job - run_phonon_displacements (b8098d3b-e91f-4971-a514-64723e88d29f)\n", + "2025-02-07 23:16:43,815 INFO Finished job - run_phonon_displacements (b8098d3b-e91f-4971-a514-64723e88d29f)\n", + "2025-02-07 23:16:43,817 INFO Starting job - phonon static 1/1 (5f879c34-b413-4a3b-9b53-7e2804c03ab2)\n" ] }, { @@ -244,7 +244,7 @@ "text": [ "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", " response = function(*self.function_args, **self.function_kwargs)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmp3s_tphxi/job_2025-02-07-22-07-57-621005-18878/POTCAR.spec is not gzipped, skipping...\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmpduvvl0_0/job_2025-02-07-22-16-43-817582-98313/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -252,11 +252,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 23:07:58,553 INFO Finished job - phonon static 1/1 (b673cf1a-5754-42e6-8471-d1c292300ece)\n", - "2025-02-07 23:07:58,554 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 23:07:58,556 INFO Starting job - store_inputs (beeac6d5-8734-4dcf-a103-ab091dd1177c, 2)\n", - "2025-02-07 23:07:58,558 INFO Finished job - store_inputs (beeac6d5-8734-4dcf-a103-ab091dd1177c, 2)\n", - "2025-02-07 23:07:58,559 INFO Starting job - generate_frequencies_eigenvectors (da446f69-6549-4de8-bc22-bbe31c13005f)\n" + "2025-02-07 23:16:44,599 INFO Finished job - phonon static 1/1 (5f879c34-b413-4a3b-9b53-7e2804c03ab2)\n", + "2025-02-07 23:16:44,600 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-07 23:16:44,600 INFO Starting job - store_inputs (b8098d3b-e91f-4971-a514-64723e88d29f, 2)\n", + "2025-02-07 23:16:44,602 INFO Finished job - store_inputs (b8098d3b-e91f-4971-a514-64723e88d29f, 2)\n", + "2025-02-07 23:16:44,602 INFO Starting job - generate_frequencies_eigenvectors (e58cd212-427e-4ef9-9e9f-92898ff3cb3f)\n" ] }, { @@ -284,8 +284,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 23:08:05,797 INFO Finished job - generate_frequencies_eigenvectors (da446f69-6549-4de8-bc22-bbe31c13005f)\n", - "2025-02-07 23:08:05,798 INFO Finished executing jobs locally\n" + "2025-02-07 23:16:51,853 INFO Finished job - generate_frequencies_eigenvectors (e58cd212-427e-4ef9-9e9f-92898ff3cb3f)\n", + "2025-02-07 23:16:51,854 INFO Finished executing jobs locally\n" ] } ], @@ -294,8 +294,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T22:08:05.822233Z", - "start_time": "2025-02-07T22:08:05.817549Z" + "end_time": "2025-02-07T22:16:51.872766Z", + "start_time": "2025-02-07T22:16:51.868017Z" } }, "cell_type": "code", @@ -324,8 +324,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T22:08:06.173641Z", - "start_time": "2025-02-07T22:08:05.865372Z" + "end_time": "2025-02-07T22:16:52.223631Z", + "start_time": "2025-02-07T22:16:51.917673Z" } }, "cell_type": "code", @@ -380,40 +380,28 @@ { "metadata": {}, "cell_type": "markdown", - "source": "One can run the same workflow with a forcefield as well. Here, we cannot consider BORN charges yet as there is no forcefield equivalent.", + "source": "One can run the same workflow with a forcefield as well. Here, we cannot consider BORN charges yet as there is no forcefield equivalent. You can find this tutorial in the force field tutorials.", "id": "c12bfc7daf5b541" }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-07T22:08:06.933660Z", - "start_time": "2025-02-07T22:08:06.180727Z" + "end_time": "2025-02-07T22:16:52.987071Z", + "start_time": "2025-02-07T22:16:52.230897Z" } }, "cell_type": "code", - "source": [ - "from atomate2.forcefields.flows.phonons import PhononMaker\n", - "flow=PhononMaker(\n", - " min_length=3.0,\n", - " born_maker=None,\n", - " use_symmetrized_structure=\"conventional\",\n", - " create_thermal_displacements=False,\n", - " store_force_constants=False,\n", - " prefer_90_degrees=False,\n", - " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", - " ).make(si_structure)\n", - "run_locally(flow, store=job_store,create_folders=True, raise_immediately=True)" - ], + "source": "", "id": "82bb21620a5679f1", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2025-02-07 23:08:06,340 INFO Started executing jobs locally\n", - "2025-02-07 23:08:06,345 INFO Starting job - structure_to_conventional (82644e48-8b91-40f2-be39-6b120242d320)\n", - "2025-02-07 23:08:06,365 INFO Finished job - structure_to_conventional (82644e48-8b91-40f2-be39-6b120242d320)\n", - "2025-02-07 23:08:06,369 INFO Starting job - Force field relax (62f866d7-c139-4b63-b789-eada2f451a71)\n" + "2025-02-07 23:16:52,394 INFO Started executing jobs locally\n", + "2025-02-07 23:16:52,399 INFO Starting job - structure_to_conventional (35821036-0bc6-4db7-9075-b37075dc772a)\n", + "2025-02-07 23:16:52,419 INFO Finished job - structure_to_conventional (35821036-0bc6-4db7-9075-b37075dc772a)\n", + "2025-02-07 23:16:52,423 INFO Starting job - Force field relax (f7dc16d0-7048-42b3-818b-48b20b5c3a79)\n" ] }, { From 68b4f4df2ebb08ff7c55ec1757b6a62e38ac6322 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:29:55 +0100 Subject: [PATCH 07/61] test tutorial test --- .github/workflows/testing.yml | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index cafdc811bb..2241abc332 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -89,6 +89,12 @@ jobs: micromamba activate a2 pytest --splits 3 --group ${{ matrix.split }} --durations-path tests/.pytest-split-durations --splitting-algorithm least_duration --ignore=tests/ase --cov=atomate2 --cov-report=xml + - name: Test tutorials with forcefields + run: | + micromamba activate a2 + pytest --nbmake ./tutorials/force_fields + + - uses: codecov/codecov-action@v1 if: matrix.python-version == '3.10' && github.repository == 'materialsproject/atomate2' with: @@ -150,7 +156,7 @@ jobs: - name: Test Notebooks run: | micromamba activate a2 - pytest --nbmake ./tutorials --ignore=./tutorials/openmm_tutorial.ipynb + pytest --nbmake ./tutorials --ignore=./tutorials/openmm_tutorial.ipynb --ignore=./tutorials/force_fields/* - name: Test ASE env: From ff23100da7cfd7705e1d04a25029899c8b078236 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:32:21 +0100 Subject: [PATCH 08/61] test tutorial test --- tutorials/force_fields/phonon_workflow.ipynb | 27 ++++++- tutorials/phonon_workflow.ipynb | 81 +------------------- 2 files changed, 25 insertions(+), 83 deletions(-) diff --git a/tutorials/force_fields/phonon_workflow.ipynb b/tutorials/force_fields/phonon_workflow.ipynb index 17578e62d4..945baad67f 100644 --- a/tutorials/force_fields/phonon_workflow.ipynb +++ b/tutorials/force_fields/phonon_workflow.ipynb @@ -42,17 +42,38 @@ " prefer_90_degrees=False,\n", " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", " ).make(si_structure)\n", - "run_locally(flow, store=job_store,create_folders=True, raise_immediately=True)" + "run_locally(flow, create_folders=True, raise_immediately=True)" ], "id": "83e78705d60d7c9b" }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "One can switch to a different force field as well!", + "id": "dc4717fc382f4bcc" + }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, - "source": "", - "id": "bf10b23db3655278" + "source": [ + "from atomate2.forcefields.jobs import ForceFieldRelaxMaker, ForceFieldStaticMaker\n", + "flow=PhononMaker(\n", + " min_length=3.0,\n", + " bulk_relax_maker=None,\n", + " use_symmetrized_structure=\"conventional\",\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + " bulk_maker=ForceFieldRelaxMaker(force_field_name=\"MACE\"),\n", + " static_energy_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", + " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE\")).make(si_structure)\n", + "\n", + "run_locally(flow, create_folders=True, raise_immediately=True)" + ], + "id": "1c49126ed2c57d30" } ], "metadata": { diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index 44c954f77b..c4256298bc 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -94,7 +94,7 @@ { "metadata": {}, "cell_type": "markdown", - "source": "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction considering very long-ranging forces by computing the `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_relax_maker`. Please make sure this is done very accurately.", + "source": "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction considering very long-ranging forces by computing the `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_maker`. Please make sure this is done very accurately.", "id": "7e042abdd5362b80" }, { @@ -382,85 +382,6 @@ "cell_type": "markdown", "source": "One can run the same workflow with a forcefield as well. Here, we cannot consider BORN charges yet as there is no forcefield equivalent. You can find this tutorial in the force field tutorials.", "id": "c12bfc7daf5b541" - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:16:52.987071Z", - "start_time": "2025-02-07T22:16:52.230897Z" - } - }, - "cell_type": "code", - "source": "", - "id": "82bb21620a5679f1", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-07 23:16:52,394 INFO Started executing jobs locally\n", - "2025-02-07 23:16:52,399 INFO Starting job - structure_to_conventional (35821036-0bc6-4db7-9075-b37075dc772a)\n", - "2025-02-07 23:16:52,419 INFO Finished job - structure_to_conventional (35821036-0bc6-4db7-9075-b37075dc772a)\n", - "2025-02-07 23:16:52,423 INFO Starting job - Force field relax (f7dc16d0-7048-42b3-818b-48b20b5c3a79)\n" - ] - }, - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'torch'", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[8], line 11\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01matomate2\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mforcefields\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mflows\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mphonons\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m PhononMaker\n\u001B[1;32m 2\u001B[0m flow\u001B[38;5;241m=\u001B[39mPhononMaker(\n\u001B[1;32m 3\u001B[0m min_length\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3.0\u001B[39m,\n\u001B[1;32m 4\u001B[0m born_maker\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 9\u001B[0m generate_frequencies_eigenvectors_kwargs\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtstep\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;241m100\u001B[39m},\n\u001B[1;32m 10\u001B[0m )\u001B[38;5;241m.\u001B[39mmake(si_structure)\n\u001B[0;32m---> 11\u001B[0m run_locally(flow, store\u001B[38;5;241m=\u001B[39mjob_store,create_folders\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, raise_immediately\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:180\u001B[0m, in \u001B[0;36mrun_locally\u001B[0;34m(flow, log, store, create_folders, root_dir, ensure_success, allow_external_references, raise_immediately)\u001B[0m\n\u001B[1;32m 177\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m encountered_bad_response\n\u001B[1;32m 179\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarted executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 180\u001B[0m finished_successfully \u001B[38;5;241m=\u001B[39m _run(flow)\n\u001B[1;32m 181\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFinished executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 183\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ensure_success \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m finished_successfully:\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:169\u001B[0m, in \u001B[0;36mrun_locally.._run\u001B[0;34m(root_flow)\u001B[0m\n\u001B[1;32m 167\u001B[0m job_dir \u001B[38;5;241m=\u001B[39m _get_job_dir()\n\u001B[1;32m 168\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m cd(job_dir):\n\u001B[0;32m--> 169\u001B[0m response, jobflow_stopped \u001B[38;5;241m=\u001B[39m _run_job(job, parents)\n\u001B[1;32m 171\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 172\u001B[0m response\u001B[38;5;241m.\u001B[39mjob_dir \u001B[38;5;241m=\u001B[39m job_dir\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:114\u001B[0m, in \u001B[0;36mrun_locally.._run_job\u001B[0;34m(job, parents)\u001B[0m\n\u001B[1;32m 111\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m 113\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m raise_immediately:\n\u001B[0;32m--> 114\u001B[0m response \u001B[38;5;241m=\u001B[39m job\u001B[38;5;241m.\u001B[39mrun(store\u001B[38;5;241m=\u001B[39mstore)\n\u001B[1;32m 115\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604\u001B[0m, in \u001B[0;36mJob.run\u001B[0;34m(self, store, job_dir)\u001B[0m\n\u001B[1;32m 601\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m bound \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(bound, types\u001B[38;5;241m.\u001B[39mModuleType):\n\u001B[1;32m 602\u001B[0m function \u001B[38;5;241m=\u001B[39m types\u001B[38;5;241m.\u001B[39mMethodType(function, bound)\n\u001B[0;32m--> 604\u001B[0m response \u001B[38;5;241m=\u001B[39m function(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_args, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_kwargs)\n\u001B[1;32m 605\u001B[0m response \u001B[38;5;241m=\u001B[39m Response\u001B[38;5;241m.\u001B[39mfrom_job_returns(\n\u001B[1;32m 606\u001B[0m response, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moutput_schema, job_dir\u001B[38;5;241m=\u001B[39mjob_dir\n\u001B[1;32m 607\u001B[0m )\n\u001B[1;32m 609\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response\u001B[38;5;241m.\u001B[39mreplace \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/forcefields/jobs.py:158\u001B[0m, in \u001B[0;36mForceFieldRelaxMaker.make\u001B[0;34m(self, structure, prev_dir)\u001B[0m\n\u001B[1;32m 143\u001B[0m \u001B[38;5;129m@forcefield_job\u001B[39m\n\u001B[1;32m 144\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmake\u001B[39m(\n\u001B[1;32m 145\u001B[0m \u001B[38;5;28mself\u001B[39m, structure: Structure, prev_dir: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m|\u001B[39m Path \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 146\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m ForceFieldTaskDocument:\n\u001B[1;32m 147\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 148\u001B[0m \u001B[38;5;124;03m Perform a relaxation of a structure using a force field.\u001B[39;00m\n\u001B[1;32m 149\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 156\u001B[0m \u001B[38;5;124;03m added to match the method signature of other makers.\u001B[39;00m\n\u001B[1;32m 157\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 158\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m revert_default_dtype():\n\u001B[1;32m 159\u001B[0m ase_result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrun_ase(structure, prev_dir\u001B[38;5;241m=\u001B[39mprev_dir)\n\u001B[1;32m 161\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtask_document_kwargs) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m:\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/contextlib.py:137\u001B[0m, in \u001B[0;36m_GeneratorContextManager.__enter__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 135\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mkwds, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunc\n\u001B[1;32m 136\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 137\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mnext\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgen)\n\u001B[1;32m 138\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m:\n\u001B[1;32m 139\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgenerator didn\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mt yield\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/forcefields/utils.py:117\u001B[0m, in \u001B[0;36mrevert_default_dtype\u001B[0;34m()\u001B[0m\n\u001B[1;32m 108\u001B[0m \u001B[38;5;129m@contextmanager\u001B[39m\n\u001B[1;32m 109\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mrevert_default_dtype\u001B[39m() \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Generator[\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m]:\n\u001B[1;32m 110\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Context manager for torch.default_dtype.\u001B[39;00m\n\u001B[1;32m 111\u001B[0m \n\u001B[1;32m 112\u001B[0m \u001B[38;5;124;03m Reverts it to whatever torch.get_default_dtype() was when entering the context.\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 115\u001B[0m \u001B[38;5;124;03m https://github.com/ACEsuit/mace/issues/328\u001B[39;00m\n\u001B[1;32m 116\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 117\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mtorch\u001B[39;00m\n\u001B[1;32m 119\u001B[0m orig \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mget_default_dtype()\n\u001B[1;32m 120\u001B[0m \u001B[38;5;28;01myield\u001B[39;00m\n", - "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'torch'" - ] - } - ], - "execution_count": 8 - }, - { - "metadata": {}, - "cell_type": "markdown", - "source": "It is possible to switch to another force field as well!", - "id": "c9b438cee59176b0" - }, - { - "metadata": {}, - "cell_type": "code", - "source": [ - "from atomate2.forcefields.jobs import ForceFieldRelaxMaker, ForceFieldStaticMaker\n", - "flow=PhononMaker(\n", - " min_length=3.0,\n", - " bulk_relax_maker=None,\n", - " use_symmetrized_structure=\"conventional\",\n", - " create_thermal_displacements=False,\n", - " store_force_constants=False,\n", - " prefer_90_degrees=False,\n", - " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", - " bulk_makre=ForceFieldRelaxMaker(force_field_name=\"MACE\"),\n", - " static_energy_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", - " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE\")).make(si_structure)\n", - "\n", - "run_locally(flow, store=job_store,create_folders=True, raise_immediately=True)" - ], - "id": "e85fc4a0867a5a4d", - "outputs": [], - "execution_count": null - }, - { - "metadata": {}, - "cell_type": "code", - "source": "", - "id": "ed2c876bf8fa9353", - "outputs": [], - "execution_count": null } ], "metadata": { From 0ce238871011e10d2931af6a146a5c6ac0d912ea Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:36:08 +0100 Subject: [PATCH 09/61] test tutorial test --- .github/workflows/testing.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index 2241abc332..f7a42e45fd 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -156,7 +156,7 @@ jobs: - name: Test Notebooks run: | micromamba activate a2 - pytest --nbmake ./tutorials --ignore=./tutorials/openmm_tutorial.ipynb --ignore=./tutorials/force_fields/* + pytest --nbmake ./tutorials --ignore=./tutorials/openmm_tutorial.ipynb --ignore=./tutorials/force_fields - name: Test ASE env: From e453f970e8fe613770e7006d1dbf444fb20bcabe Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:42:54 +0100 Subject: [PATCH 10/61] split up workflow again --- .github/workflows/testing.yml | 76 +++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index f7a42e45fd..28a90ee37b 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -171,6 +171,82 @@ jobs: token: ${{ secrets.CODECOV_TOKEN }} file: ./coverage.xml + test-force-field-notebook: + # prevent this action from running on forks + if: github.repository == 'materialsproject/atomate2' + + services: + local_mongodb: + image: mongo:4.0 + ports: + - 27017:27017 + + runs-on: ubuntu-latest + defaults: + run: + shell: bash -l {0} # enables conda/mamba env activation by reading bash profile + strategy: + matrix: + python-version: ["3.10", "3.11", "3.12"] + split: [1, 2, 3] + + steps: + - name: Check out repo + uses: actions/checkout@v4 + + - name: Set up micromamba + uses: mamba-org/setup-micromamba@main + + - name: Create mamba environment + run: | + micromamba create -n a2 python=${{ matrix.python-version }} --yes + + - name: Install uv + run: micromamba run -n a2 pip install uv + + - name: Install conda dependencies + run: | + micromamba install -n a2 -c conda-forge enumlib packmol bader openbabel openff-toolkit==0.16.2 openff-interchange==0.3.22 --yes + + - name: Install dependencies + run: | + micromamba activate a2 + python -m pip install --upgrade pip + mkdir -p ~/.abinit/pseudos + cp -r tests/test_data/abinit/pseudos/ONCVPSP-PBE-SR-PDv0.4 ~/.abinit/pseudos + uv pip install .[strict,strict-forcefields,tests,abinit] + uv pip install torch-runstats + uv pip install --no-deps nequip==0.5.6 + + - name: Install pymatgen from master if triggered by pymatgen repo dispatch + if: github.event_name == 'repository_dispatch' && github.event.action == 'pymatgen-ci-trigger' + run: | + micromamba activate a2 + uv pip install --upgrade 'git+https://github.com/materialsproject/pymatgen@${{ github.event.client_payload.pymatgen_ref }}' + + - name: Forcefield tutorial + env: + MP_API_KEY: ${{ secrets.MP_API_KEY }} + + # regenerate durations file with `pytest --store-durations --durations-path tests/.pytest-split-durations` + # Note the use of `--splitting-algorithm least_duration`. + # This helps prevent a test split having no tests to run, and then the GH action failing, see: + # https://github.com/jerry-git/pytest-split/issues/95 + # However this `splitting-algorithm` means that tests cannot depend sensitively on the order they're executed in. + run: | + micromamba activate a2 + pytest --nbmake ./tutorials/force_fields + + + - uses: codecov/codecov-action@v1 + if: matrix.python-version == '3.10' && github.repository == 'materialsproject/atomate2' + with: + token: ${{ secrets.CODECOV_TOKEN }} + name: coverage${{ matrix.split }} + file: ./coverage.xml + + + docs: runs-on: ubuntu-latest From 2a4355bec6e4ad372ac4e3d0b115a1bb764178aa Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:46:22 +0100 Subject: [PATCH 11/61] split up workflow again --- .github/workflows/testing.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index 28a90ee37b..0e77c4ceba 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -188,7 +188,6 @@ jobs: strategy: matrix: python-version: ["3.10", "3.11", "3.12"] - split: [1, 2, 3] steps: - name: Check out repo @@ -242,7 +241,7 @@ jobs: if: matrix.python-version == '3.10' && github.repository == 'materialsproject/atomate2' with: token: ${{ secrets.CODECOV_TOKEN }} - name: coverage${{ matrix.split }} + name: coverage file: ./coverage.xml From 81121a0a18600570097ee2c8733d0d3446416781 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:52:14 +0100 Subject: [PATCH 12/61] split up workflow again --- .github/workflows/testing.yml | 5 ----- tutorials/force_fields/phonon_workflow.ipynb | 6 +----- 2 files changed, 1 insertion(+), 10 deletions(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index 0e77c4ceba..bf6458f3f4 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -89,11 +89,6 @@ jobs: micromamba activate a2 pytest --splits 3 --group ${{ matrix.split }} --durations-path tests/.pytest-split-durations --splitting-algorithm least_duration --ignore=tests/ase --cov=atomate2 --cov-report=xml - - name: Test tutorials with forcefields - run: | - micromamba activate a2 - pytest --nbmake ./tutorials/force_fields - - uses: codecov/codecov-action@v1 if: matrix.python-version == '3.10' && github.repository == 'materialsproject/atomate2' diff --git a/tutorials/force_fields/phonon_workflow.ipynb b/tutorials/force_fields/phonon_workflow.ipynb index 945baad67f..a80ead6eaa 100644 --- a/tutorials/force_fields/phonon_workflow.ipynb +++ b/tutorials/force_fields/phonon_workflow.ipynb @@ -8,11 +8,7 @@ "collapsed": true }, "outputs": [], - "source": [ - "from pathlib import Path\n", - "TEST_ROOT = Path(__file__).parent.parent / \"tests\"\n", - "TEST_DIR = TEST_ROOT / \"test_data\"" - ] + "source": "from tutorials.mock_vasp import TEST_DIR" }, { "metadata": {}, From d1f47a0397acd07baf5eb1dd15bfe7034eab043a Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 7 Feb 2025 23:58:31 +0100 Subject: [PATCH 13/61] split up workflow again --- tutorials/force_fields/phonon_workflow.ipynb | 80 ++++++++++++++++---- tutorials/phonon_workflow.ipynb | 2 +- 2 files changed, 68 insertions(+), 14 deletions(-) diff --git a/tutorials/force_fields/phonon_workflow.ipynb b/tutorials/force_fields/phonon_workflow.ipynb index a80ead6eaa..35f31c2c62 100644 --- a/tutorials/force_fields/phonon_workflow.ipynb +++ b/tutorials/force_fields/phonon_workflow.ipynb @@ -2,30 +2,46 @@ "cells": [ { "cell_type": "code", - "execution_count": null, "id": "initial_id", "metadata": { - "collapsed": true + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-02-07T22:56:35.139732Z", + "start_time": "2025-02-07T22:56:35.135377Z" + } }, + "source": [ + "from pathlib import Path\n", + "TEST_ROOT = Path().cwd().parent.parent / \"tests\"\n", + "TEST_DIR = TEST_ROOT / \"test_data\"" + ], "outputs": [], - "source": "from tutorials.mock_vasp import TEST_DIR" + "execution_count": 1 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T22:56:35.488163Z", + "start_time": "2025-02-07T22:56:35.179940Z" + } + }, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": [ "from pymatgen.core.structure import Structure\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")" ], - "id": "59d1925b6558c9bb" + "id": "59d1925b6558c9bb", + "outputs": [], + "execution_count": 2 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-07T22:56:45.266340Z", + "start_time": "2025-02-07T22:56:35.600781Z" + } + }, "cell_type": "code", - "outputs": [], - "execution_count": null, "source": [ "from atomate2.forcefields.flows.phonons import PhononMaker\n", "from jobflow import run_locally\n", @@ -40,7 +56,46 @@ " ).make(si_structure)\n", "run_locally(flow, create_folders=True, raise_immediately=True)" ], - "id": "83e78705d60d7c9b" + "id": "83e78705d60d7c9b", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-07 23:56:44,550 INFO Started executing jobs locally\n", + "2025-02-07 23:56:44,562 INFO Starting job - structure_to_conventional (bf8de028-e1b9-4df1-befa-ac536c266447)\n", + "2025-02-07 23:56:44,583 INFO Finished job - structure_to_conventional (bf8de028-e1b9-4df1-befa-ac536c266447)\n", + "2025-02-07 23:56:44,587 INFO Starting job - Force field relax (d5c19be4-c96e-4f98-8656-71058365b83c)\n" + ] + }, + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'torch'", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[3], line 12\u001B[0m\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mjobflow\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m run_locally\n\u001B[1;32m 3\u001B[0m flow\u001B[38;5;241m=\u001B[39mPhononMaker(\n\u001B[1;32m 4\u001B[0m min_length\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3.0\u001B[39m,\n\u001B[1;32m 5\u001B[0m born_maker\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 10\u001B[0m generate_frequencies_eigenvectors_kwargs\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtstep\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;241m100\u001B[39m},\n\u001B[1;32m 11\u001B[0m )\u001B[38;5;241m.\u001B[39mmake(si_structure)\n\u001B[0;32m---> 12\u001B[0m run_locally(flow, create_folders\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, raise_immediately\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:180\u001B[0m, in \u001B[0;36mrun_locally\u001B[0;34m(flow, log, store, create_folders, root_dir, ensure_success, allow_external_references, raise_immediately)\u001B[0m\n\u001B[1;32m 177\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m encountered_bad_response\n\u001B[1;32m 179\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarted executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 180\u001B[0m finished_successfully \u001B[38;5;241m=\u001B[39m _run(flow)\n\u001B[1;32m 181\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFinished executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 183\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ensure_success \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m finished_successfully:\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:169\u001B[0m, in \u001B[0;36mrun_locally.._run\u001B[0;34m(root_flow)\u001B[0m\n\u001B[1;32m 167\u001B[0m job_dir \u001B[38;5;241m=\u001B[39m _get_job_dir()\n\u001B[1;32m 168\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m cd(job_dir):\n\u001B[0;32m--> 169\u001B[0m response, jobflow_stopped \u001B[38;5;241m=\u001B[39m _run_job(job, parents)\n\u001B[1;32m 171\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 172\u001B[0m response\u001B[38;5;241m.\u001B[39mjob_dir \u001B[38;5;241m=\u001B[39m job_dir\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:114\u001B[0m, in \u001B[0;36mrun_locally.._run_job\u001B[0;34m(job, parents)\u001B[0m\n\u001B[1;32m 111\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m 113\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m raise_immediately:\n\u001B[0;32m--> 114\u001B[0m response \u001B[38;5;241m=\u001B[39m job\u001B[38;5;241m.\u001B[39mrun(store\u001B[38;5;241m=\u001B[39mstore)\n\u001B[1;32m 115\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604\u001B[0m, in \u001B[0;36mJob.run\u001B[0;34m(self, store, job_dir)\u001B[0m\n\u001B[1;32m 601\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m bound \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(bound, types\u001B[38;5;241m.\u001B[39mModuleType):\n\u001B[1;32m 602\u001B[0m function \u001B[38;5;241m=\u001B[39m types\u001B[38;5;241m.\u001B[39mMethodType(function, bound)\n\u001B[0;32m--> 604\u001B[0m response \u001B[38;5;241m=\u001B[39m function(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_args, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_kwargs)\n\u001B[1;32m 605\u001B[0m response \u001B[38;5;241m=\u001B[39m Response\u001B[38;5;241m.\u001B[39mfrom_job_returns(\n\u001B[1;32m 606\u001B[0m response, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moutput_schema, job_dir\u001B[38;5;241m=\u001B[39mjob_dir\n\u001B[1;32m 607\u001B[0m )\n\u001B[1;32m 609\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response\u001B[38;5;241m.\u001B[39mreplace \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/forcefields/jobs.py:158\u001B[0m, in \u001B[0;36mForceFieldRelaxMaker.make\u001B[0;34m(self, structure, prev_dir)\u001B[0m\n\u001B[1;32m 143\u001B[0m \u001B[38;5;129m@forcefield_job\u001B[39m\n\u001B[1;32m 144\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmake\u001B[39m(\n\u001B[1;32m 145\u001B[0m \u001B[38;5;28mself\u001B[39m, structure: Structure, prev_dir: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m|\u001B[39m Path \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 146\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m ForceFieldTaskDocument:\n\u001B[1;32m 147\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 148\u001B[0m \u001B[38;5;124;03m Perform a relaxation of a structure using a force field.\u001B[39;00m\n\u001B[1;32m 149\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 156\u001B[0m \u001B[38;5;124;03m added to match the method signature of other makers.\u001B[39;00m\n\u001B[1;32m 157\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 158\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m revert_default_dtype():\n\u001B[1;32m 159\u001B[0m ase_result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrun_ase(structure, prev_dir\u001B[38;5;241m=\u001B[39mprev_dir)\n\u001B[1;32m 161\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtask_document_kwargs) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m:\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/contextlib.py:137\u001B[0m, in \u001B[0;36m_GeneratorContextManager.__enter__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 135\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mkwds, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunc\n\u001B[1;32m 136\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 137\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mnext\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgen)\n\u001B[1;32m 138\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m:\n\u001B[1;32m 139\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgenerator didn\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mt yield\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/forcefields/utils.py:117\u001B[0m, in \u001B[0;36mrevert_default_dtype\u001B[0;34m()\u001B[0m\n\u001B[1;32m 108\u001B[0m \u001B[38;5;129m@contextmanager\u001B[39m\n\u001B[1;32m 109\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mrevert_default_dtype\u001B[39m() \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Generator[\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m]:\n\u001B[1;32m 110\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Context manager for torch.default_dtype.\u001B[39;00m\n\u001B[1;32m 111\u001B[0m \n\u001B[1;32m 112\u001B[0m \u001B[38;5;124;03m Reverts it to whatever torch.get_default_dtype() was when entering the context.\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 115\u001B[0m \u001B[38;5;124;03m https://github.com/ACEsuit/mace/issues/328\u001B[39;00m\n\u001B[1;32m 116\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 117\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mtorch\u001B[39;00m\n\u001B[1;32m 119\u001B[0m orig \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mget_default_dtype()\n\u001B[1;32m 120\u001B[0m \u001B[38;5;28;01myield\u001B[39;00m\n", + "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'torch'" + ] + } + ], + "execution_count": 3 }, { "metadata": {}, @@ -57,14 +112,13 @@ "from atomate2.forcefields.jobs import ForceFieldRelaxMaker, ForceFieldStaticMaker\n", "flow=PhononMaker(\n", " min_length=3.0,\n", - " bulk_relax_maker=None,\n", " use_symmetrized_structure=\"conventional\",\n", " create_thermal_displacements=False,\n", " store_force_constants=False,\n", " prefer_90_degrees=False,\n", " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", - " bulk_maker=ForceFieldRelaxMaker(force_field_name=\"MACE\"),\n", " static_energy_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", + " bulk_relax_maker=ForceFieldRelaxMaker(force_field_name=\"MACE\"),\n", " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE\")).make(si_structure)\n", "\n", "run_locally(flow, create_folders=True, raise_immediately=True)" diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index c4256298bc..e5ba5d21c9 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -94,7 +94,7 @@ { "metadata": {}, "cell_type": "markdown", - "source": "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction considering very long-ranging forces by computing the `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_maker`. Please make sure this is done very accurately.", + "source": "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction considering very long-ranging forces by computing the `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_relax_maker`. Please make sure this is done very accurately.", "id": "7e042abdd5362b80" }, { From a27b14d1ec469fa524baab0d50da56e293d95910 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Sat, 8 Feb 2025 00:00:28 +0100 Subject: [PATCH 14/61] add automerge --- .github/workflows/testing.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index bf6458f3f4..dd5d8f93dc 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -262,7 +262,7 @@ jobs: run: sphinx-build docs docs_build automerge: - needs: [lint, test-non-ase, test-notebooks-and-ase, docs] + needs: [lint, test-non-ase, test-notebooks-and-ase, test-force-field-notebook, docs] runs-on: ubuntu-latest permissions: From cdbe273cc8992fe2313e4580c2425792590e4ec0 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Sat, 8 Feb 2025 00:02:46 +0100 Subject: [PATCH 15/61] add automerge --- docs/user/codes/vasp.md | 4 +- tutorials/force_fields/phonon_workflow.ipynb | 150 +++---- tutorials/phonon_workflow.ipynb | 403 +++++-------------- 3 files changed, 162 insertions(+), 395 deletions(-) diff --git a/docs/user/codes/vasp.md b/docs/user/codes/vasp.md index 53f09edb98..a75cc0664c 100644 --- a/docs/user/codes/vasp.md +++ b/docs/user/codes/vasp.md @@ -259,7 +259,9 @@ structure = Structure( coords=[[0, 0, 0], [0.5, 0.5, 0.5]], ) -phonon_flow = PhononMaker(min_length=15.0, store_force_constants=False).make(structure=struct) +phonon_flow = PhononMaker(min_length=15.0, store_force_constants=False).make( + structure=struct +) ``` diff --git a/tutorials/force_fields/phonon_workflow.ipynb b/tutorials/force_fields/phonon_workflow.ipynb index 35f31c2c62..172677d1bd 100644 --- a/tutorials/force_fields/phonon_workflow.ipynb +++ b/tutorials/force_fields/phonon_workflow.ipynb @@ -2,136 +2,86 @@ "cells": [ { "cell_type": "code", - "id": "initial_id", - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2025-02-07T22:56:35.139732Z", - "start_time": "2025-02-07T22:56:35.135377Z" - } - }, + "execution_count": null, + "id": "0", + "metadata": {}, + "outputs": [], "source": [ "from pathlib import Path\n", + "\n", "TEST_ROOT = Path().cwd().parent.parent / \"tests\"\n", "TEST_DIR = TEST_ROOT / \"test_data\"" - ], - "outputs": [], - "execution_count": 1 + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:56:35.488163Z", - "start_time": "2025-02-07T22:56:35.179940Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], "source": [ "from pymatgen.core.structure import Structure\n", + "\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")" - ], - "id": "59d1925b6558c9bb", - "outputs": [], - "execution_count": 2 + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:56:45.266340Z", - "start_time": "2025-02-07T22:56:35.600781Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], "source": [ - "from atomate2.forcefields.flows.phonons import PhononMaker\n", "from jobflow import run_locally\n", - "flow=PhononMaker(\n", - " min_length=3.0,\n", - " born_maker=None,\n", - " use_symmetrized_structure=\"conventional\",\n", - " create_thermal_displacements=False,\n", - " store_force_constants=False,\n", - " prefer_90_degrees=False,\n", - " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", - " ).make(si_structure)\n", + "\n", + "from atomate2.forcefields.flows.phonons import PhononMaker\n", + "\n", + "flow = PhononMaker(\n", + " min_length=3.0,\n", + " born_maker=None,\n", + " use_symmetrized_structure=\"conventional\",\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + ").make(si_structure)\n", "run_locally(flow, create_folders=True, raise_immediately=True)" - ], - "id": "83e78705d60d7c9b", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-07 23:56:44,550 INFO Started executing jobs locally\n", - "2025-02-07 23:56:44,562 INFO Starting job - structure_to_conventional (bf8de028-e1b9-4df1-befa-ac536c266447)\n", - "2025-02-07 23:56:44,583 INFO Finished job - structure_to_conventional (bf8de028-e1b9-4df1-befa-ac536c266447)\n", - "2025-02-07 23:56:44,587 INFO Starting job - Force field relax (d5c19be4-c96e-4f98-8656-71058365b83c)\n" - ] - }, - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'torch'", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[3], line 12\u001B[0m\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mjobflow\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m run_locally\n\u001B[1;32m 3\u001B[0m flow\u001B[38;5;241m=\u001B[39mPhononMaker(\n\u001B[1;32m 4\u001B[0m min_length\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m3.0\u001B[39m,\n\u001B[1;32m 5\u001B[0m born_maker\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 10\u001B[0m generate_frequencies_eigenvectors_kwargs\u001B[38;5;241m=\u001B[39m{\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtstep\u001B[39m\u001B[38;5;124m\"\u001B[39m: \u001B[38;5;241m100\u001B[39m},\n\u001B[1;32m 11\u001B[0m )\u001B[38;5;241m.\u001B[39mmake(si_structure)\n\u001B[0;32m---> 12\u001B[0m run_locally(flow, create_folders\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, raise_immediately\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:180\u001B[0m, in \u001B[0;36mrun_locally\u001B[0;34m(flow, log, store, create_folders, root_dir, ensure_success, allow_external_references, raise_immediately)\u001B[0m\n\u001B[1;32m 177\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m encountered_bad_response\n\u001B[1;32m 179\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mStarted executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 180\u001B[0m finished_successfully \u001B[38;5;241m=\u001B[39m _run(flow)\n\u001B[1;32m 181\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFinished executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 183\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ensure_success \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m finished_successfully:\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:169\u001B[0m, in \u001B[0;36mrun_locally.._run\u001B[0;34m(root_flow)\u001B[0m\n\u001B[1;32m 167\u001B[0m job_dir \u001B[38;5;241m=\u001B[39m _get_job_dir()\n\u001B[1;32m 168\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m cd(job_dir):\n\u001B[0;32m--> 169\u001B[0m response, jobflow_stopped \u001B[38;5;241m=\u001B[39m _run_job(job, parents)\n\u001B[1;32m 171\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 172\u001B[0m response\u001B[38;5;241m.\u001B[39mjob_dir \u001B[38;5;241m=\u001B[39m job_dir\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:114\u001B[0m, in \u001B[0;36mrun_locally.._run_job\u001B[0;34m(job, parents)\u001B[0m\n\u001B[1;32m 111\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m 113\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m raise_immediately:\n\u001B[0;32m--> 114\u001B[0m response \u001B[38;5;241m=\u001B[39m job\u001B[38;5;241m.\u001B[39mrun(store\u001B[38;5;241m=\u001B[39mstore)\n\u001B[1;32m 115\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 116\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604\u001B[0m, in \u001B[0;36mJob.run\u001B[0;34m(self, store, job_dir)\u001B[0m\n\u001B[1;32m 601\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m bound \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(bound, types\u001B[38;5;241m.\u001B[39mModuleType):\n\u001B[1;32m 602\u001B[0m function \u001B[38;5;241m=\u001B[39m types\u001B[38;5;241m.\u001B[39mMethodType(function, bound)\n\u001B[0;32m--> 604\u001B[0m response \u001B[38;5;241m=\u001B[39m function(\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_args, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunction_kwargs)\n\u001B[1;32m 605\u001B[0m response \u001B[38;5;241m=\u001B[39m Response\u001B[38;5;241m.\u001B[39mfrom_job_returns(\n\u001B[1;32m 606\u001B[0m response, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39moutput_schema, job_dir\u001B[38;5;241m=\u001B[39mjob_dir\n\u001B[1;32m 607\u001B[0m )\n\u001B[1;32m 609\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response\u001B[38;5;241m.\u001B[39mreplace \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/forcefields/jobs.py:158\u001B[0m, in \u001B[0;36mForceFieldRelaxMaker.make\u001B[0;34m(self, structure, prev_dir)\u001B[0m\n\u001B[1;32m 143\u001B[0m \u001B[38;5;129m@forcefield_job\u001B[39m\n\u001B[1;32m 144\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmake\u001B[39m(\n\u001B[1;32m 145\u001B[0m \u001B[38;5;28mself\u001B[39m, structure: Structure, prev_dir: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m|\u001B[39m Path \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 146\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m ForceFieldTaskDocument:\n\u001B[1;32m 147\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m 148\u001B[0m \u001B[38;5;124;03m Perform a relaxation of a structure using a force field.\u001B[39;00m\n\u001B[1;32m 149\u001B[0m \n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 156\u001B[0m \u001B[38;5;124;03m added to match the method signature of other makers.\u001B[39;00m\n\u001B[1;32m 157\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 158\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m revert_default_dtype():\n\u001B[1;32m 159\u001B[0m ase_result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrun_ase(structure, prev_dir\u001B[38;5;241m=\u001B[39mprev_dir)\n\u001B[1;32m 161\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtask_document_kwargs) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m0\u001B[39m:\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/contextlib.py:137\u001B[0m, in \u001B[0;36m_GeneratorContextManager.__enter__\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m 135\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mkwds, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfunc\n\u001B[1;32m 136\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 137\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mnext\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgen)\n\u001B[1;32m 138\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mStopIteration\u001B[39;00m:\n\u001B[1;32m 139\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgenerator didn\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mt yield\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/forcefields/utils.py:117\u001B[0m, in \u001B[0;36mrevert_default_dtype\u001B[0;34m()\u001B[0m\n\u001B[1;32m 108\u001B[0m \u001B[38;5;129m@contextmanager\u001B[39m\n\u001B[1;32m 109\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mrevert_default_dtype\u001B[39m() \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Generator[\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;28;01mNone\u001B[39;00m]:\n\u001B[1;32m 110\u001B[0m \u001B[38;5;250m \u001B[39m\u001B[38;5;124;03m\"\"\"Context manager for torch.default_dtype.\u001B[39;00m\n\u001B[1;32m 111\u001B[0m \n\u001B[1;32m 112\u001B[0m \u001B[38;5;124;03m Reverts it to whatever torch.get_default_dtype() was when entering the context.\u001B[39;00m\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 115\u001B[0m \u001B[38;5;124;03m https://github.com/ACEsuit/mace/issues/328\u001B[39;00m\n\u001B[1;32m 116\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[0;32m--> 117\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mtorch\u001B[39;00m\n\u001B[1;32m 119\u001B[0m orig \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mget_default_dtype()\n\u001B[1;32m 120\u001B[0m \u001B[38;5;28;01myield\u001B[39;00m\n", - "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'torch'" - ] - } - ], - "execution_count": 3 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "One can switch to a different force field as well!", - "id": "dc4717fc382f4bcc" + "id": "3", + "metadata": {}, + "source": [ + "One can switch to a different force field as well!" + ] }, { - "metadata": {}, "cell_type": "code", - "outputs": [], "execution_count": null, + "id": "4", + "metadata": {}, + "outputs": [], "source": [ "from atomate2.forcefields.jobs import ForceFieldRelaxMaker, ForceFieldStaticMaker\n", - "flow=PhononMaker(\n", - " min_length=3.0,\n", - " use_symmetrized_structure=\"conventional\",\n", - " create_thermal_displacements=False,\n", - " store_force_constants=False,\n", - " prefer_90_degrees=False,\n", - " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", - " static_energy_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", - " bulk_relax_maker=ForceFieldRelaxMaker(force_field_name=\"MACE\"),\n", - " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE\")).make(si_structure)\n", + "\n", + "flow = PhononMaker(\n", + " min_length=3.0,\n", + " use_symmetrized_structure=\"conventional\",\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + " static_energy_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", + " bulk_relax_maker=ForceFieldRelaxMaker(force_field_name=\"MACE\"),\n", + " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", + ").make(si_structure)\n", "\n", "run_locally(flow, create_folders=True, raise_immediately=True)" - ], - "id": "1c49126ed2c57d30" + ] } ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index e5ba5d21c9..f4186583c2 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -1,309 +1,162 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:", - "id": "5929afcd65d79f7e" + "id": "0", + "metadata": {}, + "source": [ + "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:16:39.898651Z", - "start_time": "2025-02-07T22:16:36.708061Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], "source": [ "from mock_vasp import TEST_DIR, mock_vasp\n", "\n", "ref_paths = {\n", - " \"phonon static 1/1\": \"Si_phonons_3/phonon_static_1_1\",\n", - " \"static\": \"Si_phonons_3/static\",\n", - " \"tight relax 1\": \"Si_phonons_3/tight_relax_1\",\n", - " \"tight relax 2\": \"Si_phonons_3/tight_relax_2\",\n", - " \"dielectric\": \"Si_phonons_3/dielectric\",\n", - " }" - ], - "id": "d14d39451aac0e35", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "execution_count": 1 + " \"phonon static 1/1\": \"Si_phonons_3/phonon_static_1_1\",\n", + " \"static\": \"Si_phonons_3/static\",\n", + " \"tight relax 1\": \"Si_phonons_3/tight_relax_1\",\n", + " \"tight relax 2\": \"Si_phonons_3/tight_relax_2\",\n", + " \"dielectric\": \"Si_phonons_3/dielectric\",\n", + "}" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "# Phonon Workflow", - "id": "cf25f03bbedfa1c5" + "id": "2", + "metadata": {}, + "source": [ + "# Phonon Workflow" + ] }, { - "metadata": {}, "cell_type": "raw", - "source": "This tutorial has been written based on a previous version from Aakash Naik.", - "id": "a0d30838dfd4147f" + "id": "3", + "metadata": {}, + "source": [ + "This tutorial has been written based on a previous version from Aakash Naik." + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "4", + "metadata": {}, "source": [ "## Background\n", "The Phonon workflow is based on the finite displacement approach as implemented in Phonopy.\n", "\n", "If you want to read more about Phonopy, please read Togo’s paper: https://doi.org/10.7566/JPSJ.92.012001" - ], - "id": "c0224ddf46cf7a38" + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "5", + "metadata": {}, "source": [ "## Let's run the workflow\n", "Now, we load a structure and other important functions and classes for running the phonon workflow." - ], - "id": "49d7cc42166b990f" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:16:40.294922Z", - "start_time": "2025-02-07T22:16:39.902796Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], "source": [ + "from jobflow import JobStore, run_locally\n", + "from maggma.stores import MemoryStore\n", "from pymatgen.core import Structure\n", + "\n", "from atomate2.vasp.flows.phonons import PhononMaker\n", - "from jobflow import run_locally, JobStore\n", - "from maggma.stores import MemoryStore\n", "\n", "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", - "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")\n" - ], - "id": "17de20060b45220a", - "outputs": [], - "execution_count": 2 + "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction considering very long-ranging forces by computing the `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_relax_maker`. Please make sure this is done very accurately.", - "id": "7e042abdd5362b80" + "id": "7", + "metadata": {}, + "source": [ + "Then one can use the `PhononMaker` to generate a `Flow`. For testing here, we are choosing a very small supercell length (`min_length`). Ideally, a larger cell should be chosen. For non-metallic systems with more than one element, one might need to add the non-analytical term correction considering very long-ranging forces by computing the `BORN` charges with the `born_maker`. Of course, the structure should also be relaxed in advance with the `bulk_relax_maker`. Please make sure this is done very accurately." + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:16:40.617378Z", - "start_time": "2025-02-07T22:16:40.335466Z" - } - }, "cell_type": "code", - "source": [ - "flow=PhononMaker(\n", - " min_length=3.0,\n", - " use_symmetrized_structure=None,\n", - " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", - " create_thermal_displacements=True,\n", - " store_force_constants=True,\n", - " born_maker=None,\n", - " ).make(si_structure)" - ], - "id": "94e80cd2cebc9183", + "execution_count": null, + "id": "8", + "metadata": {}, "outputs": [], - "execution_count": 3 + "source": [ + "flow = PhononMaker(\n", + " min_length=3.0,\n", + " use_symmetrized_structure=None,\n", + " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", + " create_thermal_displacements=True,\n", + " store_force_constants=True,\n", + " born_maker=None,\n", + ").make(si_structure)" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "The phonon run will first perform a bulk relaxation, then the displacements are generated and run. As we currently don’t have a way to compute BORN charges with such potentials, a non-analytical term correction is not performed here. We can visualize the flow first.", - "id": "224c77cd46856b91" + "id": "9", + "metadata": {}, + "source": [ + "The phonon run will first perform a bulk relaxation, then the displacements are generated and run. As we currently don’t have a way to compute BORN charges with such potentials, a non-analytical term correction is not performed here. We can visualize the flow first." + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:16:40.782878Z", - "start_time": "2025-02-07T22:16:40.623327Z" - } - }, "cell_type": "code", - "source": "flow.draw_graph().show()", - "id": "4c713e7285b0f2c4", - "outputs": [ - { - "data": { - "text/plain": [ - "
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NTEnB19SVlTP0k+7BgwffPC9fv65vGTZsGAIDA9G2bVuUK1cO9vb2cHZ2hoODQ6Z9v/586ujooEyZMvLLmr/H19cXixcvxs2bN5Gamirf/uVn7u7duyhbtixKlCjx3XZy4+ekePHiqFChQqZtwP9/Ln7lc/uzz9qP/PXXXwgLC0Pjxo1hYmICe3t79O7dG82aNfvpsURE9HMsbImI8olMJoNEIsHBgweh/F/x8iUdHZ0Mj7+1DwD5fN30NuvWrYslS5Z8c9+v/7j/cmQ23YkTJ9CxY0e0aNECq1evRpkyZaCqqgpvb+8s3dhGJpMBAFxcXNCvX79v7lOvXr2ftvOrsnKecrO99LsNZ6f9rwtgeZvf2JYslWboJzcYGhoiKioKhw4dwsGDB3Hw4EF4e3ujb9++8PX1zXH7mzdvhqurKzp37owJEybA0NAQysrKmDt3rnyEO6ty6+fkZ+/jr3xuc/JZq1WrFm7duoV9+/YhJCQE27dvx+rVqzF16lR4eHj89HgiIvoxFrZERDlkaGgIDQ2NTJeOAsiwzdjYGIIgoEqVKvLRx5wyNjbG5cuX0apVqyyNrn7L9u3boaGhgUOHDmUopry9vTPt+60+DAwMUKxYMUilUtjZ2f1Shq/duXMHgiBk6O/27dsAkOluvz9TqVIlAMCtW7dQtWpV+faUlBTExsZmO3P6KPzzpCTIBCFD0fooIQFA5hHa7HgYHw+JRJLpCoBKlSrh2rVrmc5LVterVVNTg6OjIxwdHSGTyTBs2DCsWbMGU6ZMyXRlga2trfxxYmIinj17hnbt2n237eDgYFStWhU7duzIkO3rS46NjY1x6NAhvH379rujtnnxc/ItefG5BX58lYO2tjZ69OiBHj16ICUlBV27dsXs2bMxceJEaGho5FoGIqKiiHNsiYhySFlZGXZ2dti1axeePn0q337nzh0cPHhQ/rhr165QVlaGh4dHphEeQRDw5s2bbPft7OyMJ0+eYN26dZme+/jxIz58Yw7nt/JLJBJI/xspBD5f7vvlHZXTaWtrZ1oiR1lZGd26dcP27dtx7dq1TMd8a+mdn3n69Cl27twpfxwfHw8/Pz+YmZlluAw5K+zs7KCmpobly5dnOO8bNmzA+/fv5Xd9ziodHR0YliqFpNRUHP9i+RmpTIa9MTHQVFFB3e9cGp4VMXFxqFGtWqaRyXbt2uHp06cZlmFKSkr67mW0X/r6s6WkpCQfjfz6kue1a9dmuJTY09MTaWlpGeaLfy19JPPL83v27Fn8+++/Gfbr1q0bBEH45ghl+rF58XPyvcy5/bkFPv+MvH//PtP2r3OrqanB1NQUgiBkON9ERPRrOGJLRJQLpk+fjtDQUDRr1gxDhw6FVCrFypUrUadOHURFRQH4PBI1a9YsTJw4Effv30fnzp1RrFgxxMbGYufOnRg8eDDGjx+frX779OmDwMBAuLu7Izw8HM2aNYNUKsXNmzcRGBiIQ4cOwcLC4odttG/fHkuWLIGDgwN69+6Nly9fYtWqVTAxMckwnxcAGjZsiLCwMCxZsgRly5ZFlSpVYGlpiXnz5iE8PByWlpZwc3ODqakp3r59i0uXLiEsLAxvv7Fm649Ur14dAwcOxPnz51G6dGls3LgRL168+OYo8s8YGBhg4sSJ8PDwgIODAzp27Ihbt25h9erVaNSoUYYbRWVVNycnrPHywpLz53EnLg6ltbVx8vFjRL95g8FmZtD6ah5oVqVKpTj97BlcBg7M9JybmxtWrlyJvn374uLFiyhTpgw2bdoErSyMDg8aNAhv375Fy5YtUb58eTx48AArVqyAmZkZatWqlWHflJQUtGrVCs7OzvLz1Lx5c3Ts2PG77Xfo0AE7duxAly5d0L59e8TGxsLLywumpqZITEyU72dra4s+ffpg+fLliImJgYODA2QyGU6cOAFbW1uMGDEiT35Ovie3P7fA55+RgIAAjB07Fo0aNYKOjg4cHR1hb28PIyMjNGvWDKVLl8aNGzewcuVKtG/fXr78EBER5UB+3X6ZiKiwO3LkiGBubi6oqakJxsbGwvr164Vx48YJGhoaGfbbvn270Lx5c0FbW1vQ1tYWatasKQwfPly4deuWfB9ra+tvLg3y9TIugvB5aZ758+cLtWvXFtTV1QV9fX2hYcOGgoeHh/D+/Xv5fgCE4cOHfzP7hg0bhGrVqgnq6upCzZo1BW9vb2HatGnC178mbt68KbRo0ULQ1NQUAGRY+ufFixfC8OHDhQoVKgiqqqqCkZGR0KpVK2Ht2rVZPYWCIHxe7qd9+/bCoUOHhHr16skzBQUFZdgvfXma8+fPZ9ievjTLl8vfCMLn5X1q1qwpqKqqCqVLlxaGDh0qxMXFZdgnq+f9+vXrAgChjoGBoKumJqgoKQmVixcX/mjUKMPSPenL/QysVy/Tsj4AhN6mphm2TbC0FAAI0dHR3zw3Dx48EDp27ChoaWkJpUqVEkaPHi2EhIT8dLmf4OBgwd7eXjA0NBTU1NSEihUrCkOGDBGePXuW6XweO3ZMGDx4sKCvry/o6OgIv//+u/DmzZtM5+nL5X5kMpkwZ84coVKlSoK6urpgbm4u7Nu375uf17S0NGHhwoVCzZo1BTU1NcHAwEBo27atcPHixQz75eTnJP0z9LVv/Qxk5XOb/pn6+jMYGxubaQmhxMREoXfv3oKenp4AQP7616xZI7Ro0UIoWbKkoK6uLhgbGwsTJkzI8DNKRES/TiIIv3h3DSIi+qnOnTvj+vXriImJETuKwqhcuTLq1KmDffv2iR3lhxzatEHUv/9iVatWvzxC+6Wk1FQMP3IEZk2aIOTQoVxImD0+Pj7o378/zp8//9NRfiIiooKGc2yJiHLJx48fMzyOiYnBgQMHYGNjI04gylOeXl5ITEvDusuXc9yWIAhYd/kyEtPS4OnllQvpiIiIihbOsSUiyiVVq1aFq6urfG1UT09PqKmp4c8//xQ7WoHw6tWrDDeo+pqamtoP1zctaKpUqYJly5fDzc0Nhlpa6GVq+kvtpEil2HTtGg7FxmLx4sXQ1NTE8+fPM+xTvHjxby7VRERERJ+xsCUiyiUODg7w9/fH8+fPoa6ujiZNmmDOnDmoVq2a2NEKhEaNGuHBgwfffd7a2hoRERH5FygXDBo0CC9evMDkyZPxMikJbvXrZ+uy5KTUVMw/cwbn/ytkx40bh3HjxmXaz9vbG66urrkVm4iIqNDhHFsiIsoXp06dynS59pf09fXRsGHDfEyUczKZDGvWrMHbt28xd/Zs6KiooK+pKZqXLw9Vpe/P9kmVSnHyyRP4RUfjfUoKmjZrhr/++gtK3zmmdu3aKFOmTF69DCIiIoXHwpaIiCibZDIZgoOD8eeff+LBgwfo1asXZs+ejaHu7jgUGgp9LS00LVMG1fT1UVFXF+rKykiWSvEwPh4xcXE4/ewZ4pKS0MbeHmrq6ti7dy8qVaqEhQsXolu3bt8tcImIiOjbWNgSERFlkVQqRVBQEKZNm4bbt2/Lt+/cuROdO3cGAERHR8PLywuHDx3CrZgYfPlrViKRoEa1amjdpg2GDh2KWrVqYceOHejWrZt8nxo1asDDwwPdu3eHsrJyvr02IiIiRcbCloiIKAtiYmLQvn17xMTEQElJCTKZTP7cw4cPUaFChUzHJCYmon379pDJZFixYgVMTEygo6OTYZ+HDx+iUqVK8sfpbVerVg379+/nHG0iIqIs4M2jiIiIsiA+Ph6PHj3KVNQWK1YM5cuX/+YxOjo6KF68OADAzMzsm/tUqFABxYoVQ0JCAoDPlzkrKSnh0aNHSExMzN0XQUREVEhxEg8REVEWNGzYEBcvXkTZsmUzbDc3N4dEIvnldiUSCerXr59hW7ly5RAZGQlzc/NfbpeIiKgoYWFLRESURSVLlkRqaipKly4t39agQYMct/vl3aCNjIyQmpqqUGv6EhERiY2FLRERURZIpVK4uLgAACIjI7Fx40ZoamrC1tY2x223bNkSmpqa8Pb2xqVLlyAIAlxcXCCVSnPcNhERUVHAm0cRERFlgYeHBzw8PHD48GG0atUKwOdi92d3Lu7YsSMAYM+ePT/c78u2jhw5gtatW8PDwwNTpkzJhfRERESFG0dsiYiIfuLIkSPw8PDA9OnT5UUtgFxdjufLtlq1aoVp06Zh2rRpOHr0aK71QUREVFhxxJaIiOgHnj17BjMzM9SrVw8hISHZLmazOmL7NalUCgcHB1y9ehVRUVEwMjLK1vFERERFCUdsiYiIviMtLQ29evWCsrIytmzZkqsjtD+T3qeSkhJ69+7N+bZEREQ/wMKWiIjoO6ZPn44TJ07A398fhoaG+d6/oaEh/P39cezYMXh4eOR7/0RERIqChS0REdE3HDp0CHPmzMGsWbNgbW0tWg5ra2vMnDkTs2bNQmhoqGg5iIiICjLOsSUiIvrK48ePYW5ujkaNGmHfvn1QUvr174F/dY7tl2QyGdq3b48LFy4gKioK5cqV++W2iIiICiOO2BIREX0hNTUVvXr1goaGBvz8/HJU1OYWJSUlbNq0CRoaGujVqxfS0tLEjkRERFSgiP/bmoiIqACZPHky/v33X2zbtg2lSpUSO45cqVKlsG3bNpw+fZpr2xIREX2FhS0REdF/9u3bhwULFmDevHlo1qyZ2HEyadasGebOnYt58+Zh//79YschIiIqMDjHloiICMDDhw9hbm6OZs2aYffu3ZBIJLnSbm7Msf2STCZDp06dcPr0aURGRqJixYq50i4REZEi44gtEREVeSkpKejRoweKFSsGHx+fXCtq84KSkhJ8fX2ho6ODnj17IjU1VexIREREomNhS0RERd7//vc/XLx4EQEBAShRooTYcX6qRIkSCAwMxIULFzBx4kSx4xAREYmOhS0RERVpu3btwtKlS7Fw4UJYWlqKHSfLLC0tsWDBAixevBi7d+8WOw4REZGoOMeWiIiKrNjYWJibm6NVq1YIDg7Ok0uQc3uO7ZcEQUC3bt0QHh6OS5cuoUqVKrneBxERkSLgiC0RERVJycnJcHZ2RokSJbBhw4YCPa/2eyQSCTZu3Ah9fX306NEDKSkpYkciIiISBQtbIiIqksaPH48rV64gKCgIenp6Ysf5ZXp6eggKCsLly5cxYcIEseMQERGJgoUtEREVOUFBQVi5ciWWLl2Khg0bih0nxxo2bIglS5Zg+fLlCA4OFjsOERFRvuMcWyIiKlLu3LmDBg0aoF27dvD398/zS5Dzco7tlwRBQI8ePXDo0CFcunQJxsbGedofERFRQcIRWyIiKjI+ffoEJycnGBkZYe3atQo5r/Z7JBIJ1q9fD0NDQzg5OeHTp09iRyIiIso3LGyJiKjIGDNmDG7cuIHAwEDo6uqKHSfX6erqIigoCNHR0Rg7dqzYcYiIiPKNitgBiIiI8oO/vz/WrFmDtWvXwszMLN/67dWrV771BQBmZmZYvnw5hgwZAisrq3zvn4iISAycY0tERIXerVu3YGFhgU6dOmHTpk35eglyWloaAEBFJf++SxYEAS4uLtizZw8uXLiAGjVq5FvfREREYmBhS0REhVpSUhJ+++03pKam4vz589DR0RE7Ur5ITExEo0aNoKamhjNnzkBTU1PsSERERHmGc2yJiKhQGzlyJO7cuYOgoKAiU9QCgI6ODoKCghATE4NRo0aJHYeIiChPsbAlIqJCy8/PDxs3bsTq1atRp04dsePkuzp16mDVqlVYv349Nm3aJHYcIiKiPMNLkYmIqFCKjo5Go0aN4OzsDG9vb7HjiMrV1RVBQUE4f/48TE1NxY5DRESU61jYEhFRofPhwwc0atQISkpKOHfuHLS0tPKt7+nTp2PXrl2IiorK9bZ9fHwwZswYvHv3LlvHffjwAZaWlhAEAefOnYO2tnauZyMiIhITL0UmIqJCRRAEDB06FA8fPkRQUFC+FrUFlba2NoKCgvDgwQMMGzYM/E6biIgKGxa2RERUqGzcuBGbNm2Cl5cXatWqle3jk5OTMWrUKBgaGkJDQwPNmzfH+fPnAXweMdXT08uw/65du+TLB/n4+MDDwwOXL1+GRCKBRCKBj48PAEAikcDT0xNt27aFpqYmqlatiuDgYHk7ERERkEgkGUZjo6KiIJFIcP/+fURERKB///54//69vO3p06dn+XXVqlULXl5e8PPzK/KXZhMRUeHDwpaIiAqNK1euYMSIEXBzc4OLi8svtfHnn39i+/bt8PX1xaVLl2BiYoI2bdrg7du3Pz22R48eGDduHGrXro1nz57h2bNn6NGjh/z5KVOmoFu3brh8+TJ+//139OzZEzdu3MhSrqZNm2LZsmXQ1dWVtz1+/PhsvTYXFxcMGjQIw4cPx9WrV7N1LBERUUHGwpaIiAqFhIQEODk5oUaNGvjnn39+qY0PHz7A09MTCxcuRNu2bWFqaop169ZBU1MTGzZs+Onxmpqa0NHRgYqKCoyMjGBkZJRh/VgnJycMGjQI1atXx8yZM2FhYYEVK1ZkKZuamhqKFy8OiUQib/tXli9avnw5qlevDicnJyQkJGT7eCIiooKIhS0RESk8QRAwePBgPH36FIGBgRmKyey4e/cuUlNT0axZM/k2VVVVNG7cOMsjqz/SpEmTTI9zo93s0NTURFBQEJ48eQJ3d3fOtyUiokKBhS0RESm8NWvWYNu2bVi/fj2qV6+eZ/0oKSllKgRTU1NzrW0AGdrPrba/Vr16daxfvx5bt27F2rVr86QPIiKi/MTCloiIFFpkZCTGjBmDYcOGZZjP+iuMjY2hpqaGU6dOybelpqbK1381MDBAQkICPnz4IH/+62V91NTUIJVKv9n+mTNnMj1Ov8GVgYEBAODZs2e/1HZ29ejRA0OHDsXo0aMRGRmZK20SERGJhYUtEREprPfv38PJyQm1a9fGkiVLctyetrY2hg4digkTJiAkJATR0dFwc3NDUlISBg4cCEtLS2hpaWHSpEm4e/cutm7dKr/rcbrKlSsjNjYWUVFReP36NZKTk+XPBQUFYePGjbh9+zamTZuGc+fOYcSIEQAAExMTVKhQAdOnT0dMTAz279+PxYsXZ2o7MTERR44cwevXr5GUlJSj17tkyRLUrl0bzs7OiI+Pz1FbREREYmJhS0RECkkQBAwcOBCvXr1CYGAg1NXVc6XdefPmoVu3bujTpw8aNGiAO3fu4NChQ9DX10eJEiWwefNmHDhwAHXr1oW/v3+mJXe6desGBwcH2NrawsDAAP7+/vLnPDw8sG3bNtSrVw9+fn7w9/eHqakpgM9zef39/XHz5k3Uq1cP8+fPx6xZszK03bRpU7i7u6NHjx4wMDDAggULcvRaNTQ0EBgYiJcvX2LQoEGcb0tERApLIvC3GBERKaAVK1Zg1KhRCA4ORrdu3cSO81MSiQQ7d+5E586dxY6Syfbt29G9e3esWLFCPoJMRESkSDhiS0RECuf8+fMYN24cRo8erRBFbUHXrVs3jBo1CmPHjsWFCxfEjkNERJRtHLElIiKFEhcXhwYNGsDQ0BAnTpyAmpqa2JGypCCP2AJASkoKrKys8OrVK1y6dAl6enpiRyIiIsoyFrZERKQwBEFAly5dcOzYMURGRqJy5cpiRypU7t+/D3Nzc9jY2GDHjh2QSCRiRyIiIsoSXopMREQKY+nSpdi9ezd8fX1Z1OaBypUrw9fXF7t27cKyZcvEjkNERJRlHLElIiKFcObMGVhZWWHMmDFYuHCh2HEKtfHjx+Off/7BiRMn8Ntvv4kdh4iI6KdY2BIRUYH35s0bmJubo0KFCoiIiICqqqrYkQq11NRUWFtb48mTJ4iMjESJEiXEjkRERPRDvBSZiIgKNJlMhr59+yIpKQnbtm1jUZsPVFVVERAQgA8fPqBfv36QyWRiRyIiIvohFrZERFSgLVy4EAcOHMCmTZtQoUIFseNk24QJEzBhwgSxY2RbhQoVsGnTJuzbtw+LFi0SOw4REdEPsbAlIqIC6+TJk/j7778xceJEtG3bVuw4v+TWrVu4deuW2DF+Sdu2bfG///0PkyZNwsmTJ8WOQ0RE9F2cY0tERAXSq1evYGZmBhMTExw5cgQqKipiR/olHTt2BADs2bNH5CS/Ji0tDS1btsS9e/cQGRkJAwMDsSMRERFlwhFbIiIqcGQyGVxcXJCamgp/f3+FLWoLAxUVFfj7+yMlJQV9+vThfFsiIiqQWNgSEVGBM2fOHBw+fBhbtmxB2bJlxY5T5JUrVw5btmxBaGgo5s6dK3YcIiKiTFjYEhFRgRIeHo5p06ZhypQpaN26tdhx6D+tW7fG5MmTMXXqVERERIgdh4iIKAPOsSUiogLjxYsXMDMzg6mpKUJDQ6GsrCx2pBxT9Dm2X5JKpWjdujVu3LiBqKgolC5dWuxIREREADhiS0REBYRUKkXv3r0hCAK2bNlSKIrawkZZWRlbt26FIAj4/fffIZVKxY5EREQEgIUtEREVEDNmzEBERAT8/f1hZGQkdhz6DiMjI/j7+yM8PBwzZ84UOw4REREAFrZERFQAHD58GDNnzoSHhwdsbW3FjkM/YWtri+nTp2PGjBkICwsTOw4RERHn2BIRkbiePn0KMzMzmJub4+DBg1BSKlzfuRamObZfkkqlaNu2LS5fvoyoqCiUKVNG7EhERFSEFa6/HoiISKGkpaWhV69eUFVVxebNmwtdUVuYKSsrY/PmzVBRUUGvXr2QlpYmdiQiIirC+BcEERGJZurUqTh16hS2bdsGAwMDseNQNhkaGmLbtm04efIkpk2bJnYcIiIqwljYEhGRKA4ePIi5c+di9uzZsLKyEjsO/SIrKyvMmjULc+bMQUhIiNhxiIioiOIcWyIiynePHj2Cubk5LC0tsXfv3kJ9CXJhnWP7JZlMBkdHR5w9exZRUVEoX7682JGIiKiIKbx/SRARUYGUmpqKnj17QktLC35+foW6qC0qlJSU4OfnBy0tLfTs2ROpqaliRyIioiKGf00QEVG+mjRpEs6dO4eAgACULFlS7DiUS0qWLImAgACcPXsWf//9t9hxiIioiGFhS0RE+Wbv3r1YtGgR5s+fjyZNmogdh3JZkyZNMG/ePCxcuBD79u0TOw4RERUhnGNLRET54sGDBzA3N0eLFi2wc+dOSCQSsSPli6Iwx/ZLgiCgc+fOOHHiBCIjI1GpUiWxIxERURHAwpaIiPJcSkoKrKys8PLlS1y6dAn6+vpiR8pT9+/fx8qVKyGVSrF7924AQKdOnaCsrIwRI0agcuXK4gbMY3FxcWjQoAFKly6N48ePQ01NTexIRERUyLGwJSKiPDdmzBisXr0ap06dQqNGjcSOk+d27NiBbt26QVlZGTKZDMDnGyxJpVJs374dXbt2FTlh3jt//jyaNWuG4cOHY+nSpWLHISKiQo6FLRER5an0Im/58uUYOXKk2HHyRVpaGkxMTPDw4UOk/5qVSCSoWLEi7ty5AxUVFZET5o/ly5dj9OjR2LFjB7p06SJ2HCIiKsRY2BIRUZ65d+8eGjRogNatWyMwMLDIzKsFAB8fH/Tv3z/Ttn79+omUKP8JggAnJyeEhYXh0qVLqFq1qtiRiIiokGJhS0REeeLTp09o1qwZ3r9/j4sXL6J48eJiR8pX6aO2Dx48AABUqlSpSI3Wpnv//j0aNGgAfX19nDp1Curq6mJHIiKiQojL/RARUZ4YN24crl27hsDAwCJX1AKAiooKpk+fLn/s4eFR5IpaAChevDiCgoJw9epVjBs3Tuw4RERUSHHEloiIcl1AQAB69uwJT09PuLu7ix1HNGlpafKi/v3790WysE3n6emJYcOGISAgAM7OzmLHISKiQoaFLRER5aqYmBg0bNgQ7du3x9atW4vUvNqvJSYmIjAwEKmpqbC0tISJiQl0dHTEjiUKQRDQq1cvHDhwABcvXkS1atXEjkRERIUIC1siIso1Hz9+RJMmTfDx40dcuHABxYoVEztSvouOjoaXlxdCQ0Jw+84dfPlrViKRoLqJCewdHODu7g5TU1MRk+a/hIQEWFhYQEtLC6dPn4ampqbYkYiIqJBgYUtERLlm8ODB2LRpE86ePYt69eqJHSdfxcbGYqi7Ow6FhkJfSwtNy5RBNX19VNTVhbqKCpLT0vAwPh4xcXE4/ewZ4pKS0MbeHp5eXqhSpYrY8fPNlStXYGlpib59+2LNmjVixyEiokKChS0REeWKLVu2wMXFBevXr8fAgQPFjpOv1q9fjzGjRkFHRQV9TU3RvHx5qCp9//6MqTIZTj5+DL/oaCSmpWHZ8uUYNGhQPiYW1/r16+Hm5oYtW7agd+/eYschIqJCgHdFJiKiHLt58yaGDBmCPn36YMCAAfLtNjY2qFOnjojJ8t7s2bPh5uaG5kZGWNWqFWwrVsxU1C45dw6u+/fLH6sqKWHh2bNoUbYsmhsZwc3NDbNnz861TPfv34dEIoGPj0+utZmbBg4cCBcXFwwePBg3b94UOw4RERUCLGyJiChHkpKS0L17d1SsWBGenp5F6mZR69evx+TJk9Gndm2MtrCAlqpqto5XVVbGaAsLuNSujcmTJ2PDhg15lLRgkUgk8PT0RMWKFeHk5ISkpKR86zspKQnTp09HREREvvVJRER5j4UtERHlyPDhwxEbG4ugoCBoa2uLHSffxMbGYsyoUWhTpQp6/cJNoHZ17YpetWoBAHrVqoU2Vapg9MiRiI2Nze2oBZKOjg6CgoJw7949jBgxIt/6TUpKgoeHBwtbIqJChoUtEZGC+vDhg9gR4OPjAx8fH6xevRq1a9cWO06+GuruDh0VFbjVr/9Lx6spK0P5v0uWJRIJ3OrXh46KCoYWoXV/a9eujdWrV8Pb2xu+vr5ixyEiIgXGwpaISAFMnz4dEokE0dHR6N27N/T19dG8eXPY2NjAxsYm0/6urq6oXLmy/HH6nMtFixZh7dq1MDY2hrq6Oho1aoTz589nK0tERIS8LTc3N2hqamLYsGHo2LEjHj169M1joqOjYWtrCy0tLZQrVw4LFizItM/Lly8xcOBAlC5dGhoaGqhfv36mYie7r+Po0aOwsrKCtrY29PT00KlTJ9y4cSPDPunn9s6dO3B1dYWenh6KFy+O/v37f/cS2ejoaBwKDUVfU9NMlx+ffvIEQw8dQqft2zH00CGcfvLkm220CwrC5uvXM2wrr62NQ6GhUFNTg6GhIVq3bo1Lly7Jn0+fs3zx4kU0bdoUmpqaqFKlCry8vL7Zx5euXLkCV1dXVK1aFRoaGjAyMsKAAQPw5s2bTPs+efIEAwcORNmyZaGuro4qVapg6NChSElJke/z7t07jBkzBhUqVIC6ujpMTEwwf/58yGQy+T5fvl+rVq1C1apVoaWlBXt7ezx69AiCIODhw4fQ0tKCq6srWrZsibdv32bKc/DgQfn7WKxYMbRv3x7Xvzp3rq6u0NHRwZMnT9C5c2fo6OjAwMAA48ePh1QqlecxMDAAAHh4eEAikUAikWD69OkAgOfPn6N///4oX7481NXVUaZMGXTq1An379//6fklIiJxqYgdgIiIss7JyQnVqlXDnDlzIAgCAgMDs3X81q1bkZCQgCFDhkAikWDBggXo2rUr7t27B9Vszg+dPHkyVFRUMG3aNLx79w7Lli2DnZ0doqKiMqxPGhcXBwcHB3Tt2hXOzs4IDg7GX3/9hbp166Jt27YAPq9/a2Njgzt37mDEiBGoUqUKgoKC4Orqinfv3mH06NHZfh1hYWFo27YtqlatiunTp+Pjx49YsWIFmjVrhkuXLmUo/AHA2dkZVapUwdy5c3Hp0iWsX78ehoaGmD9/fqbX7uXlBX0tLTQvXz7D9kvPn2PO6dOooKsL17p1EZ+SgqXnz6NUFtZrXXnpEi6/fAl1FRU0s7JCmzZtcPLkSdy4cQMNGjTIcD7btWsHZ2dn9OrVC4GBgRg6dCjU1NQy3Ljra4cPH8a9e/fQv39/GBkZ4fr161i7di2uX7+OM2fOyOdGP336FI0bN8a7d+8wePBg1KxZE0+ePEFwcDCSkpKgpqaGpKQkWFtb48mTJxgyZAgqVqyI06dPY+LEiXj27BmWLVuWoe8tW7YgJSUFI0eOxNu3b7FgwQI4OzujZcuWiIiIwIwZMzB37lyEh4dj9OjR2LRpk/zYTZs2oV+/fmjTpg3mz5+PpKQkeHp6onnz5oiMjMzwPkqlUrRp0waWlpZYtGgRwsLCsHjxYhgbG2Po0KEwMDCAp6cnhg4dii5duqBr164AIF+aqlu3brh+/TpGjhyJypUr4+XLlzh8+DAePnyY6fNCREQFjEBERAXetGnTBABCr169Mmy3trYWrK2tM+3fr18/oVKlSvLHsbGxAgChZMmSwtu3b+Xbd+/eLQAQ9u7dm+UsR48eFQAIEolEuHDhgnx7YGCgAED4559/MuQDIPj5+cm3JScnC0ZGRkK3bt3k25YtWyYAEDZv3izflpKSIjRp0kTQ0dER4uPjs/06zMzMBENDQ+HNmzfybZcvXxaUlJSEvn37yreln9sBAwZkeJ1dunQRSpYs+c1zUKNaNaG9sbFwwMkpw7+qenpCCQ0NIahzZ/m2WS1aCAAEQy2tDPsCEHqbmsofa6uqCh2MjYX2xsZCzerVv9lv+vlcvHhxhvOZ/lpTUlIynCdvb2/5fklJSZna8/f3FwAIx48fl2/r27evoKSkJJw/fz7T/jKZTBAEQZg5c6agra0t3L59O8Pz//vf/wRlZWXh4cOHGXIYGBgI7969k+83ceJEAYBQv359ITU1VRAEQbhx44agrKwsKCkpCR8/fhQEQRASEhIEPT09wc3NLUM/z58/F4oXL55he79+/QQAwowZMzLsa25uLjRs2FD++NWrVwIAYdq0aRn2i4uLEwAICxcuzPS6iYio4OOlyERECsQ9h/Mve/ToAX19ffljKysrAMC9e/ey3Mb+/5atcXR0RMOGDeXbu3fvjjJlyuDAgQMZ9tfR0YGLi4v8sZqaGho3bpyhzwMHDsDIyAi9evWSb1NVVcWoUaOQmJiIY8eOZet1PHv2DFFRUXB1dUWJEiXk+9WrVw+tW7fOlBHIfG6trKzw5s0bxMfHZ9iekJCA23fuoNoX/QPA248fce/dO7SqXBnaX4x+NyhdGhV1dTP19zVtVVXcevsWZbS1cSsmBomJid/cT0VFBUOGDJE/VlNTw5AhQ/Dy5UtcvHjxu+1/OYr+6dMnvH79Gr/99hsAyC93lslk2LVrFxwdHWFhYZGpjfRR3aCgIFhZWUFfXx+vX7+W/7Ozs4NUKsXx48czHOfk5ITixYvLH1taWgIAXFxcoKLy+eKxmjVronfv3pDJZFi6dCmAz6PM7969Q69evTL0o6ysDEtLS4SHh2fK+K33MSufb01NTaipqSEiIgJxcXE/3Z+IiAoWFrZERAqkSpUqOTq+YsWKGR6nF4dZ/UP+8uXLWL58OQCgc+fOGZ6TSCQwMTHJNB+xfPnymZYA0tfXz9DngwcPUK1aNSh9tf5rrf/uGvzgwYNsvY70/WvUqJHpNdSqVQuvX7/OdPOtrJ6bu3fvQhCETMXqy//m45bT0cnUZ/lixTJt+9qAevXw4P17bLhyBYIgYPz48d8syMqWLZvp7tPVq1cHgB/OBX379i1Gjx6N0qVLQ1NTEwYGBvLP0/v37wEAr169Qnx8/E/XHo6JiUFISAgMDAwy/LOzswPweb70l74+t+lFboUKFTJsb9myJYDP856vXLmCmJgY+fav+woNDc3Uj4aGhnwObbqvP2vfo66ujvnz5+PgwYMoXbo0WrRogQULFuD58+c/PZaIiMTHObZERApE86u5mhKJBIIgZNov/WY5X1NWVv7m9m+18bX4+Hg4OTmhYsWKuHv3bhbS5rzPgthmcnIyAEBdJXd/hbaoUAF1SpXC3jt3EHDzJnx9feHr64sdO3bI5yLnhLOzM06fPo0JEybAzMwMOjo6kMlkcHBwyHDDp6yQyWRo3bo1/vzzz28+n15op/veuf3e9kqVKqF79+7o3bs3gM/zbI2MjDLtp/LVe/C99rJqzJgxcHR0xK5du3Do0CFMmTIFc+fOxdGjR2Fubp6jtomIKG+xsCUiUmD6+vrfHNX7eoQzpwRBgJubG54/fw5PT0+4uLjIR9O+3OfOnTvyG/FkR6VKlXDlyhXIZLIMo7Y3b96UP5/d9gDg1q1bmZ67efMmSpUq9ctr7qqrqwMAktPSMmw31NICADz5xiXEjxMSstR2CU1NWJYti4CbN7F//3707dsXs2fPzlDYPn36FB8+fMiQ//bt2wDw3RscxcXF4ciRI/Dw8MDUqVPl279+Dw0MDKCrq4tr1679MKexsTESExPlI7S5bd68eXB1dcXhw4cBAIaGhrnW19dXD3zN2NgY48aNw7hx4xATEwMzMzMsXrwYmzdvzpX+iYgob/BSZCIiBWZsbIybN2/i1atX8m2XL1/GqVOncrUfT09PBAYGYsOGDShXrhwAwM/PDwlfFGzBwcF49uzZL40utmvXDs+fP0dAQIB8W1paGlasWAEdHR1YW1tnq70yZcrAzMwMvr6+ePfunXz7tWvXEBoainbt2mU7YzoTExNIJBI8/GrubQlNTVTV08OR+/fxITVVvv3SixeZ9v2aVBDkxzyMj4dEIkHjxo1RtmxZ+QhxurS0NKxZs0b+OCUlBWvWrIGBgUGGOc9fSh/J/Hr0+eu7FyspKaFz587Yu3cvLly4kKmd9OOdnZ3x77//4tChQ5n2effuHdK+Kvqzq2LFitiwYQNOnz4NDQ0NzJkzB6lfnNN0X37us0rrvy8gvvxcAEBSUhI+ffqUYZuxsTGKFSuW6T0gIqKChyO2REQKbMCAAViyZAnatGmDgQMH4uXLl/Dy8kLt2rUz3fToV128eBF//PEHRowYAScnJ0RERAAASpQogebNm6N///548eIFli1bBhMTE7i5uWW7j8GDB2PNmjVwdXXFxYsXUblyZQQHB+PUqVNYtmwZimVhjurXFi5ciLZt26JJkyYYOHCgfLmf4sWLy9ct/RU6OjqobmKCmLg42H8159m1bl1MP3EC448ehX2VKkhIScHeO3dQSVcXH39Q7H1MTUXfffvQvHx5PElMRGkDAwwcOBDnz5/H4sWLM+xbtmxZzJ8/H/fv30f16tUREBCAqKgorF279rtLNunq6srnjKampqJcuXIIDQ1FbGxspn3nzJmD0NBQWFtbY/DgwahVqxaePXuGoKAgnDx5Enp6epgwYQL27NmDDh06wNXVFQ0bNsSHDx9w9epVBAcH4/79+yhVqtQvnN3/5+TkhOHDh8PLywsnTpxAgwYN0LNnTxgYGODhw4fYv38/mjVrhpUrV2arXU1NTZiamiIgIADVq1dHiRIlUKdOHaSlpaFVq1ZwdnaGqakpVFRUsHPnTrx48QI9e/bM0WshIqK8x8KWiEiB1apVC35+fpg6dSrGjh0LU1NTbNq0CVu3bpUXoDnx7t07ODs7o169eli0aFGG5yZNmoQrV65g7ty5SEhIQKtWrbB69Wr5iFh2aGpqIiIiAv/73//g6+uL+Ph41KhRA97e3nB1df2l7HZ2dggJCcG0adMwdepUqKqqwtraGvPnz8/xTbjsHRywecMGDJbJoPrFpdMWRkaY2KQJ/K5dg8/Vqyijo4M/GjXCmSdPcOUHo4vqKipob2KCS8+fI/b9e6iqquLWrVtYvXo1hg4dmmFffX19+Pr6YuTIkVi3bh1Kly6NlStX/vQLha1bt2LkyJFYtWoVBEGAvb09Dh48iLJly2bYr1y5cjh79iymTJmCLVu2ID4+HuXKlUPbtm3l762WlhaOHTuGOXPmICgoCH5+ftDV1UX16tXh4eGR4Q7IObF48WKcOXMGT58+RenSpbFw4UIkJyejXLlysLKyQv/+/X+p3fXr12PkyJH4448/kJKSgmnTpmHkyJHo1asXjhw5gk2bNkFFRQU1a9ZEYGAgunXrliuvh4iI8o5EyMmdNoiIqNASBAHdunXD0aNHERkZKS8GIyIiYGtri6CgIHTv3l3klOKIjo5G7dq1McHSErZf3fE3J8IfPsTCs2cRHR0tvyP0l2xsbPD69eufzoEtTO7du4cGDRqgVatWCA4O/ukcWSIiKpo4x5aIiL5p+fLl2LlzJ3x8fHI8wlnYmJqaoo29Pfyio5H0jbmfvyIpNRV+0dFoY2//zaK2qKpatSq8vb2xY8cOrFixQuw4RERUQLGwJSIiAJ9vQvT8+XM8f/4cBw8exPjx4zF48GD89ttv8u0fP34UO6Yo3r9/Lz8H6f9mzJyJ+NRUrLx4EW+/uulQdgmCgHWXLyMxLQ2eXl65lLrw6NKlC8aMGYPx48fj3LlzYschIqICiIUtEREBAE6fPo0yZcqgTJkyaNeuHdLS0rB27Vr5tjJlymS4a3FRMnr06AznoUyZMrC0tMTHT58Q8egRXPbu/eW2BUGA/40bOBQbi39WrODo+HfMnz8f5ubmcHZ2RlxcnNhxiIiogOEcWyIiAvB5rdMLFy5g6tSpuHbtGry8vFC6dOkM+9SuXRtlypQRKaF4oqOj8fTpUwCfl9uJjIzE9u3bcffuXXTu3Bk+Pj5oU6UK3OrXh9Z37kz8LUmpqVh3+TIOxcaiTp062Lp1K+rWrZtXL0PhPXjwAObm5rCyssKuXbs435aIiORY2BIRkdyiRYswYcIE7N27Fx06dBA7ToGRkpKCI0eOIDAwEDt27JAvpaSpqYkPHz5gw4YNGDNqFHRUVNDX1BTNy5fPcLfkr6VKpTj55An8oqORmJaGrt27Y9OmTQAAMzMzDBgwQL60DWW0b98+ODo6YtGiRRg3bpzYcYiIqIBgYUtERAA+X4rcokULjBs3DvPnzxc7ToHx6tUr1KpVC2/evIGKigrS/luPViKR4I8//pCvMxsbG4vBbm4IO3IEehoaaFauHKrp66Oiri7UlZWRLJXiYXw8YuLicPrZM8QlJaGNvT08/xsZL168eIa2lZSU4ODggIkTJ6JZs2aivf6C6M8//8TSpUtx/PhxNGnSROw4RERUALCwJSIivH79Gubm5qhUqRLCw8Ohmo3LaQu71NRUWFtb4+zZs5DJZBmeO3/+PCwsLOSPN2/ejD59+qBv3744d+YMbsXE4MtfsxKJBDWqVUPrNm0wdOjQDHc/bt++PQ4ePIivfy07Ojpiz549efTqFFNqaipsbGzw8OFDREZGolSpUmJHIiIikbGwJSIq4mQyGdq3b48LFy4gMjIS5cuXFztSgZOQkICGDRsiJiZGvq1ChQp48OBBhnmerVq1glQqRUREBAAgMTERjo6OkEqlWL58OUxMTKCjo/PNPtauXQt3d3d5YSuRSFCvXj2EhYWxcPuGx48fw8zMDI0bN8a+ffug9INLv4mIqPDjbwEioiJu/vz5OHToEDZt2sSi9jsOHTqEu3fvomTJklBWVoaysjJ69+6doah98OABjh49iv79+8u3vX79GhEREThx4gT09fW/W9QCQIcOHTIUtYIgYODAgSxqv6N8+fLYvHkzDh48iAULFogdh4iIRMbCloioCDt+/DgmT56MSZMmwcHBQew4BVJQUBB69uyJnj174vbt22jQoAGkUimcnZ0z7Ofr6wsdHR10795dvm327NmQSCSQSCSYPXv2D/spW7YszMzMAHwe+R00aBBGjx6NwMDAXH9NhYWDgwMmTZqEyZMn48SJE2LHISIiEfFSZCKiIurly5cwMzND9erVERYWBhUVFbEjFTgBAQH4/fff0atXL/j4+EBZWRkJCQk4c+YMWrduLd9PJpPBxMQENjY22LhxIwDg/v37MDExgVQqBQAoKyvj7t27qFSp0nf72759O44dO4aFCxdCVVUV/fr1w7Zt27B79260a9cub1+sgkpLS4OdnR1iYmIQGRkJQ0NDsSMREZEIWNgSERVBUqkUDg4OuHLlCqKioork2rQ/s23bNri4uKB3797w9vaGsrLyd/c9duwYbGxscPz4cVhZWQEA3Nzc4O3tnaGwHTBgANauXZvlDGlpaXByckJISAhCQkJgbW2dsxdVSD19+hTm5uaoX78+Dh48+MP3ioiICideikxEVATNnj0bR44cwdatW1nUfsPWrVvx+++/4/fff/9pUQsA3t7eMDY2RvPmzQF8Hq39sqgFPn+ZsHHjRjx48CDLOVRUVLBt2zZYWVmhQ4cOOHfu3K+9oEKubNmy2Lp1K8LCwjBnzhyx4xARkQhY2BIRFTFHjx7F9OnTMW3aNLRq1UrsOAXOli1b5Ev2bNy48adFbWJiIoKDg+Hq6iq/mdS5c+cyFLXppFJptotTdXV17Ny5E/Xq1YODgwOuXbuWreOLilatWmHq1KmYPn06wsPDxY5DRET5jJciExEVIc+ePYO5uTnq1q2LkJAQXrL5lc2bN6Nfv37o168f1q9fn6UlZLy9vTFw4EDcv38fFStWlG9/8eIFpFIpXF1dAUA+R7d06dK/lO3du3ewtbXF8+fPceLECZiYmPxSO4WZVCpFmzZtcO3aNURFRcHIyEjsSERElE9Y2BIRFRFpaWlo3bo1bt68iaioqF8usAorPz8/uLq6yufBZnVd1BYtWkBdXR2HDx/+5vMdO3YEAOzZsyfHGV++fIkWLVrg06dPOHHiBCpUqJDjNgubFy9ewMzMDLVq1cLhw4f55Q0RURHBS5GJiIoIDw8PHD9+HNu2bWNR+xVfX1+4urpi4MCB2Spq79y5gxMnTmRYuzYvGRoaIiwsDADQunVrvHz5Ml/6VSSlS5fGtm3bcOzYMXh4eIgdh4iI8gkLWyKiIiA0NBSzZ8/GzJkzeWfdr/j4+KB///5wc3PDmjVrslzUAp8LYl1dXXTp0iUPE2ZUvnx5hIWF4f3797C3t0dcXFy+9a0orK2tMWPGDMyaNQuhoaFixyEionzAS5GJiAq5J0+ewMzMDBYWFti/f3+2CrfCbuPGjRg0aBAGDx6M1atXZ+vcyGQyVK5cGW3btsWaNWu+u19uXor8pWvXrsHa2ho1atRAaGgodHR0crV9RSeTydCuXTtcunQJkZGRKFeunNiRiIgoD/GvGyKiQiwtLQ09e/aEuro6Nm3axKL2Cxs2bMDAgQPh7u6e7aIW+Hx36UePHuXbZchfq1OnDkJCQnDt2jV07twZnz59EiVHQaWkpIRNmzZBTU0NvXr1QlpamtiRiIgoD/EvHCKiQmzy5Mn4999/ERAQgFKlSokdp8BYt24dBg0ahGHDhmHVqlW/VPB7e3ujZs2asLS0zIOEWdOoUSPs3bsXp06dQs+ePZGamipaloLIwMAAAQEBOH36NKZMmSJ2HCIiykMsbImICqn9+/dj/vz5mDt3Lpo1ayZ2nAJj7dq1GDx4MEaMGIGVK1fK157Njvfv32PHjh0Z1q4Vi7W1NXbs2IEDBw6gf//+kMlkouYpaJo1a4Y5c+Zg3rx5OHDggNhxiIgoj7CwJSIqhB4+fIi+ffuiQ4cOGDdunNhxCgwvLy8MGTIEI0eOxPLly3+5KA0ICEBKSgr69OmTywl/Tdu2bbFlyxb4+/tj+PDh4O0zMho/fjzat2+PPn364NGjR2LHISKiPMDCloiokElJSUGPHj2go6MDX19fzqv9j6enJ4YOHYrRo0fjn3/+ydFIq7e3N9q0aYOyZcvmYsKccXJywrp16+Dl5YX//e9/LG6/oKSkBF9fX2hra6NHjx68ZJuIqBDiXztERIXMxIkTceHCBQQEBKBEiRJixykQVq1ahWHDhmHMmDFYunRpjoramzdv4syZM6LdNOpHBgwYgGXLlmHBggWYO3eu2HEKlJIlSyIwMBDnz5/HxIkTxY5DRES5TEXsAERElHt2796NJUuWYOnSpfjtt9/EjlMgrFy5EiNHjsTYsWOxaNGiHM+J9fHxgb6+vnwZn4Jm9OjRiI+Px99//41ixYph5MiRYkcqMH777TcsWLAAY8eORYsWLQrse0hERNnHwpaIqJCIjY2Fq6srunTpgtGjR4sdp0BYvnw5Ro8ejXHjxmHhwoU5LmqlUik2bdqE3r17Q11dPZdS5r7Jkyfj/fv3GDVqFHR1ddGvXz+xIxUYY8aMwfHjx9GvXz9ERkaicuXKYkciIqJcwEuRiYgKgeTkZPTo0QP6+vrYuHGj6HfqLQiWLVuG0aNHY8KECblS1AJAaGgonj59WiAvQ/6SRCLBwoUL4ebmhgEDBmD79u1iRyowJBIJNm7cCD09PTg7OyMlJUXsSERElAtY2BIRFQITJkzA5cuXERgYCD09PbHjiG7p0qX4448/8Ndff2H+/Pm5Vuh7e3ujbt26aNCgQa60l5ckEgk8PT3h7OyMXr164dChQ2JHKjD09fURFBSEy5cvY8KECWLHISKiXMDClohIwQUHB2PFihVYsmQJLCwsxI4juiVLlmDs2LH43//+h7lz5+ZaUfv27Vvs3r27QKxdm1XKysrw8/ODg4MDunTpghMnTogdqcCwsLDA4sWLsXz5co5oExEVAixsiYgU2N27dzFw4EA4OTlh2LBhYscR3aJFizBu3DhMmjQJc+bMydUC1N/fHzKZDC4uLrnWZn5QVVVFYGAgmjRpgg4dOuDixYtiRyowhg8fDicnJwwYMAB3794VOw4REeWAROBCd0RECunTp09o2rQpEhIScPHiRejq6oodSVQLFizAX3/9hcmTJ2PGjBm5PqpqYWGBcuXKYffu3dk6Lv3Ou3v27MnVPNmVmJgIOzs73LlzB8ePH4epqamoeQqK9+/fo2HDhtDV1cXp06ehoaEhdiQiIvoFHLElIlJQf/zxB6KjoxEUFFTki9r58+fjr7/+wpQpU/KkqL169SouXrxY4G8a9SM6Ojo4cOAAypUrh9atW+PevXtiRyoQihcvjqCgIERHR2Ps2LFixyEiol/EEVsiIgXk7++P3r17Y82aNRg8eLDYcUQ1d+5cTJo0CdOmTcP06dPzpI979+7h5cuXaNy4MZSUsved8PXr1wEAtWvXzoto2fby5Us0b94caWlpOHHiBMqVKyd2pAJhzZo1cHd3x7Zt29CjRw+x4xARUTaxsCUiUjC3bt2ChYUFOnbsiM2bNyvMjYzywuzZszF58mRMnz4d06ZNEzuOwnj8+DGaNm0KHR0dHDt2DAYGBmJHEp0gCPj999+xd+9eXLx4EdWrVxc7EhERZQMLWyIiBfLx40dYWloiJSUFFy5cgI6OjtiRRDNr1ixMmTIFHh4emDp1qthxFM7t27dhZWWF8uXL4+jRoyhevLjYkUSXkJAACwsLaGho4MyZM9DU1BQ7EhERZRHn2BIRKZCRI0fizp07CAoKKtJF7YwZMzBlyhTMnDmTRe0vql69Og4fPox79+6hQ4cOSEpKEjuS6IoVK4bg4GDcvn0bo0aNEjsOERFlAwtbIiIFsWnTJmzYsAGrVq1C3bp1xY4jmvTLjtMvQ6ZfV69ePRw8eBCRkZHo0qULkpOTxY4kurp162LVqlVYv349Nm/eLHYcIiLKIha2REQKIDo6Gu7u7ujXr59C35k3JwRBwLRp0+Dh4YE5c+Zg0qRJOW7TxsYGY8aMAQBUrlwZy5Yty3Gbiua3337Dnj17cOzYMfTu3RtpaWliRxJd//790bdvXwwZMgTR0dFixyEioizgHFsiogLuw4cPaNy4MQDg3Llz0NbWFjlR/ksvamfOnIl58+bhr7/+ypV2bWxsYGZmhmXLluHVq1fQ1taGlpZWrrStaPbu3YuuXbvi999/x8aNG7N99+fChj93RESKpWj/1iIiKuAEQcCwYcNw//59BAUFFck/rgVBkM+nXbBgQa4VtV8zMDD4YVGbmpqaJ/0WFI6OjvDz84Ofnx9Gjx6Nov69t7a2NoKCgnD//n0MHz5c7DhERPQTLGyJiAowb29v+Pn5Yc2aNTA1NRU7Tr4TBAGTJ0/G7NmzsXDhQkyYMOGX2/rw4QP69u0LHR0dlClTBosXL87w/NeXIkskEnh6eqJjx47Q1tbG7Nmzf9h+REQEJBIJjhw5AgsLC2hpaaFp06a4deuWfJ+7d++iU6dOKF26NHR0dNCoUSOEhYVlyjFr1ix51kqVKmHPnj149eoVOnXqBB0dHdSrVw8XLlzIcNzJkydhZWUFTU1NVKhQAaNGjcKHDx+ydY569eqFNWvWYOXKlZgyZUq2ji2MTE1N4eXlBV9fX3h7e4sdh4iIfoCFLRFRAXX16lUMHz4cgwYNgouLi9hx8p0gCJg0aRLmzJmDxYsXY/z48Tlqb8KECTh27Bh2796N0NBQRERE4NKlSz88Zvr06ejSpQuuXr2KAQMGZKmfv//+G4sXL8aFCxegoqKS4bjExES0a9cOR44cQWRkJBwcHODo6IiHDx9maGPp0qVo1qwZIiMj0b59e/Tp0wd9+/aFi4sLLl26BGNjY/Tt21c+qnr37l04ODigW7duuHLlCgICAnDy5EmMGDEim2cJcHNzw6JFizB79mzMnz8/28cXNn369MHAgQMxfPhwXL16Vew4RET0PQIRERU48fHxQo0aNYR69eoJSUlJYsfJdzKZTPjzzz8FAMKSJUty3F5CQoKgpqYmBAYGyre9efNG0NTUFEaPHi0IgiBUqlRJWLp0qfx5AMKYMWOy3Ed4eLgAQAgLC5Nv279/vwBA+Pjx43ePq127trBixQr540qVKgkuLi7yx8+ePRMACFOmTJFv+/fffwUAwrNnzwRBEISBAwcKgwcPztDuiRMnBCUlpR/2/SNTp04VAAirV6/+peMLk6SkJKFu3bpCjRo1hPj4eLHjEBHRN3DEloiogBEEAUOGDMGTJ08QFBQETU1NsSPlK0EQ8Ndff2HBggVYtmwZ/vjjjxy3effuXaSkpMDS0lK+rUSJEqhRo8YPj7OwsMh2X/Xq1ZP/d5kyZQAAL1++BPB5xHb8+PGoVasW9PT0oKOjgxs3bmQasf2yjdKlSwNAhiWe0relt3v58mX4+PhAR0dH/q9NmzaQyWSIjY3N9msAPo9Wjx49GsOHDy/yy95oamoiKCgIT548gbu7e5Gff0xEVBCpiB2AiIgyWrt2Lfz9/bFt2zZUr15d7Dj5ShAETJgwAYsXL8by5csxcuRIUfP8ys26VFVV5f8tkUgAADKZDAAwfvx4HD58GIsWLYKJiQk0NTXRvXt3pKSk/LSNH7WbmJiIIUOGYNSoUZnyVKxYMduvIb2PJUuWICEhAa6urtDR0UHnzp1/qa3CoEaNGli3bh169eoFa2trDB48WOxIRET0BRa2REQFSGRkJEaPHo2hQ4eiR48eYsfJV4IgYNy4cVi6dClWrFjxS/NDv8fY2Biqqqo4e/asvNCLi4vD7du3YW1tnWv9/MypU6fg6uqKLl26APhckN6/fz/H7TZo0ADR0dEwMTHJcVtfUlJSwtq1a5GQkIAePXpg//79sLOzy9U+FEnPnj1x7NgxjBo1Co0bN4aZmZnYkYiI6D+8FJmIqIB4//49nJ2dYWpqiiVLlogdJ18JgoCxY8di6dKlWLlyZa4WtQCgo6ODgQMHYsKECTh69CiuXbsGV1fXfF+rtVq1atixYweioqJw+fJl9O7dWz7qmhN//fUXTp8+jREjRiAqKgoxMTHYvXt3rpxHZWVlbN68Ga1atUKnTp1w+vTpHLepyJYuXQpTU1M4OTkhPj5e7DhERPQfFrZERAWAIAgYNGgQXr58iaCgIGhoaIgdKd8IgoAxY8Zg2bJlWL16dZ6tGbpw4UJYWVnB0dERdnZ2aN68ORo2bJgnfX3PkiVLoK+vj6ZNm8LR0RFt2rRBgwYNctxuvXr1cOzYMdy+fRtWVlYwNzfH1KlTUbZs2VxIDaipqSE4OBgWFhZo164doqKicqVdRaShoYHAwEC8fPkSgwYN4nxbIqICQiLw/8hERKJbuXIlRo4cieDgYHTr1k3sOPlGEASMHj0aK1asgJeXF4YMGSJ2JPqB+Ph4tGrVCg8ePMDx48dRs2ZNsSOJZvv27ejevTtWrlyZZ1/GEBFR1rGwJSIS2YULF9C0aVMMHToU//zzj9hx8o0gCBg5ciRWrVqFNWvW8GY8CuLNmzewtrbGu3fvcPLkSVSuXFnsSKIZNWoU1qxZg1OnTv3SHbSJiCj3sLAlIhJRXFwcGjRoAAMDA5w8eRJqampiR8oXMpkMI0aMgKenJ9atW4dBgwaJHemn3N3dv7vsjYuLC7y8vPI5kXiePXsGKysrAMCJEyfkyxoVNcnJybCyssLr169x6dIl6OnpiR2JiKjIYmFLRCQSQRDQpUsXHDt2DJGRkUVm5Esmk2H48OFYs2YN1q1bh4EDB4odKUtevnz53ZsF6erqwtDQMJ8Tiev+/fto3rw59PT0cOzYMZQsWVLsSKKIjY1FgwYNYGtri+3bt8uXYiIiovzFwpaISCRLly7F2LFjsXv3bnTs2FHsOPlCJpNh2LBhWLt2LdavX48BAwaIHYly4ObNm2jRogUqV66MsLAw6Orqih1JFLt370bnzp2xdOlSjBkzRuw4RERFEgtbIiIRnDlzBlZWVhg9ejQWLVokdpx8IZPJ4O7ujvXr12Pjxo1wdXUVOxLlgsjISNja2qJ+/foICQmBpqam2JFEMW7cOCxfvhwnTpzAb7/9JnYcIqIih4UtEVE+e/PmDRo0aIBy5crh2LFjUFVVFTtSnpPJZBgyZAg2bNgAb29v9OvXT+xIlItOnz6N1q1bw8bGBjt37iwyc8W/lJqaihYtWuDp06eIjIxEiRIlxI5ERFSkcB1bIqJ8JJPJ0K9fPyQmJiIgIKDIFLVubm7YsGEDfH19WdQWQk2bNsWuXbsQFhYGFxcXSKVSsSPlO1VVVQQEBCAxMRH9+vWDTCYTOxIRUZHCwpaIKB8tWrQI+/fvx6ZNm1ChQgWx4+Q5qVSKgQMHwsfHB35+fujTp4/YkTKZMGECmjZtmmeFyIABA4rEXOLWrVsjICAAO3bswODBg4tkYVexYkX4+flh3759WLx4sdhxiIiKFBa2RET55OTJk5g0aRL+97//oV27dmLHyXPpRa2fnx82bdoEFxcXsSNl8uHDB3h5ecHe3h5KSnnzK/H169d4/fp1nrRd0HTu3Bk+Pj7YuHEjxo4di6I426l9+/b466+/MHHiRJw6dUrsOERERYaK2AGIiIqCV69eoWfPnmjatClmzpwpdpw8J5VKMWDAAGzevBmbN29Gr169xI70TTt27JBfOkq5w8XFBQkJCRg2bBiKFy8ODw8PsSPlu1mzZuHUqVPo0aMHIiMjYWBgIHYkIqJCjyO2RER5TCaToU+fPkhJSYG/vz9UVAr3d4pSqRSurq7YsmULtm7dWmCLWgDw9vaGra0tqlSpInaUQmXo0KGYN28eZsyYUSQvyVVRUcG2bduQnJyMPn36FMnLsomI8hsLWyKiPDZ37lyEhoZi8+bNKFeunNhx8pRUKkW/fv3g7++PrVu3okePHmJH+q779+8jPDycyw7lkb/++guTJk3C+PHjsW7dOrHj5Lty5cphy5YtCA0Nxbx588SOQ0RU6BXuYQMiIpFFRERg6tSpmDx5Muzt7cWOk6fS0tLQr18/BAQEwN/fH05OTmJH+iFfX1/o6OigW7duYkcptGbNmoX4+HgMGTIExYoVQ8+ePcWOlK/s7e3x999/Y8qUKWjWrBmsra3FjkREVGhxHVsiojzy4sULmJmZoVatWjh8+DCUlZXFjpRn0tLS0KdPHwQHB2Pbtm0FvliUyWQwNjZGy5YtsWHDhjztq2PHjgCAPXv25Gk/BZVMJkP//v2xdetW7Ny5Ex06dBA7Ur6SSqWws7PDzZs3ERUVhdKlS4sdiYioUOKlyEREeUAqlaJ3794QBAFbt24t9EWti4sLgoODERAQUOCLWgA4fvw47t+/j/79+4sdpdBTUlLChg0b0LFjR3Tv3h3h4eFiR8pXysrK2Lp1KwRBwO+//14k1/glIsoPLGyJiPLAzJkzERERAX9/fxgZGYkdJ8+kpqaid+/e2L59OwIDA9G1a1exI2WJt7c3TExM0KxZM7GjFAkqKirYunUrrK2t4ejoiLNnz4odKV+VKVMGW7duxdGjRzFr1iyx4xARFUosbImIcllYWBhmzJiB6dOnw9bWVuw4eSa9qN25cyeCgoLQpUsXsSNlSUJCAoKDg+Hq6gqJRCJ2nCJDXV0dO3bsgJmZGdq2bYsrV66IHSlftWzZEtOnT4eHhweOHDkidhwiokKHc2yJiHLR06dPYW5ujvr16+PgwYOF9hLk1NRU9OrVC3v27EFQUBA6deokdqQs27hxIwYNGoQHDx6gQoUKed5fUZ9j+7X379+jZcuWePLkCY4fP47q1auLHSnfSKVSODg44MqVK4iKikKZMmXEjkREVGhwxJaIKJekpaWhV69eUFFRwebNmwttUZuSkoIePXpgz5492L59u0IVtcDny5Bbt26dL0UtZVa8eHGEhISgRIkSsLOzw8OHD8WOlG+UlZWxZcsWKCsro1evXkhLSxM7EhFRocHClogol0ybNg2nTp2Cv78/DA0NxY6TJ9KL2v3792PHjh1wdHQUO1K23LlzBydPnuTatSIzMDDA4cOHoaKiAjs7O7x48ULsSPnG0NAQ27Ztw4kTJzB9+nSx4xARFRosbImIckFISAjmzJmDWbNmoUWLFmLHyRMpKSlwdnbGgQMHsGPHDoVctsXHxwfFixdH586dxY5S5JUrVw5hYWFITEyEvb094uLixI6Ub1q0aIFZs2Zh9uzZCAkJETsOEVGhwDm2REQ59OjRI5ibm8PS0hJ79+6FklLh+84wOTkZTk5OCA0Nxc6dO9G2bVuxI2WbVCpF5cqV0b59e3h5eeVbv5xj+2PR0dFo0aIFTExMcPjwYRQrVkzsSPlCJpOhQ4cOOHfuHKKiolC+fHmxIxERKbTC99cXEVE+Sk1NRc+ePaGpqQlfX99CW9R2794doaGh2LVrl0IWtQBw9OhRPH78mGvXFjCmpqY4dOgQoqOj0blzZ3z69EnsSPlCSUkJfn5+0NTURM+ePZGamip2JCIihVb4/gIjIspHf//9N86dO4eAgACUKlVK7Di5Ljk5Gd26dcPhw4exe/duODg4iB3pl3l7e6NWrVpo3Lix2FHoKw0bNsT+/fvx77//wtnZucgUeaVKlUJAQADOnj2LyZMnix2HiEihsbAlIvpF+/btw8KFCzFv3jw0bdpU7Di57tOnT+jatSuOHDmCPXv2oE2bNmJH+mXv3r3Dzp07uXZtAWZlZYUdO3YgJCQE/fr1g1QqFTtSvmjatCnmzp2LBQsWYN++fWLHISJSWCxsiYh+wYMHD9C3b1907NgRY8eOFTtOrvv06RO6dOmCo0ePYu/evbC3txc7Uo4EBAQgNTUVffr0ETsK/YCDgwP8/f0REBCAoUOHoqjcBmTcuHFwdHRE37598eDBA7HjEBEpJBa2RETZlH53YF1dXfj4+BS6EcBPnz6hc+fOOHbsGPbt2wc7OzuxI+WYt7c3HBwcUKZMGbGj0E9069YNGzZswLp16zBhwoQiUdxKJBL4+PhAV1cXPXr0QEpKitiRiIgUDgtbIqJs+uuvvxAZGYnAwEDo6+uLHSdXffz4EZ06dcLx48exb98+tGrVSuxIOXbjxg2cPXuWa9cqEFdXVyxfvhyLFy/GrFmzxI6TL0qUKIGAgABcunQJ//vf/8SOQ0SkcFTEDkBEpEh27tyJZcuW4Z9//il0NyFKSkpCp06dcOrUKezfvx+2trZiR8oVPj4+KFGiBBwdHcWOQtkwcuRIxMfHY/LkydDV1cXo0aPFjpTnLC0tsXDhQowZMwZWVlbo0qWL2JGIiBQGC1sioiy6d+8e+vfvj27dumHkyJFix8lVSUlJ6NixI/79918cOHAANjY2YkfKFWlpafDz80Pv3r2hrq4udhzKpkmTJiE+Ph5jxoxBsWLFMGDAALEj5blRo0bh+PHj6N+/P+rXr4+qVauKHYmISCHwUmQioixITk6Gs7MzSpYsiQ0bNhSqebVJSUlwdHTEmTNncPDgwUJT1AJAaGgonj9/zrVrFZREIsG8efPg7u4ONzc3BAUFiR0pz0kkEmzYsAElSpSAs7MzkpOTxY5ERKQQWNgSEWXBuHHjcPXqVQQFBaF48eJix8k1Hz58QIcOHXD27FkcPHgQLVq0EDtSrvL29ka9evVgbm4udhT6RRKJBKtWrUKvXr3w+++/4+DBg2JHynN6enoICgrC1atXMX78eLHjEBEpBBa2REQ/ERgYiFWrVmHZsmVo0KCB2HFyTXpRe/78eYSEhMDKykrsSLnqzZs32LNnD9euLQSUlJTg7e2Ndu3aoWvXrjh27JjYkfJcw4YNsXTpUqxcubJIjFQTEeUUC1sioh+IiYnBoEGD0KNHD7i7u4sdJ9ckJiaiXbt2uHDhAkJCQtC8eXOxI+U6f39/yGQyuLi4iJYhMTER79+/R1xcHKKiopCYmChaFkWnqqqKbdu2oVmzZnB0dMT58+fFjpTnhg4dih49emDgwIGIiYkROw4RUYEmEYrCAnFERL9AKpUiOjoaMpkMderUgbKystiRckV6URsVFYWQkBA0bdpU7Eh54tq1a1BTU0P16tXztd/o6Gh4eXkhNCQEt+/cybAOq0QiQXUTE9g7OMDd3R2mpqb5mq0wSExMhL29PW7duoVjx46hTp06YkfKU/Hx8bCwsIC2tjb+/fdfaGhoiB2JiKhAYmFLRFSEJCQkoF27drh8+TIOHTqEJk2aiB2p0IiNjcVQd3ccCg2FvpYWmpYpg2r6+qioqwt1FRUkp6XhYXw8YuLicPrZM8QlJaGNvT08vbxQpUoVseMrlLi4ONja2uLFixc4ceIETExMxI6Upy5fvgxLS0u4urrCy8tL7DhERAUSC1sioiIiISEBbdu2xdWrV3Ho0CH89ttvYkcqNNavX48xo0ZBR0UFfU1N0bx8eagqfX+2T6pMhpOPH8MvOhqJaWlYtnw5Bg0alI+JFd/Lly/RokULfPr0CSdPnkT58uXFjpSn1q1bh8GDB2PLli3o3bu32HGIiAocFrZEREVAfHw82rZti2vXriE0NBSWlpZiRyo0Zs+ejcmTJ6NNlSpwq18fWqqqWT42KTUV6y5fxqHYWMyaNQupqanw8PAAfzVnzaNHj9C8eXNoaWnh2LFjMDQ0FDtSnhEEAX369MGuXbtw4cIF1KxZU+xIREQFCgtbIqJCLj4+Hg4ODoiOjkZoaCgaN24sdiSFtnXrVrx8+RJjxozB+vXr4ebmhj61a6NXFufLfkpLQ/CtW6hnYIB6/xViW6Ojsfn6dTg6OmLv3r0sbLPhzp07sLKygpGREcLDw6Gnpyd2pDyTmJiIRo0aQUVFBWfPnoWWlpbYkYiICgzeFZmIqBB7//492rRpgxs3biAsLIxFbS7YunUrli1bhtjYWIwZNQptqlTJclELAMlSKbZGR+PKq1fybb1q1UKbKlVw5PBh3LhxIy9iF1omJiYIDQ3FgwcP0L59e3z48EHsSHlGR0cHQUFBuHv3LkaOHCl2HCKiAoWFLREVGdOnT4eZmVmetO3j41PgRorevXsHe3t73Lx5E2FhYbCwsBA7Uq4pCO/lUHd36KiowK1+/Rz3KZFI4Fa/PoqpqmLM6NE5bq+oqVu3LkJCQnDlyhV06dIFycnJYkfKM3Xq1MHq1auxceNG+Pr6ih2HiKjAYGFLRFQIpRe1MTExOHLkCBo2bCh2JIXx8eNHfPz4EZUrV4a6ujoMDQ3RunVrXLp0CTY2Nti/fz8ePHiAQ6GheJGQgGGhoQA+3xBq07VrGHX4MLrv3IkuO3ZgQng4Lr98KW/7xYcP6LVnD4DPlx+3CwpCu6AgbL5+HVqqqjDW1cWh0NBMo7abN29G48aNoaWlBX19fbRo0QKh//VLnzVu3Bh79+7FiRMn0LNnT6SlpYkdKc+4urrC1dUVQ4cOxfXr18WOQ0RUILCwJSKFkZycjFGjRsHQ0BAaGhpo3rw5zp8/D+Dbo2y7du2CRCKRP+/h4YHLly9DIpFAIpHAx8cHwOfRMk9PT7Rt2xaampqoWrUqgoOD5e1ERERAIpHg3bt38m1RUVGQSCS4f/8+IiIi0L9/f7x//17e9vTp0/PyVPxQXFwcWrdujbt37+LIkSNo0KCBaFm+pyC/l8OGDUNKSgoePHiAlJQU1K9fH5qamrhx4wb+/vtvmJmZQUNDA1pqavijUSMM/m/kOCk1FYdiY1HX0BD969XD77Vr431yMqYcP467//VXXF0dw/97P5qWK4fxjRtjfOPGaFauHACgYrFiAABPT095Pg8PD/Tp0weqqqqYMWMGPDw8UKFCBRw9ejRX3ovCxMbGBsHBwdi3bx/69+8PmUwmdqQ8s2rVKlStWhVOTk5ITEwUOw4RkehUxA5ARJRVf/75J7Zv3w5fX19UqlQJCxYsQJs2bXDnzp2fHtujRw9cu3YNISEhCAsLAwAUL15c/vyUKVMwb948/PPPP9i0aRN69uyJq1evolatWj9tu2nTpli2bBmmTp2KW7duAfg8F04M6UVtbGwsjhw5kmeX6+ZUQX4vNTQ0IJVK8fDhQwCf38sv389y5crh2rVraFOpElpXrizfrqOmBu/27TMs8+NQpQqGhIRgb0wMxjRqBA0VFTQvXx6rLl1C5eLF0bJSpQz9K/137OFDhwB8vjHSjBkz0KVLFwQHB8ufB8AbTH1H+/btsXnzZvTq1QvFihXDqlWr5F+KFCZaWloICgpCo0aNMGzYMPj6+hbK10lElFUcsSUihfDhwwd4enpi4cKFaNu2LUxNTbFu3Tpoampiw4YNPz1eU1MTOjo6UFFRgZGREYyMjKCpqSl/3snJCYMGDUL16tUxc+ZMWFhYYMWKFVnKpqamhuLFi0MikcjbFqOwffv2Lezs7HD//n0cPXq0wBa1Bf291NbWhkwmg0wm++Z7mZaWhrS0NFTT18+wXVkikRe1MkFAQkoKpIIAkxIlcOeLEeKsuBUTg8TEROzatQsymQxTp07NUNQCYBHzAz169MC6devg6emJSZMmiR0nz9SqVQteXl7YtGkTNm7cKHYcIiJRccSWiBTC3bt3kZqaimbNmsm3qaqqonHjxrhx4wYMDAxy1H6TJk0yPY6KispRm/kpvah99OgRjh49inr16okd6bsK+nvp7OwMT09PVKhQAQ0bNkS7du3Qt29fVK1aFQCQlJQEAKioq5vp2LD797Hj9m08jo9H2hcjqkba2tl6DYIg4M6dO7h79y6UlJRgmo27LtNnAwcOREJCAv744w/o6upi4sSJYkfKEy4uLjh+/DhGjBiBRo0aFeiffSKivMQRWyIqFJSUlDJdmpmampprbQMZL/3MrbZzw5s3b9CqVSuFKGqzQuz3snHjxihWrBhWrFiBsmXLYuHChahduzYOHjwIAPJ5m+oqGb8bPvrgAZacP48y2toY3agRZlpZYXaLFqhvaAjZL1w2XJjv7JtfxowZg+nTp2PSpElYtWqV2HHyzD///IMaNWrAyckJCQkJYschIhIFC1siUgjGxsZQU1PDqVOn5NtSU1Nx/vx5mJqawsDAAAkJCRnWsPx6lE5NTQ1SqfSb7Z85cybT4/Q5mekjiM+ePfultvPS69ev0apVKzx58gTh4eGoW7duvmfILkV4LwVBwLBhw7Br1y7ExsaiZMmSmD17NgBAWVkZAJD81V13Tz5+DCNtbUxu2hStKlVCQyMjmJcujZSvcmb1AmJ1dXUYGxtDJpMhOjo6i0fR16ZOnYqxY8dixIgR8PPzEztOntDU1ERgYCCePn2KwYMHc/41ERVJLGyJSCFoa2tj6NChmDBhAkJCQhAdHQ03NzckJSVh4MCBsLS0hJaWFiZNmoS7d+9i69at8jvlpqtcuTJiY2MRFRWF169fZxgRCwoKwsaNG3H79m1MmzYN586dw4gRIwAAJiYmqFChAqZPn46YmBjs378fixcvztR2YmIijhw5gtevX8svV81L6UXts2fPEB4ejjp16uR5n7mhIL+XUqkUpUqVyvBe6ujooGzZsvI+0ovjh/HxGTIp/Tfn9cuS4uabN7j55k2G/dJHej/8YBRaIpHAxMQEnTt3hpKSEmbMmJHpDr8sXrJGIpFg0aJFGDRoEPr3748dO3aIHSlPVK9eHevXr8e2bduwZs0aseMQEeU/gYhIQXz8+FEYOXKkUKpUKUFdXV1o1qyZcO7cOfnzO3fuFExMTARNTU2hQ4cOwtq1a4Uv/zf36dMnoVu3boKenp4AQPD29hYEQRAACKtWrRJat24tqKurC5UrVxYCAgIy9H3y5Emhbt26goaGhmBlZSUEBQUJAITY2Fj5Pu7u7kLJkiUFAMK0adPy8lQIL1++FOrWrSsYGhoK169fz9O+8kJBfS/j4uIEbW1toXr16oKWlpYAQKhdu7YAQFi8eLEgCIKwYMECAYBQpXhx4U9LS2Fas2bCAScnYYyFhQBA+K1sWWFkw4aCU82ago6qqlBJV1cw1NISDjg5yf9V1NUV9DU0hGHm5sJflpbCant74YCTk9Db1FQAINSsXl2ed8qUKQIAoWnTpsKiRYuEFStWCH379hX+97//5eE7VPikpaUJPXr0EFRVVYWQkBCx4+SZYcOGCWpqasLFixfFjkJElK8kgsCvfImoaJNIJNi5cyc6d+4sdpQsefnyJVq1aoVXr17h6NGjvLHQF3L6XqakpGDy5MkIDQ3FvXv3IJPJYGJigiFDhmDo0KEAPt/VuUGDBoi5fRsCAEMtLfi0bw9BEBB48yYO3ruHuE+fUFFXF33q1MHJR49w5dUr+LRvL+/nxps38IyMxP3375Emk6G3qSlcateG39Wr2HbzJkaOHInly5fL9/f29saKFSsQHR0NLS0t1KtXD5MnT4adnV1OTleRk5qaii5duuDo0aMIDQ1F8+bNxY6U65KTk9G0aVO8e/cOly5dyrAUFhFRYcbCloiKPEUqbF++fImWLVvizZs3CA8PR82aNcWOVKDk13sZHR2N2rVrY4KlJWwrVsy1dsMfPsTCs2cRHR2dpXV3Kfs+fvyIdu3a4dKlSwgPD0eDBg3EjpTr7t69iwYNGqB169YICgri0lBEVCRwji0RkYJ48eIFbG1tWdQWAKampmhjbw+/6Ggk5dIdm5NSU+EXHY029vYsavOQpqYm9uzZg5o1a6JNmza4ceOG2JFynbGxMby9vbF9+3asXLlS7DhERPmCI7ZERArg+fPnaNmyJd69e4fw8HDUqFFD7EhFXmxsLOrWro3mRkYYbWGRo7YEQcDyixdx4vlzXIqM5PubD96+fQtra2u8ffsWJ0+eRJUqVcSOlOvGjBmD1atX4+TJk2jcuLHYcYiI8hQLWyKiAu7Zs2do2bIl4uPjER4ejurVq4sdif6zfv16uLm5oU/t2uj1i3OdBUGA/40b2Hz9OoDPl1Obm5ujefPmaNy4MRo3bgwTExNeTpoHnj9/DisrK8hkMpw4cQJly5YVO1KuSklJgZWVFV68eIHIyEjo6+uLHYmIKM+wsCUiKsCePXsGW1tbJCYmIjw8HNWqVRM7En1l4sSJmDdvHtpUqQK3+vWhpaqa5WOTUlOx7vJlHIqNxezZs7Fjxw5cvHgRAKCiooK0/9bK1dPTw5EjRwrlfFCxPXjwAFZWVihWrBiOHTuGUqVKiR0pV92/fx/m5uawtrbGzp07+QUJERVanGNLRFRAPX36FDY2Nvjw4QMiIiJY1BZAUqkUFy9ehI6ODk48f47hR44g/OFDpH615uzXUqVShD98iOFHjuDk8+dYv349Jk2alOFOyOlFrUQigUwmk6+fS7mrUqVKCAsLw+vXr+Hg4ID379+LHSlXVa5cGb6+vti9ezeWLl0qdhwiojzDEVsiKpSkUilSUlKgoqIC1WyMoBUUT548ga2tLT59+oTw8HAYGxuLHalASU1NRVpaGjQ0NEQdgZo5cyamTZuG0NBQGBsbY6i7Ow6FhkJfSwtNy5RBNX19VNTVhbqyMpKlUjyMj0dMXBxOP3uGuKQktLG3h6eXV4b5nU2bNsXZs2ch+6I4PnjwIBwcHMR4iUXG5cuXYWNjg7p16yIkJARaWlpiR8pVEyZMwLJly3D8+HE0adJE7DhERLmOhS0RFTqvX7+Gubk5KlasiIiICIUrbB8/fgxbW1skJycjIiICVatWFTtSgSIIAqpXr45mzZrBx8dHtBzh4eGws7PD5MmT4eHhId8eHR0NLy8vHD50CLdiYvDlr1mJRIIa1aqhdZs2GDp06Dfvfrx//3506NAhw7YuXbrA19cXxYoVy7sXRPj333/RunVrNG/eHLt374a6urrYkXJNamoqbGxs8OjRI0RGRqJkyZJiRyIiylUsbImoUJHJZOjQoQPOnTuHqKgolC9fXuxI2fL48WPY2NggNTUV4eHhLGq/4eTJk7CyskJ4eDhsbGxEyfD8+XOYmZmhdu3aCA0NhbKy8jf3S0xMxJ07d5CcnAx1dXWYmJhAR0fnh20LggBTU1PcvHkTXbp0Qb9+/dCnTx+UL18eO3fu5B2T89iRI0fQrl07ODo6Ytu2bVBRURE7Uq559OgRzM3NYWlpib1790JJiTPSiKjw4P/RiKhQWbBgAQ4ePIjNmzcrXFH76NEj2NjYIC0tjSO1P+Dt7Y0qVaqgRYsWovQvlUrRu3dvSCQSbNmy5btFLQDo6OjAzMwMlpaWMDMz+2lRC3we1V26dCnat28PX19fdOrUCefPn4cgCGjcuDH27NmTmy+HvtKqVSsEBQVh165dcHNzy3BJuKKrUKECNm3ahAMHDmDhwoVixyEiylUsbImo0Dhx4gQmT56MSZMmKdx8xIcPH2Yoagvjmpq54cOHDwgMDES/fv1EG23y8PDAsWPH4O/vDyMjozzpw8HBAfv27ZNfelyjRg2cPXsWrVq1QqdOnTBt2rRCVXAVNB07doSfnx98fX0xZswYFKaL29q2bYuJEyfi77//xokTJ8SOQ0SUa3gpMhEVCi9fvoS5uTmqVauGsLAwhbp88MGDB7C1tYUgCIiIiEClSpXEjlRg+fn5oV+/foiNjUXlypXzvf/Q0FA4ODhgxowZmDx5cr73L5PJMG/ePEyePBnt27fHpk2boKenl+85ioo1a9bA3d0dkydPxsyZM8WOk2vS0tLQqlUr3LlzB5GRkTA0NBQ7EhFRjrGwJSKFJ5VK0bZtW0RFRSEqKgply5YVO1KW3b9/H7a2tpBIJIiIiEDFihXFjlSg2draQklJCUeOHMn3vp88eQJzc3OYm5vj4MGDos5PPHjwIHr37g0DAwPs3LkTtWvXFi1LYbdo0SJMmDABCxYswIQJE8SOk2uePn0KMzOzAvF5JiLKDfy/GBEpvDlz5iAsLAxbt25VuKLWxsYGSkpKLGqz4N69e4iIiICrq2u+952WloZevXpBTU0NmzdvFr0IaNu2LS5cuAANDQ1YWloiODhY1DyF2fjx4zF58mT8+eefWLNmjdhxck3ZsmWxZcsWHD58GHPmzBE7DhFRjrGwJSKFFh4ejunTp2Pq1Kmws7MTO06WxcbGwtraGioqKixqs8jPzw/FihVD165d873vKVOm4PTp09i2bRsMDAzyvf9vMTY2xr///gtHR0c4OTlh4sSJkEqlYscqlGbMmIFRo0Zh6NCh2Lp1q9hxck3r1q0xZcoUTJs2DeHh4WLHISLKEV6KTEQKK33JlTp16uDQoUM/vDttQXLv3j3Y2tpCTU0N4eHhCnf3ZjHIZDJUrVoVdnZ2WL9+fb72feDAAbRv3x7z5s3DX3/9la99Z4UgCFiyZAn+/PNP2NnZwd/fHyVKlBA7VqEjk8kwcOBAbNq0Cdu3b0enTp3EjpQrpFIp7O3tcf36dURFReXZDdGIiPIaC1siUkhSqRStW7fGjRs3EBUVhdKlS4sdKUvu3r0LW1tbaGhoIDw8HOXKlRM7kkIIDw9Hy5YtcfLkSTRr1izf+n306BHMzMzw22+/Ffh1P48cOYIePXpAV1cXO3fuRP369cWOVOikX5K+Z88e7N+/X6GuEvmR9C8JTU1NcfjwYYX5kpCI6EsF9zc0EdEPfLnkiqIUtXfu3IGNjQ00NTVZ1GaTt7c3qlWrhqZNm+Zbn6mpqejRowe0tbXh5+dXoIta4PP6qxcvXoSenh6aNGlSqC6ZLShUVFSwZcsWtGzZEp06dcK///4rdqRcYWRkBH9/fxw7dgwzZswQOw4R0S8p2L+liYi+ITQ0FLNmzcKMGTNgY2MjdpwsSS9qtbS0WNRmU3x8PIKDg+Hq6gqJRJJv/U6cOBHnz59HYGAgSpYsmW/95kSlSpVw6tQpODk54ffff8fYsWORlpYmdqxCRU1NDdu3b0fDhg3Rrl07REVFiR0pV9ja2sLDwwMzZ85EaGio2HGIiLKNlyITkUJJX3KlQYMGOHDgQIEfRQOAmJgY2NjYoFixYggPD0eZMmXEjqRQNmzYgMGDB+PBgwf5Nh95z5496NSpExYvXoyxY8fmS5+5SRAErFy5EmPHjkXz5s0REBDAtUpzWXx8PFq2bImHDx/ixIkTqFGjhtiRckwmk6Ft27aIjIxUuKXTiIhY2BKRwkhLS0PLli1x7949REZGFpi70/7I7du3YWNjg+LFiyM8PJw3ZvkFzZs3h46ODkJCQvKlv/v378Pc3BzW1tbYuXNnvo4S57bjx4/DyckJ6urq2LFjBywsLMSOVKi8fv0a1tbWiI+Px8mTJ1GpUiWxI+XYq1evYGZmBmNjYxw9ehQqKipiRyIiypKCP9RBRPSfgrjkyo/cunULNjY20NPTQ0REBIvaX3D79m2cOnUq39auTUlJgbOzM/T09ODt7a3QRS0AtGjRAhcvXkSZMmXQvHlz+Pj4iB2pUClVqhQOHz4MNTU12NnZ4fnz52JHyjEDAwNs27YNp0+fxtSpU8WOQ0SUZSxsiUghHDhwAPPmzcOcOXPQvHlzseP81M2bN2FjYwN9fX2Eh4crzA2uChpfX18UL14cnTt3zpf+JkyYgKioKAQGBkJfXz9f+sxr5cuXx/Hjx9GnTx/0798fw4cPR0pKitixCo2yZcsiLCwMHz9+ROvWrfH27VuxI+WYlZUVZs+ejblz5+LAgQNixyEiyhJeikxEBV76kitNmjTBnj17Cvy82hs3bsDW1halSpXC0aNHObfxF0mlUlSqVAmOjo7w9PTM8/62b9+O7t27Y/ny5Rg5cmSe9yeGtWvXYsSIEWjcuDGCg4N5FUEuunHjBlq0aIEqVargyJEjKFasmNiRckQmk8HR0RFnzpxBVFQUKlSoIHYkIqIfYmFLRAVaamoqrK2t8fjxY0RGRhb4u9NGR0ejZcuWMDAwwNGjRxXikumCKjQ0FG3atMHZs2fRuHHjPO3r7t27aNCgAezt7REYGKjwlyD/yL///otu3boB+FzMN2nSROREhcelS5dga2sLc3NzHDx4EJqammJHypE3b97A3Nwc5cuXx7Fjx6Cqqip2JCKi7yrYwx5EVOQp0pIr169fh62tLQwNDVnU5gJvb2+YmpqiUaNGedrPp0+f4OTkBAMDA6xfv75QF7UA0KRJE1y8eBFVq1aFtbU11q5dK3akQiP9bu3nz5+Hk5OTwl/yXbJkSQQEBOD8+fOYNGmS2HGIiH6IhS0RFVh79uzB4sWLsWDBAvz2229ix/mha9euwdbWFkZGRixqc0FcXBx27tyZL2vXjh07FtHR0QgMDETx4sXztK+CokyZMjh69CgGDx6MIUOGwM3NDcnJyWLHKhSaNWuGnTt34vDhw+jTpw+kUqnYkXKkSZMmmD9/PhYtWoQ9e/aIHYeI6Lt4KTIRFUjpS67Y2Nhgx44dBXoU7erVq2jVqhXKli2LI0eOFPiRZUXg5eWFESNG4PHjx3k6DzQgIAA9e/aEp6cn3N3d86yfgszHxwfu7u4wMzNDcHBwvq0VXNjt3LkTTk5OcHV1xbp16wr0/8N+RhAEdOnSBceOHUNkZCQqV64sdiQiokxY2BJRgZOSkoLmzZvj1atXuHTpUoG+O+2VK1fQqlUrlC9fHmFhYSxqc4mlpSUMDQ2xd+/ePOvj9u3baNiwITp06ICtW7cqdOGRUxcuXEDXrl2RnJyMoKAgtGjRQuxIhYKfnx/69euHP/74A4sXL1boz1hcXBwaNGgAQ0NDnDhxAmpqamJHIiLKgJciE1GBM2HCBFy+fBlBQUEFuqi9fPkyWrZsiQoVKnCkNhdFR0fj3Llzebp27cePH+Hk5ISyZcti7dq1Cl1w5AYLCwtcvHgRpqamaNWqFZYvXw5+751zffv2xcqVK7F06VLMmDFD7Dg5oq+vj8DAQERGRuLPP/8UOw4RUSYqYgcgIvrS9u3bsXz5cqxYsQIWFhZix/muqKgo2NnZoVKlSjh8+DBKlCghdqRCw8fHByVLloSjo2Oe9TFq1Cjcvn0bZ8+eVfhlWXKLgYEBDh8+jL/++gujR4/GhQsXsGbNGoW/s6/Yhg8fjoSEBEycOBG6urr4448/xI70yxo1aoTFixdj1KhRsLKykt9dm4ioIOClyERUYKQvudKmTRsEBAQU2FG0yMhI2NnZoUqVKjh8+HCBHlVWNGlpaahQoQKcnJywfPnyPOlj8+bN6NOnD9avX4+BAwfmSR+KbuvWrRg0aBBq1qyJHTt2cE5lLpg4cSLmzZuHdevWYdCgQWLH+WWCIMDZ2RmhoaG4dOkSjI2NxY5ERASAhS0RFRCfPn1C06ZNER8fj4sXLxbYu9NeunQJdnZ2MDExQWhoKPT09MSOVKjs378fHTp0wKVLl2Bubp7r7d+4cQMWFhbo1q0bfH19C+yXJwXB5cuX0aVLF8THxyMgIACtWrUSO5JCEwQBI0aMgKenJ/z9/dGjRw+xI/2y9+/fo2HDhihevDhOnToFDQ0NsSMREXGOLREVDOlLrgQFBRXYovbixYuws7NDtWrVWNTmEW9vb9SvXz9PitoPHz7AyckJlSpVgqenJ4van6hfvz4uXLiAhg0bwt7eHosWLeK82xyQSCRYsWIFXFxc4OLigv3794sd6ZcVL14cgYGBuHbtGsaNGyd2HCIiAByxJaICIH3JFS8vLwwZMkTsON+UnJyMly9fQlVVFYaGhlBS4veCuU0mkyExMRHq6upQV1fP9fY/fvyI1NRUaGtrQ1lZOdfbL6ykUikmT56MefPmoUePHtiwYQO0tbXFjqWw0tLS4OzsjAMHDuDgwYOwtbUVO9IvS0hIQFxcHEqWLMnPBBGJjoUtEYkqfckVR0dHbNmyhaNoRAVUcHAwXF1dUbVqVezcuZNzK3MgOTkZjo6O+PfffxEWFgZLS0uxIxERKTwWtkQkmo8fP+K3337Dp0+fcOHCBd6dlqiAu379Orp06YJXr17B398fDg4OYkdSWB8+fIC9vT1u3LiBY8eOoW7dumJHIiJSaLyWjohEk77kSnBwMItaIgVQu3ZtnDt3Ds2aNUO7du0wZ84czrv9Rdra2ti/fz8qVaqE1q1bIyYmRuxIREQKjYUtEYli8+bNWL9+PVatWsWRCiIFoqenhz179mDKlCn4+++/0a1bNyQkJIgdSyHp6ekhNDQU+vr6sLOzw6NHj8SORESksFjYElG+i46OxpAhQ9C3b1/0799f7DgKwcbGBhKJBBKJBFFRUWLHUSjTp0+HmZmZ/LGrqys6d+4sWp785urqKv/s7Nq1K1faVFJSgoeHB3bv3i2fI3rr1q1cabuoMTAwwOHDh6GkpAQ7Ozu8ePFC7EhERAqJhS0R5av0JVcqV66M1atX5+vNor4ucHKTj49Pni//4+bmhmfPnqFOnToAgPv37xe6m21VrlwZERERWd4/IiIClStXzlYf//zzD3x8fLJ1jCKZPn06XF1d5Y//+ecfPHv2LE/66tixI86fPw8AaNy4Mfbs2ZMn/RR25cuXR1hYGBISEtCmTRvExcXla//5/WVPbn7JQkSUjoUtEeWr4cOH4/7/sXffcTX2/x/AX6epZUeiIWVkteg20qSsWyRZiawQlXmblT3ulBARZZWWbJUW2VLZkpRE9ixpnPP5/XF/nZ+jUKlO8X4+Hh4P5xqfz/s6V9c5531dn5GVhdDQUJoeooKkpaWhoKAAMTExYYcioKioSNghVEiDBg1+eBOirh3PzzRo0AAKCgrVVn67du1w+fJlmJmZYciQIXB1dQWPx6u2+n5Xbdq0wenTp/H48WMMGDAAeXl5wg6plOLiYmGHIOB3u1YJIb+GEltCSI3x9/fHnj17sH37dmhqalZ4/8LCQsyaNQvNmjVDvXr10Lt3b/7TorKemB4+fJj/RDMgIADu7u64fv06v1nml6d2HA4H27ZtQ//+/SElJQU1NTWEhYXxy0lISACHw8G7d+/4y1JTU8HhcJCVlYWEhARMmDAB79+/55ft5uZW4eOrCufOnYOBgQGkpKSgpKSEWbNmIT8/n79eVVUVq1evhr29PeTk5KCsrIwdO3YIlPH48WOMGDECDRs2ROPGjTFkyBBkZWXx1395urNq1SooKiqiXbt2AIALFy5AS0sL9erVg56eHv/9T01NBWMM6urq+PfffwXq+vI+PnjwoMreg7Vr16J58+aQk5PDxIkT8fnzZ4H13z6dMjIygqOjI5ydndG0aVOYm5v/tA4OhwM/Pz8MHToU0tLS0NDQEHhayeVyMXHiRLRu3RpSUlJo164dNm3aVGYcq1evRvPmzdGwYUMsX74cJSUlmDdvHho3boxWrVrB399fYL+fnR9hkJOTQ1hYGFavXo0VK1ZgyJAhAtcLKZ+OHTsiKioKt2/fxpAhQ0r97f6qsLAwdO7cGVJSUmjSpAnMzMwwb9487NmzB0eOHOF/fiUkJPBbhAQHB8PQ0BD16tXDgQMHymz54uXlVarlxO7du9GxY0dISkqiRYsWcHR0BAD+dkOHDgWHw+G/LuupsbOzM4yMjPivv3et3rp1C/3794esrCyaN28OW1tbvHr1qqreNkJIHUGJLSGkRty8eRMzZszAxIkTYWtrW6ky5s+fj/DwcOzZswfJyclQV1eHubk53rx589N9bWxsMGfOHHTs2BG5ubnIzc2FjY0Nf/3SpUthZWWF69evY8yYMRg5ciTu3r1brrh69uwJLy8v1K9fn1/23LlzK3WMvyIjIwMWFhawsrLCjRs3EBwcjHPnzvF/UH7h4eEBPT09pKSkYPr06Zg2bRq/f2RxcTHMzc0hJyeHxMREnD9/HrKysrCwsBB4OhIbG4u0tDScPn0ax48fx4cPHzB48GB07twZycnJWLFiBRYsWMDfnsPhwN7evlSS5u/vjz59+kBdXb1K3oOQkBC4ublh9erVSEpKQosWLeDj4/PT/fbs2QMJCQmcP38e27dvL1dd7u7uGDFiBG7cuIEBAwZgzJgx/L9FHo+HVq1aITQ0FHfu3MGyZcuwaNEihISECJQRFxeHp0+f4uzZs9i4cSNcXV0xaNAgNGrUCJcvX4aDgwOmTp2KnJwcAOU/P8LA4XCwcOFCnDx5EufOnUP37t1x+/ZtocZUF+np6eH48eO4cOECbGxsquwpaW5uLkaNGgV7e3vcvXsXCQkJGDZsGFxdXTFixAhYWFjwP7969uzJ3++ff/6Bk5MT7t69W66bPgCwbds2zJgxA1OmTMHNmzdx9OhR/jX+5Wakv78/cnNz+a/L69tr9d27dzAxMYG2tjaSkpIQGRmJ58+fY8SIERUqlxDyG2CEEFLNPnz4wNq1a8c6d+7MPn36VKky8vLymLi4ODtw4AB/WVFREVNUVGTr169n/v7+rEGDBgL7REREsK8/5lxdXVnXrl1LlQ2AOTg4CCzT19dn06ZNY4wxFh8fzwCwt2/f8tenpKQwACwzM5MxxsqsvyoZGhoyJyenH24zceJENmXKFIFliYmJTEREhBUUFDDGGFNRUWFjx47lr+fxeKxZs2Zs27ZtjDHG9u3bx9q1a8d4PB5/m8LCQiYlJcWioqIYY4zZ2dmx5s2bs8LCQv4227ZtY02aNOHXwxhjO3fuZABYSkoKY4yxJ0+eMFFRUXb58mXG2H/nr2nTpiwgIKCC78b39ejRg02fPl1gmb6+vsB5t7OzY0OGDOG/NjQ0ZNra2hWqBwBbsmQJ/3VeXh4DwE6dOvXdfWbMmMGsrKwE4lBRUWFcLpe/rF27dszAwID/uqSkhMnIyLCgoCDGWPnOz49ijoiIKPcx/ooHDx6wzp07MxkZGRYaGlojdf5uTp48ycTFxdno0aMF/kYq69q1awwAy8rKKrXu22uCMcYyMzMZAObl5SWwvKzPUU9PT6aiosJ/raioyBYvXvzdWMr6WywrBicnJ2ZoaMh/Xda1umLFCtavXz+BZY8fP2YAWFpa2ndjIIT8fuiJLSGkWjHG4ODggCdPniA0NBRSUlKVKicjIwPFxcXo1asXf5m4uDi6d+9e7ierP9KjR49Sr6ui3Jp0/fp1BAQEQFZWlv/P3NwcPB4PmZmZ/O26dOnC/z+Hw4GCggJevHjBL+PBgweQk5Pjl9G4cWN8/vwZGRkZ/P06d+4MCQkJ/uu0tDR06dIF9erV4y/r3r27QHyKiooYOHAgdu/eDQA4duwYCgsLYW1tXWXvwd27d6Gvry+w7NtzWxZdXd0K1/X1+ygjI4P69evz30cA2Lp1K3R1dSEvLw9ZWVns2LED2dnZAmV07NgRIiL//1XcvHlzgemvREVF0aRJkwqfH2Fr06YNLl68iMGDB8Pa2hr//PMPuFyusMOqU/r3748DBw7g4MGDmD59+i/PF9y1a1eYmpqic+fOsLa2xs6dO8s1SJWenl6F6nnx4gWePn0KU1PTyob6Q99eq9evX0d8fLzA51779u0BoFZdE4SQ6le7RiAhhPx2du7cicDAQAQFBfH7YlYHERGRUj/8qqoJ35fE4+vya9sgKgCQl5eHqVOnYtasWaXWKSsr8/8vLi4usI7D4fAH+8nLy4Ouri4OHDhQqgx5eXn+/ys78NekSZNga2sLT09P+Pv7w8bGBtLS0pUqqypV5nh+9D4ePHgQc+fOhYeHB3r06AE5OTls2LABly9f/mkZVXF+agMZGRkEBgZCT08P8+fPR0pKCgIDA9GkSRNhh1ZnWFtbIy8vD/b29qhfvz7WrVtX6ZHQRUVFcfr0aVy4cAHR0dHYvHkzFi9eXOpv8lvfXhs/+6yt7M3L8n6GfxtPXl4eBg8ejHXr1pXatkWLFpWKhRBSN1FiSwipNqmpqZg1axYcHBwwcuTIXyqrTZs2/H5VKioqAP770XP16lU4OztDXl4eHz9+RH5+Pv+Hz7fzvUpISHz3qdGlS5cwbtw4gdfa2toA/j9hyM3NRaNGjSpcdk3R0dHBnTt3fqm/qo6ODoKDg9GsWTPUr1+/3Pu1a9cO+/fvR2FhISQlJQGgzL5zAwYMgIyMDLZt24bIyEicPXu20rGWpUOHDrh8+XKpc1nTzp8/j549e2L69On8ZVXx9Kiy50dYOBwO5syZAy0tLdjY2EBPTw8RERHVNu3W72jChAn48OEDnJ2d0aBBAyxevLjSZXE4HPTq1Qu9evXCsmXLoKKigoiIiAp9fsnLy+PZs2dgjPGT7K8/D+Xk5KCqqorY2FgYGxuXWYa4uHip+uTl5XHr1i2BZampqaVu9HxLR0cH4eHhUFVVrXUjxhNCahY1RSaEVIsPHz7A2toampqa8PT0/OXyZGRkMG3aNMybNw+RkZG4c+cOJk+ejE+fPmHixInQ19eHtLQ0Fi1ahIyMDAQGBpaaq1RVVRWZmZlITU3Fq1evUFhYyF8XGhqK3bt34/79+3B1dcWVK1f4gy6pq6tDSUkJbm5uSE9Px4kTJ+Dh4VGq7Ly8PMTGxuLVq1f49OnTLx9zRS1YsAAXLlyAo6MjUlNTkZ6ejiNHjpQaPOpHxowZg6ZNm2LIkCFITExEZmYmEhISMGvWLP4ARmUZPXo0eDwepkyZgrt37yIqKoo/AvLXT5hERUUxfvx4LFy4EBoaGuVqJlwRTk5O2L17N/z9/fnnUhgDGGloaCApKQlRUVG4f/8+li5dWuFBcspS2fMjbKamprh27RoaNWqEnj17IjAwUNgh1SlOTk5YsWIFlixZAm9v70qVcfnyZf6gatnZ2Th06BBevnyJDh06QFVVFTdu3EBaWhpevXr1wxYpRkZGePnyJdavX4+MjAxs3boVp06dEtjGzc0NHh4e8Pb2Rnp6OpKTk7F582b++i+J77Nnz/jNoU1MTJCUlIS9e/ciPT0drq6upRLdssyYMQNv3rzBqFGjcPXqVWRkZCAqKgoTJkwQ+s1GQkjNosSWEFLlGGOYNGkSXrx4gZCQEIF+l79i7dq1sLKygq2tLXR0dPDgwQNERUWhUaNGaNy4Mfbv34+TJ0+ic+fOCAoKKjXljpWVFSwsLGBsbAx5eXkEBQXx17m7u+PgwYPo0qUL9u7di6CgIP6UROLi4ggKCsK9e/fQpUsXrFu3DitXrhQou2fPnnBwcICNjQ3k5eWxfv36KjnmiujSpQvOnDmD+/fvw8DAANra2li2bBkUFRXLXYa0tDTOnj0LZWVlDBs2DB06dOBPmfOjJ4T169fHsWPHkJqaCi0tLSxevBjLli0DgFLnf+LEiSgqKsKECRN+Go+RkRHGjx9f7vhtbGywdOlSzJ8/H7q6unj06BGmTZtW7v2rytSpUzFs2DDY2NhAX18fr1+/Fnh6W1mVPT+1gYqKCs6fPw9ra2uMGTMGs2fPRklJibDDqjMWL16MuXPnwsnJqdTo4uVRv359nD17FgMGDEDbtm2xZMkSeHh4oH///pg8eTLatWsHPT09yMvL4/z5898tp0OHDvDx8cHWrVvRtWtXXLlypdQo8HZ2dvDy8oKPjw86duyIQYMGIT09nb/ew8MDp0+fhpKSEr9ljLm5Of/a7datGz5+/CjQ8uJ7FBUVcf78eXC5XPTr1w+dO3eGs7MzGjZsKNB/nRDy++OwXx2NgBBCvrF161Y4OjoiLCwMVlZWwg7npzgcDiIiIkrNoVibGBkZQUtLC15eXsIOpdwOHDjAn9/36353iYmJMDU1xePHj9G8efMflqGiogJ3d/cKJbekbLXl75wxhi1btmD27Nno3bs3v2k1+bkvg/H5+fkhODgYw4cPF3ZIhBBSa9CtLEJIlUpKSsLs2bMxc+bMOpHU1iU+Pj6QlZXFzZs3hR1Kmfbu3Ytz584hMzMThw8fxoIFCzBixAh+UltYWIicnBy4ubnB2tr6p0nt7du30aBBg3I9tSHf5+DgAFlZWWGHwcfhcDBz5kzExsbizp070NPTQ1JSkrDDqhM4HA58fHwwcuRIjB49GpGRkcIOiRBCag16YksIqTLv3r2Djo4OmjZtisTERP4gQrVdbXmS9SNPnjxBQUEBgP9GOP56qp3aYv369fDx8cGzZ8/QokULWFpaYtWqVfxRjwMCAjBx4kRoaWnh6NGjaNmypZAjLtuBAwcwderUMtepqKgIpc/ur3jx4gU+fPgA4L9RYis7onV1yMnJgZWVFa5fv47t27fTk/lyKi4uhpWVFWJiYhAZGYk+ffoIOyRCCBE6SmwJIVWCMQYrKyvEx8cjOTkZrVu3FnZIhFTKx48f8fz58zLXiYuL80flJlWjsLAQjo6O8PPzw/Tp0+Hp6Vkrb9zUNp8/f8bAgQNx9epVxMXFVXi+WUII+d1QYksIqRJeXl5wcXHB4cOHMWTIEGGHQwipY3bs2AFHR0d0794dYWFhUFBQEHZItV5eXh769u2L9PR0nDlzBh07dhR2SIQQIjSU2BJCftmlS5dgYGCAWbNmlZoGhxBCyuvixYsYPnw4GGMIDw+v8umgfkdv377lT8Fz7tw5qKmpCTskQggRCkpsCSG/5M2bN9DW1oaioiLOnj0LcXFxYYdECKnDnj17Bmtra1y+fBmbN2/GlClTBOZCJqU9f/4cBgYGKC4uxrlz52pt/3VCCKlONCoyIaTSeDwe7OzskJeXh+DgYKEntQUFBXj8+DFevXoFumdX++Tn5yM/P7/C+/F4PHz8+BH5+fl0Xv8ACgoKiI2NxZQpU+Dg4IDJkyfj8+fPwg6rVmvevDliYmLA4/FgZmaGly9fCjukcispKcGTJ0/w8uVLur4JIb+EEltCSKV5eHjg+PHj2LdvH5SVlYUaS0REBOrXr4/Zs2ejQYMG9ISnlsnOzoacnBxCQkIqtB+Xy4W5uTk0NDTw4cMHOq9/CAkJCWzZsgX+/v7Yv38/DA0NkZOTI+ywajVlZWXExMTg7du3MDc3x7t374QdUrmIiYkhJSUFzZo1w7///ivscAghdRgltoSQSjl//jwWLlyIBQsWYMCAAUKN5dChQxgxYgSGDRuGwMBAoT85JqXt3bsX0tLSsLa2rtB+K1euRGxsLA4cOIAWLVpUU3Sktho/fjzOnTuH3Nxc6Orq4uzZs8IOqVbT0NBAdHQ0srKyMGjQoEq1kBCGQYMGYf78+Vi4cCHOnz8v7HAIIXUU9bElhFTYy5cvoa2tjdatWyM+Ph5iYmJCiyU8PBwjR46ElZUV9u/fL9RYSNkYY9DQ0EDv3r0REBBQ7v1iY2PRt29fuLq6wtXVtfoCJLXey5cvMWLECJw7dw4eHh6YOXMmPb3/gcuXL8PU1BQ9e/bEsWPH6sSc4sXFxTA2NkZWVhZSU1PRtGlTYYdECKlj6IktIaRCeDwebG1tUVhYiIMHDwo1kQwNDYWNjQ2GDx9OSW0tlpiYiIyMDEyYMKHc++Tm5mL06NEwNTXFkiVLqjE6UhfIy8vj9OnTmDVrFpycnGBnZ4eCggJhh1Vr6evr49ixYzh79ixGjRqFkpISYYf0U+Li4jh48CAKCwtha2sLHo8n7JAIIXUMJbaEkApZu3YtoqOjceDAAaGOvBkSEoJRo0bBxsYG+/bto6S2FgsICICamhoMDAzKtX1JSQlGjx4NUVFRHDhwAKKiotUcIakLxMTE4OHhgQMHDiAsLAy9evVCVlaWsMOqtYyNjREWFoZjx47B3t6+TiSKrVq1wr59+xAVFYW1a9cKOxxCSB1DiS0hpNzOnDmDpUuXYvHixejXr5/Q4ggODsbo0aMxcuRI7N27l5LaWiwvLw8hISGws7ODiEj5vnLc3d1x9uxZBAUFoVmzZtUcIalrRo8ejYsXL+Ldu3fQ09NDbGyssEOqtQYNGoR9+/Zh//79mDVrVp0YddjCwgKLFi3C0qVLcebMGWGHQwipQ6iPLSGkXJ4/fw4tLS20b98eMTExQnuKFhQUhLFjx2LMmDHw9/enp3m13J49ezB+/HhkZWVBRUXlp9tHRUWhf//+WLlyJRYtWlQDEZK66s2bNxg1ahRiYmKwbt06zJkzh/rdfsfOnTsxZcoULFy4EKtXrxZ2OD9VUlICMzMz3L9/HykpKWjevLmwQyKE1AGU2BJCfurLlCu3bt1CSkqK0EanDQwMhK2tLcaOHYvdu3dTUlsHGBkZQVRUtFxP1XJycqCtrQ09PT2cOHGi3E94yZ+Ly+ViyZIlWLt2LWxsbLBr1y7IyMgIO6xaaePGjZgzZw7WrFmDf/75R9jh/FRubi60tLTQpUsXREZG0uc9IeSn6FcDIeSnVq5cibi4OAQGBgotqT1w4ABsbW0xbtw4SmrriIcPH+LMmTPlGjSqpKQEo0aNQr169bBv3z5Kakm5iIqKYs2aNQgNDcXx48fRo0cPZGRkCDusWmn27NlwdXXFwoUL4ePjI+xwfqpFixYIDAxEbGwsVq1aJexwCCF1AP1yIIT8UGxsLNzd3eHm5gYTExOhxLBv3z6MGzcOdnZ28PPzo6S2jtizZw/k5OQwbNiwn267ZMkSXLx4EQcPHqRpPkiFDR8+HJcvX8bnz5+hp6eHyMhIYYdUK7m6usLZ2RkzZszAvn37hB3OT5mamsLV1RVubm7Ul5oQ8lPUFJkQ8l21oSnY3r17MX78eNjb22PHjh30JK+O4PF4UFNTQ9++fbFz584fbnv8+HEMHjwYGzZswNy5c2soQvI7evfuHWxtbXHixAmsXLkSCxcupH6332CMYfLkyQgICEBoaCiGDh0q7JB+iMvlwsLCAjdu3EBqaqrQWg0RQmo/SmwJIWX6evCO1NRUoYxOu2fPHkyYMAETJ06Er68vJbV1SGxsLMzMzHD+/Hn07Nnzu9tlZ2dDW1sbvXr1wpEjRygJIb+Mx+Nh+fLlcHd3x9ChQ/ktB8j/43K5GD16NA4fPozjx4+jb9++wg7ph74evPD06dM0Ej4hpEz0K5EQUiY3NzckJibi4MGDQklq/f39MWHCBEyaNImS2jooICAAbdu2RY8ePb67TVFREWxsbCAnJ4eAgABKakmVEBERgZubG44cOYLY2Fh0794daWlpwg6rVhEVFcW+fftgZmYGS0tLnD9/Xtgh/VDz5s1x8OBBnD17Fu7u7sIOhxBSS9EvRUJIKZGRkVi1ahVWrlyJPn361Hj9u3fvxsSJEzFlyhRs376dkto65v379wgPD8f48eN/mKwuXLgQ165dQ3BwMBo3blyDEZI/wd9//40rV66Aw+GgW7duOHLkiLBDqlUkJCQQFhaGbt26YcCAAUhJSRF2SD9kaGiIFStWYNWqVYiKihJ2OISQWoiaIhNCBOTk5EBLSwvdu3fH8ePHazyp9PPzw+TJkzFt2jRs2bKFkto6yM/PD1OnTkV2djZatmxZ5jaHDx/G0KFD4eXlBScnpxqOkPxJPn78iPHjx+PQoUNYunQp3Nzc6HPlKx8+fICZmRkyMzNx9uxZdOjQQdghfRePx8PAgQORlJSElJQUtGrVStghEUJqEUpsCSF8xcXFMDY2xqNHj5CSklLjo9Pu2LEDU6dOxfTp07FlyxZqmlpH9erVC3Jyct8dmTYzMxPa2towNTVFWFgYnWdS7RhjWLt2LRYvXowBAwZg//79aNiwobDDqjVev34NIyMjvH37FomJiWjdurWwQ/quV69eQUtLC61bt0Z8fDz1tyWE8NEtS0II35IlS3D58mUEBwfXeFLr6+uLqVOnwtHRkZLaOiwtLQ0XLlz47ty1hYWFGDFiBBo3boxdu3bReSY1gsPhYOHChTh58iTOnz+Pbt264fbt28IOq9Zo0qQJoqOjUa9ePZiZmeHp06fCDum7mjZtiuDgYFy8eBFLliwRdjiEkFqEEltCCID/plxZv3491qxZ88NRbKvD9u3b4eDggJkzZ8Lb25uSnTpsz549aNiwIYYMGVLm+nnz5uHGjRsIDQ2lJ2akxllYWCApKQlSUlLQ19dHWFiYsEOqNVq0aIGYmBgUFRWhb9++ePXqlbBD+q5evXphzZo1WLduHY4fPy7scAghtQQ1RSaE4NGjR9DW1kbv3r1rfMoVHx8fzJgxA05OTvD09KSktg7jcrlQUVHB33//DR8fn1Lrw8LCYG1tja1bt2L69OlCiJCQ/+Tn52PSpEk4ePAgFixYgFWrVgllnu7a6N69e+jTpw+UlZURFxeH+vXrCzukMvF4PAwZMgQXLlxASkoKlJWVhR0SIUTIKLEl5A9XVFSEPn364NmzZ0hOTq7R0Wm3bt0KR0dHODs7Y+PGjZTU1kGZmZnw8vLC4MGDUVRUhIEDB+LKlSvo1q2bwHYPHjyAjo4OBgwYgKCgIDrXROgYY9i4cSPmz58PMzMzBAYGokmTJsIOq1ZITU2FkZERunbtilOnTkFaWlrYIZXpzZs30NbWhqKiIs6cOQMJCQlhh0QIESJKbAn5w82ePRtbtmxBYmIi9PX1a6zezZs3Y9asWZg9ezb+/fdfSnTqqMDAQIwZMwYAUK9ePcjKyuLixYtQV1fnb/P582f06NED+fn5SEpKqrVPgMifKTY2lj+fckREBLS0tIQdUq1w4cIF9O3bF3369MGRI0dqbdJ4+fJlGBgYYObMmfDw8BB2OIQQIaI+toT8wQ4fPgxPT09s2LChRpPaTZs2YdasWZgzZw4ltXWcoqIi//+fP3/G69evoaGhgRYtWiApKQkA4OLigrt37yI0NJSSWlLrmJqa4tq1a2jUqBF69uyJwMBAYYdUK/Ts2RNHjhxBXFwcxowZg5KSEmGHVCZ9fX2sX78eGzduxOHDh4UdDiFEiOiJLSF/qIcPH0JHR6fGp1zx8vKCi4sL5s2bh3Xr1lFSW8elp6ejbdu2Za6TlJTEhAkTsH37duzYsQOTJ0+u4egIKb+CggI4ODhg7969cHFxwfr162kqGQBHjhyBlZUVxo0bBz8/v1o5BzBjDFZWVoiLi0NKSkqtnq6IEFJ9KLEl5A9UWFiIXr164c2bN0hOTq6x0Wk9PT0xe/ZsLFiwAGvWrKGk9jeQl5cHOTk5/mtRUVE0btwYb968AZfLBQCoqanh5s2btbafHiFfMMawdetWuLi4oHfv3ggODkazZs2EHZbQ7d+/H+PGjcPMmTPh5eVVKz+73717Bx0dHTRp0gTnzp2DpKSksEMihNSw2nfbjRBS7ebOnYubN2/W6JQrHh4emD17Nv755x9Kan8jsrKykJKSAvDfXKE9e/aEpqYmP6kFgKysLOjp6SErK0tIURJSPhwOB46OjoiLi8OdO3egp6fHb1L/Jxs7dix8fHzg7e0NV1dXYYdTpoYNGyIkJAQ3btzAvHnzhB0OIUQIKLEl5A8TGhqKLVu2wNPTE7q6ujVS54YNGzB37lwsWrQIq1evpqT2N/PlyciAAQMQHR2Nmzdvltrm7t27OHXqVE2HRkilGBgYIDk5GYqKiujduzf8/f2FHZLQOTg4YN26dVixYgX+/fdfYYdTJj09PWzcuBGbN29GaGiosMMhhNQwaopMyB9EGFOurF+/HgsWLMCSJUuwfPlySmrrsLy8PDx48ACFhYWQlJSEuro6ZGVl0adPHzDGEB8fj1evXqFFixYAABEREfB4PHTr1g0rVqxAv3796PyTOqWwsBAzZ87Ezp07MX36dHh6etba0YFryuLFi7F69Wr4+vpiypQpwg6nFMYYbGxsEBkZieTkZIER2gkhvzdKbAn5QwhjypW1a9di4cKFWLp0Kdzd3SmpqYPu3LmD7du3IzoyEvcfPMDXXxkcDgdt1dXRz8ICDg4O0NTUxJ49ezB+/HgAwF9//YXly5fDzMyMzj2p03bs2AFHR0d0794doaGh/Js3fyLGGIqLi8HlciEuLl4rB9hijKGwsBDAfy1K6POHkD8DJbaE/CEcHBwQEBCAy5cvo2vXrtVe3+rVq7F48WK4urrCzc2t2usjVSszMxPTHBwQFR2NRtLS6NmiBTQaNYJy/fqQFBNDYUkJsj98QPrbt7iQm4u3nz7BvF8/jLW1hZubGzZt2oQBAwbQD0ry27h48SKGDx8OxhjCw8PRo0cPYYdECCHkK5TYEvIHCAoKwujRo2tsypVVq1ZhyZIlcHNzq7UDjZDv8/Pzg/OsWZAVE8M4TU30btUK4j+Y4qOYx8O5nBzsvXMHeSUl8PL2xqRJk2owYkJqxrNnz2BtbY3Lly9j8+bNmDJlCt28IYSQWoIGjyLkN3fv3j1MnjwZY8aMqZFkY8WKFViyZAnc3d1/+6R2w4YNUFNTg6ioKLS0tIQdTpVYtWoVJk+ejN4KCthqagpjZeUfJrUAIC4iAmNlZWw1NUVvBQVMnjwZq1atQkJCAjgcDhISEmom+F/A4XCoZQH5KQUFBcTGxmLKlClwcHDA5MmT8fnzZ2GHRQghBJTYEvJb+/TpE6ytraGkpITt27dX+5MFd3d3LFu2DCtWrMCyZcuqta4fWb16NQ4fPlytdURHR2P+/Pno1asX/P39sXr16mqtryb4+flhyZIlsO3YEU56epAWF6/Q/tLi4nDS08PYjh2xZMkSnDhxopoiJdWhJq6b34GEhAS2bNkCf39/7N+/H4aGhsjJyRF2WIQQ8sejpsiE/MYmTpyIoKAgXLlyBZ06darWutzc3ODu7o5Vq1Zh0aJF1VrXz8jKymL48OEICAiotjr++ecfbNiwAQUFBb/FKKmZmZno3LEjeisowElP75fKYozB+9o1JObm4lpKCtq2bQuRnzz1FbbPnz9DTEysVg6EU1Nq4rr53SQlJWHYsGEoLCxEaGgo+vTpI+yQCCHkj1W7f2kQQipt79692L17N3x8fKo1qWWMwdXVFe7u7li9enWVJ7U8Hq9WNvV78eIFpKSkfprU1tb4vzXNwQGyYmKYXAUDi3E4HEzu2hVy4uJwdnKq9UktANSrV++PTmqrS35+vrBDqFZ6enq4du0aNDU1YWpqCm9vb9TF5wXjx4+HpaVljdXH4XCE1jqAy+UKpV5CSPWr/b82CCEVdvv2bUybNg3jx4/nT73yRUJCAvT09FCvXj20adMGvr6+cHNzK9VMef/+/dDV1YWUlBQaN26MkSNH4vHjxwLbGBkZoXnz5li+fDnU1NSwYsUKtGzZEuvXry8VU2FhIVxdXaGurg5JSUkoKSlh/vz5/CkZvuBwOHB0dMSBAwfQsWNHSEpKIjIyEgDw77//omfPnmjSpAmkpKSgq6uLsLCwUvvn5+djz5494HA44HA4Au/BkydPYG9vj+bNm0NSUhIdO3bE7t27K/T+cjgc+Pv7Iz8/n1/Hl6dcP4q/vHXn5OTA0tISMjIyaNasGVxcXBAVFVWqv6qqqmqp8wv8d16MjIwElv3o/b9z5w6ioqMxTlMTww8fhk9yMi48eYJpUVH4OzwcDlFRSHr2rFQ9rwoK4HX1KsYeO4a/w8Mx4cQJbLl2DcU8HqTFxWHUqhWioqOxZ88egf0uX74MCwsLNGjQANLS0jA0NMT58+cFtvn48SOcnZ2hqqoKSUlJNGvWDH379kVycnI5ztD/e/fuHZydnaGkpMSfe3fdunXg8XgC25XVx7a81wpQ/uulU6dOuHPnDoyNjSEtLV3qenn+/DnExMTg7u5eqo60tDRwOBxs2bKlwsfH4/GwadMmdO7cGfXq1YO8vDwsLCyQlJTEP/4fXTcpKSno378/6tevD1lZWZiamuLSpUsCdQQEBIDD4eDMmTOYPn06mjVrhlatWgGouvNZG8nLy+P06dOYNWsWnJycYGdnh4KCAmGHVS2Ki4uFHYKAoqKiCu/D5XLx8uXLaoiGECJ0jBDyW/n48SPr0KED69ixI8vPzxdYl5yczCQlJZmqqipbu3YtW7VqFVNUVGRdu3ZlX38crFy5knE4HGZjY8N8fHyYu7s7a9q0KVNVVWVv375ljDHG4/GYsrIyA8AaNmzInJycmI+PDzMxMWEA2MmTJ/nlcblc1q9fPyYtLc2cnZ2Zr68vc3R0ZGJiYmzIkCECMQJgHTp0YPLy8szd3Z1t3bqVpaSkMMYYa9WqFZs+fTrbsmUL27hxI+vevTsDwI4fP87ff9++fUxSUpIZGBiwffv2sX379rELFy4wxhh79uwZa9WqFVNSUmLLly9n27ZtY3///TcDwDw9Pcv9Hu/bt48ZGBgwSUlJfh0ZGRk/jL+8dX/69Im1bduW1atXj82fP595eXkxXV1d1qVLFwaAxcfH87dVUVFhdnZ2peIzNDRkhoaG5X7/Z86cyRpJS7MjVlYMAFNr0IA1rleP2XbsyKZoaTEFGRkmKSrKDv79Nztpbc1OWluzfYMGsSb16jFJUVFmqaHBHHV02KgOHZiSnBwLGTKEnbS2ZisNDBgANnToUH4ssbGxTEJCgvXo0YN5eHgwT09P1qVLFyYhIcEuX77M32706NFMQkKCzZ49m/n5+bF169axwYMHs/3795f7POXn57MuXbqwJk2asEWLFrHt27ezcePGMQ6Hw5ycnAS2BcBcXV35r8t7rTBWvuvly3lRVFRkSkpKP7xeTExMmKamZqnjcXd3Z6KiouzZs2cVPr7x48czAKx///7My8uL/fvvv2zIkCFs8+bNjLEfXze3bt1iMjIyrEWLFmzFihVs7dq1rHXr1kxSUpJdunSJX4e/vz8DwDQ1NZmhoSHbvHkzW7t2LWOsas5nXXDgwAEmJSXFtLW1WWZmprDDKSU0NJR16tSJ1atXjzVu3JiZmpqyuXPnMgAC/+Lj41lmZiYDwA4ePMj69OnDJCUlmb+/P3N1dWVdu3YVKNfT05OpqKgILNu1axfT1NRkEhISTEFBgc2YMYMx9t/n1td1fdnPzs6u1PeBk5OTwGeZoaEhmzFjBnNycmJNmjRhRkZGjDHGbt68ySwsLJiMjAxr1qwZGzt2LHv58mWZ70FRURHbtm0by8vLq/T7SAipnSixJeQ3wuPxmK2tLZORkWF37twptX7w4MFMWlqaPXnyhL8sPT2diYmJ8X+sZ2VlMVFRUbZq1SqBfW/evMnExMTYqlWrGI/HYwsXLuT/MNm7dy9/u8LCQqagoMCsrKz4y/bt28dERERYYmKiQJnbt29nANj58+f5ywAwERERdvv27VLxf/r0SeB1UVER69SpEzMxMRFYLiMjU2bCN3HiRNaiRQv26tUrgeUjR45kDRo0KFX+j9jZ2TEZGZlSy78Xf3nr9vLyYgBYSEgIf5v8/Hymrq5e6cT2Z++/cqtWbGCbNuyktTUDwMRERNiu/v35SezWvn0ZADZNW5u/zFRFhYkAzMvUlL/sy78Tw4ezk9bWbK2hIQPAlFq1Yoz99/epoaHBzM3NGY/H48fx6dMn1rp1a9a3b1/+sgYNGvB/CFfWihUrmIyMDLt//77A8n/++YeJioqy7Oxs/rJvE9vyXCuMle96+cLwf+/Hz64XX19fBoDdvHlToExNTU2Bv/XyHl9cXBwDwGbNmlXqPfr6PHzvurG0tGQSEhL8mzeMMfb06VMmJyfH+vTpw1/2JbHt3bs3KykpESijKs5nXZGamspat27NmjRpwmJiYoQdDt/Tp0+ZmJgY27hxI8vMzGQ3btxgW7duZR8/fmQjRoxgFhYWLDc3l+Xm5rLCwkJ+YquqqsrCw8PZw4cP2dOnT8uV2Pr4+LB69eoxLy8vlpaWxq5cucK/gffixQsGgPn7+7Pc3Fz24sULxlj5E1tZWVk2b948du/ePXbv3j329u1bJi8vzxYuXMju3r3LkpOTWd++fZmxsXGZ7wOPx2Nr1qxhtra2An//hJC6j5oiE/Ib2b17N/bt2wdfX1906NBBYB2Xy0VMTAwsLS2hqKjIX66uro7+/fvzXx86dAg8Hg8jRozAq1ev+P8UFBSgoaGB+Ph4LFy4EGvWrEGbNm0gKyuLsWPH8veXkJBA9+7d8fDhQ/6y0NBQdOjQAe3btxco08TEBAAQHx8vEKuhoSE0NTVLHZ+UlBT//2/fvsX79+9hYGBQruaMjDGEh4dj8ODBYIwJxGFubo73799XWbPIb+OvSN0nT55EixYtMHz4cP7+0tLSmDJlSqXj+dn7n52TA41GjfjbazdrhhaysvzXrRs2hLSYGHLz8gAAPMZw8ckTdFdURNvGjUvV921T3cc5OcjLy0NqairS09MxevRovH79mh9Hfn4+TE1NcfbsWX4T2oYNG+Ly5ct4+vTpLx23gYEBGjVqJHDcZmZm4HK5OHv2bJn7lfdaAcp3vXytPNfLsGHDICYmhuDgYP6yW7du4c6dO7Cxsanw8YWHh4PD4ZQ5/dbPRkrncrmIjo6GpaUl1NTU+MtbtGiB0aNH49y5c/jw4YPAPpMnT4aoqKjAsqo4n3VF165dkZSUBF1dXfTr1w///vtvreh3m5ubi5KSEgwbNgyqqqro3Lkzpk+fDllZWUhJSUFSUhIKCgpQUFAQGDvA2dkZw4YNQ+vWrdGiRYty1bVy5UrMmTMHTk5OaNu2Lbp16wZnZ2cA/zXdBv77m1BQUOC/Li8NDQ2sX78e7dq1Q7t27bBlyxZoa2tj9erVaN++PbS1tbF7927Ex8fj/v37pfbncDgYOHAg9u3bV+FuKISQ2o1GyiDkN3Hjxg04Ojry56z91osXL1BQUAB1dfVS675elp6eDsYYNDQ0yqzn1atXiImJwcaNG3HkyBGIi4uX+nHcqFEj3LhxQ6DMu3fvfvcHzIsXLwRet27dusztjh8/jpUrVyI1NVWgb255pjF6+fIl3r17hx07dmDHjh3liqOyvo2/InU/evQI6urqpY6pXbt2lY7nZ+8/ACjXr8//v7y0dKn1shISyPtf/7r3hYX4VFIClQYNyh3DgwcPkJ6eDgCws7P77nbv379Ho0aNsH79etjZ2UFJSQm6uroYMGAAxo0bJ5Bc/Ux6ejpu3LhR7r+7r5eX51r5UsePrhfxb6ZMatWq1U+vl6ZNm8LU1BQhISFYsWIFACA4OBhiYmIYNmxYhY8vIyMDioqKaFzGTYifefnyJT59+lTm31+HDh3A4/Hw+PFjdOzYkb+8rOu3Ks5nXdK4cWOcPHkSS5cuxbx585CUlIRdu3ZBRkZGaDF17doVpqam6Ny5M8zNzdGvXz8MHz4cjb66qVUWvQqOkv7ixQs8ffoUpqamvxLud+nq6gq8vn79OuLj4yH71c24LzIyMtC2bdtSyzt37oxJkybB0dER3bp1Q5cuXaolVkJIzaLElpDfwMePH2FtbY127dph06ZNv1QWj8cDh8PBqVOnBJ66MMawc+dOhIaGwsvLC05OTjhy5EipJzNfb/91mZ07d8bGjRvL3FZJSUng9ddPZr9ITEzE33//jT59+sDHxwctWrSAuLg4/P39ERgYWK7jAoCxY8d+N7Gqqh8338ZfXXV/L6HncrkC5+VH7//du3cxa9YsSH41IrDI924U/MJTp8LCQv77sGHDBmhpaZW53ZcfpyNGjICBgQEiIiIQHR2NDRs2YN26dTh06FCpp6bfw+Px0LdvX8yfP7/M9WX94K2o710vX3z7Y7s81wsAjBw5EhMmTEBqaiq0tLQQEhICU1NTNG3aVKDu6j6+yijr+q2K81nXiIqKYvXq1dDV1YWdnR169OiBiIgItGnTRmjxnD59GhcuXEB0dDQ2b96MxYsX4/Llyz/c79tkXEREpNTf69eDSpV1/svjZ+V+L568vDwMHjwY69atK7Xtj54we3t748qVK7C2tkZSUhLk5OQqFTchpPagxJaQOo4xhilTpiA3NxdJSUnf/VHRrFkz1KtXDw8ePCi17utlbdq0AWMMrVu35v8wZoxh7ty5CA0Nhbe3N2bOnFmhGNu0aYPr16/D1NS0XE9XyxIeHo569eohKioKkpKS/OX+/v6lti2rDnl5ecjJyYHL5cLMzKxSMVRWRepWUVHBrVu3wBgTOI60tLRS2zZq1Ajv3r0rtfzRo0cCT8J+9P5/SZQKS0rKfTwNJCUhLSaGR+/fl3sfSUlJ/g/6+vXrl+sctGjRAtOnT8f06dPx4sUL6OjoYNWqVeVOhNq0aYO8vLwKn+/yXitf6vj2eqkKlpaWmDp1Kr858v3797Fw4cJSdZfn+Nq0aYOoqCi8efPmh09tv3fdSEtLl/n3d+/ePYiIiJS6MfU9v3o+6yorKyu0b98eQ4cOhZ6eHgIDA4V2zBwOB7169UKvXr2wbNkyqKioICIiAhISEuWeBkdeXh7Pnj0T+IxKTU3lr5eTk4OqqipiY2NhbGxcZhni4uKl6pOXl8etW7cElqWmppZq9fAtHR0dhIeHQ1VVtUJTdklJSSE0NBS6urqYOnUqDhw4UOnvJ0JI7UB9bAmp43x9fXHw4EH4+fn98Ie1qKgozMzMcPjwYYF+bg8ePMCpU6f4r4cNGwZRUVG4u7uD/TfAHObMmYONGzfC29sbo0ePrnCMI0aMwJMnT7Bz585S6woKCso116WoqCg4HI7Aj6GsrKwy50KUkZEplfCJiorCysoK4eHhpX48AajW6R8qUveAAQPw9OlTgWmMPn36VGYT5jZt2uDSpUsCU14cP3681DQzP3r/W7ZsCQDI/qaf5I+IcDjo0bIlrjx9ivtv3pRaX1Z/QnV1dejq6qJNmzb4999/kfe//rpf+/I+cLlcvP8maW7WrBkUFRVLTQ/1IyNGjMDFixcRFRVVat27d+9Q8p1kvrzXClD6evkaYwyvX78ud7xfa9iwIczNzRESEoKDBw9CQkKi1Dyj5T0+KysrMMbKnELo65i/d93069cPR44cQVZWFn/58+fPERgYiN69e6P+V83Yy1JV57Mu69ixI65cuYLevXtj4MCBWLVqVakpmarb5cuXsXr1aiQlJSE7OxuHDh3Cy5cv0aFDB6iqquLGjRtIS0vDq1evfjitj5GREV6+fIn169cjIyMDW7duLXVduLm5wcPDA97e3khPT0dycjI2b97MX/8l8X327Bnevn0LADAxMUFSUhL27t2L9PR0uLq6lvl5+a0ZM2bgzZs3GDVqFK5evYqMjAxERUVhwoQJP03W27ZtCz8/PwQFBX23mwghpO6gJ7aE1GHJyclwcnLC9OnTMWLEiJ9u7+bmhujoaPTq1QvTpk0Dl8vFli1b0KlTJ/4d9zZt2mDlypVYuHAhsrKyICIignPnzsHMzAybN29GYWEh5s6dW6E4bW1tERISAgcHB8THx6NXr17gcrm4d+8eQkJCEBUV9dN+XAMHDsTGjRthYWGB0aNH48WLF9i6dSvU1dUF+icC//XB+tIPWFFREa1bt4a+vj7Wrl2L+Ph46OvrY/LkydDU1MSbN2+QnJyMmJgYvCkjSasq5a178uTJ2LJlC8aNG4dr166hRYsW2LdvH6TL6Pc6adIkhIWFwcLCAiNGjEBGRgb2799fqqnjz95/FSUlpL99i37f6dtcFrvOnZH8/DkWJCSgv5oalOrXx5uCApzLycEGY2PIfjX4jFKrVvwmuX5+fujfvz86duyICRMmoGXLlnjy5Ani4+NRv359HDt2DB8/fkSrVq0wfPhwdO3aFbKysoiJicHVq1fh4eFR7hjnzZuHo0ePYtCgQRg/fjx0dXWRn5+PmzdvIiwsDFlZWQJNe79WnmsFKH29WFpaQk5ODpmZmYiIiMCUKVMqfL18YWNjg7Fjx8LHxwfm5uZo2LBhpY7P2NgYtra2/CTDwsICPB4PiYmJMDY2hqOjI4DvXzcrV67E6dOn0bt3b0yfPh1iYmLw9fVFYWFhmXNWf6uqzmdd17BhQxw5cgTLly/HkiVLkJSUhD179vz0xkBVqV+/Ps6ePQsvLy98+PABKioq8PDwQP/+/aGnp8eftzkvLw/x8fFQVVUts5wOHTrAx8cHq1evxooVK2BlZYW5c+cKJIZ2dnb4/PkzPD09MXfuXDRt2lRgQDwPDw/Mnj0bO3fuRMuWLZGVlQVzc3MsXboU8+fPx+fPn2Fvb49x48bh5s2bPzwuRUVFnD9/HgsWLEC/fv1QWFgIFRUVWFhYQETk589vbGxscObMGTg5OaF79+7Q1tYu3xtKCKl9amz8ZUJIlXr37h1TU1NjOjo67PPnz+XeLzY2lmlrazMJCQnWpk0b5ufnx+bMmcPq1asnsF1YWBhTVFRkAJikpCRr3749mzFjBktLS+NvY2hoyDp27FiqDjs7u1JzGhYVFbF169axjh07MklJSdaoUSOmq6vL3N3d2fv37/nbAfjutCC7du1iGhoa/Hi+zKn47UfZvXv3WJ8+fZiUlBQDIDCFyfPnz9mMGTOYkpISExcXZwoKCszU1JTt2LGjvG8h/xi/N93P9+Ivb92PHj1if//9N5OWlmZNmzZlTk5OLDIystR0P4wx5uHhwVq2bMkkJSVZr169WFJSUqnpfgoLC9mCBQuYgYEBk5eXZ2JiYkxaWpqpqqqy/v37s4kTJwrMYzvof1P/fP2vmbQ0M1NREVgWMHAgM1VRYQ0kJZm4iAhTkJFhg9q0YUesrATmsdXV1WX+/v7M19eXeXh4MAcHB6ahocHq1avHJCQkmIqKChsxYgSLjY3lxztv3jzWtWtXJicnx2RkZFjXrl2Zj49Phc4RY//N67xw4UKmrq7OJCQkWNOmTVnPnj3Zv//+y4qKigTO29fT/TBW/muFMcbCw8NZ7969mYyMDJORkfnl64Uxxj58+MD/G/7efK/lPb6SkhK2YcMG1r59eyYhIcHk5eVZ//792bVr1/jb/Oi6SU5OZubm5kxWVpZJS0szY2Nj/jy3X3yZ7ufq1asCy6vyfP4ujhw5wurXr8/at2/P7t27J+xw/ngFBQVMW1ubtWnThr17907Y4RBCKonDWC0Yg54QUiGMMVhbWyMmJgbJycm/PLKopaUlbt++zR+1ljGGWbNmYcuWLdi+fTumTp1aFWGTX5CQkABjY2PEx8fDyMioQvu+f/8eTZs2BZfLhZiYGHg8nkATvdmzZ2Pjxo2Yp68PY2XlKos5PjsbG74ZmEZUVBQiIiL8GCIiIko1sa3Nvr1WCKmstLQ0DB06FDk5Odi3bx+GDBki7JD+aBkZGdDR0UG/fv0QEhJC/W0JqYOojy0hddCWLVsQHh4Of3//Cie1BQUFAq/T09Nx8uRJfrLEGIOjoyO2bNkCX19fSmp/Aw0aNMDIkSMhIiKC4uJigaT2r7/+wrp162Derx/23rmDTz/oW1cRn4qLsffOHZj364dly5bxl3O5XH4MCgoKGDhwYJXUVx1+dq0Q8ivatWuHy5cvo2/fvrC0tMSyZctqvN8t+X9t2rTB7t27ERYWhq1btwo7HEJIJVAfW0LqmKtXr/Invh86dGiF91dTU8P48eOhpqaGR48eYdu2bZCQkMD8+fPB4/Hg6OiIbdu2YefOnZg0aVI1HEHt9vLlyx8OOCIhIVGp+UCF6fr163j//r3AcYmKikJDQwPR0dEQExPDtu3b0bljR+y8fh1OFZy38luMMey8fh15JSXYtn07VFVVkZubCz8/P4HBiqSlpXHq1CkMGjSoXH3hvigoKCg1GNG3GjduDImv+vlWxo+uFUKqgpycHMLCwrB27VosXrwYycnJ2L9/f6n+1KRmWFlZYdasWZg9ezb09fXRrVs3YYdECKkAaopMSB3y9u1baGtro3nz5khMTKzUD/cJEyYgPj4ez549g6SkJHr06IHVq1dDS0sLM2bMgK+vL3bu3ImJEydWwxHUfqqqqnj06NF31xsaGiIhIaHmAvqfijZF5vF4OHnyJDw9PREXFwclJSWIiooiOzsbwH8DySQnJ6P1VwNG+fn5YfLkybDt2BGjNDUrFSdjDEF372L/7dvw8/Pj/x0VFRXB1NQUFy9eBJfLhbi4OLS0tHD16lWoq6vDyckJ48ePLzXva1kCAgIwYcKEH25TmSbb3/retaKjo/NL5RJSlsjISIwaNQpNmzbF4cOH0bFjR2GH9EcqKipC79698fLlSyQnJ6NRo0bCDokQUk6U2BJSRzDGMHToUJw9exbJycnfHbGyMng8HqZNm4adO3fCz88P9vb2VVZ2XXP+/PlSTVC/1qhRI+jq6tZgRBWTn5+PPXv2YNOmTbh//z709fXh4uKCYcOG4cSJExg6dChERUURFxeHPn36lNp/1apVWLJkCcxbt8bkrl0h/ZM5JL/2qbgYO69fR1RmJlatWoVFixYJrH/16hV0dHTw+PFjTJs2DT4+Prh8+TI8PT0RFhYGOTk5TJ48GTNnzvzh3Ki5ubm4ffv2D2PR1dWlH6SkzsnIyMDQoUPx8OFDBAQECIwkTGpOVlYWtLW1YWhoiIiICOpvS0gdQYktIXXExo0bMWfOHBw9ehSDBw+usnJ5PB6mTp2KXbt2Yffu3Rg/fnyVlU1qTk5ODrZs2YIdO3bg/fv3sLKygouLC3r06MHfhsvlYvjw4bCyssLYsWO/W5afnx+cZ82CrJgYxmlqonerVhD/QVPhYi4X5548wd47d5BXUoJNmzd/94n/7du34ejoiN27dws8Lc7OzubHn5eXh+HDh8PFxQX6+vqVeDcIqbvy8/MxadIkHDx4EAsWLMCqVasgKioq7LD+OEePHsWQIUOwceNGuLi4CDscQkg5UGJLSB1w8eJF9OnTB87OztiwYUOVlcvj8TBlyhTs3r0b/v7+sLOzq7KySc24evUqPD09ERoaCmlpaf4TTxUVlV8qNzMzE9McHBAVHY1G0tLo2aIFNBo1gnL9+pAUFUUhl4vsDx+Q/vYtLuTm4u2nTzDv1w/btm8XSFgrKi8vD/7+/ti0aRMyMjLQo0cPuLi4YOjQoRATo2EhyJ+BMQZPT0/MmzcPZmZmCAwMRJMmTYQdVrUqKSnB3bt3UVxcDE1NTdSrV0/YIWHu3LnYtGkTEhMT8ddffwk7HELIT1BiS0gt9/r1a2hra0NJSQkJCQkQr0DT0B/h8XiYNGkSAgICsGfPHtja2lZJuaT6cblcHD58GJ6enjh//jzU1NTg5OSECRMmQE5OrkrrunPnDrZv347TUVFIS08XGPyJw+GgnYYG+pqbY9q0aejQoUOV1cvlcnH8+HF4enrizJkzUFFRwcyZMzFp0iQ0aNCgyuohpDaLi4uDjY0NZGVlERERAS0tLWGHVK1evHiBPn364PPnz0hMTPxhl4SaUFxcDENDQ+Tk5CAlJeW3v7lASF1HiS0htRiPx8PgwYNx+fJlpKSkVNmXPJfLxaRJk7B3717s2bPnh81SSe3x4cMH7Nq1C97e3sjKykKfPn3g4uKCwYMH10hTxby8PDx48ACFhYWQlJSEurp6uQZ7+lUpKSnw9PTEwYMHISkpCXt7ezg5Of3y/M2E1AWPHj3CsGHDcPfuXezcuRNjxowRdkjV6vHjxzAwMICkpCQSExPRrFkzocejra2Nv/76C0ePHq3QCO6EkBrGCCG11tq1axkAdvLkySors6SkhNnZ2TERERF24MCBKiuXVJ+HDx8yZ2dnJicnx8TExNjYsWNZUlKSsMOqcU+ePGGLFy9mTZo0YRwOhw0dOpSdPXuW8Xg8YYdGSLX69OkTGzduHAPAnJ2dWVFRkbBDqlbp6elMQUGBde3alb1580bY4bCTJ08yAGzdunXCDoUQ8gP0xJaQWioxMRHGxsaYP38+Vq9eXSVlcrlcTJgwAQcOHMD+/fsxatSoKimXVD3GGM6fPw9PT08cPnwYDRs2xNSpUzFjxgy0bNlS2OEJ1adPn7B//354eXnh7t270NXVhYuLC6ytrX957lpCaivGGLZu3QoXFxf06tULISEhQn+aWZ1u3boFQ0NDtGvXDtHR0TXSOuRHFi5ciA0bNiAhIQG9e/cWaiyEkLJRYktILfTixQtoa2tDXV0dsbGxVTJoDpfLxfjx4xEUFIQDBw7AxsamCiIlVa24uBihoaHw9PREUlIS2rVrB2dnZ4wbNw7S0tLCDq9W4fF4iI6OhqenJ6Kjo6GoqAhHR0dMmTKF+sKR31ZiYiKGDx8OCQkJHDp0CN26dRN2SNXm6tWrMDExgb6+Po4fPy7UAaVKSkpgYmKCjIwMpKamQl5eXmixEELKRh0FCKlleDwebG1tUVxcjKCgoCpJaktKSjBu3DgEBQUhMDCQktpa6O3bt1i3bh1at26NMWPGoGHDhjhx4gTu3LkDBwcHSmrLICIiAgsLC0RFReHWrVsYMGAA3N3doaSkhGnTpiEtLU3YIRJS5QwMDJCcnIyWLVvCwMAA/v7+wg6p2nTr1g3Hjx/H+fPnMXLkSBQXFwstFjExMQQFBaG4uBi2trbg8XhCi4UQUjZKbAmpZVavXo3Tp08jMDAQioqKv1zel6Q2ODgYQUFBGDFiRBVESarK/fv3MWPGDLRq1Qqurq4wNzfHjRs3cPr0aQwYMIAGKimnjh07YufOnXj8+DEWLlyIiIgItG/fHgMHDkRMTAyocRL5nbRs2RJnzpzBuHHjYG9vjxkzZqCoqEjYYVULQ0NDhIeH48SJE5gwYYJQE8qWLVviwIEDiI6Oxpo1a4QWByHkO4TYv5cQ8o24uDgmIiLCli1bViXlFRcXs5EjRzIxMTEWFhZWJWWSX8fj8VhsbCwbNGgQA8CaNWvG3Nzc2PPnz4Ud2m/j8+fPLCAggHXt2pUBYJ07d2a7du1iBQUFwg6NkCrl6+vLxMXFWa9evdjTp0+FHU61CQ4OZiIiIszBwUHoA8YtXbqUiYiIsLi4OKHGQQgRRH1sCaklnj17Bi0tLXTs2BHR0dG/PH1LSUkJxowZg0OHDiE4OBjDhg2rokhJZRUWFiIoKAienp64ceMGOnfuDBcXF4waNUqofcd+Z4wxJCQkwNPTE8ePH4e8vDymTZuGadOmoXnz5sIOj5AqcfHiRQwfPhyMMYSHh6NHjx7CDqla7N69GxMnTsT8+fOxdu1acDgcocTB5XLRt29f3L17F6mpqfRZQkgtQW3cCKkFuFwuRo8eDQ6HgwMHDvxyUltcXIzRo0fj0KFDCAkJoaRWyF6+fInly5dDRUUFEyZMgJKSEmJiYnD9+nVMmDCBktpqxOFwYGxsjKNHjyItLQ3W1tbYsGEDlJWVYW9vj5s3bwo7REJ+WY8ePXDt2jW0adMGhoaG8PX1/S2b39vb28PT0xPr168XalNgUVFRBAYGgjGG0aNHg8vlCi0WQshXhPm4mBDyn2XLljEREREWHx//y2UVFRUxKysrJi4uzg4fPvzrwZFKu3nzJps4cSKTlJRkUlJSbNq0aezevXvCDuuP9/r1a7Z27VrWsmVLBoCZmpqy48ePMy6XK+zQCPklhYWFbMaMGQwAmzhx4m/b9N7d3Z0BYN7e3kKN40v3IVdXV6HGQQj5DyW2hAhZdHQ043A4bMWKFb9cVlFRERs2bBgTFxdnR44cqYLoSEVxuVx28uRJ1rdvXwaAKSoqsjVr1rDXr18LOzTyjaKiIhYYGMi6devGALB27doxHx8flpeXJ+zQCPkl/v7+TFJSknXv3p09fvxY2OFUOR6Px+bMmcMAsICAAKHGsnz5csbhcFh0dLRQ4yCEUB9bQoTq6dOn0NLSgra2Nk6dOvVLI+AWFRVh5MiROH78OMLDwzF48OAqjJT8zKdPn7Bv3z54eXnh3r170NPTg4uLC6ytrSEuLi7s8MgPMMZw4cIFeHp6IiIiAg0aNICDgwNmzJiBli1bCjs8QiolKSkJw4YNQ2FhIUJDQ9GnTx9hh1SlGGOYOnUqdu3ahZCQEFhZWQklDi6Xi/79+yM1NRWpqalVMpsBIaRyKLElREi+TPb+8OFDpKSk/NJk70VFRRgxYgROnTqF8PBwDBo0qAojJT/y9OlTbN26Fb6+vnj79i0sLS3h4uKCXr16CW1gE1J5mZmZ2Lx5M/z8/FBQUAAbGxu4uLhAV1dX2KERUmEvX76EjY0NEhMT4eHhgZkzZ/5Wn0tcLhdjx45FeHg4jh07BnNzc6HE8eLFC2hra0NdXR2xsbFVMv88IaTiaPAoQoRk2bJluHDhAg4ePPjLSa21tTVOnTqFiIgISmprSHJyMmxtbaGqqorNmzfD1tYW6enpCA8PR+/evX+rH49/ktatW2Pjxo3IycnBhg0bcOHCBejp6aFPnz6IiIigQWJInSIvL4/o6Gg4OTnByckJdnZ2KCgoEHZYVUZUVBR79+6Fubk5hg4disTERKHE0axZMxw8eBDnz5+Hq6urUGIghNATW0KE4tSpUxgwYADWrl2LBQsWVLqcwsJCWFtbIzo6GhEREejfv38VRkm+xeVycezYMXh6euLs2bNQUVHBrFmzMHHiRDRo0EDY4ZFqwOVyceTIEXh6euLcuXNQU1PDrFmzYG9vDzk5OWGHR0i5BQUFYeLEiWjfvj0OHToEVVVVYYdUZQoKCjBw4EAkJSUhPj5eaC0s1q5di4ULF+LkyZP0fUyIEFBiS0gNe/z4MbS0tPDXX3/h2LFjle5XW1hYCCsrK8TExODw4cOwsLCo4kjJFx8/foS/vz+8vb2RkZGBnj17wsXFBZaWltTk7A+SlJQET09PhISEQFpaGpMmTcLMmTN/qwSB/N6uX7+OoUOH4sOHDzh48CDMzMyEHVKV+fjxI/r27YsHDx7g7Nmz0NTUrPEYeDweBg8ejMuXLyMlJQVKSko1HgMhfzJKbAmpQcXFxTA0NEROTg5SUlLQpEmTSpXz+fNnWFlZITY2FkeOHBFav6LfXXZ2NjZv3oydO3ciLy8P1tbWcHFxQffu3YUdGhGinJwcbNmyBTt27MD79+8xbNgwuLi4oEePHtQEndR6b968wahRoxATE4O1a9di7ty5v83f7Zs3b2BkZIRXr17xW1jUtNevX0NbWxtKSkpISEigwQMJqUHUx5aQGrRo0SJcvXoVISEhv5TUDhs2DHFxcTh69CgltdXg0qVLsLGxgZqaGvz8/DB16lRkZmYiKCiIklqCVq1aYe3atXj8+DG2bNmCGzduoFevXvjrr79w8OBBFBcXCztEQr6rcePGOHnyJBYsWID58+dj5MiRyM/PF3ZYVaJx48aIjo6GrKwszMzM8OTJkxqPoUmTJggODsaVK1ewePHiGq+fkD8ZPbElpIYcPXoUQ4YMgYeHB2bPnl2pMj5//oyhQ4ciISEBx44d+62akQlbSUkJDh06BE9PT1y6dAnq6upwdnaGnZ0dZGVlhR0eqcV4PB5OnToFT09PxMbGolWrVpg5cyYmT56MRo0aCTs8Qr4rPDwcdnZ2aN26NSIiIqCuri7skKpEdnY2evfuDVlZWZw5c+aXBmisLA8PD8ydOxdHjx6l6fcIqSGU2BJSA7KysqCtrQ1DQ0NERERUqtlXQUEBLC0tkZiYiGPHjsHU1LQaIv3zvHv3Dn5+fti8eTOys7NhbGwMFxcXDBw48JfmFSZ/puvXr8PLywuBgYEQFxfH+PHj4eTkBA0NDWGHRkiZbt++jaFDh+Lly5cIDAz8bQY9un//PgwMDNCqVSvExcXV+AB/jDH+d3ZKSgpUVFRqtH5C/kSU2BJSzYqKimBgYIAXL14gOTm5Uk9wCgoKMGTIEJw7dw7Hjx+HiYlJNUT6Z8nIyMCmTZuwe/duFBUVYfTo0XB2doaWlpawQyO/gWfPnmHbtm3Ytm0bXr16hUGDBsHFxQVGRka/TX9G8vt49+4dbG1tceLECaxYsQILFy78LW7s3bhxA4aGhujUqROioqIgLS1do/W/ffsWOjo6aNasGRITEyEhIVGj9RPyp6n7n1qE1HLz589HSkoKQkJCKpXUfvr0CX///TfOnz+PkydPUlL7CxhjOHPmDCwtLaGhoYGgoCDMnj0b2dnZCAgIoKSWVBkFBQW4u7sjOzsbO3fuxMOHD2FiYgIdHR3s2bMHhYWFwg6REL6GDRviyJEjWLZsGZYsWQIrKyt8+PBB2GH9si5duuDUqVNISUnB0KFDa/y6a9SoEUJCQpCSkvJLU/sRQsqHntgSUo0OHToEKysreHt7Y+bMmRXe/0tSe/HiRZw8eRKGhobVEOXvr6ioCMHBwfD09ERKSgo0NTXh4uKCMWPGQEpKStjhkT8AYwwxMTHw9PTEqVOnoKCggBkzZsDBwQFNmzYVdniE8B09ehS2trZQVFTE4cOH0a5dO2GH9Mvi4uIwYMAADBw4EMHBwTU+TZu3tzecnJwQHh6OYcOG1WjdhPxJKLElpJpkZGRAR0cH/fr1Q0hISIWbH+bn52Pw4MG4cuUKTp48iT59+lRTpL+vV69ewdfXF1u3bkVubi4sLCzg4uKCvn37UnNQIjR3797Fpk2bsHfvXjDGYGtrC2dnZ6HMu0lIWdLS0jB06FDk5ORg3759GDJkiLBD+mXHjh3D0KFDMWbMGPj7+9doU2vGGKytrRETE4Pk5GShTENEyJ+AEltCqsHnz5/Rq1cvvH//HteuXavwoBX5+fkYNGgQrl69ilOnTsHAwKCaIv093b17F15eXti7dy8AUOJAaqXXr1/D19cXW7ZsQW5uLszNzeHi4oJ+/frRjRcidB8/fsT48eNx6NAhLF26FG5ubnW+321QUBDGjBmDGTNmwNvbu0avs/fv30NHRweNGjXC+fPnISkpWWN1E/KnqNufUITUUnPmzMHt27cRGhpaqaR24MCBSEpKQmRkJCW15cQYw+nTpzFgwABoamri6NGjWLx4MR4/fowdO3ZQUktqnSZNmmDRokXIysrCvn378PLlS1hYWKBTp07YuXMnCgoKhB0i+YPJyckhLCwMmzdvRkBAAOzt7fH+/Xthh/VLRo0ahX379uHw4cPYsGFDjdbdoEEDHD58GG/fvsWKFStqtG5C/hT0xJaQKhYcHIyRI0di27ZtcHBwqNC+eXl5GDhwIJKTkxEZGYlevXpVU5S/F8YYbt26BR8fH1y6dAkuLi6wsbGhO+KkTmGMITExEZ6enjhy5AiaNGkCBwcHzJgxAwoKCsIOjxBCCKnVKLElpArdv38furq6GDRoEAIDAyvUzCkvLw8DBgxAamoqIiMj0bNnz2qM9PfD4/EgIiICxhg14yR13oMHD+Dt7c2fjmrUqFFwcXGhkbsJIYSQ76DElpAqUlBQgL/++gufP39GUlIS5OTkyr3vx48fMWDAAFy/fh1RUVHo0aNHNUZKCKkr3r17Bz8/P2zevBnZ2dkwMjKCi4sLBg0aVOf7OxJCCCFVib4VCakiTk5OuH//PkJDQyuU1H748AEWFha4ceMGoqOjKaklhPA1bNgQc+fORUZGBoKDg/H582cMGTIE7dq1w9atW5GXlyfsEAkhhJBagZ7YElIF9u/fD1tbW/j5+WHixInl3u9LUnvnzh1ER0eje/fu1RglIeR3cOnSJXh6eiI8PBxycnKYMmUKHB0doaSkJOzQCCGEEKGhJ7aE/KK7d+9i6tSpsLW1hb29fbn3e//+PczNzXHnzh2cPn2aklpCSLn89ddfCA4OxsOHDzFp0iT4+vqidevWGDVqFK5cuSLs8H5LRkZG4HA44HA4SE1NLfd+AQEBaNiwIf+1m5sb9ZMm5fblb+7rvyFCyPdRYkvIL8jPz4e1tTVUVFSwbdu2cg9a9CWpvXfvHmJiYtCtW7dqjrRmVeePt29/KBLyp1JWVsaGDRvw+PFjeHp64urVq9DX10evXr0QFhaGkpISYYf4W5k8eTJyc3PRqVMnAEBWVlaFB6qbO3cuYmNjqyO8WiEgIABGRkYV2sfIyAgBAQHl2lZVVRVeXl4VjqsyEhISwOFw8O7duxqpD/jv+BISEvivc3Nza+x4CfkdUGJLyC9wdHREZmYmwsLCICMjU6593r17h379+uH+/fuIiYmBnp5eNUdJCPmdycnJYebMmUhLS8Phw4chLi4Oa2trqKurY+PGjXV+7tHaQlpaGgoKChATE6t0GbKysmjSpMl31xcVFVW6bPIfLpcLHo8n7DAEFBcXV2o/BQUFNGjQoIqjIeT3RYktIZXk7++PgIAAbNu2DZqamuXa50tSm56ejpiYGOjq6lZzlJVTWFiIWbNmoVmzZqhXrx569+6Nq1evAij7ienhw4f5Ty4CAgLg7u6O69ev85tRfbkbz+FwsG3bNvTv3x9SUlJQU1NDWFgYv5yy7pCnpqaCw+EgKysLCQkJmDBhAt6/f88v283NrTrfCkLqDFFRUQwZMgQJCQm4du0a+vTpg3/++QdKSkpwdnbGw4cPhR3iby8gIADKysqQlpbG0KFD8fr1a4H137ZmGT9+PCwtLbFq1SooKiqiXbt2P61DVVUVq1evhr29PeTk5KCsrIwdO3YIbLNgwQK0bdsW0tLSUFNTw9KlSwWSqy9x7N69G8rKypCVlcX06dPB5XKxfv16KCgooFmzZli1apVAue/evcOkSZMgLy+P+vXrw8TEBNevX6/EO1U2xhjc3NygrKwMSUlJKCoqYtasWQD+e7L76NEjuLi48D//gf//Tjp69Cg0NTUhKSnJH0Hc2dlZoHxLS0uMHz+e/7qwsBALFiyAkpISJCUloa6ujl27diErKwvGxsYAgEaNGoHD4fD3K+upsZaWlsB30Zfvur///hsyMjL89/HIkSPQ0dFBvXr1oKamBnd3d2pZQUgVosSWkEq4desWZsyYAXt7e4wbN65c+7x9+xZ9+/ZFRkYGYmNjoaOjU81RVt78+fMRHh6OPXv2IDk5Gerq6jA3N8ebN29+uq+NjQ3mzJmDjh07Ijc3F7m5ubCxseGvX7p0KaysrHD9+nWMGTMGI0eOxN27d8sVV8+ePeHl5YX69evzy547d26lj5OQ35WOjg727t2LrKwszJo1C/v374eGhgaGDRuGxMRE0LiRVe/y5cuYOHEiHB0dkZqaCmNjY6xcufKn+8XGxiItLQ2nT5/G8ePHy1WXh4cH9PT0kJKSgunTp2PatGlIS0vjr5eTk0NAQADu3LmDTZs2YefOnfD09BQoIyMjA6dOnUJkZCSCgoKwa9cuDBw4EDk5OThz5gzWrVuHJUuW4PLly/x9rK2t8eLFC5w6dQrXrl2Djo4OTE1Ny/XdUB7h4eHw9PSEr68v0tPTcfjwYXTu3BkAcOjQIbRq1QrLly/nf/5/8enTJ6xbtw5+fn64ffs2mjVrVq76xo0bh6CgIHh7e+Pu3bvw9fWFrKwslJSUEB4eDgBIS0tDbm4uNm3aVKFjcXNzw9ChQ3Hz5k3Y29sjMTER48aNg5OTE+7cuQNfX18EBASUunlACPkFjBBSIR8/fmTt27dnnTp1Yvn5+eXa582bN0xXV5c1btyYpaSkVG+AvygvL4+Ji4uzAwcO8JcVFRUxRUVFtn79eubv788aNGggsE9ERAT7+uPE1dWVde3atVTZAJiDg4PAMn19fTZt2jTGGGPx8fEMAHv79i1/fUpKCgPAMjMzGWOszPoJIT+Wn5/PfH19WYcOHRgApqury/bv388KCwuFHVqdYGhoyJycnH64zahRo9iAAQMEltnY2Ah8Xn372WhnZ8eaN29eofOgoqLCxo4dy3/N4/FYs2bN2LZt2767z4YNG5iurq5AHNLS0uzDhw/8Zebm5kxVVZVxuVz+snbt2rE1a9YwxhhLTExk9evXZ58/fxYou02bNszX17fc8f+Ih4cHa9u2LSsqKipzvYqKCvP09BRY5u/vzwCw1NRUgeVlnbMhQ4YwOzs7xhhjaWlpDAA7ffp0mXWV9X30vRi6du3KXF1d+a8BMGdnZ4FtTE1N2erVqwWW7du3j7Vo0aLM+r8+PvrOI6R86IktIRXAGIODgwNycnIQFhYGaWnpn+7z5s0bmJmZISsrC3FxcbV+RMyMjAwUFxejV69e/GXi4uLo3r17uZ+s/si38/T26NGjSsolhHyftLQ0pkyZglu3buHUqVNo3Lgxxo4di9atW2PNmjVV9sTtT3b37l3o6+sLLCvPvOSdO3eGhIREherq0qUL//8cDgcKCgp48eIFf1lwcDB69eoFBQUFyMrKYsmSJcjOzhYoQ1VVVWDO9ebNm0NTUxMiIiICy76Ue/36deTl5aFJkyaQlZXl/8vMzERGRkaF4v8ea2trFBQUQE1NDZMnT0ZERES5mupKSEgIvCflkZqaClFRURgaGlY23B/6dvyM69evY/ny5QLv3ZcByT59+lQtMRDyp6HElpAK8PPzw4EDB7Bjx45y9YV6/fo1TE1NkZ2djbi4OHTt2rUGoqxeIiIipZoxVnZgjLLKBiBQflWVTQj57xqzsLBAdHQ0bt68if79+8Pd3R2tWrUq1ZyV1IzyDjz4NXFxcYHXHA6HP2DSxYsXMWbMGAwYMADHjx9HSkoKFi9eXGpgqrLK+FG5eXl5aNGiBVJTUwX+paWlYd68eRU+hrIoKSkhLS0NPj4+kJKSwvTp09GnT5+ffg9ISUmVGqH6Z99VUlJSlYqxvN+B357XvLw8uLu7C7x3N2/eRHp6OurVq1epWAghgiixJaScUlNTMXPmTEydOhWjRo366favX7+GmZkZcnJyEBcXV+G7ycLSpk0bSEhI4Pz58/xlxcXFuHr1KjQ1NSEvL4+PHz8iPz+fv/7beR0lJCTA5XLLLP/SpUulXnfo0AEAIC8vDwACfacqUjYhpPw6deoEPz8/ZGdn459//kFERATat2+PgQMHIiYmhvrhVlCHDh0E+qMCpT/vasKFCxegoqKCxYsXQ09PDxoaGnj06NEvl6ujo4Nnz55BTEwM6urqAv+aNm1aBZH/R0pKCoMHD4a3tzcSEhJw8eJF3Lx5E0DFPv/l5eUFvku4XC5u3brFf925c2fweDycOXOmzP2/PEX/tr5vy/3w4QMyMzN/Go+Ojg7S0tJKvXfq6uoCT8kJIZVHVxIh5fDhwwdYW1ujQ4cO5ZpT7tWrVzA1NcWTJ08QHx/PH/yiLpCRkcG0adMwb948REZG4s6dO5g8eTI+ffqEiRMnQl9fH9LS0li0aBEyMjIQGBhYag5CVVVVZGZmIjU1Fa9evUJhYSF/XWhoKHbv3o379+/D1dUVV65cgaOjIwBAXV0dSkpKcHNzQ3p6Ok6cOAEPD49SZefl5SE2NhavXr2iJlyE/KJmzZph2bJlePToEfz9/ZGTk4O+ffuia9eu8Pf3x+fPn4UdYp0wa9YsREZG4t9//0V6ejq2bNmCyMjIGo9DQ0MD2dnZOHjwIDIyMuDt7Y2IiIhfLtfMzAw9evSApaUloqOjkZWVhQsXLmDx4sVISkqqgsj/G+F4165duHXrFh4+fIj9+/dDSkoKKioqAP77/D979iyePHmCV69e/bAsExMTnDhxAidOnMC9e/cwbdo0gRH3VVVVYWdnB3t7exw+fBiZmZlISEhASEgIAEBFRQUcDgfHjx/Hy5cvkZeXxy933759SExMxM2bN2FnZwdRUdGfHtuyZcuwd+9euLu74/bt27h79y4OHjyIJUuWVPLdIoR8ixJbQn6CMYbJkyfj+fPnCA0N/WmToZcvX8LExAS5ubmIj49Hp06daijSqrN27VpYWVnB1tYWOjo6ePDgAaKiotCoUSM0btwY+/fvx8mTJ9G5c2cEBQWVmnLHysoKFhYWMDY2hry8PIKCgvjr3N3dcfDgQXTp0gV79+5FUFAQf7okcXFxBAUF4d69e+jSpQvWrVtXalTRnj17wsHBATY2NpCXl8f69eur/f0g5E8gKSmJ8ePHIzU1FbGxsVBRUYG9vT1UVFTg7u4u0IeTlPbXX39h586d2LRpE7p27Yro6GihJC1///03XFxc4OjoCC0tLVy4cAFLly795XI5HA5OnjyJPn36YMKECWjbti1GjhyJR48eoXnz5mXu82UKt6ysrHLV0bBhQ+zcuRO9evVCly5dEBMTg2PHjvHn/l2+fDmysrLQpk0bfguf77G3t4ednR3GjRsHQ0NDqKmp8afw+WLbtm0YPnw4pk+fjvbt22Py5Mn81kgtW7aEu7s7/vnnHzRv3px/A3bhwoUwNDTEoEGDMHDgQFhaWqJNmzY/PTZzc3McP34c0dHR6NatG/766y94enryk3ZCyK/jMGprRMgP+fj4YMaMGQgNDcXw4cN/uO3Lly9hamqKFy9eIC4urtzz2/4pOBwOIiIiYGlpKexQCCHlcP/+fWzatAkBAQHgcrkYM2YMnJ2d61QrlKpgZGQELS2tcrXYIf/P398fq1evxp07d0r13yXlExAQAGdnZ4GnzYSQstETW0J+4Nq1a/w73z9Lal+8eAETExO8ePEC8fHxlNQSQuq8tm3bYuvWrXj8+DHc3d0RFRWFLl26oG/fvjh58iR/YKE/gY+PD2RlZfn9PcnPnTx5EqtXr6aktpJkZWXh4OAg7DAIqTPoiS0h3/Hu3Tvo6OigSZMmOHfuHCQlJb+77fPnz2FiYoI3b94gPj4e7du3r8FI6w56YktI3VZcXIywsDBs3LgRSUlJaNeuHZydnTFu3LhyTX9WVz158gQFBQUAAGVl5QpPz1NeiYmJ6N+//3fXf+nnSf4MDx48AACIioqidevWQo6GkNqPEltCysAYg5WVFeLi4pCSkvLDL5QvSe3bt28RHx9frmmACCGkLmOM4fz58/D09MThw4fRsGFDTJ06FTNmzEDLli2FHV6dVVBQgCdPnnx3vbq6eg1GQwghdQsltoSUYdOmTXB2dv7p08Vnz57BxMQE7969o6SWEPJHyszMhLe3N3bt2oWCggLY2NjAxcUFurq6wg6NEELIH4QSW0K+cfnyZRgYGMDR0REbN2787na5ubkwMTHBhw8fEB8fj7Zt29ZglIQQUrt8+PABu3btgre3N7KysmBgYAAXFxf8/fff5ZoOhRBSPl/m1qXrihBBNHgUIV958+YNRowYAR0dHaxdu/a72+Xm5sLY2BgfP35EQkJCnUxquVwu7t27BwAoKSkBl8sFYwx0r4sQUhn169eHi4sLHjx4gLCwMPB4PAwbNgxt27bFpk2b8PHjR2GHSEidxxhDSUkJRo8ejTdv3gg7HEJqFUpsCfkfxhjGjx+PvLw8hISEfHdwkKdPn8LIyAj5+flISEiAhoZGDUf6a/Ly8rB582a0a9cOHTp0wNixY3H//n1wOBz+P0IIqSxRUVFYWVnh3LlzuHLlCvT19TF37ly0atUKc+fOxaNHj4QdIqkDXr58ifPnz+PatWv8gbtqMx6Ph5s3b+L8+fP48OFDtdXD4XDw8uVLnD59GhMmTKCb0YR8hRJbQv7Hw8MDx44dw969e6GsrFzmNk+ePIGRkREKCgqQkJBQpwbyyM7Oxrx589CqVSu4uLigW7duuHz5Mvbv3w9NTU2IiNDHASGkanXr1g2BgYHIzMzEtGnTsHv3bqipqWHEiBG4ePGisMMjtZi8vDwaNmyIUaNGQVFREadOnRJ2SD8kIiICNTU1LFiwACoqKrhx40a11dWqVSvs3bsXR48e/WGXKUL+NNTHlhAA58+fh6GhIebMmYN169aVuU1OTg6MjY1RVFSE+Ph4qKmp1XCUlXPp0iV4enoiPDwccnJymDJlChwdHaGkpCTs0Aghf5j8/Hzs2bMHXl5eSE9Ph76+PlxcXGBlZQUxMTFhh0dqoXfv3sHW1hYnTpzAihUrsHDhwlp9I/b9+/cwMTHBkydPcPbs2WrtqjR//nxs3LgRZ8+eRc+ePautHkLqCkpsyR/v1atX0NLSgqqqKuLj48ucSP7x48cwNjZGcXExEhISav18ciUlJTh06BA8PT1x6dIlqKurw9nZGXZ2dpCVlRV2eISQPxyPx8PJkyfh6emJuLg4KCkpYebMmZg8eTIaNmwo7PBILcPj8bBixQq4ubnB0tISe/bsQf369YUd1ne9fPkSffr0QX5+Ps6dO/fdVmC/qri4GMbGxnj06BFSUlLQtGnTaqmHkLqCElvyR+PxeBg4cCCSkpKQkpKCVq1aldomOzsbxsbG4HK5iI+Pr9VJ7bt37+Dn54fNmzfz43ZxccHAgQNr9R1uQsif6/r16/Dy8kJgYCDExcUxYcIEODk51amuHqRmHDt2DGPHjoWioiIiIiLQvn17YYf0XU+ePIGBgQHExMSQmJiI5s2bV0s9OTk50NbWRrdu3XD8+HH6rid/NPrrJ3+0devWISoqCvv37/9uUmtkZAQej1ern9RmZGTAyckJSkpKWLRoEYyNjZGSkoK4uDgMHjyYvugIIbVW165d4e/vj0ePHmHOnDkIDg5G27ZtMWTIECQkJNDgOIRv8ODBuHr1KjgcDrp3744jR44IO6TvatmyJWJiYpCXl4d+/frh7du31VJPq1atsG/fPpw6dQrr16+vljoIqSvoiS35Y505cwYmJiZYuHAhVq5cWWr9o0ePYGxsDACIj4+HiopKTYf4Q4wxJCYmwtPTE0eOHEGTJk3g4OCA6dOno0WLFsIOjxBCKuXz5884cOAAPD09cfv2bWhpacHFxQUjR4787mj15M/y8eNHjB8/HocOHcLSpUvh5uZWa2/g3r59G4aGhlBXV8fp06chJydXLfUsXrwYa9euRXx8PPr06VMtdRBS21FiS/5Iz58/h7a2Ntq2bYuYmJhSg5ZkZWXB2NgYHA4HCQkJ1dY/pjKKiooQEhICT09PJCcnQ1NTE87Ozhg7diykpKSEHR4hhFQJxhhOnz4NT09PREZGQkFBATNmzICDgwP1JSRgjGHt2rVYvHgx+vfvjwMHDtTa/tnXrl2DsbEx9PT0cPLkSdSrV6/K6ygpKYGZmRnu37+P1NRUNGvWrMrrIKS2o8SW/HG4XC4sLCxw48YNpKamlnq6mZWVBSMjI4iKiiIhIaHWjB78+vVr+Pr6YsuWLcjNzYW5uTlcXFzQr18/mnuWEPJbu3v3Lry8vLB3714AgK2tLZydnaGpqSnkyIiwRUZGYvTo0WjSpAkiIiLQqVMnYYdUpsTERJibm8PMzAzh4eFlDlT5q3Jzc6GlpYWuXbvi1KlTEBUVrfI6CKnName7DUKq0apVqxAbG4vAwMBSSW1mZiYMDQ0hJiaGM2fO1Iqk9t69e3BwcICSkhJWrFiBQYMG4fbt24iMjIS5uTkltYSQ316HDh3g6+uLx48fY8mSJTh+/Dg6duwICwsLREVFUT/cP5iFhQWSkpIgLS2Nv/76C6GhocIOqUwGBgY4dOgQIiMjYWdnBy6XW+V1tGjRAoGBgYiJicHq1aurvHxCajtKbMkfJTY2Fm5ubnB1dYWpqanAuocPH8LQ0BASEhJISEgoczCpmvKlCd6AAQPQoUMHHDlyBIsWLUJ2djZ27NhBTykIIX+kpk2bYvHixcjKysLevXvx4sULWFhYoFOnTvDz80NBQYGwQyRCoKamhgsXLmDw4MEYMWIEFixYUC2J46+ysLBAUFAQgoODMW3atGq5IWNqaoply5bBzc0NcXFxVV4+IbUZNUUmf4wvTXS6dOmCyMhIgSY6GRkZMDIygpSUFOLj49GyZUuhxPhl0BQvLy/cunWLP2iKjY0NJCUlhRITIYTUVowxnD17Fp6enjh69CiaNGmCadOmYfr06VBQUBB2eKSGMcbg6emJefPmwdTUFEFBQWjSpImwwyolICAAEyZMwJw5c7Bhw4Yqb3nF5XJhbm6OW7duITU1la4F8segxJb8EUpKStC3b1+kpaUhJSVFYD65Bw8ewNjYGNLS0oiPj4eiomKNx/f8+XP4+Phg27ZtePXqFQYPHgwXFxcYGhpSU2NCCCmHBw8ewNvbG7t370ZxcTFGjRoFFxcXdO3aVdihkRoWFxcHGxsbyMrKIiIiAlpaWsIOqZTNmzdj1qxZWL58OZYuXVrl5T9//hxaWlro0KEDTp8+Tf1tyR+BmiKTP4K7uzvOnj2LoKAggaQ2PT0dRkZGkJGREUpSe+PGDUyYMAHKysrw8PCAjY0N0tLScOTIERgZGVFSSwgh5aSurg5vb2/k5ORg1apViIuLg5aWFkxMTHDs2DHweDxhh0hqiImJCZKSktC4cWP07NkTBw4cEHZIpcycORMrV67EsmXLsGnTpiovv3nz5jh48CDOnDkDd3f3Ki+fkFqJEfKbi4yMZBwOh61atUpgeVpaGlNUVGTt27dnT58+rbF4uFwuO3bsGDMxMWEAmJKSElu/fj178+ZNjcVACCG/u+LiYnbw4EGmr6/PADANDQ22ZcsWlpeXJ+zQSA359OkTGzduHAPAnJ2dWVFRkbBDEsDj8di8efMYALZr165qqWPlypWMw+GwqKioaimfkNqEmiKT31pOTg60tbWhp6eHEydO8CdwT0tLg7GxMRo2bIi4uLga6X+Sn5+PPXv2YNOmTbh//z709fXh4uKCYcOGVcuw/4QQQv5z6dIleHp6Ijw8HHJycpgyZQocHR1rxcj3pHoxxrB161a4uLigV69eCAkJqVVzvDLGMH36dOzYsQMHDx6EtbV1lZbP4/EwYMAAXLt2DampqUIbQ4SQmkCJLfltlZSUwNjYGJmZmUhNTUXTpk0B/Dd9jomJCRo1aoS4uDiBpsnV4cmTJ9iyZQt8fX3x/v17DBs2DLNnz0aPHj2qtV5CCCGCHj16hC1btmDnzp3Iy8uDtbU1XFxc0L17d2GHRqpZYmIirK2tIS4ujkOHDqFbt27CDomPx+PB1tYWoaGhOHz4MAYMGFCl5b969QpaWlpQU1NDXFwcxMTEqrR8QmoL6mNLfltLlizBxYsXERwcLJDUGhsbo3HjxtWe1CYlJWHMmDFQVVWFj48PJkyYgIyMDISGhlJSSwghQqCiooINGzbg8ePH2LhxI65cuQJ9fX306tULYWFhKCkpEXaIpJoYGBjg2rVraNmyJQwMDLB7925hh8QnIiKCgIAA9O/fH1ZWVjhz5kyVlt+0aVMEBwfjwoUL1TJQFSG1BSW25Ld04sQJrFu3DmvWrEGvXr0AAHfv3oWRkRGaNm1abUktl8vFoUOHYGBggG7duuHixYv4999/kZOTAw8PD6iqqlZ5nYQQQipGTk4Os2bNwv379xEREQExMTFYW1tDQ0MDGzduxPv374UdIqkGLVu2xJkzZ2BnZ4eJEydi+vTpKCoqEnZYAABxcXEEBwejV69eGDx4MK5evVql5ffq1QurV6/G2rVrcfLkySotm5Dagpoik99OdnY2tLW10bNnTxw5cgQiIiK4c+cOjI2N0bx5c8TGxkJeXr5K6/zw4QN2794Nb29vZGZmwsDAAC4uLvj7779piH1CCKkDkpOT4enpiYMHD0JKSgr29vaYNWsW1NTUhB0aqQY7d+6Eo6Mj9PT0EBYWhhYtWgg7JABAXl4e+vXrh7S0NJw5cwadOnWqsrJ5PB6GDBmCCxcuICUlBcrKylVWNiG1ASW25LdSVFQEQ0NDPH36FCkpKWjcuDFu374NY2NjtGjRAjExMVWa1GZlZcHb2xt+fn4oKCiAjY0NXFxcoKurW2V1EEIIqTlPnz7F1q1bsX37drx79w5DhgyBi4sLevfuTVOw/WYuXboEKysrMMYQFhaGnj17CjskAMDbt29hbGyM58+fIzExEerq6lVW9ps3b6Ctrc1/ek2DV5LfCTVFJr+VhQsX4tq1awgJCUHjxo1x69YtGBsbQ1FRscqe1DLGcP78eQwfPhxt2rTBnj174OjoiKysLOzfv5+SWkIIqcMUFRWxatUqPH78GD4+Prh79y769OmD7t27IzAwEMXFxcIOkVSRv/76C9euXUObNm1gZGSE7du3ozY872nUqBGio6NRv359mJmZIScnp8rKbty4MYKDg3H16lUsXLiwysolpDagxJb8No4cOYKNGzdi/fr10NfXx82bN2FsbIyWLVsiNjaWP4BUZRUXFyMoKAj6+vro3bs3bt26ha1bt+Lx48dYvXo1DaFPCCG/EWlpaUydOhW3b9/GyZMn0ahRI4wZMwatW7fG2rVr8ebNG2GHSKqAgoICYmNjMXXqVEybNg2TJk3C58+fhR0WmjVrhpiYGDDG0LdvX7x48aLKyv7rr7+wfv16eHh44MiRI1VWLiHCRk2RyW8hMzMT2traMDExQXh4OG7evAkTExMoKyvj9OnTaNKkSaXLfvv2LXbs2IEtW7YgJycHZmZmcHFxgYWFBX9eXEIIIb+/W7duwcvLC/v374eoqCjs7Ozg5OSEdu3aCTs0UgX27NmDqVOnokuXLggPD68V8xynp6fDwMAALVq0QHx8PBo2bFgl5TLGMGzYMCQkJCA5ORmtW7euknIJESZKbEmdV1hYiN69e+P169dITk7Go0ePYGpqChUVFZw+fRqNGzeuVLnp6enYtGkT/P39UVJSgjFjxsDZ2RldunSp4iMghBBSl7x48QLbt2/H1q1b8eLFCwwcOBAuLi4wMTGhfrh13LVr1zBs2DAUFBQgNDQUhoaGwg4JN2/ehKGhITp06IDo6GjIyMhUSbnv3r2Djo4OmjZtinPnzkFCQqJKyiVEWOhxE6nz5s2bhxs3biA0NBRZWVkwMTGBqqpqpZJaxhji4+Px999/o127dggJCcG8efOQnZ2N3bt3U1JLCCEEzZo1w7Jly/Do0SPs3r0b2dnZMDMzg5aWFvz9/VFYWCjsEEkl6erqIikpCZ06dYKpqSm8vb2F3u+2c+fOiIyMxI0bN2BpaVllTaUbNmyIkJAQXL9+HfPmzauSMgkRJkpsSZ0WFhaGzZs3Y+PGjRAREYGJiQnU1NQqnNQWFhZiz549/ObMWVlZ8PPzQ3Z2Ntzc3KplzltCCCF1W7169TBhwgRcv34dMTExUFJSgr29PZSVleHu7l6l/SJJzZGXl0d0dDScnZ3h5OSEcePG4dOnT0KNqXv37jh27BjOnTuHUaNGoaSkpErK1dPTg4eHB7y9vREeHl4lZRIiLNQUmdRKeXl5ePDgAQoLCyEpKQl1dXXIysoKbPPgwQPo6OjAwsICCxYsQN++faGuro7o6Ohy90F5+fIlvznZ8+fPMWDAALi4uMDU1JSakxFCCKmwrKwsHDx4EMePHwePx4OFhQVGjx5dpVO2kJoTFBSEiRMnon379jh06BBUVVWFGs+JEydgaWmJkSNHYs+ePVUy1gdjDDY2NoiKikJycjLatGlTBZESUvMosSW1xp07d7B9+3ZER0bi/oMHAk1/OBwO2qqro5+FBRwcHKCmpoYePXogLy8Pfn5+sLS0RNu2bREVFVWupPb27dv8AUA4HA5/AJD27dtX4xESQgghpK65fv06hg4dig8fPuDgwYMwMzMTajzBwcEYNWoUHBwcsHXr1iq5Ef/hwwfo6upCTk4OFy5cQL169aogUkJqFiW2ROgyMzMxzcEBUdHRaCQtjZ4tWkCjUSMo168PSTExFJaUIPvDB6S/fYsLubl4++kTVJWV8fTZM+zevRuOjo5o164doqKi0KBBg+/WwxhDVFQUPD09ER0dDUVFRTg6OmLKlCm/NGoyIYQQQn5vb968wejRo3H69GmsWbMG8+bNE2rLrl27dmHSpEn4559/sGbNmiopMzU1FX/99Rfs7e3h4+NTJWUSUpMosSVC5efnB+dZsyArJoZxmpro3aoVxH/QrKaYx8O5nBz437yJD8XFEBUTQ9euXREVFYX69euXuU9BQQH27dsHLy8v3L17F7q6unBxcYG1tTWNAEgIIYSQcuFyuVi2bBlWr16NESNGYNeuXaW6SdUkT09PzJ49G6tXr8bChQurpExfX184ODggKCgII0eOrJIyCakpNHgUEZpVq1Zh8uTJ6K2ggK2mpjBWVv5hUgsA4iIiMFZWxvZ+/WDcqhUKCgpgZmZWZlKbm5uLJUuWQElJCQ4ODmjfvj3Onj2Lq1evYsyYMaWSWlVVVYwfP57/OiEhARwOBwkJCVVxuHxubm7Uf5cQQgipY0RFRbFq1SqEh4fj5MmT6NGjBx48eCC0eFxcXODm5oZFixZhy5YtVVLmlClTMHr0aEyePBlpaWlVUiYhNYUS299EYGAgvLy8hB1Gufn5+WHJkiWw7dgRTnp6kBYXr9D+0uLicNLTw9iOHbFy5Urs2rWLvy4lJQXjxo2DiooKNm3ahLFjx+LBgwc4dOgQDAwMKKkkhBBCSKUNGzYMly9fRmFhIbp164aTJ08KLZZly5bBxcUFM2fOxJ49e365PA6HA19fX7Rs2RIjRoxAQUFBFURJSM2gxPY3UZcS28zMTDjPmgXz1q0xSlPzl8oa1aEDzFu3xqyZM7Fjxw4YGRlBR0cHZ8+exdq1a5GTkwMvLy+oqalVUfS/bsmSJfRFQQgh3zAyMgKHwwGHw0Fqamq59wsICBAYNNDNzQ1aWlpVHh/5PX35myvvbApfaGpq4urVqzAwMMCgQYOwcuVK8Hi86gnyBzgcDjw8PDBp0iTY29vj0KFDv1ymrKwsQkNDkZ6ejlmzZlVBlITUDEpsSY2b5uAAWTExTO7atVL7M8ZQyOUC+O8DfXLXrpAC4DB1KoqKihAaGooHDx5g9uzZPxxMSljExMRotEFCCCnD5MmTkZubi06dOgH4b+qciraymTt3LmJjY6sjvFohICAARkZGFdrHyMgIAQEB5dpWVVW1xm6Uf+ny8+7duxqpD/jv+L7uYpSbm1vp423QoAEOHz4MV1dXLF26FFZWVvjw4UPVBFoBHA4H27dvh7W1NUaOHImoqKhfLrNz587YsmUL/Pz8sG/fviqIkpDqR4mtECUkJEBPTw/16tVDmzZt4OvrW2b/y/3790NXVxdSUlJo3LgxRo4cicePH/PXGxkZ4cSJE3j06BH/zmNF5llLSkqCubk5mjZtCikpKbRu3Rr29vYCcZbV1/TLD46vvyzHjx8PWVlZPHz4EObm5pCRkYGioiKWL18Oxhju3LmDqOhojNPURD0xMRy+fx8OUVEYEh6O0UePYvO1a/hYVCRQz/gTJ+B67hyuPXuGWTExsDx0CKcyMgAAeUVF2H/7Noq5XDAADx8+xNGjRwW+JAsLC+Hq6gp1dXVISkpCSUkJ8+fPR2FhYbnfo/IqLi6Gu7s7NDQ0UK9ePTRp0gS9e/fG6dOn+dt8e47Hjx/PP2/f/nNzcxPKcRBCiDBIS0tDQUEBYmJilS5DVlb2hyPdF33zHUMqjsvlCuXp5I8UFxdXaj8FBYVfugkuIiICV1dXHD16FHFxcdDX18e9e/cqXV5liYqKYt++fejXrx+GDh2Kc+fO/XKZEyZMgJ2dHRwcHHDnzp0qiJKQ6kWJrZCkpKTAwsICr1+/hru7OyZOnIjly5fj8OHDAtutWrUK48aNg4aGBjZu3AhnZ2fExsaiT58+/ORt8eLF0NLSQtOmTbFv3z7+CMDl8eLFC/Tr1w9ZWVn4559/sHnzZowZMwaXLl2q9LFxuVxYWFigefPmWL9+PXR1deHq6gpXV1ds374djaSl0btVK2y+dg27btyAZpMmmKqlhb6qqoh/9AhLz55FyTdfmE8+fsS6S5eg3bw5pmppQa1hQxSUlGBefDyOPniAvxQVISb/HJgAAF5zSURBVC0ujtatW+PevXvIyckBAPB4PPz999/4999/MXjwYGzevBmWlpbw9PSEjY1NpY/xe9zc3ODu7g5jY2Ns2bIFixcvhrKyMpKTk7+7z9SpU/nn7cu/MWPGAACaNWsmlOMghJDaKiAgAMrKypCWlsbQoUPx+vVrgfXfNkUeP348LC0tsWrVKigqKqJdu3Y/rUNVVRWrV6+Gvb095OTkoKysjB07dghss2DBArRt2xbS0tJQU1PD0qVLBZKrL3Hs3r0bysrKkJWVxfTp08HlcrF+/XooKCigWbNmWLVqlUC57969w6RJkyAvL4/69evDxMQE169fr8Q7VTbGGNzc3KCsrAxJSUkoKirym5saGRnh0aNHcHFx4d9gBf6/uffRo0ehqakJSUlJZGdnw8jICM7OzgLlW1paCgzEWFhYiAULFkBJSQmSkpJQV1fHrl27kJWVBWNjYwBAo0aNwOFw+PuV9dRYS0tL4GYvh8PBtm3b8Pfff0NGRob/Ph45cgQ6OjqoV68e1NTU4O7ujpKSkip7/75n8ODBuHr1KjgcDrp3744jR45Ue53fEhcXR2hoKPT19TFw4MAf/vYoDw6Hg61bt0JVVRXW1tbIz8+vokgJqR6VvyVKfomrqytERUVx/vx5KCoqAgBGjBiBDh068Ld59OgRXF1dsXLlSixatIi/fNiwYdDW1oaPjw8WLVqEvn37omXLlnj79i3Gjh1boTguXLiAt2/fIjo6Gnp6evzlK1eurPSxff78GRYWFvD29gYATJ8+HYMHD8a6deug1KoVerZogftv3iAqMxPz9PVhrKzM37dLs2ZYmpiIxJwcgeVP8/KwwsAAugoK/GX7b9/Gow8fsKRnT/Rs2RLioqLIePMGd9PS8GUWq8DAQMTExODMmTPo3bs3f99OnTrBwcEBFy5cQM+ePSt9rN86ceIEBgwYUOoH0I/06NEDPXr04L9+8OABHB0d0bdvX0ydOlUox0EIIbXR5cuXMXHiRKxZswaWlpaIjIyEq6vrT/eLjY1F/fr1BVrP/IyHhwdWrFiBRYsWISwsDNOmTYOhoSE/MZaTk0NAQAAUFRVx8+ZNTJ48GXJycpg/fz6/jIyMDJw6dQqRkZHIyMjA8OHD8fDhQ7Rt2xZnzpzBhQsXYG9vDzMzM+jr6wMArK2tISUlhVOnTqFBgwbw9fWFqakp7t+/j8aNG1fwHSstPDwcnp6eOHjwIDp27Ihnz57xE+dDhw6ha9eumDJlCiZPniyw36dPn7Bu3Tr4+fmhSZMm/BuvPzNu3DhcvHgR3t7e6Nq1KzIzM/Hq1SsoKSkhPDwcVlZWSEtLQ/369SElJVWhY3Fzc8PatWvh5eUFMTExJCYmYty4cfD29oaBgQEyMjIwZcoUACjX38mvatu2LS5fvsy/mbJ06VK4ublB5CczPlQlKSkpHD16FGZmZjA3N8fZs2cFfltWlIyMDEJDQ9GtWzfMmDGj3E3aCREGemIrBFwuFzExMbC0tOQntQCgrq6O/v37818fOnQIPB4PI0aMwKtXr/j/FBQUoKGhgfj4+F+O5ctgCcePH690M56yODo68v/P4XDg6OiIoqIiZDx8CI1GjZD4+DFkxMWh07w53hcW8v+pN2oEKTEx3HjxQqA8BRkZgaQWAM7n5ECtQQP0bNkSAKDRqBHS0tORl5fHv8scGhqKDh06oH379gLvoYmJCQBUyXv4tYYNG+L27dtIT0+v1P75+fkYOnQoGjVqhKCgIIiKigKo+eMghJDaQFVVlX+jEgA2bdoECwsLzJ8/H23btsWsWbNgbm7+03JkZGTg5+eHjh07omPHjuWqe8CAAZg+fTrU1dWxYMECNG3aVOCzdsmSJejZsydUVVUxePBgzJ07FyEhIQJl8Hg87N69G5qamhg8eDCMjY2RlpYGLy8vtGvXDhMmTEC7du345Z47dw5XrlxBaGgo9PT0oKGhgX///RcNGzZEWFgYgP+eQFd0GrqEhAT+09Ds7GwoKCjAzMwMysrK6N69Oz+Jbdy4MURFRSEnJwcFBQUofPW9W1xcDB8fH/Ts2RPt2rWDtLT0T+u9f/8+QkJCsHv3bgwdOhRqamowNTWFjY0NREVF+Yl6s2bNKtUkePTo0ZgwYQLU1NSgrKwMd3d3/PPPP7Czs4Oamhr69u2LFStWwNfXl79PVlZWhfsoV4ScnBzCwsKwevVqrFy5EoMHD67RPsRfYjh16hT/PGdmZv5SeZqamti+fTv27NkDf3//KoqSkKpHT2yF4MWLFygoKIC6unqpdV8vS09PB2MMGhoaZZYjXsEpcspiaGgIKysruLu7w9PTE0ZGRrC0tMTo0aMhKSlZqTJFRERKjULctm1b/v+V69fHuZwc5BcXY9TRo2WW8f6bfqPNZWRKbZObl4derVoJlMsYw4MHD6ClpQXGGG7duoWHDx9CXl6+zHpefJNA/6rly5djyJAhaNu2LTp16gQLCwvY2tqiS5cu5dp/8uTJyMjIwIULFwT6iKWnp+Pu3bs1dhyEEFIb3b17F0OHDhVY1qNHD0RGRv5wv86dO5eau/xnvv7c5nA4UFBQEPisDQ4Ohre3NzIyMpCXl4eSkpJSc6qrqqpCTk6O/7p58+YQFRUVeILXvHlzfrnXr19HXl5eqT7CBQUFyPjf2BK/ytramj9bgIWFBQYMGIDBgwf/tF+zhIREub/LvkhNTYWoqCgMDQ1/JeTv+rqlGfDf+3f+/HmB5t1cLhefP3/Gp0+fypWMVwUOh4OFCxdCW1sbo0ePRrdu3RAREcEfFK0mNG7cGKdPn4aBgQFMTU2RmJiIlv97EFAZtra2OHv2LKZPnw49PT107ty5CqMlpGpQYluL8Xg8cDgcnDp1iv/k7muysrK/XAeHw0FYWBguXbqEY8eOISoqCvb29vDw8MClS5cgKyv73REpuf8bmbiiJMXEwBhDQ0lJzPtf06tvNfgmqZYo4/hLlfu/bW7cuIGQkBAEBQUhKyuLv37cuHGwtbUV2EdJSamC0f9Ynz59kJGRgSNHjiA6Ohp+fn7w9PTE/7V353E15f8fwF8t2pWyJNo3+1KYsStb2WYqsg7JWqS6M5hByDpjTCr7yBrGLsYaRpRlGLuR0aKEQohKSXXP7w/f7s/VjUp1i9fz8ejx0L2f8zmfc1HndT7nvD+rV6/GmDFjPrhtcHAwtm3bhi1bthRaqkIsFqNZs2ZYsmSJzG3L+jiIiD4nmjIujn7M+xePFRQUJAWTzp8/j2HDhmHOnDlwcHCAjo4Otm/fjoCAgI/28aF+MzMzYWBgIHNGtqTL0RTFyMgId+7cwYkTJ3D8+HFMmDABixcvxunTpz94wVxdXb3Q+YCioqLUjDogXcSppLcWF7ffAu//vWZmZmLOnDlwcXEp1FYeqxE4Ojri0qVLcHZ2Rtu2bbF+/XoMHDiwwvZft25dnDhxAh07dkSPHj0QGRmJWrVqlbq/pUuX4uLFi3B1dcU///wjddGGqDJgsJWDOnXqQE1NDXFxcYXee/c1CwsLCIIAMzMzqRlPWUq6HML72rZti7Zt22LBggX4448/MGzYMGzfvh1jxoyBrq4uABS6lebevXsy+xKLxZJniArExMRI/pyTl4e6Wlq4+uQJGteqJQmkJWWgpYV7L1/+f7//C9pubm4y29vb26N79+6l2ldJ6Onpwd3dHe7u7sjMzETnzp3h7+//wWAbFRWFyZMnw9fXV1I46l0WFha4fv06unXr9sl/10REVVWjRo1w4cIFqdc+pdhhaZ07dw4mJiaYMWOG5LWifieWhK2tLR49egRlZeUSrW5QUurq6ujXrx/69euHiRMnomHDhrh58yZsbW2hoqJS7AvXtWvXRkpKiuT7/Px8/Pvvv5KiUM2aNYNYLMbp06dl/v4tmEV/f3/v95uenl6s22ltbW1x584dmXfEyYu5uTnOnTuHsWPHYtCgQbh8+TIWLFjwSZW/S8LExAQnTpxAp06d4OjoiL/++qvUVaDV1dWxa9cutGrVCh4eHtiyZQvPSahS4TO2cqCkpITu3btj3759SE5OlrweFxeHI0eOSL53cXGBkpIS5syZU+jKpSAIUpUgNTU18fKdkFdcaWlphfoumC0sWEbGxMQESkpKiIyMlGq3cuXKIvtdvny51FiXL18uuRKclJ6OzkZGEAsCtskoH58vFiOzGMsxdDA0xN2XL3Hu4UNJvwoKCkX+wHZ3d0f9+vVhb2+P8ePH4+eff8bOnTvx33//ldnyD+9X59TS0oKlpeUHl+RJSUnBwIED0bFjRyxevFhmm4EDB+Lhw4cICQkp9F52djYrFRLRF8Hb2xtHjx7Fb7/9htjYWCxfvvyjtyGXBysrKyQlJWH79u2Ij4/H0qVLERYW9sn9du/eHe3atYOTkxOOHTuGxMREnDt3DjNmzMClS5fKYORvKxyvW7dO8qjOli1boK6uDhMTEwBvb5+OjIzEw4cP8fTp0w/21bVrVxw6dAiHDh3Cf//9B09PT6mL4KampnBzc8OoUaOwb98+JCQk4NSpU5JnkU1MTKCgoICDBw8iNTUVmZmZkn43b96MqKgo3Lx5E25ubjLvXHvfrFmzEBoaijlz5uDWrVu4ffs2tm/fDj8/v1J+WmVDU1MTW7duxZIlSxAQEIBevXoVOl8oTw0aNMDx48cRHx+Pvn37Iisrq9R9WVtbIyQkBH/88YfMcxIieeKMrZz4+/vj2LFj6NChAzw9PZGfn4/ly5ejadOmuHbtGoC3s3Tz58/HtGnTkJiYCCcnJ1SvXh0JCQkICwvDuHHjMHnyZABAq1atsGPHDnz//fdo06YNtLS00K9fv4+OY9OmTVi5ciWcnZ1hYWGBjIwMhISEQFtbG7179wbwdgFyV1dXLFu2DAoKCrCwsMDBgweLfK5TTU0NR48ehZubG77++mscOXIEhw4dwvTp07Fn1y7EpqWhp5kZepmbY+d//+Huixew1deHkqIikjMzceb+fYy3sUHHd56flaV/gwY48+ABFp4/j56mpniQkYFaNWvC1MwMhoaGhU4y2rRpg5o1a+Lx48fYu3ev1C9sRUVFKCoq4vjx4/D29oa1tTWys7MBlOyW68aNG8POzg6tWrWCnp4eLl26hN27d0sV03qft7c3UlNTMXXqVGzfvl3qvebNm6N58+YYPnw4du7cCQ8PD0RERKBDhw7Iz8/Hf//9h507dyI8PLzQs0ZERJ+btm3bIiQkBLNnz8asWbPQvXt3+Pn5Yd68eRU6jm+++QYikQheXl7IyclBnz59JBVwP4WCggIOHz6MGTNmwN3dHampqahbty46d+4MfX19mducOnUK9vb2SEhIKNYsb40aNfDLL7/g+++/R35+Ppo1a4YDBw5InuudO3cuxo8fDwsLC+Tk5BS6+P2uUaNG4fr16xgxYgSUlZUhEokks7UFVq1ahenTp2PChAl49uwZjI2NJSs91K9fX1Lwyd3dHSNGjMDGjRsxbdo0JCQkoG/fvtDR0cG8efOKNWPr4OCAgwcPYu7cuVi0aBGqVauGhg0bfvRRoIqgoKAAkUiEli1bYuDAgWjdujX27t0LGxubCtl/ixYtcPjwYfTo0QMuLi7Yv39/qWupDB48GJGRkfD29sZXX31V6PEpIrkRSG7++usvwcbGRlBRUREsLCyEtWvXCj/88IOgpqYm1W7Pnj1Cx44dBU1NTUFTU1No2LChMHHiROHOnTuSNpmZmcLQoUOFGjVqCAAEExOTYo3hypUrwpAhQwRjY2NBVVVVqFOnjtC3b1/h0qVLUu1SU1OF/v37CxoaGoKurq4wfvx44d9//xUACBs2bJC0c3NzEzQ1NYX4+HihZ8+egoaGhqCvry/Mnj1byM/PFyZNmiToamgI+/v3Fw67ugrerVoJlrq6gqqSkqCurCyY6ugIAxo0ELb07SscdnUVDru6CnU0NIQ2BgaS79/92vHtt0I/S0uhppqaAEDQ0tIS3NzchKdPnwr79+8XdHV1BQCChoaG0KRJE0FVVVXQ1dUVWrVqJfj7+wt37twRTp06JYSEhAja2tqCkZGR0LhxY0FFRUUAIAAQlJWVhcaNGwvffvutMGXKFCEkJEQ4ffq0kJycLIjFYqnPaf78+cJXX30l1KhRQ1BXVxcaNmwoLFiwQHjz5o2kzezZs4V3/+t16dJFsq/3v2bPni1p9+bNG2HRokWFjmPOnDnCy5cvi/X3TURUWXXp0kXw8fGR9zCqnPXr1wuWlpZSv2eoZDZs2CDo6OhU2P7u3bsntGrVSlBTUxM2b95cYfsVBEE4ceKEoKKiIvTv31/Izc0tdT/Z2dmCjY2NYGlpyXMQqjQUBOEDl+Kowjk5OX3ScjHyNnLkSOzevVtyO9H7oqOj0aRJk0Lr136qiKQkLL5wAdHR0VLrtT158gQeHh6oWbNmiW6Zyc/PR1JSEmJjYxETE4OYmBjJnxMTEyWFPrS0tGBtbQ0rKytYW1tL/bng2WQiIvo4Ozs7nDt3DioqKjh//jyrrhaTq6srBg4cCFdXV3kPpUrS0tJCXl4e1NTUKnRZnuzsbHh6emLTpk3w8fHB4sWLy2S1i+L4888/4eLiguHDh2PdunWlXmc3Pj4etra2cHBwwI4dO/i8Lckdg60cZWdnS1UMjI2NRZMmTeDm5lZln1v4WLAFAEcHB1w7fx4runWDRhn8EM/KzcXEv/5Cy3btcDQ8/JP7+5icnBwkJCQUCrwxMTFSz0zXqlVLZuC1tLQsVYVOIqLP2cOHDyWPgBgbG5d4eZ7iioqKkloz/n0f+v1Fn5+Cop1KSkowMzOr0H0LgoCVK1fC19cXHTp0wM6dO1GnTp0K2fcff/yB7777Dl5eXggODi51KN2zZw8GDBiA5cuXY+LEiWU8SqKSYbCVIwMDA4wcORLm5ua4d+8eVq1ahZycHFy9erXItWtLIjU19YPPh6qoqEgWRy8rxQm2CQkJaNakCTrWrQufT3wuVBAELL18GWcePcLNW7fK7ZdSdnb2R4tz6enp4c2bN4iLi5MZep8/fy5pW79+/UKB19raGmZmZuV2MkdERG9/nj/8X9FBWSpTRV36MkRFRcHV1RXVqlXD3r170aZNmwrZ7++//w4PD49Pfk7dx8cHq1evxtmzZ1nvg+SKwVaO3N3dERERgUePHkFVVRXt2rXDwoULYWtrWyb9m5qafnD5gS5dushcK+9TFCfYAsDatWsxduxYDG/SBEMaNy7VvgRBwLbbt7Hl1i2sXbsWo0ePLlU/xbFx40a4u7t/sE1ERATs7OyKfP/Zs2eSoPv+Lc4FVY2VlJRgamoqM/QaGRmV+nYhIiIiqrwePnyI/v3749q1a1i5ciVGjRpVIftdvHgxpk6dikWLFmHq1Kml6uPNmzfo2LEjnj59iitXrpTZmstEJcVg+xk7e/as5LYuWXR1ddGqVasKHJG0BQsWwM/PDw5mZhjbokWJbkvOys1FyPXrCE9IwIIFCyQVFstLSkoKbt269cE2rVq1KtVztYIgICUlRWbgjYuLkyxKr6qqCktLS5nP9Orr6/PZFiIioiosJycH3t7eWLNmDTw9PREUFFQhd3HNnDkT8+fPx6pVq+Dh4VGqPhITE2FjYwN7e3vs2bOH5yQkFwy2JFdr166Fr7c3tJSVMaJxY3Q0NES1D8xK5ubn48zDhwiNjkZmXh6Cly0r15laecvLyytUxKog9CYmJkqWYahevXqRRax45ZSIiEpLEATJF/B22ZqCLyofISEh8PLyQuvWrbF7924YGBiU6/4EQYCPjw+WL1+OzZs3Y9iwYaXqZ//+/XByckJgYCB8fX3LdpBExcBgS3KXkJAATw8PhB87Bl0NDbQ3MICVri6MtbWhqqSEnPx8JKWnIzYtDedSUpCWlQWHnj2xavXqCi/0UJm8fv0ad+/elVm5OSUlRdKudu3aRRax0tDQkOMREBFRVZGbm4uEhATExsYiMzMTtWrVgrW1NerXr8/HZMrB33//jf79+0MQBOzevRvt27cv1/2JxWKMHj0amzdvxp49e/Dtt9+Wqp/JkycjODgYUVFRaNu2bRmPkujDGGyp0oiOjsbq1atxPDwcd2JjpRaFV1BQQAMrK/RwcICnp6fUkj5UWEZGhswiVnfu3JFazsDIyEhm6DUzM6uwZQeIiKjqyM/Px4EDBxAYGIjIyEiYmprC29sbo0ePhra2tryH91l59OgRXF1dceHCBQQHB8PDw6NcZ8rz8vIwZMgQ/Pnnnzh06BC6d+9e4j5yc3PRpUsXPHz4EFevXi3zIqVEH8JgS5VSZmYm4uLikJOTI3m2VEtLS97DqvIEQZAqYvVu6I2NjUVWVhaA/1/2QFYRK0NDQ16dJyIiXL58GUFBQdi+fTvU1dUxevRoeHt7f9F3U5W1N2/e4IcffsDy5csxatQorFixAmpqauW6v2+//RaRkZE4ceIE2rVrV+I+7t+/j5YtW6J9+/bYv38/zxmowjDYEhGAt6E3OTlZ5lJFd+/elRSxUlNTk1nEytraGrVr1+ZzV0REX5jk5GSsWLECq1evxosXL+Dk5ASRSIQOHTrwd0IZ2bRpEzw8PNCsWTPs2bMHRkZG5bavrKwsODo64ubNm4iIiEDLli1L3Mfhw4fRp08f/Prrr5gyZUrZD5JIBgZbIvqovLw83Lt3T2bl5nv37kluG9fW1pY5y2tlZQUdHR05HwUREZWnrKwshIaGIigoCHfu3EHr1q0hEokka7TSp7l8+TJcXFyQnZ2NXbt2oUuXLuW2r/T0dHTt2hVJSUmIiopCgwYNStzHtGnTsHjxYpw+fRodOnQoh1ESSWOwJaJP8vr1a8THx8us3Pzo0SNJuzp16sgMvJaWllBXV5fjERARUVkSi8U4evQoAgMDceLECdSvXx9eXl4YN24cn7n8RKmpqRg8eDBOnz6NgIAAeHt7l9us+NOnT9GlSxekp6fjzJkzMDExKdH2eXl56Nq1K+7evYurV6+idu3a5TJOogIMtkRUbtLT0xEbGyvzmd53i1gZGxvLLGJlamrKq/xERFXYzZs3ERQUhK1bt0JJSQlubm7w9fWFtbW1vIdWZeXl5eGnn35CQEAAhg0bhjVr1pTbKgfJycno1KkTFBQUEBUVVeKlhx4+fAgbGxvY2tri8OHDfN6WyhWDLRFVOEEQ8PTp0yKLWGVnZwMAlJWVYW5uLjP0cokJIqKq48mTJ1i1ahVWrlyJJ0+eoE+fPhCJROjatSufwy2l7du3Y9SoUWjQoAHCwsJgampaLvtJSEhAx44doaenh9OnT5d41v3YsWNwdHTE/PnzMX369HIZIxHAYEtElYxYLP5gEau8vDwAgLq6uqSI1fu3ONeqVYsnSkREldDr16+xbds2BAYG4ubNm2jevDlEIhGGDBkCVVVVeQ+vyrlx4wacnZ3x4sULbN++HT169CiX/dy+fRudO3eGmZkZ/vrrL1SvXr1E28+aNQsLFizAyZMny/XZYPqyMdgSUZWRl5eHxMREmaH3/v37kiJWOjo6RRax4jqLRETyJwgCTp48icDAQBw6dAj6+vqYMGECPDw8UKdOHXkPr0p5/vw5hg4diuPHj+Pnn3/GlClTyuXi7pUrV2Bvbw8bGxscOXKkRPUx8vPz0aNHD9y+fRvXrl2Dvr5+mY+PiMGWiD4L2dnZiI+Pl1m5+fHjx5J2+vr6MpcqsrCwKNe1AYmISLY7d+4gODgYGzduhFgsxnfffQdfX180bdpU3kOrMvLz8zFr1iwsXLgQAwcOxLp166ClpVXm+zl79ix69uwJe3t77N27FyoqKsXe9tGjR2jZsiWaNm2K8PBwKCkplfn46MvGYEtEn72XL1/KLGJ1584dpKenAwAUFBRgbGwsc5bX1NQUysrKcj4KIqLP27NnzxASEoJly5YhOTkZPXr0gEgkgoODA2sqFNPevXvh5uYGU1NThIWFwdLSssz3cezYMfTr1w9OTk74448/ShRQT548iR49emDWrFmYPXt2mY+NvmwMtkT0xRIEAampqUUWsXr9+jWAt0WsLCwsZBaxqlevHk+4iIjKUG5uLnbt2oXAwEBcunQJDRs2hK+vL4YPH15u1X8/J9HR0XByckJqaiq2bt2K3r17l/k+wsLC4OrqipEjRyIkJKREtz7PmzcPs2fPxvHjx9GtW7cyHxt9uRhsiYhkEIvFePDggczQe/fuXeTn5wMANDQ0iixiVbNmTRaxIiIqJUEQcPbsWQQGBmLfvn3Q1dXF+PHjMXHiRNSrV0/ew6vUXr58ieHDh+PgwYOYM2cOZsyYUeYXYUNDQyXLNy1ZsqTYv+/y8/PRq1cvXL9+HdeuXSvxEkJERWGwJSIqodzc3A8WsSpQo0YNqed4C0KvlZVViStKEhF9ye7evYulS5di3bp1yMnJwaBBgyASiWBrayvvoVVaYrEY8+bNg7+/P7799luEhoaWeQHFFStWwMvLC7Nnz4a/v3+xt3vy5AlsbGxgZWWFEydO8HEfKhMMtkREZSgrK0tSxOr90JuamippV7duXZmzvBYWFlzygoioCC9fvsS6deuwdOlS3Lt3D507d4ZIJEK/fv1YjKgIBw4cwHfffQcDAwPs27cPDRs2LNP+f/75Z0yfPh0BAQH4/vvvi71dZGQk7O3tMW3aNMyfP79Mx0RfJgZbIqIK8uLFC6lneN8Nv+8WsTIxMZFZxMrExIRXtYmI8Hb5t3379iEwMBDnzp2DhYUFvL294e7uzjtiZIiJiYGTkxMePHiA0NBQODk5lWn/06ZNwy+//IKQkBCMGTOm2Nv98ssvmDZtGo4cOQJHR8cyHRN9eRhsiYjkTBAEPHnyRGbgjY2NRU5ODgCgWrVqsLCwkBl669Wrx+d5ieiLdPHiRQQGBmLXrl3Q0tLCmDFjMGnSJJiYmMh7aJVKRkYG3N3dsWfPHvj5+cHf37/MZrkFQYCXlxdWrVqFbdu2YdCgQcXaTiwWo1+/frhw4QKuXbsGQ0PDMhkPfZkYbImIKjGxWIz79+/LLGKVkJAgKWKlqakJKysrmZWba9asKeejICIqf/fv38fy5cuxZs0aZGRkwMXFBSKRCO3atZP30CoNQRCwaNEiTJ8+HY6Ojti6dSt0dXXLpG+xWIyRI0di27Zt2LdvH/r06VOs7Z4+fQobGxuYmJggIiIC1apVK5Px0JeHwZaIqIp68+YNEhISZIbeBw8eSNrp6enJDLxWVlbQ0tKS4xEQEZW9zMxMbNq0CUFBQYiLi0Pbtm0hEong4uLCxzn+Jzw8HEOGDIGenh727duHpk2blkm/eXl5GDhwIA4fPowjR47A3t6+WNudO3cOXbp0wffff49FixaVyVjoy8NgS0T0GcrKykJcXJzMIlZPnz6VtDMwMJBZxMrc3JxFrIioShOLxTh06BACAwMREREBY2NjTJo0CWPGjEGNGjXkPTy5u3v3LpydnREXF4cNGzZg4MCBZdJvTk4O+vXrh/Pnz+PEiRP4+uuvi7VdQEAAJk+ejAMHDqBv375lMhb6sjDYEhF9YdLS0oosYpWRkQEAUFRUlBSxej/0Ghsbs/ooEVUp165dQ1BQEP744w+oqKhg1KhR8PHxgYWFhbyHJlevXr3C2LFjsW3bNkydOhULFiwok1ntV69eoWfPnrh9+zZOnz6NZs2afXQbQRDg5OSEqKgoXL16lc9IU4kx2BIREYC3JxWPHz8uFHhjYmIQHx8vKWKloqJSZBErAwMDFrEiokrr0aNHWLlyJVatWoVnz57hm2++gUgkQufOnb/Yn12CICAoKAhTpkyBvb09tm/fXia1GV68eAF7e3ukpKQgKioKVlZWH90mLS0NNjY2qFu3LiIjI6GiovLJ46AvB4MtERF9VH5+Pu7fvy9zljchIQFisRjA2yJWsgKvtbU19PT05HwURERvZWdnY8uWLQgKCkJ0dDRsbGwgEokwaNCgLzZMRUREYODAgdDS0sLevXthY2PzyX2mpqaic+fOyMrKQlRUFIyNjT+6zcWLF9GxY0d4eXlhyZIlnzwG+nIw2BIR0Sd58+YN7t69K7OI1cOHDyXtatasKRV4C0KvlZUVNDU15XgERPSlEgQBx44dQ2BgIMLDw2FgYICJEydi/PjxqFWrlryHV+GSkpLg4uKCW7duISQkBN99990n9/ngwQN07NgRqqqqiIyMhL6+/ke3Wbp0KXx8fBAWFlbma+7S54vBloiIys2rV6+KLGL17NkzSbv69evLnOU1Nzf/YmdPiKhiRUdHIygoCJs3bwYAjBgxAr6+vmjUqJGcR1axsrOz4enpiU2bNsHHxweLFy/+5CV44uPj0alTJ9SuXRunTp366BJDgiDA1dUVJ06cwJUrV2Bubv5J+6cvA4MtERHJxfPnz2XO8sbExODVq1cA3haxMjU1lVnEysjIiEWsiKjMpaam4vfff8eKFSvw6NEjODo6QiQSoUePHl/Mc7iCIGDlypXw9fVF+/btsXPnzmLNtH7IrVu30LlzZ1hbW+P48eMfXW7u5cuXsLW1ha6uLs6ePctK/fRRDLZERFSpCIKAR48eyQy88fHxePPmDQBAVVVVUsTq/fV569at+8WcgBJR+cjJycGOHTsQGBiIa9euoUmTJvD19cV3330HNTU1eQ+vQkRFRcHV1RXKysrYu3cvvvrqq0/q79KlS+jatSvatGmDQ4cOffRzvHLlCtq1a4dx48Zh2bJln7Rv+vwx2BIRUZWRn5+PpKQkmZWb7927JylipaWlJXOW18rK6qO3wBERvUsQBJw+fRqBgYE4cOAAatWqBU9PT0yYMOGTZzGrgocPH6J///64evUqVq5cidGjR39Sf5GRkXBwcEDPnj2xe/fuj97mvGrVKkyYMAE7d+6Eq6vrJ+2bPm8MtkRE9FnIycnB3bt3ZVZuTk5OlrSrVauWzMBraWnJIlZE9EGxsbFYunQpNmzYgNzcXAwdOhS+vr5o0aKFvIdWrnJycuDt7Y01a9bAw8MDwcHBn1T/4MiRI/j222/h6uqK0NDQDz5WIggChgwZgsOHD+PKlSuwtLQs9X7p88ZgS0REn72MjAzExcUVCrx37txBWlqapJ2hoaHMys1mZmYsYkVEEmlpaVi7di2WLVuG+/fvo2vXrhCJROjduzcUFRXlPbxyExISAi8vL7Ru3Rq7du1CvXr1St3Xrl27MHjwYIwZMwarV6/+4OMj6enpaN26NTQ1NXH+/Pkv5lZwKhkGWyIi+qI9e/asyCJWWVlZAAAlJSWYmZnJrNxsZGT0WZ/IElHRcnNzsXfvXgQGBuLChQuwtraGj48P3NzcPts7QC5cuID+/fsjPz8fu3fvRocOHUrd14YNGzBq1ChMnjwZv/766wfD7fXr19G2bVu4ublh9erVpd4nfb4YbImIiGQQBAEpKSlSz/EWhN74+Hjk5uYCANTU1GQWsbK2tkadOnVYxIroC3H+/HkEBgZiz5490NHRwbhx4+Dl5QVDQ0N5D63MPX78GK6urvj7778RHBwMDw+PUv+sCw4Ohq+vL+bNmwc/P78Ptl27di3Gjh2LP/74A0OGDCnV/ujzxWBLRERUQnl5eZIiVu/P8t67dw8Fv1qrV68uFXj9/Pw+eT1IIqrcEhMTsWzZMqxduxZZWVlwdXWFSCRCmzZt5D20MpWbm4sffvgBy5Ytw6hRo7BixYpS3yI8f/58zJw5E8HBwfD29i6ynSAIGDFiBMLCwnDp0iU0bNiwtMOnzxCDLRERURl6/fp1kUWs7ty5g+rVq5eov5EjR+LFixfYt29f+Qz4PQoKCggLC4OTk1OF7I/oc5WRkYH169cjODgYCQkJ6NChA0QiEZycnD6rNbhDQ0Mxfvx4NGvWDHv27IGRkVGJ+xAEAVOnTsVvv/2G9evXw93dvci2mZmZaNOmDZSVlXHhwgVoaGh8yvDpM8JgS0REVEEEQSjx7XrFDba5ubllMhtcVsH2zZs3LLhFhLfLlP35558IDAxEVFQUTE1N4e3tjdGjR0NbW1vewysTly9fhouLC7Kzs7Fr1y506dKlxH0IggAPDw+sXbsWO3bswIABA4ps+++//+Krr77CkCFDsG7duk8ZOn1GWO2CiIiognwo1O7evRvNmjWDuro6atasie7du2PKlCnYtGkT9u/fDwUFBSgoKODUqVNITEyEgoICduzYgS5dukBNTQ1bt26Fv78/WrZsKdVvUFAQTE1NpV5bv349mjRpAlVVVRgYGMDLywsAJO2cnZ2hoKAg+X7kyJGFgq6vry/s7Owk39vZ2cHLywu+vr6oVasWHBwcALw9Ae3Vqxe0tLSgr6+P4cOH4+nTpyX+7IiqKiUlJTg7OyMyMhKXLl1Chw4dMHXqVBgaGkIkEiEhIUHeQ/xkrVq1wqVLl9CsWTN069YNwcHBKOncmYKCAlauXInBgwdj6NChOHr0aJFtmzZtilWrVmH9+vUIDQ391OHTZ4LBloiISM5SUlIwZMgQjBo1Crdv38apU6fg4uKC2bNnY+DAgXB0dERKSgpSUlLQvn17yXY//fQTfHx8cPv2bUmQ/JhVq1Zh4sSJGDduHG7evIk///xTsi7kP//8A+BtpdKUlBTJ98W1adMmqKio4OzZs1i9ejVevHiBrl27wsbGBpcuXcLRo0fx+PFjDBw4sET9En0uWrVqhS1btiAxMRFeXl4IDQ2FpaUl+vfvjzNnzpQ4DFYmtWvXRnh4OHx9feHr64vhw4dLKssXl5KSEjZu3AhHR0e4uLggMjKyyLZubm5wd3eHp6cnbt269anDp8+AsrwHQERE9KVLSUlBXl4eXFxcYGJiAgBo1qwZAEBdXR05OTmoW7duoe18fX3h4uJSon3Nnz8fP/zwA3x8fCSvFRS1qV27NgCgRo0aMvf3MVZWVvj111+l9mVjY4OFCxdKXlu/fj2MjIwQExMDa2vrEu+D6HNQv359LFy4EH5+fggNDUVQUBA6deqE1q1bQyQSwdXVtUoWmlNWVsZvv/2G1q1bY9SoUbh16xb27t0LMzOzYvdRrVo17Ny5E3369EHfvn1x8uRJtG7dWmbb5cuX459//oGrqysuXrwILS2tsjoUqoI4Y0tERCRnLVq0QLdu3dCsWTO4uroiJCQEaWlpH92uqJO9ojx58gTJycno1q1baYf6Qa1atZL6/vr164iIiICWlpbkq6CKaXx8fLmMgagq0dDQgIeHB6Kjo3Ho0CHUqFEDw4YNg5mZGRYtWlSsnwOV0eDBg/H3338jPT0drVu3xvHjx0u0vZqaGvbv348mTZrAwcGhyBlZDQ0N7Nq1C0lJSZgwYUKVnvGmT8dgS0REJGdKSko4fvw4jhw5gsaNG2PZsmVo0KDBR5+909TUlPpeUVGx0IldwXq7wNvZ39L4WL9FjSczMxP9+vXDtWvXpL5iY2PRuXPnUo2F6HOkqKiI3r174/jx47hx4wYcHBwwa9YsGBoaYuLEiYiJiZH3EEusefPm+Oeff9CmTRs4Ojri119/LVHw1NLSwuHDh2FoaIgePXoUeTGsYcOGWLNmDTZv3oz169eX1fCpCmKwJSIiqgQUFBTQoUMHzJkzB1evXoWKigrCwsKgoqKC/Pz8YvVRu3ZtPHr0SOrk8dq1a5I/V69eHaampvjrr7+K7KNatWqF9le7dm2kpKRIvfZuv0WxtbXFrVu3YGpqCktLS6mv90MwEb3VrFkzrFu3DklJSZgyZQp27dqFBg0aoF+/fjh58mSVmpXU09PDoUOH8NNPP+HHH3/EoEGDkJmZWeztdXV1cezYMWhpaaF79+548OCBzHZDhw7FuHHj4OXlhRs3bpTV8KmKYbAlIiKSswsXLmDhwoW4dOkSkpKSsHfvXqSmpqJRo0YwNTXFjRs3cOfOHTx9+lTmTGkBOzs7pKam4tdff0V8fDxWrFiBI0eOSLXx9/dHQEAAli5ditjYWFy5cgXLli2TvF8QfB89eiS5DbJr1664dOkSQkNDERsbi9mzZ+Pff//96HFNnDgRz58/x5AhQ/DPP/8gPj4e4eHhcHd3L3ZYJ/pS6evrw9/fH0lJSVi3bh3u3buHbt26oWXLlti4cSNycnLkPcRiUVJSwoIFC7Bnzx4cOXIEbdu2RVxcXLG319fXx4kTJyAWi9GjRw+kpqbKbBcUFIQGDRrA1dUVGRkZZTV8qkIYbImIiORMW1sbkZGR6N27N6ytreHn54eAgAD06tULY8eORYMGDdC6dWvUrl0bZ8+eLbKfRo0aYeXKlVixYgVatGiBixcvYvLkyVJt3NzcEBQUhJUrV6JJkybo27cvYmNjJe8HBATg+PHjMDIygo2NDQDAwcEBM2fOxNSpU9GmTRtkZGRgxIgRHz2uevXq4ezZs8jPz0fPnj3RrFkz+Pr6okaNGlBU5CkIUXGoqalh1KhRuH79Oo4fPw5DQ0O4u7vDxMQEc+fOLTLoVTYuLi64cOECcnNz0bp1axw6dKjY2xobG+PEiRNIS0uDg4MDXrx4UaiNuro6du3ahZSUFIwbN65KzWxT2VAQ+LdORERElUBMTAx+/fVXWFlZwdraGtbW1rCwsICampq8h0ZUqfz3338IDg7Gpk2bIBaL8d1338HX1xdNmzaV99A+6uXLlxg+fDgOHjyIOXPmYMaMGcW+0HXjxg3Y2dmhcePGCA8Pl/lIw86dOzFo0CCsWrUKHh4eZT18qsQYbImIiKhSSExMhKurK2JiYpCeng7g7bPHxsbGkqD7bug1MTGBsjJXLqQv17Nnz7BmzRosX74cycnJ6NGjB0QiERwcHCr1XRFisRjz58/H7Nmz8e233yI0NBTa2trF2vbChQvo1q0b2rdvjwMHDkBVVbVQGy8vL4SEhOD8+fOwtbUt6+FTJcVgS0RERJWKIAhITU1FTEwMYmJiEBsbK/lzXFwcXr9+DeBtoStzc/NCgdfKygr169eHgoKCnI+EqGK8efMGu3btQmBgIC5fvoyGDRvC19cXw4cPh4aGhryHV6SDBw9i2LBhMDAwwL59+yTLgX1MREQEevXqhd69e2Pnzp2FLnDl5OSgQ4cOSEtLw5UrV6Cjo1Mew6dKhsGWiIiIqgyxWIwHDx4UCryxsbG4e/eupCiVhoYGrKysCgVea2tr1KxZk6GXPkuCIODMmTMIDAzEvn37oKenh/Hjx2PixImoV6+evIcnU0xMDJydnXH//n2EhobCycmpWNsdPHgQzs7OGDJkCDZu3Fhohvru3buwtbVF9+7dsWvXLv6f/wIw2BIREdFnITc3FwkJCVKBtyD03r9/X9JOV1dXZuC1srJC9erV5XgERGXn7t27WLp0KdatW4ecnBwMGjQIIpGoUt6am5GRAXd3d+zZswd+fn7w9/eHkpLSR7fbvn07hg4dCk9PTyxfvrxQeA0LC4OLiwuWLl2KSZMmldfwqZJgsCUiIqLPXlZWFuLi4grN8sbExEhVlTUwMJAZei0sLGQ+y0dU2b18+RLr1q3D0qVLce/ePXTu3BkikQj9+vUrVnisKIIgYNGiRZg+fTocHR2xdetW6OrqfnS7tWvXYuzYsZg2bRoWLlxY6H2RSIQVK1bg7NmzaNOmTXkMnSoJBlsiIiL6or148UJm4I2JiZGsh6mgoAATE5Mii1hVpoBAJEteXh727duHwMBAnDt3DhYWFvD29oa7u3ululMhPDwcQ4YMgZ6eHsLCwtCsWbOPbrNkyRL88MMP+Pnnn/HTTz9JvffmzRt06tQJjx8/xtWrV4sVlqlqYrAlIiIikkEQBDx58qTIIlY5OTkA3haxsrCwkFnEql69eny2jyqdixcvIjAwELt27YKWlhbGjh2LSZMmwdjYWN5DA/D2NmpnZ2fExcVhw4YNGDhw4Ee38ff3x5w5c7BixQpMmDBB6r179+7BxsYGnTt3RlhYGP9PfqYYbImIiIhKKD8/X1LE6v3Qm5iYKClipamp+cEiVkTydP/+fSxfvhy///47MjMz0b9/f4hEIrRt21beQ8OrV68wduxYbNu2DVOmTMHChQs/uLyXIAj4/vvvERQUhNDQUAwfPlzq/YMHD6Jfv34ICAjA999/X97DJzlgsCUiIiIqQ2/evEFCQoLMys0PHjyQtNPT05MKvAWh18rKClpaWnI8AvrSZGZmYuPGjQgODkZcXBzatm0LkUgEFxcXua4VLQgCgoKCMGXKFNjZ2WH79u2oVavWB9uPHTsWGzduxK5du+Ds7Cz1/tSpUxEYGIjIyEi0a9euvIdPFYzBloiIiKiCvHr1qlARq4LQ+/TpU0m7evXqyZzlNTc3ZxErKjdisRgHDx5EYGAgTp06BWNjY0yaNAljxoxBjRo15DauiIgIDBw4EJqamggLC4ONjU2RbfPz8zF06FDs27cPBw4cQM+ePSXv5ebmws7ODvfv38fVq1d518RnhsGWiIiIqBJ4/vw5YmNjZRayyszMBAAoKioWWcTK2NiYRayozFy7dg2BgYHYtm0bVFRUMGrUKPj4+MDCwkIu40lKSoKLiwtu3bqFNWvWFLrV+F1v3ryBs7MzIiIicPz4cXTo0EHy3oMHD9CyZUt8/fXXOHDgQKH1b6nqYrAlIiIiqsQEQcCjR49kBt64uDi8efMGAKCiolJkESsDAwMWzKFSSUlJwcqVK7Fq1So8f/4c33zzDUQiETp37lzh/6ays7MxYcIEbNy4Ed7e3vjtt99QrVq1Itv26tULV69eRUREhNT6vUePHkWvXr3wyy+/4Mcff6yo4VM5Y7AlIiIiqqLy8/Nx//79IotYicViAICWlpYk7L4fevX09OR8FFQVZGdnY8uWLQgKCkJ0dDRsbGwgEokwaNAgqKioVNg4BEHAqlWr4OPjg/bt22Pnzp3Q19eX2TY9PR3du3dHQkICIiMj0ahRI8l7M2bMwKJFixAREYFOnTpV1PCpHDHYEhEREX2GcnJyJEWs3g+9ycnJknY1a9aUGXitrKygqakpxyOgykgQBBw7dgyBgYEIDw+HgYEBJk6ciPHjx3+wsFNZO3PmDAYMGABlZWXs3bsXX331lcx2z549g52dHdLS0hAVFQUzMzMAb9f17d69O2JjY3H16lXUqVOnwsZO5YPBloiIiOgLk5mZibi4OJmVm589eyZpV79+/SKLWFXkLB1VTtHR0QgKCsLmzZsBACNGjICvr6/UzGh5evjwIQYMGIArV65g5cqVGD16tMx2KSkp6NSpEwRBQFRUFOrVqwcASE5ORsuWLWFjY4MjR47wedsqjsGWiIiIiCSePXtWZBGrV69eAXhbxMrU1FRmESsjIyMWsfrCpKam4vfff8eKFSvw6NEj9OrVCyKRCN27dy/353BzcnLg4+OD33//HR4eHggODpZ50SUxMRGdOnWCtrY2Tp8+LZldPnHiBHr27Im5c+fCz8+vXMdK5YvBloiIiIg+ShAEpKSkFFnEKjc3FwCgqqoqKWL1fujV19dnEavPWE5ODnbs2IHAwEBcu3YNTZs2ha+vL4YNGwY1NbVy3ffatWsxceJEtGrVCrt375bMyr7rzp076NSpE4yNjXHy5Eloa2sDAPz9/TFv3jycOHEC9vb25TpOKj8MtkRERET0SfLz83Hv3j2ZoTcxMREFp5taWloyA6+VlRV0dXXlfBRUVgRBwOnTpxEYGIgDBw6gVq1a8PT0xIQJE4os9FQWLly4gP79+yM/Px+7d++WWuanwLVr12BnZ4cWLVrgyJEj0NDQQH5+PhwcHHDr1i1cvXoVdevWLbcxUvlhsCUiIiKicpOTk4O7d+/KLGKVkpIiaVerVi2ZgdfS0pJFrKqw2NhYBAcHY8OGDcjLy8PQoUMhEonQvHnzctnf48eP4erqivPnzyM4OBienp6F7hI4d+4cevTogc6dO2P//v1QUVHB48eP0bJlSzRq1AjHjx/n7fRVEIMtEREREclFRkaGzCJWMTExSEtLk7QzNDSUCrwFodfMzIxFrKqItLQ0hISEYNmyZXjw4AG6du0KkUiE3r17l3nRptzcXPzwww9YtmwZ3N3dsXLlykK3Qp84cQJ9+vTBN998g23btkFZWRmnTp1Ct27d4Ofnhzlz5pTpmKj8MdgSERERUaXz7NkzmbO8sbGxyMrKAgAoKSnBzMxMZuVmIyMjVrmthHJzc7Fnzx4EBgbi4sWLsLa2ho+PD9zc3Mp8Zj40NBTjx49H06ZNsXfvXhgZGUm9v3//fvTv3x/Dhw/HunXroKioiAULFmDmzJkIDw9Hjx49ynQ8VL4YbImIiIioyhAEAcnJyTIDb3x8vFQRK0tLS5nP9NapU4dFrORMEAScP38egYGB2Lt3L3R0dDBu3Dh4eXnB0NCwzPZz+fJluLi4IDs7Gzt37oSdnZ3U+1u2bMGIESMwadIkBAUFQRAE9O7dG1euXMG1a9dkFqGiyonBloiIiIg+C3l5eUUWsbp3756kiFX16tWLLGJVo0YN+R7EFygxMRHLli3D2rVrkZWVBVdXV4hEIrRp06ZM+k9NTcXgwYNx+vRp/Pbbb/Dx8ZG6sLF69Wp4enpi5syZmDt3LlJTU2FjYwNzc3OcPHkSysrKZTIOKl8MtkRERET02Xv9+rVUEat3Q++jR48k7WrXrl1kESsNDQ05HsHnLz09HRs2bEBwcDASEhLQoUMHiEQiODk5fXIxp7y8PPz0008ICAjA0KFDERISIvX3+euvv+LHH3/E4sWLMXnyZJw5cwZ2dnaYOnUqFi5c+KmHRhWAwZaIiIiIvmjp6emSIlbv3+L84sULSTsjIyOZodfMzAzVqlWT3wF8ZvLz8/Hnn38iMDAQUVFRMDU1hbe3N0aPHi1Ze7a0tm/fjtGjR8Pa2hp79+6FmZmZ5D0/Pz8sWLAAv//+O8aNGycJu4cPH0avXr0+9bConDHYEhERERHJIAjCB4tYZWdnA3hbxMrc3FxmEStDQ0MWsfoEly9fRmBgIHbs2AF1dXWMHj0a3t7eUoG0pG7cuAFnZ2e8ePEC27dvlxSJEgQBPj4+WL58ObZs2YLBgwfjm2++wd9//42rV68WKj5FlQuDLRERERFRCYnFYkkRq/dD7927d5GXlwcAUFNTK7KIVe3atVnEqpgePnyIFStWYPXq1Xj58iWcnZ0hEonQvn37Un2Gz58/x7Bhw3Ds2DH8/PPPmDJlChQUFCAWizFq1Chs2bIFe/fuRYcOHWBjYwNDQ0OcPn2aM/OVGIMtEREREVEZysvLQ2JiosxZ3qSkJEkRK21t7SKLWOno6Mj5KCqnV69eITQ0FEFBQYiJiUGbNm0gEokwYMCAEofO/Px8zJo1CwsXLoSrqyvWr18PLS0t5OXlYfDgwTh48CAOHToETU1NdOrUCb6+vli8eHE5HRl9KgZbIiIiIqIKkp2djfj4eJmVmx8/fixpV6dOHZmh18LCAurq6nI8gspBLBbjyJEjCAwMxF9//QVDQ0N4eXlh3Lhx0NXVLVFfe/fuhZubG0xMTBAWFgYrKyvk5OTg22+/xZkzZ3D8+HH8/fff+P777/Hnn3+iX79+5XRU9CkYbImIiIiIKoH09HSpwPtu6H358iUAQEFBocgiVqampl/krbI3btxAUFAQtm7dCmVlZYwcORI+Pj6wtrYudh/R0dFwdnbG48ePsXXrVvTp0wdZWVlwdHTEzZs3ERERgTlz5uD06dO4cuUKTE1Ny++AqFQYbImIiIiIKjFBEJCamipzljc2NhavX78GACgrKxdZxKp+/fqffRGrx48fY9WqVVi5ciVSU1PRt29fiEQi2NvbF+s53JcvX2L48OE4ePAg/P394efnh4yMDHTt2hUPHjzAwYMHMXDgQNSpUwdRUVFQUVGpgKOi4mKwJSIiIiKqosRiMR4+fCiziFVCQoKkiJW6urpUEat3Q2+tWrU+qyJWr1+/xh9//IHAwED8+++/aNGiBXx9fTFkyBCoqqp+cFuxWIz58+dj9uzZ+OabbxAaGorc3Fx06dIFGRkZWLFiBQYMGABPT08EBQVVzAFRsTDYEhERERF9hnJzcz9YxKpAjRo1ZM7yWllZffK6sfIkCAL++usvBAYG4vDhw9DX18eECRPg6emJ2rVrf3DbgwcPYtiwYTAwMEBYWBh0dHTQsWNHKCkpwd3dHTNmzMCePXvg4uJSQUdDH8NgWwEyMzMRFxeHnJwcqKqqwtLSElpaWvIeFhERERF9obKyshAfHy8z9D558kTSTl9fv8giVmpqanI8gpL577//EBwcjE2bNkEsFuO7776Dr68vmjZtWuQ2MTExcHZ2RlJSEkJDQ9GiRQt06tQJNWvWhLm5OU6dOoXLly/DwsKi0LY8/694DLblJDo6GqtXr8axo0cRExeHdz9mBQUFWFtaoqejIzw8PNC4cWM5jpSIiIiI6P+9ePECsbGxMp/pTU9PB/D2fNbY2LjIIlbKyspyPgrZnj17hjVr1mD58uVITk5Gjx49IBKJ4ODgIPMZ5IyMDLi7u2PPnj2YMWMGBg0aBHt7e5iamuL58+fQ1dXF2bNnoaamxvN/OWOwLWMJCQnw9PBA+LFj0NXQQHsDA1jp6sJYWxuqysrIyctDUno6YtPScC4lBWlZWXDo2ROrVq+GmZmZvIdPRERERCSTIAh48uSJzMrNsbGxyMnJAQBUq1atyCJW9erVqxRFrN68eYNdu3YhMDAQly9fRqNGjeDj44Phw4dDQ0NDqq0gCFi0aBGmT58OR0dHTJkyBU5OTrC2tsaNGzcwduxYxMXG8vxfzhhsy9DatWvh6+0NLWVljGjcGB0NDVHtA/9xc8VinHnwAKHR0cjMy0PQ0qUYM2ZMBY6YiIiIiOjTicVi3L9/X+Ysb0JCAvLz8wEAGhoaRRaxqlmzZoUXsRIEAWfOnEFgYCD27dsHPT09jB8/HhMnTkS9evWk2oaHh2PIkCHQ09PDzJkz4enpiQYNGuC/27eho6LC8385q/LB1tTUFHZ2dti4cWOptm3atCkOHjz4yeNYsGAB/Pz84GBmhrEtWkCjBGuIZeXmIuT6dYQnJGD+/PmYMWMG7OzsAACnTp365LEREREREclLbm4uEhISZFZufvDggaSdrq5ukUWsqlevLtVneWSAu3fvYunSpVi3bh1ycnIwaNAgiEQi2NraSrVxdnZGXFwcevbsiX379pXZ+T8AZoBPUDlvfn/PuXPncOzYMfj6+qJGjRoVvv/o6Gjs3LkTI0eOlLkY89q1a+Hn54fhTZpgSCnul9eoVg0+rVujtoYG/Pz8ULdu3TIYdekcO3YMO3bswIULF3D79m0YGRkhMTFRbuMhIiIioqqtWrVqkrD6vqysLMTFxRUKvOHh4UhNTZW0q1u3rlTgzcrKwosXLyTFmcqCubk5goKCMGfOHKxduxYBAQHYsmULvv76a0ybNg19+/aFubk5zp07B2dnZ+zbt69Mz/9Hjx5dJsdRUllZWdiwYQP279+PmzdvIjMzE5aWlhg3bhzGjRsHJSUluYyrpKrEjO1vv/2GKVOmICEhoVCwzMnJgaKiIqqV4ApJgeLO2O7evRuurq6IiIiQXEUpkJCQgGZNmqBj3brwad26xGN4lyAIWHr5Ms48eoSmzZtDTU2twq/WjBw5Ejt27ICtrS2SkpKgpKTEYEv0hbOzs8Pp06cBAFevXkXLli3lO6AvhKmpKXx9feHr6wvgbeGRsLAwODk5yXVcFaXgdkQdHR28ePFCvoMhIrmYO3cuZs+ejSVLluDFixdSM74ZGRkA3v6sMDExkVnEysTERGYRq+JmgB07dmDw4MFo3LgxoqOjYWFhAR8fH9jb26PtV1+V+fn/zVu34O7uDqBiZ2z//fdfNG/eHN26dUPPnj2hra2N8PBwhIWFYcSIEdi0aVOFjeVTVIkZ2w8pqys0peXp4QEtZWWMbdHig+1e5+VB7SPV4RQUFDC2RQtce/oUMTExaN68eVkOtVgWLlyIkJAQVKtWDX379sW///5b4WMgospn7NixmDt3LmrVqgUASExMhJmZGYpzbfTUqVOwt7dHWlpahdx1M3LkSLx48QL79u0r930Bb49v5MiRJboIWHAHkL+/f7G3SUlJga6ubskHWEWYmppi48aNkgvIKSkp2LFjB2bPni3fgRGR3BQUcXJ2dpaa3BIEAY8fPy40y3vq1CmsXbtWqoiVhYVFodBb8LzvxxTMVK5YsQLq6uoIDAyESCSCpoZGsc7/gY9ngHfP/z09PIo1rrJWt25d3Lx5E02aNJG8Nn78eIwaNQobNmzAzJkzYWlpKZexlYT8S5J9hL+/P6ZMmQIAMDMzg4KCAhQUFCQnEKamphg5cqTUNjdu3ECXLl2grq4OQ0NDzJ8/Hxs2bJDa7l1nzpzBV199BTU1NZibmyM0NFTy3saNG+Hq6goAsLe3l+z/1KlTiI6ORvixYxjRuLHUPfVLLl6Ey969SMnMxKyoKPQPC8OvFy4AAMSCgH0xMfAID8e3e/Zg6J9/Ytnly8h48wbA29sSRjRujLS0NGRlZUn6fPPmDWbNmoVWrVpBR0cHmpqa6NSpEyIiIqSOZfbs2VBUVMRff/0l9fq4ceOgoqKC69evf/DzrlevXqlmv4no86ahoYG6deuW6/INb/73c7CyyM/Ph1gslvcwJOrWrfvBi7m5ubkVOJryV7duXejo6Mh7GEQkJx/KAAoKCmjbti3Wr1+P0aNHY9GiRQgLC8Mff/yBr776CqqqqqhVqxYcHBxgaGiIP//8E7t27YKnpyfs7e3x4MEDHD16FFZWVtDT04OysjLq1KmDWbNm4dmzZwAKZ4C2bdtix44dWLRoEdIzMgqd/wOlzwAF5//hx45Jnf8DFZMBatWqJRVqCzg7OwMAbt++/dG/r8qg0gdbFxcXDBkyBAAQGBiIzZs3Y/Pmzahdu7bM9g8fPoS9vT1u3bqFadOmQSQSYevWrQgODpbZPi4uDgMGDECPHj0QEBAAXV1djBw5Erdu3QIAdO7cGd7e3gCA6dOnS/bfqFEjrF69GroaGuhoaFio33xBgF9kJGqoqmJ08+bo8L82yy5fxrobN9C4Zk2Mb9kSPUxNEXHvHmZGRiLvfydQHevXh7KiIpKTkyX9paenY+3atbCzs8OiRYvg7++P1NRUODg44Nq1a5J2fn5+aNmyJUaPHi25RSM8PBwhISGYNWsWWhTjyhIRUUndu3cP/fr1g66uLjQ1NdGkSRMcPnwYiYmJsLe3B/C2KIiCgoLkYqSdnR28vLzg6+srOQEpOGF59+faixcvJBcUC9y6dQt9+/aFtrY2qlevjk6dOiE+Ph7+/v7YtGkT9u/fL3Uh8tSpU1BQUJC6pfXatWtSFzw3btyIGjVq4M8//0Tjxo2hqqqKpKQk5OTkYPLkyahfvz40NTXx9ddfl/ktYk+ePEG/fv2grq4OMzMzbN26tVAbBQUFySx0wee0Y8cOdOnSBWpqajK3eVfB8YWHh6NRo0bQ0tKCo6MjUlJSJG3++ecf9OjRA7Vq1YKOjg66dOmCK1euFBrH77//jr59+0JDQwONGjXC+fPnERcXBzs7O2hqaqJ9+/aIj4+X2m7//v2wtbWVXESeM2cO8vLySvmJEdHnrrQZIDo6GtOnT8dPP/2EuLg4yXO6p0+fRnZ2Nm7fvo06depAT08PDx8+hLa2NrS0tJCamop58+ahVq1aUFZWhr+/Pxo0aAAAcHJywrx58xASEoLbt28Xef4PlD4DdKxfH7oaGlLn/4B8M8CjR48AQHK3VmVX6W9Fbt68OWxtbbFt2zY4OTnJLN70rkWLFiEtLQ1XrlyRPAfm7u4OKysrme3v3LmDyMhIdOrUCQAwcOBAGBkZYcOGDfjtt99gbm6OTp06YenSpejRo4fUM7bHjh5FewMDmSW9c8VidDQygnuzZpLXbj19ivCEBEz5+mvYGxv//zHWqYOZUVGIevAA9sbGqKakBG0VFTx//lzSRldXF4mJiVBRUZG8NnbsWDRs2BDLli3DunXrALy95SI0NBStWrXC999/j8WLF2P06NFo3bo1fvrppw9+dkREpTVx4kS8efMGkZGR0NTURHR0NLS0tGBkZIQ9e/agf//+uHPnDrS1taGuri7ZbtOmTfD09MTZs2eLva+HDx+ic+fOsLOzw8mTJ6GtrY2zZ88iLy8PkydPxu3bt5Geno4NGzYAAPT09HDu3Lli9Z2VlYVFixZh7dq1qFmzJurUqQMvLy9ER0dj+/btqFevHsLCwuDo6IibN28W+bulpEaOHInk5GRERESgWrVq8Pb2xpMnTz663U8//YSAgADY2NhATU3to+2zsrLw22+/YfPmzVBUVMR3332HyZMnS0JxRkYG3NzcsGzZMgiCgICAAPTu3RuxsbFSFUnnzZuHJUuWYMmSJfjxxx8xdOhQmJubY9q0aTA2NsaoUaPg5eWFI0eOAACioqIwYsQILF26VHIRYty4cQDAW42JSKbyyAAqKipo2LAh1NXVce/ePakMkJiYiAYNGsDIyAjx8fG4d++e5C7Gffv2SS4saqipwb5+/SKX9PmUDNDewAAn3wu28soAb968QVBQEMzMzNCmTZsSby8PlT7YltTRo0fRrl07qeImenp6GDZsGJYtW1aofePGjSX/oAGgdu3aaNCgAe7evfvB/WRkZCAmLg69WrUqsk0fCwup76Pu34dmtWqw1dfHy//d+w8Alrq6UFdWxo0nTyT/2NWVlfE8MxNXr16FpqamVD9isRjp6ekQi8WSK+UxMTGS91VUVDBp0iQEBATg4sWLSE1NxZo1az56TO979eoV8vLypPomos9XjRo1ULt27WKtIWhqair1fG1SUhL69++PZv/7RW5ubi55T09PDwBQp06dQs/YWllZ4ddff5V8X5znVFesWAEdHR1s375dctLxbqVNdXV15OTklKrCfG5uLlauXCm5sp2UlIQNGzYgKSlJsp7h5MmTcfToUWzYsAELFy6EnZ1diYvsvbs8RUxMDI4cOYKLFy9KTh7WrVuHRo0afbQfX19fuLi4FHu/ubm5WL16NSz+9/vJy8sLc+fOlbzftWtXqfZr1qxBjRo1cPr0afTt21fyuru7OwYOHAgA+PHHH9GuXTvMnDkTDg4OAAAfHx9JARQAmDNnDn766Se4ubkBePvvY968eZg6daok2Jb0MxQEAampqSwsRfQZK5htvXv3bqHHVfLy8pCeni45Tz1w4ABatmwJDQ0NqXPXPn36YPPmzVJ95OXlwdLSEvr6+lJtzczMkJOTg2rVqiE3N1fyiEft2rUxfPhwWFtbw9PTE1YfqXdQ2gxgpauLQ/HxUs8AKykpSZ71FYvFePHiBcRiMVq3bl3ojpqmTZtizpw5mDZtGm7cuIGnT5/i2LFjpXqMqOCi7qFDh8r1MaSyVDVGWQL37t1Du3btCr1e1APPxu9cNSmgq6uLtLS0D+4nPj4egiDAWFtb5vtKCgqo9c6sBAAkZ2biVW4uhvz5p8xt3v2HXvCQ+bvrZn1Iwa0S77tx4waAt/+pS6uovono8+Ll5YWgoKBSlfX39vaGp6cnjh07hu7du6N///7FKoDX6gMXB4ty7do1dOrUqVzqAaioqEiN++bNm8jPzy+0REVOTg5q1qxZJvu8ffs2lJWVpT6Lhg0bFqvQVusSVuPU0NCQhFoAMDAwkJoZfvz4Mfz8/HDq1Ck8efIE+fn5yMrKQlJSklQ/735G+vr6ACC5qFHw2uvXr5Geng5tbW1cv34dZ8+exYIFCyRt8vPz8fr1a2RlZUkKxJSEWCzG3LlzsWLFihJvS0RVS7du3WS+HhYWhrCwMMn3BbOuxe2juOe4qampWLJkCby9vT94/g98WgYo6Dc7O1vq/U2bNiEgIAD//fefVD0FMzOzQn1NmTIF27dvx8WLF7Fw4UI0LsVSRIsXL0ZISAjmzZuH3r17l3h7efnsgm1JFXUC97FKnwXV1lSLuIJRTVERiu/NegiCgBqqqpjy9dcyt9F5pyhIwbarV69G48aNcezYMcyfPx+dOnVC586doaurC0VFRWzZsgXJycnYuXOnVF/379/H6NGj8fr1a3Tv3h2zZs364PHI8uOPPyIhIaFQ30T0eapXrx4Ui7i16mPGjBkDBwcHHDp0CMeOHcPPP/+MgIAATJo06YPbvX9HSsH+3/0Z/H5RJPX3ThiKozj9FvT97ox1ZmYmlJSUcPny5UK/L7S0tEo8jrL2/uf3Me9fDFBQUJD6TNzc3PDs2TMEBwfDxMQEqqqqaNeuXaGZknf7Kfi8ZL1WUHwrMzMTc+bMkTm7XJxbqGVRVFSEr68vBg0aVKrtiajy27ZtG1atWoUdO3bAwMBA6r2BAweiZcuWmD59OoC3wbVr166YMWOGVLvdu3dj6dKlUn0MHDgQZmZmWLRokVRbb29vJCcn4/nz51I1ADQ1NTFu3Di4uLhg6dKlRZ7/A5+WAVTfmZktsGXLFowcORJOTk6YMmUK6tSpAyUlJfz888+FahkAb2e3Y2NjAby9OFtSGzduxI8//ggPDw/4+fmVeHt5qhLBtji3xRUwMTFBXFxcoddlvfYp+y+oTJlTgsIXdbW0cPXJEzSuVUvyD7co4v+daHz99ddo2bIlAgICYG5ujtOnT0uNZ/fu3VBVVZW6nVosFqNTp06oUaMGRo0ahYULF8LT07NEt6sBb28fTE5OluqbiKgoRkZG8PDwgIeHB6ZNm4aQkBBMmjRJ8lxQcZZXKCgKkpKSAhsbGwCQKo4BvJ0t3LRpE3Jzc2XO2qqoqBTa17v9FiyZ836/stjY2CA/Px9Pnjwpt5+FDRs2RF5eHi5fviy5FfnOnTtyucX27NmzWLlypeQK/f379/H06dNP7tfW1hZ37twp0+UiFBQUYGlpWSWWoCCi0rl48SIA4Kuvvir0jK2qqir09fUlP5tNTU2RkZFR6Gf1rl27CvWhqqoKPT29Qm11dHTw7Nkz5OXlQUFBAdWrV0d6ejr27NkjVaypJOf/QPEzQM7/fne9e5F59+7dMDc3x969e6UygKz6BGKxGCNHjoS2tjZ8fX2xcOFCDBgwoNgZYP/+/RgzZgxcXFyq5N0wlb4qMvD/V6SL80vewcEB58+flzphef78+UerRZZ0/5aWllBQUEBSenqx++lsZASxIGBbdHSh9/LFYmS+c0X89f/+wxT8wi6YKXj3yvqFCxdw/vz5Qn0tWbIE586dw5o1azBv3jy0b98enp6eZXJyQkQki6+vL8LDw5GQkIArV64gIiJC8oyoiYkJFBQUcPDgQaSmpiIzM7PIftTV1dG2bVv88ssvuH37Nk6fPl3oirGXlxfS09MxePBgXLp0CbGxsdi8eTPu3LkD4O3JzY0bN3Dnzh08ffoUubm5sLS0hJGREfz9/REbG4tDhw4hICDgo8dlbW2NYcOGYcSIEdi7dy8SEhJw8eJF/Pzzzzh06NAnfGL/r0GDBnB0dMT48eNx4cIFXL58GWPGjCnVzPSnsrKywubNm3H79m1cuHABw4YNK5NxzJo1C6GhoZgzZw5u3bqF27dvY/v27VVuNoCIKpY8MoC6ujqMjIwQFBQkWQK04Nbg0pz/A8XPAAX9vvtzt6IyQGRkJAYPHozOnTtj69atpb6DS56qxIgLnjuaMWMGNm/ejO3bt+PVq1cy206dOhU6Ojro0aMH5s6di4CAAHTo0EHyLG1JZn8LtGzZEkpKSli0aBE2bdqE7du3IysrC9aWloj9yLO472pWuzZ6mZtj53//YVZUFPbFxOBAXBx+v3YNIw8dwrV3nnPKzsuDurq65Fa3vn374u7du3B2dsaaNWswbdo0ODo6Frpv/vbt25g5cyZGjhyJfv36QVFRERs3bkRmZiYmTJjw0THeuHED8+fPx/z58xEXF4eXL19Kvj9w4ECxj5WIviz5+fmYOHEiGjVqBEdHR1hbW2PlypUAgPr160uKB+nr68PLy+uDfa1fvx55eXlo1aoVfH19MX/+fKn3a9asiZMnTyIzMxNdunRBq1atEBISIpm9HTt2LBo0aIDWrVujdu3aOHv2LKpVq4Zt27bhv//+Q/PmzbFo0aJC/RZlw4YNGDFiBH744Qc0aNAATk5O+Oeff2TWaAD+fymekiwJtGHDBtSrVw9dunSBi4sLxo0bhzp16hR7+7Kybt06pKWlwdbWFsOHD4e3t3eZjMPBwQEHDx7EsWPH0KZNG7Rt2xaBgYEwMTEpg1ET0edKHhnAwMAASUlJ8Pb2xtdffy2VAQ4ePAhzU9MSnf8Dxc8AsWlpUFdXl3r0pSIywL179/DNN99AQUEBAwYMwK5du7BlyxbJV0HNnkpPqCLmzZsn1K9fX1BUVBQACAkJCYIgCIKJiYng5uYm1fbq1atCp06dBFVVVcHQ0FD4+eefhaVLlwoAhEePHknamZiYCH369Cm0ry5dughdunSRei0kJEQwNzcXlJSUBABCRESEMGnSJEFXQ0PY37+/cNjVVfLV3cREUFNSknrt3S/vVq0ES11dQVVJSVBXVhZMdXSEAQ0aCFv69hUOu7oK+11cBGVFRaF+/fqS/YvFYmHhwoWCiYmJoKqqKtjY2AgHDx4U3NzcBBMTE0EQBCEvL09o06aNYGhoKLx48UJq/MHBwQIAYceOHR/8nDds2CAAkPn1/udMRF+GLl26CD4+PvIeRpVx8uRJoUaNGsLz58/lPZQqb8OGDYKOjo68h0FEclTZMoCzs7PM8/9PzQD7XVwEXQ0NoX79+lJjqIgMEBERUeT5PwBh9uzZH/17qgwUBOEjVZI+E76+vvj9998lhUDKQnR0NJo0aVJoTapPFZGUhMUXLiA6OrpYyz0QEZUnOzs7nDt3DioqKjh//rxU9VsqrKC4x5QpU+Q9lCpNS0sLeXl5UFNT45I+RFRqZZ0BeP5feX2WwTY7O1vq3vRnz57B2toatra2OH78eJnuy9HBAdfOn8eKbt2gUQZLT2Tl5mLiX3+hZbt2OBoeXgYjJCL6NA8fPpQ8X2RsbCy1SDxVHr169UJUVJTM96ZPny6pHFpVFBR9VFJSkrmkBRHR+yoqA/D8v3L6LINty5YtYWdnh0aNGuHx48dYt24dkpOT8ddff6Fz585luq+EhAQ0a9IEHevWhU8J1xN8nyAIWHr5Ms48eoSbt27xFzkRERXbuxcg3qenpwc9Pb0KHhERUcWqqAzA8//KqUos91NSvXv3xu7du7FmzRooKCjA1tYW69atK/NQC7xdGDlo6VKMHTsWdTQ0MKQUiyADb/9Rb7t9G+EJCVi7di3/URMRUYnUr19f3kMgIpKrisoAPP+vnD7LGVt5WLBgAfz8/OBgZoaxLVqU6LaErNxchFy/jvCEBCxYsKDK3S5GRERERPSl4fl/5cJgW4bWrl0LX29vaCkrY0TjxuhoaIhqH1gDKjc/H2cePkRodDQy8/IQvGwZRo8eXYEjJiIiIiKi0uL5f+XBYFvGEhIS4OnhgfBjx6CroYH2Bgaw0tWFsbY2VJWUkJOfj6T0dMSmpeFcSgrSsrLg0LMnVq1ezdsPiIiIiIiqGJ7/Vw4MtuUkOjoaq1evxvHwcNyJjcW7H7OCggIaWFmhh4MDPD09WdKbiIiIiKiK4/m/fDHYVoDMzEzExcUhJycHqqqqsLS0hJaWlryHRURERERE5YDn/xWPwZaIiIiIiIiqtKKfbCYiIiIiIiKqAhhsiYiIiIiIqEpjsCUiIiIiIqIqjcGWiIiIiIiIqjQGWyIiIiIiIqrSGGyJiIiIiIioSmOwJSIiIiIioiqNwZaIiIiIiIiqNAZbIiIiIiIiqtIYbImIiIiIiKhKY7AlIiIiIiKiKo3BloiIiIiIiKo0BlsiIiIiIiKq0hhsiYiIiIiIqEpjsCUiIiIiIqIqjcGWiIiIiIiIqjQGWyIiIiIiIqrSGGyJiIiIiIioSmOwJSIiIiIioiqNwZaIiIiIiIiqNAZbIiIiIiIiqtIYbImIiIiIiKhKY7AlIiIiIiKiKo3BloiIiIiIiKo0BlsiIiIiIiKq0hhsiYiIiIiIqEpjsCUiIiIiIqIqjcGWiIiIiIiIqjQGWyIiIiIiIqrSGGyJiIiIiIioSmOwJSIiIiIioiqNwZaIiIiIiIiqNAZbIiIiIiIiqtIYbImIiIiIiKhKY7AlIiIiIiKiKo3BloiIiIiIiKo0BlsiIiIiIiKq0hhsiYiIiIiIqEpjsCUiIiIiIqIqjcGWiIiIiIiIqjQGWyIiIiIiIqrSGGyJiIiIiIioSmOwJSIiIiIioiqNwZaIiIiIiIiqNAZbIiIiIiIiqtIYbImIiIiIiKhKY7AlIiIiIiKiKo3BloiIiIiIiKq0/wOTdUs8FC555QAAAABJRU5ErkJggg==" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 4 + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], + "source": [ + "flow.draw_graph().show()" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`", - "id": "9c97c5b1152715b0" + "id": "11", + "metadata": {}, + "source": [ + "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:16:51.864590Z", - "start_time": "2025-02-07T22:16:40.797793Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", - " run_locally(flow, create_folders=True,\n", + " run_locally(\n", + " flow,\n", + " create_folders=True,\n", " ensure_success=True,\n", " raise_immediately=True,\n", - " store=job_store)" - ], - "id": "3f4db4f33192b6a3", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-07 23:16:40,808 INFO Started executing jobs locally\n", - "2025-02-07 23:16:40,814 INFO Starting job - tight relax 1 (a2da27b5-def6-41e6-b5c5-6a9552fe839c)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-07 23:16:41,672 INFO Finished job - tight relax 1 (a2da27b5-def6-41e6-b5c5-6a9552fe839c)\n", - "2025-02-07 23:16:41,672 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 23:16:41,673 INFO Starting job - tight relax 2 (8cb9dcbd-a140-449a-a3c8-1bfc0e6a82a1)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmpduvvl0_0/job_2025-02-07-22-16-41-673155-76431/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-07 23:16:42,983 INFO Finished job - tight relax 2 (8cb9dcbd-a140-449a-a3c8-1bfc0e6a82a1)\n", - "2025-02-07 23:16:42,983 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 23:16:42,984 INFO Starting job - get_supercell_size (34bedd82-f290-4404-9b1f-074b4321526c)\n", - "2025-02-07 23:16:42,997 INFO Finished job - get_supercell_size (34bedd82-f290-4404-9b1f-074b4321526c)\n", - "2025-02-07 23:16:42,998 INFO Starting job - static (c239b33e-cfc3-457b-858a-e28029d45430)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmpduvvl0_0/job_2025-02-07-22-16-42-997738-44040/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-07 23:16:43,649 INFO Finished job - static (c239b33e-cfc3-457b-858a-e28029d45430)\n", - "2025-02-07 23:16:43,649 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 23:16:43,650 INFO Starting job - generate_phonon_displacements (c07ffa41-28c9-4309-b86f-bc3848f59936)\n", - "2025-02-07 23:16:43,707 INFO Finished job - generate_phonon_displacements (c07ffa41-28c9-4309-b86f-bc3848f59936)\n", - "2025-02-07 23:16:43,708 INFO Starting job - run_phonon_displacements (b8098d3b-e91f-4971-a514-64723e88d29f)\n", - "2025-02-07 23:16:43,815 INFO Finished job - run_phonon_displacements (b8098d3b-e91f-4971-a514-64723e88d29f)\n", - "2025-02-07 23:16:43,817 INFO Starting job - phonon static 1/1 (5f879c34-b413-4a3b-9b53-7e2804c03ab2)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/atomate2/common/files.py:268: UserWarning: /tmp/tmpduvvl0_0/job_2025-02-07-22-16-43-817582-98313/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-07 23:16:44,599 INFO Finished job - phonon static 1/1 (5f879c34-b413-4a3b-9b53-7e2804c03ab2)\n", - "2025-02-07 23:16:44,600 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-07 23:16:44,600 INFO Starting job - store_inputs (b8098d3b-e91f-4971-a514-64723e88d29f, 2)\n", - "2025-02-07 23:16:44,602 INFO Finished job - store_inputs (b8098d3b-e91f-4971-a514-64723e88d29f, 2)\n", - "2025-02-07 23:16:44,602 INFO Starting job - generate_frequencies_eigenvectors (e58cd212-427e-4ef9-9e9f-92898ff3cb3f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-07 23:16:51,853 INFO Finished job - generate_frequencies_eigenvectors (e58cd212-427e-4ef9-9e9f-92898ff3cb3f)\n", - "2025-02-07 23:16:51,854 INFO Finished executing jobs locally\n" - ] - } - ], - "execution_count": 5 + " store=job_store,\n", + " )" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:16:51.872766Z", - "start_time": "2025-02-07T22:16:51.868017Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], "source": [ "from pymatgen.phonon.bandstructure import PhononBandStructureSymmLine\n", "from pymatgen.phonon.dos import PhononDos\n", "from pymatgen.phonon.plotter import PhononBSPlotter, PhononDosPlotter\n", - "from jobflow import SETTINGS\n", "\n", "job_store.connect()\n", "\n", @@ -314,82 +167,44 @@ " \"output.phonon_bandstructure\",\n", " ],\n", " load=True,\n", - " sort={\"completed_at\": -1} # to get the latest computation\n", + " sort={\"completed_at\": -1}, # to get the latest computation\n", ")" - ], - "id": "1bea502d9378bf28", - "outputs": [], - "execution_count": 6 + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-07T22:16:52.223631Z", - "start_time": "2025-02-07T22:16:51.917673Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], "source": [ - "ph_bs = PhononBandStructureSymmLine.from_dict(result['output']['phonon_bandstructure']) # get pymatgen bandstructure object\n", - "ph_dos = PhononDos.from_dict(result['output']['phonon_dos']) # get pymatgen phonon dos object\n", + "ph_bs = PhononBandStructureSymmLine.from_dict(\n", + " result[\"output\"][\"phonon_bandstructure\"]\n", + ") # get pymatgen bandstructure object\n", + "ph_dos = PhononDos.from_dict(\n", + " result[\"output\"][\"phonon_dos\"]\n", + ") # get pymatgen phonon dos object\n", "\n", "# initialize dos plotter and visualize dos plot\n", "dos_plot = PhononDosPlotter()\n", - "dos_plot.add_dos(label='a', dos=ph_dos)\n", + "dos_plot.add_dos(label=\"a\", dos=ph_dos)\n", "dos_plot.get_plot()\n", "\n", "# initialize Phonon bandstructure plotter and visualize band structure plot\n", "bs_plot = PhononBSPlotter(bs=ph_bs)\n", "bs_plot.get_plot()" - ], - "id": "3f7ab2d88f97fba7", - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 7 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "One can run the same workflow with a forcefield as well. Here, we cannot consider BORN charges yet as there is no forcefield equivalent. You can find this tutorial in the force field tutorials.", - "id": "c12bfc7daf5b541" + "id": "15", + "metadata": {}, + "source": [ + "One can run the same workflow with a forcefield as well. Here, we cannot consider BORN charges yet as there is no forcefield equivalent. You can find this tutorial in the force field tutorials." + ] } ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", From 6ffb680e1220cf2125d608cb782da861f8a630b8 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 15:50:32 +0100 Subject: [PATCH 16/61] add mock lobster and a lobster tutorial --- src/atomate2/utils/testing/lobster.py | 145 +++++++++++ tests/conftest.py | 2 +- tests/vasp/lobster/conftest.py | 142 +---------- tutorials/lobster_workflow.ipynb | 353 ++++++++++++++++++++++++++ tutorials/mock_lobster.py | 40 +++ tutorials/mock_vasp.py | 1 + 6 files changed, 548 insertions(+), 135 deletions(-) create mode 100644 src/atomate2/utils/testing/lobster.py create mode 100644 tutorials/lobster_workflow.ipynb create mode 100644 tutorials/mock_lobster.py diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py new file mode 100644 index 0000000000..6e6e51da3e --- /dev/null +++ b/src/atomate2/utils/testing/lobster.py @@ -0,0 +1,145 @@ +from __future__ import annotations + +import logging +import shutil +from pathlib import Path +from typing import TYPE_CHECKING, Literal + +import pytest +from pymatgen.io.lobster import Lobsterin + +import atomate2.lobster.jobs +import atomate2.lobster.run + +if TYPE_CHECKING: + from collections.abc import Sequence + +logger = logging.getLogger("atomate2") + +_LFILES = "lobsterin" +_DFT_FILES = ("WAVECAR", "POSCAR", "INCAR", "KPOINTS", "POTCAR") +_LOBS_REF_PATHS = {} +_FAKE_RUN_LOBSTER_KWARGS = {} + + +@pytest.fixture(scope="session") +def lobster_test_dir(test_dir): + return test_dir / "lobster" + + + +def monkeypatch_lobster(monkeypatch, lobster_test_dir): + """ + This is provided as a generator and can be used as by conextmanagers and + pytest.fixture ("mock_lobster"). + + It works by monkeypatching (replacing) calls to run_lobster that will + work when the lobster executables + are not present. + The primary idea is that instead of running LOBSTER to generate the output files, + reference files will be copied into the directory instead. As we do not want to + test whether LOBSTER is giving the correct output rather that the calculation inputs + are generated correctly and that the outputs are parsed properly, this should be + sufficient for our needs. + To use the fixture successfully, the following steps must be followed: + 1. "mock_lobster" should be included as an argument to any test that would + like to use its functionally. + 2. For each job in your workflow, you should prepare a reference directory + containing two folders "inputs" (containing the reference input files + expected to be produced by Lobsterin.standard_calculations_from_vasp_files + and "outputs" (containing the expected + output files to be produced by run_lobster). These files should reside in a + subdirectory of "tests/test_data/lobster". + 3. Create a dictionary mapping each job name to its reference directory. + Note that you should supply the reference directory relative to the + "tests/test_data/lobster" folder. For example, if your calculation + has one job named "lobster_run_0" and the reference files are present in + "tests/test_data/lobster/Si_lobster_run_0", the dictionary + would look like: ``{"lobster_run_0": "Si_lobster_run_0"}``. + 4. Optional: create a dictionary mapping each job name to custom + keyword arguments that will be supplied to fake_run_lobster. + This way you can configure which lobsterin settings are expected for each job. + For example, if your calculation has one job named "lobster_run_0" + and you wish to validate that "basisfunctions" is set correctly + in the lobsterin, your dictionary would look like + ``{"lobster_run_0": {"lobsterin_settings": {"basisfunctions": Ba 5p 5s 6s}}``. + 5. Inside the test function, call `mock_lobster(ref_paths, fake_lobster_kwargs)`, + where ref_paths is the dictionary created in step 3 + and fake_lobster_kwargs is the + dictionary created in step 4. + 6. Run your lobster job after calling `mock_lobster`. + """ + + def mock_run_lobster(*args, **kwargs): + from jobflow import CURRENT_JOB + + name = CURRENT_JOB.job.name + ref_path = lobster_test_dir / _LOBS_REF_PATHS[name] + fake_run_lobster(ref_path, **_FAKE_RUN_LOBSTER_KWARGS.get(name, {})) + + monkeypatch.setattr(atomate2.lobster.run, "run_lobster", mock_run_lobster) + monkeypatch.setattr(atomate2.lobster.jobs, "run_lobster", mock_run_lobster) + + def _run(ref_paths, fake_run_lobster_kwargs): + _LOBS_REF_PATHS.update(ref_paths) + _FAKE_RUN_LOBSTER_KWARGS.update(fake_run_lobster_kwargs) + + yield _run + + monkeypatch.undo() + _LOBS_REF_PATHS.clear() + + +def fake_run_lobster( + ref_path: str | Path, + check_lobster_inputs: Sequence[Literal["lobsterin"]] = _LFILES, + check_dft_inputs: Sequence[Literal["WAVECAR", "POSCAR"]] = _DFT_FILES, + lobsterin_settings: Sequence[str] = (), +): + """ + Emulate running LOBSTER and validate LOBSTER input files. + Parameters + ---------- + ref_path + Path to reference directory with VASP input files in the folder named 'inputs' + and output files in the folder named 'outputs'. + check_lobster_inputs + A list of lobster input files to check. Supported options are "lobsterin.gz". + lobsterin_settings + A list of LOBSTER settings to check. + """ + logger.info("Running fake LOBSTER.") + ref_path = Path(ref_path) + + # Checks if DFT files have been copied + for file in check_dft_inputs: + Path(file).exists() + logger.info("Verified copying of VASP files successfully") + # zipped or not zipped? + if "lobsterin" in check_lobster_inputs: + verify_inputs(ref_path, lobsterin_settings) + + logger.info("Verified LOBSTER inputs successfully") + + copy_lobster_outputs(ref_path) + + # pretend to run LOBSTER by copying pre-generated outputs from reference dir + logger.info("ran fake LOBSTER, generated outputs") + + +def verify_inputs(ref_path: str | Path, lobsterin_settings: Sequence[str]): + user = Lobsterin.from_file("lobsterin") + + # Check lobsterin + ref = Lobsterin.from_file(ref_path / "inputs" / "lobsterin") + + for key in lobsterin_settings: + if user.get(key) != ref.get(key): + raise ValueError(f"lobsterin value of {key} is inconsistent!") + + +def copy_lobster_outputs(ref_path: str | Path): + output_path = ref_path / "outputs" + for output_file in output_path.iterdir(): + if output_file.is_file(): + shutil.copy(output_file, ".") diff --git a/tests/conftest.py b/tests/conftest.py index 7def0a6906..1edbd41ce8 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -64,7 +64,7 @@ def tmp_dir(): @pytest.fixture(scope="session") def debug_mode(): - return False + return True @pytest.fixture(scope="session") diff --git a/tests/vasp/lobster/conftest.py b/tests/vasp/lobster/conftest.py index 9743334bd3..9839df85f8 100644 --- a/tests/vasp/lobster/conftest.py +++ b/tests/vasp/lobster/conftest.py @@ -1,143 +1,17 @@ -from __future__ import annotations +from atomate2.utils.testing.lobster import * +from pytest import MonkeyPatch -import logging -import shutil -from pathlib import Path -from typing import TYPE_CHECKING, Literal +from typing import TYPE_CHECKING, Any, Final -import pytest -from pymatgen.io.lobster import Lobsterin +from collections.abc import Generator, Callable -import atomate2.lobster.jobs -import atomate2.lobster.run - -if TYPE_CHECKING: - from collections.abc import Sequence - -logger = logging.getLogger("atomate2") - -_LFILES = "lobsterin" -_DFT_FILES = ("WAVECAR", "POSCAR", "INCAR", "KPOINTS", "POTCAR") -_LOBS_REF_PATHS = {} -_FAKE_RUN_LOBSTER_KWARGS = {} - - -@pytest.fixture(scope="session") -def lobster_test_dir(test_dir): - return test_dir / "lobster" @pytest.fixture -def mock_lobster(monkeypatch, lobster_test_dir): - """ - This fixture allows one to mock (fake) running LOBSTER. - It works by monkeypatching (replacing) calls to run_lobster that will - work when the lobster executables - are not present. - The primary idea is that instead of running LOBSTER to generate the output files, - reference files will be copied into the directory instead. As we do not want to - test whether LOBSTER is giving the correct output rather that the calculation inputs - are generated correctly and that the outputs are parsed properly, this should be - sufficient for our needs. - To use the fixture successfully, the following steps must be followed: - 1. "mock_lobster" should be included as an argument to any test that would - like to use its functionally. - 2. For each job in your workflow, you should prepare a reference directory - containing two folders "inputs" (containing the reference input files - expected to be produced by Lobsterin.standard_calculations_from_vasp_files - and "outputs" (containing the expected - output files to be produced by run_lobster). These files should reside in a - subdirectory of "tests/test_data/lobster". - 3. Create a dictionary mapping each job name to its reference directory. - Note that you should supply the reference directory relative to the - "tests/test_data/lobster" folder. For example, if your calculation - has one job named "lobster_run_0" and the reference files are present in - "tests/test_data/lobster/Si_lobster_run_0", the dictionary - would look like: ``{"lobster_run_0": "Si_lobster_run_0"}``. - 4. Optional: create a dictionary mapping each job name to custom - keyword arguments that will be supplied to fake_run_lobster. - This way you can configure which lobsterin settings are expected for each job. - For example, if your calculation has one job named "lobster_run_0" - and you wish to validate that "basisfunctions" is set correctly - in the lobsterin, your dictionary would look like - ``{"lobster_run_0": {"lobsterin_settings": {"basisfunctions": Ba 5p 5s 6s}}``. - 5. Inside the test function, call `mock_lobster(ref_paths, fake_lobster_kwargs)`, - where ref_paths is the dictionary created in step 3 - and fake_lobster_kwargs is the - dictionary created in step 4. - 6. Run your lobster job after calling `mock_lobster`. +def mock_lobster( + monkeypatch: MonkeyPatch, lobster_test_dir: Path +) -> Generator[Callable[[Any, Any], Any], None, None]: """ - def mock_run_lobster(*args, **kwargs): - from jobflow import CURRENT_JOB - - name = CURRENT_JOB.job.name - ref_path = lobster_test_dir / _LOBS_REF_PATHS[name] - fake_run_lobster(ref_path, **_FAKE_RUN_LOBSTER_KWARGS.get(name, {})) - - monkeypatch.setattr(atomate2.lobster.run, "run_lobster", mock_run_lobster) - monkeypatch.setattr(atomate2.lobster.jobs, "run_lobster", mock_run_lobster) - - def _run(ref_paths, fake_run_lobster_kwargs): - _LOBS_REF_PATHS.update(ref_paths) - _FAKE_RUN_LOBSTER_KWARGS.update(fake_run_lobster_kwargs) - - yield _run - - monkeypatch.undo() - _LOBS_REF_PATHS.clear() - - -def fake_run_lobster( - ref_path: str | Path, - check_lobster_inputs: Sequence[Literal["lobsterin"]] = _LFILES, - check_dft_inputs: Sequence[Literal["WAVECAR", "POSCAR"]] = _DFT_FILES, - lobsterin_settings: Sequence[str] = (), -): - """ - Emulate running LOBSTER and validate LOBSTER input files. - Parameters - ---------- - ref_path - Path to reference directory with VASP input files in the folder named 'inputs' - and output files in the folder named 'outputs'. - check_lobster_inputs - A list of lobster input files to check. Supported options are "lobsterin.gz". - lobsterin_settings - A list of LOBSTER settings to check. """ - logger.info("Running fake LOBSTER.") - ref_path = Path(ref_path) - - # Checks if DFT files have been copied - for file in check_dft_inputs: - Path(file).exists() - logger.info("Verified copying of VASP files successfully") - # zipped or not zipped? - if "lobsterin" in check_lobster_inputs: - verify_inputs(ref_path, lobsterin_settings) - - logger.info("Verified LOBSTER inputs successfully") - - copy_lobster_outputs(ref_path) - - # pretend to run LOBSTER by copying pre-generated outputs from reference dir - logger.info("ran fake LOBSTER, generated outputs") - - -def verify_inputs(ref_path: str | Path, lobsterin_settings: Sequence[str]): - user = Lobsterin.from_file("lobsterin") - - # Check lobsterin - ref = Lobsterin.from_file(ref_path / "inputs" / "lobsterin") - - for key in lobsterin_settings: - if user.get(key) != ref.get(key): - raise ValueError(f"lobsterin value of {key} is inconsistent!") - - -def copy_lobster_outputs(ref_path: str | Path): - output_path = ref_path / "outputs" - for output_file in output_path.iterdir(): - if output_file.is_file(): - shutil.copy(output_file, ".") + yield from monkeypatch_lobster(monkeypatch, lobster_test_dir) \ No newline at end of file diff --git a/tutorials/lobster_workflow.ipynb b/tutorials/lobster_workflow.ipynb new file mode 100644 index 0000000000..ab49c4ebaf --- /dev/null +++ b/tutorials/lobster_workflow.ipynb @@ -0,0 +1,353 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "raw", + "source": "The first lines are needed to ensure that we can mock VASP and LOBSTER runs.", + "id": "709fcbd6cc50b3a6" + }, + { + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-02-10T14:41:58.803952Z", + "start_time": "2025-02-10T14:41:55.187930Z" + } + }, + "cell_type": "code", + "source": [ + "from mock_vasp import mock_vasp, TEST_DIR\n", + "from mock_lobster import mock_lobster\n", + "\n", + "\n", + "ref_paths = {\n", + " \"relax 1\": \"Si_lobster_uniform/relax_1\",\n", + " \"relax 2\": \"Si_lobster_uniform/relax_2\",\n", + " \"static\": \"Si_lobster_uniform/static\",\n", + " \"non-scf uniform\": \"Si_lobster_uniform/non-scf_uniform\",\n", + " }\n", + "ref_paths_lobster = {\n", + " \"lobster_run_0\": \"Si_lobster/lobster_0\",\n", + " }" + ], + "id": "initial_id", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We first load a structure that we want to analyze with bonding analysis.", + "id": "87fa2e22363a6b0d" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T14:41:59.020661Z", + "start_time": "2025-02-10T14:41:58.807606Z" + } + }, + "cell_type": "code", + "source": [ + "from jobflow import JobStore, run_locally\n", + "from maggma.stores import MemoryStore\n", + "from pymatgen.core import Structure\n", + "\n", + "from atomate2.vasp.flows.lobster import VaspLobsterMaker, LobsterMaker\n", + "from atomate2.vasp.powerups import update_user_incar_settings\n", + "\n", + "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", + "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")" + ], + "id": "edefa4c433c5fed1", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Then, we initialize a workflow:", + "id": "168042f064c6aaa8" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T14:41:59.247945Z", + "start_time": "2025-02-10T14:41:59.064896Z" + } + }, + "cell_type": "code", + "source": [ + "job = VaspLobsterMaker(LobsterMaker(\n", + " task_document_kwargs={\n", + " \"analyze_outputs\": False\n", + " },\n", + " user_lobsterin_settings={\n", + " \"COHPstartEnergy\": -5.0,\n", + " \"COHPEndEnergy\": 5.0,\n", + " \"cohpGenerator\": \"from 0.1 to 3.0 orbitalwise\",\n", + " },\n", + " ),\n", + " delete_wavecars=True,\n", + " ).make(si_structure)\n", + "job = update_user_incar_settings(job, {\"NPAR\": 4})\n", + "\n", + "# run the flow or job and ensure that it finished running successfully\n", + "\n" + ], + "id": "423ed97c01c1eb4a", + "outputs": [], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We then run this workflow locally to show-case the capabilities. In real-life, you would omit the `mock*` parts.", + "id": "59e2c8121196a5a0" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T14:42:02.930099Z", + "start_time": "2025-02-10T14:41:59.254350Z" + } + }, + "cell_type": "code", + "source": [ + "with mock_vasp(ref_paths) as mf:\n", + " with mock_lobster(ref_paths_lobster) as mf2:\n", + " responses = run_locally(\n", + " job, store=job_store, create_folders=True, ensure_success=True)" + ], + "id": "81ea06138ad6e7dc", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 15:41:59,267 INFO Started executing jobs locally\n", + "2025-02-10 15:41:59,276 INFO Starting job - relax 1 (5514118e-8ab4-4ce2-b425-6e9f80df4852)\n", + "2025-02-10 15:41:59,461 INFO Finished job - relax 1 (5514118e-8ab4-4ce2-b425-6e9f80df4852)\n", + "2025-02-10 15:41:59,462 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 15:41:59,463 INFO Starting job - relax 2 (a2305a3b-d986-4e3b-98f8-6a04d8a3c3ef)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpl5qlnwgk/job_2025-02-10-14-41-59-463299-39059/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 15:41:59,708 INFO Finished job - relax 2 (a2305a3b-d986-4e3b-98f8-6a04d8a3c3ef)\n", + "2025-02-10 15:41:59,709 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 15:41:59,710 INFO Starting job - get_basis_infos (004fbffb-c50b-4e3d-abfa-e1dd1a5c9b92)\n", + "2025-02-10 15:41:59,759 INFO Finished job - get_basis_infos (004fbffb-c50b-4e3d-abfa-e1dd1a5c9b92)\n", + "2025-02-10 15:41:59,759 INFO Starting job - update_user_incar_settings_maker (d0be51c6-b23a-45cc-ae99-08879eeb30ec)\n", + "2025-02-10 15:41:59,898 INFO Finished job - update_user_incar_settings_maker (d0be51c6-b23a-45cc-ae99-08879eeb30ec)\n", + "2025-02-10 15:41:59,900 INFO Starting job - static (fd86b2d0-7261-4438-a4ec-d1d3cfef55df)\n", + "2025-02-10 15:42:00,126 INFO Finished job - static (fd86b2d0-7261-4438-a4ec-d1d3cfef55df)\n", + "2025-02-10 15:42:00,127 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 15:42:00,128 INFO Starting job - non-scf uniform (4341f72c-7c4c-4648-ae45-04177b4e02f0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpl5qlnwgk/job_2025-02-10-14-41-59-900399-76176/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpl5qlnwgk/job_2025-02-10-14-42-00-128422-46501/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 15:42:00,855 INFO Finished job - non-scf uniform (4341f72c-7c4c-4648-ae45-04177b4e02f0)\n", + "2025-02-10 15:42:00,856 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 15:42:00,857 INFO Starting job - store_inputs (d0be51c6-b23a-45cc-ae99-08879eeb30ec, 2)\n", + "2025-02-10 15:42:00,858 INFO Finished job - store_inputs (d0be51c6-b23a-45cc-ae99-08879eeb30ec, 2)\n", + "2025-02-10 15:42:00,859 INFO Starting job - get_lobster_jobs (8dc8329e-1ac3-4c54-b31c-ba4a0aab13db)\n", + "2025-02-10 15:42:00,900 INFO Finished job - get_lobster_jobs (8dc8329e-1ac3-4c54-b31c-ba4a0aab13db)\n", + "2025-02-10 15:42:00,901 INFO Starting job - lobster_run_0 (5116b80d-e10d-4e8c-82d3-48b19fef0b8e)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/lobster/inputs.py:699: UserWarning: Always check and test the provided basis functions. The spilling of your Lobster calculation might help\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/lobsterpy/cohp/analyze.py:870: UserWarning: The bonding, antibonding integral/percent values are numerical estimate. These values are sensitive to COHPstartEnergy parameter. If COHPstartEnergy value does not cover entire range of VASP calculations then absolute value of ICOHP_sum might not be equivalent to (bonding- antibonding) integral values.\n", + " ) = self._integrate_antbdstates_below_efermi(cohp, start=self.start)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 15:42:02,296 INFO lobster_run_0 failed with exception:\n", + "Traceback (most recent call last):\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py\", line 117, in _run_job\n", + " response = job.run(store=store)\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py\", line 604, in run\n", + " response = function(*self.function_args, **self.function_kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/jobs.py\", line 110, in make\n", + " return LobsterTaskDocument.from_directory(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/monty/dev.py\", line 216, in decorated\n", + " return _callable(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py\", line 877, in from_directory\n", + " calc_quality_summary = CalcQualitySummary.from_directory(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/monty/dev.py\", line 216, in decorated\n", + " return _callable(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py\", line 600, in from_directory\n", + " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/lobsterpy/cohp/analyze.py\", line 1707, in get_lobster_calc_quality_summary\n", + " dos_vasp.get_dos_fp_similarity(fp_lobster_orb, fp_vasp_orb, metric=\"tanimoto\"),\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/electronic_structure/dos.py\", line 1374, in get_dos_fp_similarity\n", + " rescale = np.linalg.norm(vec1) ** 2 + np.linalg.norm(vec2) ** 2 - np.dot(vec1, vec2)\n", + " ^^^^^^^^^^^^^^^^^^\n", + "ValueError: shapes (2,) and (256,) not aligned: 2 (dim 0) != 256 (dim 0)\n", + "\n", + "2025-02-10 15:42:02,298 INFO Starting job - store_inputs (8dc8329e-1ac3-4c54-b31c-ba4a0aab13db, 2)\n", + "2025-02-10 15:42:02,300 INFO Finished job - store_inputs (8dc8329e-1ac3-4c54-b31c-ba4a0aab13db, 2)\n", + "2025-02-10 15:42:02,301 INFO Starting job - delete_lobster_wavecar (ab8990d8-1d35-40f3-bc5e-903edeccabc5)\n", + "2025-02-10 15:42:02,303 INFO delete_lobster_wavecar failed with exception:\n", + "Traceback (most recent call last):\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py\", line 117, in _run_job\n", + " response = job.run(store=store)\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py\", line 593, in run\n", + " self.resolve_args(store=store)\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py\", line 709, in resolve_args\n", + " resolved_kwargs = find_and_resolve_references(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 473, in find_and_resolve_references\n", + " resolved_references = resolve_references(\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 356, in resolve_references\n", + " cache[uuid][index] = store.get_output(\n", + " ^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/store.py\", line 523, in get_output\n", + " return find_and_resolve_references(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 473, in find_and_resolve_references\n", + " resolved_references = resolve_references(\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 361, in resolve_references\n", + " resolved_references[ref] = ref.resolve(\n", + " ^^^^^^^^^^^^\n", + " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 166, in resolve\n", + " raise ValueError(\n", + "ValueError: Could not resolve reference - 5116b80d-e10d-4e8c-82d3-48b19fef0b8e not in store or index=None, cache={'5116b80d-e10d-4e8c-82d3-48b19fef0b8e': {}}\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:600: UserWarning: Oxidation states from BVA analyzer cannot be determined. Thus BVA charge comparison will be skipped\n", + " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:600: UserWarning: Consider using DOSCAR.LSO.lobster, as non LSO DOS from LOBSTER can have negative DOS values\n", + " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:600: UserWarning: Minimum energy range requested for DOS comparisons is not available in VASP or LOBSTER calculation. Thus, setting min_e to -5 eV\n", + " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 15:42:02,304 INFO Finished executing jobs locally\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Flow did not finish running successfully", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mRuntimeError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[4], line 3\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m mock_vasp(ref_paths) \u001B[38;5;28;01mas\u001B[39;00m mf:\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m mock_lobster(ref_paths_lobster) \u001B[38;5;28;01mas\u001B[39;00m mf2:\n\u001B[0;32m----> 3\u001B[0m responses \u001B[38;5;241m=\u001B[39m run_locally(\n\u001B[1;32m 4\u001B[0m job, store\u001B[38;5;241m=\u001B[39mjob_store, create_folders\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, ensure_success\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", + "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:184\u001B[0m, in \u001B[0;36mrun_locally\u001B[0;34m(flow, log, store, create_folders, root_dir, ensure_success, allow_external_references, raise_immediately)\u001B[0m\n\u001B[1;32m 181\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFinished executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 183\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ensure_success \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m finished_successfully:\n\u001B[0;32m--> 184\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFlow did not finish running successfully\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 186\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mdict\u001B[39m(responses)\n", + "\u001B[0;31mRuntimeError\u001B[0m: Flow did not finish running successfully" + ] + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "id": "96f7ec288b224e17", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "id": "b9975aba14ceaf39", + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/mock_lobster.py b/tutorials/mock_lobster.py new file mode 100644 index 0000000000..bbd5d3d5fd --- /dev/null +++ b/tutorials/mock_lobster.py @@ -0,0 +1,40 @@ +"""Mock LOBSTER functions for executing tutorials.""" + +import contextlib +import os +import shutil +import tempfile +from collections.abc import Generator +from pathlib import Path + +from pytest import MonkeyPatch + +from atomate2.utils.testing.lobster import monkeypatch_lobster + +TEST_ROOT = Path(__file__).parent.parent / "tests" +TEST_DIR = TEST_ROOT / "test_data" + + +@contextlib.contextmanager +def mock_lobster(ref_paths: dict) -> Generator: + """Mock LOBSTER functions. + + Parameters + ---------- + ref_paths (dict): A dictionary of reference paths to the test data. + + Yields + ------ + function: A function that mocks calls to VASP. + """ + for mf in monkeypatch_lobster(MonkeyPatch(), TEST_DIR / "lobster"): + fake_run_lobster_kwargs = {k: {"lobsterin_settings": ()} for k in ref_paths} + old_cwd = os.getcwd() + new_path = tempfile.mkdtemp() + os.chdir(new_path) + try: + yield mf(ref_paths, fake_run_lobster_kwargs) + finally: + os.chdir(old_cwd) + #shutil.rmtree(new_path) + diff --git a/tutorials/mock_vasp.py b/tutorials/mock_vasp.py index cc99676163..3e1153b8cf 100644 --- a/tutorials/mock_vasp.py +++ b/tutorials/mock_vasp.py @@ -37,3 +37,4 @@ def mock_vasp(ref_paths: dict) -> Generator: finally: os.chdir(old_cwd) shutil.rmtree(new_path) + From 61c82f870109638e349e8078a119e4d69676769b Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 17:24:25 +0100 Subject: [PATCH 17/61] fix bug in tutorial and fix calc summary --- src/atomate2/lobster/jobs.py | 2 + src/atomate2/lobster/schemas.py | 20 ++-- tutorials/lobster_workflow.ipynb | 192 ++++++++++++------------------- tutorials/lobster_workflow.py | 64 +++++++++++ tutorials/mock_lobster.py | 5 +- 5 files changed, 154 insertions(+), 129 deletions(-) create mode 100644 tutorials/lobster_workflow.py diff --git a/src/atomate2/lobster/jobs.py b/src/atomate2/lobster/jobs.py index aef254c64d..e440cd0591 100644 --- a/src/atomate2/lobster/jobs.py +++ b/src/atomate2/lobster/jobs.py @@ -79,6 +79,8 @@ def make( basis_dict: dict A dict including information on the basis set """ + print(self.task_document_kwargs) + # copy previous inputs # VASP for example copy_lobster_files(wavefunction_dir) diff --git a/src/atomate2/lobster/schemas.py b/src/atomate2/lobster/schemas.py index ae4352669e..d89f2d5b3a 100644 --- a/src/atomate2/lobster/schemas.py +++ b/src/atomate2/lobster/schemas.py @@ -597,6 +597,7 @@ def from_directory( "bva_comp": True, **calc_quality_kwargs, } + print(calc_quality_kwargs_updated) cal_quality_dict = Analysis.get_lobster_calc_quality_summary( path_to_poscar=structure_path, path_to_vasprun=vasprun_path, @@ -844,6 +845,7 @@ def from_directory( calc_quality_text = None describe = None describe_ionic = None + print(analyze_outputs) if analyze_outputs: if ( icohplist_path.exists() @@ -872,16 +874,16 @@ def from_directory( lobsterpy_kwargs=lobsterpy_kwargs, which_bonds="cation-anion", ) - # Get lobster calculation quality summary data + # Get lobster calculation quality summary data - calc_quality_summary = CalcQualitySummary.from_directory( - dir_name, - calc_quality_kwargs=calc_quality_kwargs, - ) + calc_quality_summary = CalcQualitySummary.from_directory( + dir_name, + calc_quality_kwargs=calc_quality_kwargs, + ) - calc_quality_text = Description.get_calc_quality_description( - calc_quality_summary.model_dump() - ) + calc_quality_text = Description.get_calc_quality_description( + calc_quality_summary.model_dump() + ) # Read in charges charges = None @@ -971,7 +973,7 @@ def from_directory( if describe_ionic is not None else None, strongest_bonds_cation_anion=sb_ionic, - calc_quality_summary=calc_quality_summary, + calc_quality_summary=calc_quality_summary if calc_quality_summary is not None else None, calc_quality_text=" ".join(calc_quality_text) if calc_quality_text is not None else None, diff --git a/tutorials/lobster_workflow.ipynb b/tutorials/lobster_workflow.ipynb index ab49c4ebaf..afff3c1efe 100644 --- a/tutorials/lobster_workflow.ipynb +++ b/tutorials/lobster_workflow.ipynb @@ -10,8 +10,8 @@ "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2025-02-10T14:41:58.803952Z", - "start_time": "2025-02-10T14:41:55.187930Z" + "end_time": "2025-02-10T16:23:35.273002Z", + "start_time": "2025-02-10T16:23:31.749277Z" } }, "cell_type": "code", @@ -52,8 +52,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T14:41:59.020661Z", - "start_time": "2025-02-10T14:41:58.807606Z" + "end_time": "2025-02-10T16:23:35.505765Z", + "start_time": "2025-02-10T16:23:35.277556Z" } }, "cell_type": "code", @@ -81,15 +81,16 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T14:41:59.247945Z", - "start_time": "2025-02-10T14:41:59.064896Z" + "end_time": "2025-02-10T16:23:35.796368Z", + "start_time": "2025-02-10T16:23:35.619909Z" } }, "cell_type": "code", "source": [ - "job = VaspLobsterMaker(LobsterMaker(\n", - " task_document_kwargs={\n", - " \"analyze_outputs\": False\n", + "job = VaspLobsterMaker(lobster_maker=LobsterMaker(\n", + " task_document_kwargs={\n", + " \"calc_quality_kwargs\": {\"potcar_symbols\": [\"Si\"], \"n_bins\": 10},\n", + " \"add_coxxcar_to_task_document\": True,\n", " },\n", " user_lobsterin_settings={\n", " \"COHPstartEnergy\": -5.0,\n", @@ -117,8 +118,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T14:42:02.930099Z", - "start_time": "2025-02-10T14:41:59.254350Z" + "end_time": "2025-02-10T16:23:41.141393Z", + "start_time": "2025-02-10T16:23:35.803301Z" } }, "cell_type": "code", @@ -134,11 +135,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-10 15:41:59,267 INFO Started executing jobs locally\n", - "2025-02-10 15:41:59,276 INFO Starting job - relax 1 (5514118e-8ab4-4ce2-b425-6e9f80df4852)\n", - "2025-02-10 15:41:59,461 INFO Finished job - relax 1 (5514118e-8ab4-4ce2-b425-6e9f80df4852)\n", - "2025-02-10 15:41:59,462 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 15:41:59,463 INFO Starting job - relax 2 (a2305a3b-d986-4e3b-98f8-6a04d8a3c3ef)\n" + "2025-02-10 17:23:35,816 INFO Started executing jobs locally\n", + "2025-02-10 17:23:35,824 INFO Starting job - relax 1 (cf0e7b23-c5f8-4ac3-8a2d-4762f2cf5e38)\n", + "2025-02-10 17:23:36,015 INFO Finished job - relax 1 (cf0e7b23-c5f8-4ac3-8a2d-4762f2cf5e38)\n", + "2025-02-10 17:23:36,015 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 17:23:36,016 INFO Starting job - relax 2 (b8a483e9-ea94-4b3a-9e35-6d54310ccd0f)\n" ] }, { @@ -148,7 +149,7 @@ "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", "\n", " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpl5qlnwgk/job_2025-02-10-14-41-59-463299-39059/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpdzzrblpr/job_2025-02-10-16-23-36-016113-19978/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -156,25 +157,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-10 15:41:59,708 INFO Finished job - relax 2 (a2305a3b-d986-4e3b-98f8-6a04d8a3c3ef)\n", - "2025-02-10 15:41:59,709 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 15:41:59,710 INFO Starting job - get_basis_infos (004fbffb-c50b-4e3d-abfa-e1dd1a5c9b92)\n", - "2025-02-10 15:41:59,759 INFO Finished job - get_basis_infos (004fbffb-c50b-4e3d-abfa-e1dd1a5c9b92)\n", - "2025-02-10 15:41:59,759 INFO Starting job - update_user_incar_settings_maker (d0be51c6-b23a-45cc-ae99-08879eeb30ec)\n", - "2025-02-10 15:41:59,898 INFO Finished job - update_user_incar_settings_maker (d0be51c6-b23a-45cc-ae99-08879eeb30ec)\n", - "2025-02-10 15:41:59,900 INFO Starting job - static (fd86b2d0-7261-4438-a4ec-d1d3cfef55df)\n", - "2025-02-10 15:42:00,126 INFO Finished job - static (fd86b2d0-7261-4438-a4ec-d1d3cfef55df)\n", - "2025-02-10 15:42:00,127 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 15:42:00,128 INFO Starting job - non-scf uniform (4341f72c-7c4c-4648-ae45-04177b4e02f0)\n" + "2025-02-10 17:23:36,276 INFO Finished job - relax 2 (b8a483e9-ea94-4b3a-9e35-6d54310ccd0f)\n", + "2025-02-10 17:23:36,277 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 17:23:36,277 INFO Starting job - get_basis_infos (9ca5ae0e-5139-4396-8578-432628fe0efe)\n", + "2025-02-10 17:23:36,327 INFO Finished job - get_basis_infos (9ca5ae0e-5139-4396-8578-432628fe0efe)\n", + "2025-02-10 17:23:36,328 INFO Starting job - update_user_incar_settings_maker (28e68e55-6a8f-495c-a21e-915c4f257a52)\n", + "2025-02-10 17:23:36,469 INFO Finished job - update_user_incar_settings_maker (28e68e55-6a8f-495c-a21e-915c4f257a52)\n", + "2025-02-10 17:23:36,471 INFO Starting job - static (50c6b38c-3d3b-48e5-ab9f-41d0d2e50487)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpl5qlnwgk/job_2025-02-10-14-41-59-900399-76176/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpl5qlnwgk/job_2025-02-10-14-42-00-128422-46501/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpdzzrblpr/job_2025-02-10-16-23-36-471242-51151/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -182,13 +178,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-10 15:42:00,855 INFO Finished job - non-scf uniform (4341f72c-7c4c-4648-ae45-04177b4e02f0)\n", - "2025-02-10 15:42:00,856 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 15:42:00,857 INFO Starting job - store_inputs (d0be51c6-b23a-45cc-ae99-08879eeb30ec, 2)\n", - "2025-02-10 15:42:00,858 INFO Finished job - store_inputs (d0be51c6-b23a-45cc-ae99-08879eeb30ec, 2)\n", - "2025-02-10 15:42:00,859 INFO Starting job - get_lobster_jobs (8dc8329e-1ac3-4c54-b31c-ba4a0aab13db)\n", - "2025-02-10 15:42:00,900 INFO Finished job - get_lobster_jobs (8dc8329e-1ac3-4c54-b31c-ba4a0aab13db)\n", - "2025-02-10 15:42:00,901 INFO Starting job - lobster_run_0 (5116b80d-e10d-4e8c-82d3-48b19fef0b8e)\n" + "2025-02-10 17:23:36,711 INFO Finished job - static (50c6b38c-3d3b-48e5-ab9f-41d0d2e50487)\n", + "2025-02-10 17:23:36,714 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 17:23:36,715 INFO Starting job - non-scf uniform (3d72d6fa-880f-433d-afbe-b76fb5c669c5)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpdzzrblpr/job_2025-02-10-16-23-36-715060-90187/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 17:23:37,457 INFO Finished job - non-scf uniform (3d72d6fa-880f-433d-afbe-b76fb5c669c5)\n", + "2025-02-10 17:23:37,457 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 17:23:37,458 INFO Starting job - store_inputs (28e68e55-6a8f-495c-a21e-915c4f257a52, 2)\n", + "2025-02-10 17:23:37,459 INFO Finished job - store_inputs (28e68e55-6a8f-495c-a21e-915c4f257a52, 2)\n", + "2025-02-10 17:23:37,459 INFO Starting job - get_lobster_jobs (588c1175-8b0b-4436-af11-2e4137f64a37)\n", + "2025-02-10 17:23:37,498 INFO Finished job - get_lobster_jobs (588c1175-8b0b-4436-af11-2e4137f64a37)\n", + "2025-02-10 17:23:37,500 INFO Starting job - lobster_run_0 (fd14a831-a6f6-4079-a6df-f3325e2480e6)\n", + "{'calc_quality_kwargs': {'potcar_symbols': ['Si'], 'n_bins': 10}, 'add_coxxcar_to_task_document': True}\n", + "True\n" ] }, { @@ -213,80 +228,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-10 15:42:02,296 INFO lobster_run_0 failed with exception:\n", - "Traceback (most recent call last):\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py\", line 117, in _run_job\n", - " response = job.run(store=store)\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py\", line 604, in run\n", - " response = function(*self.function_args, **self.function_kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/jobs.py\", line 110, in make\n", - " return LobsterTaskDocument.from_directory(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/monty/dev.py\", line 216, in decorated\n", - " return _callable(*args, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py\", line 877, in from_directory\n", - " calc_quality_summary = CalcQualitySummary.from_directory(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/monty/dev.py\", line 216, in decorated\n", - " return _callable(*args, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py\", line 600, in from_directory\n", - " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/lobsterpy/cohp/analyze.py\", line 1707, in get_lobster_calc_quality_summary\n", - " dos_vasp.get_dos_fp_similarity(fp_lobster_orb, fp_vasp_orb, metric=\"tanimoto\"),\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/electronic_structure/dos.py\", line 1374, in get_dos_fp_similarity\n", - " rescale = np.linalg.norm(vec1) ** 2 + np.linalg.norm(vec2) ** 2 - np.dot(vec1, vec2)\n", - " ^^^^^^^^^^^^^^^^^^\n", - "ValueError: shapes (2,) and (256,) not aligned: 2 (dim 0) != 256 (dim 0)\n", - "\n", - "2025-02-10 15:42:02,298 INFO Starting job - store_inputs (8dc8329e-1ac3-4c54-b31c-ba4a0aab13db, 2)\n", - "2025-02-10 15:42:02,300 INFO Finished job - store_inputs (8dc8329e-1ac3-4c54-b31c-ba4a0aab13db, 2)\n", - "2025-02-10 15:42:02,301 INFO Starting job - delete_lobster_wavecar (ab8990d8-1d35-40f3-bc5e-903edeccabc5)\n", - "2025-02-10 15:42:02,303 INFO delete_lobster_wavecar failed with exception:\n", - "Traceback (most recent call last):\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py\", line 117, in _run_job\n", - " response = job.run(store=store)\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py\", line 593, in run\n", - " self.resolve_args(store=store)\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py\", line 709, in resolve_args\n", - " resolved_kwargs = find_and_resolve_references(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 473, in find_and_resolve_references\n", - " resolved_references = resolve_references(\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 356, in resolve_references\n", - " cache[uuid][index] = store.get_output(\n", - " ^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/store.py\", line 523, in get_output\n", - " return find_and_resolve_references(\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 473, in find_and_resolve_references\n", - " resolved_references = resolve_references(\n", - " ^^^^^^^^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 361, in resolve_references\n", - " resolved_references[ref] = ref.resolve(\n", - " ^^^^^^^^^^^^\n", - " File \"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/reference.py\", line 166, in resolve\n", - " raise ValueError(\n", - "ValueError: Could not resolve reference - 5116b80d-e10d-4e8c-82d3-48b19fef0b8e not in store or index=None, cache={'5116b80d-e10d-4e8c-82d3-48b19fef0b8e': {}}\n", - "\n" + "{'e_range': [-20, 0], 'dos_comparison': True, 'n_bins': 10, 'bva_comp': True, 'potcar_symbols': ['Si']}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:600: UserWarning: Oxidation states from BVA analyzer cannot be determined. Thus BVA charge comparison will be skipped\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Oxidation states from BVA analyzer cannot be determined. Thus BVA charge comparison will be skipped\n", " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:600: UserWarning: Consider using DOSCAR.LSO.lobster, as non LSO DOS from LOBSTER can have negative DOS values\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Consider using DOSCAR.LSO.lobster, as non LSO DOS from LOBSTER can have negative DOS values\n", " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:600: UserWarning: Minimum energy range requested for DOS comparisons is not available in VASP or LOBSTER calculation. Thus, setting min_e to -5 eV\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Minimum energy range requested for DOS comparisons is not available in VASP or LOBSTER calculation. Thus, setting min_e to -5 eV\n", " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n" ] }, @@ -294,26 +247,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-10 15:42:02,304 INFO Finished executing jobs locally\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "Flow did not finish running successfully", - "output_type": "error", - "traceback": [ - "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[0;31mRuntimeError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[0;32mIn[4], line 3\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m mock_vasp(ref_paths) \u001B[38;5;28;01mas\u001B[39;00m mf:\n\u001B[1;32m 2\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m mock_lobster(ref_paths_lobster) \u001B[38;5;28;01mas\u001B[39;00m mf2:\n\u001B[0;32m----> 3\u001B[0m responses \u001B[38;5;241m=\u001B[39m run_locally(\n\u001B[1;32m 4\u001B[0m job, store\u001B[38;5;241m=\u001B[39mjob_store, create_folders\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, ensure_success\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", - "File \u001B[0;32m~/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/managers/local.py:184\u001B[0m, in \u001B[0;36mrun_locally\u001B[0;34m(flow, log, store, create_folders, root_dir, ensure_success, allow_external_references, raise_immediately)\u001B[0m\n\u001B[1;32m 181\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFinished executing jobs locally\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 183\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m ensure_success \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m finished_successfully:\n\u001B[0;32m--> 184\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mRuntimeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mFlow did not finish running successfully\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m 186\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mdict\u001B[39m(responses)\n", - "\u001B[0;31mRuntimeError\u001B[0m: Flow did not finish running successfully" + "2025-02-10 17:23:40,414 INFO Finished job - lobster_run_0 (fd14a831-a6f6-4079-a6df-f3325e2480e6)\n", + "2025-02-10 17:23:40,416 INFO Starting job - store_inputs (588c1175-8b0b-4436-af11-2e4137f64a37, 2)\n", + "2025-02-10 17:23:40,417 INFO Finished job - store_inputs (588c1175-8b0b-4436-af11-2e4137f64a37, 2)\n", + "2025-02-10 17:23:40,418 INFO Starting job - delete_lobster_wavecar (d1fbf5bd-91e4-454b-af3a-21deff67d474)\n", + "2025-02-10 17:23:41,137 INFO Finished job - delete_lobster_wavecar (d1fbf5bd-91e4-454b-af3a-21deff67d474)\n", + "2025-02-10 17:23:41,137 INFO Finished executing jobs locally\n" ] } ], "execution_count": 4 }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T16:23:41.153939Z", + "start_time": "2025-02-10T16:23:41.152239Z" + } + }, "cell_type": "code", "source": "", "id": "96f7ec288b224e17", @@ -321,7 +272,12 @@ "execution_count": null }, { - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T16:23:41.196023Z", + "start_time": "2025-02-10T16:23:41.194191Z" + } + }, "cell_type": "code", "source": "", "id": "b9975aba14ceaf39", diff --git a/tutorials/lobster_workflow.py b/tutorials/lobster_workflow.py new file mode 100644 index 0000000000..503ebe4552 --- /dev/null +++ b/tutorials/lobster_workflow.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +# coding: utf-8 +#The first lines are needed to ensure that we can mock VASP and LOBSTER runs. + + +from mock_vasp import mock_vasp, TEST_DIR +from mock_lobster import mock_lobster + + +ref_paths = { + "relax 1": "Si_lobster_uniform/relax_1", + "relax 2": "Si_lobster_uniform/relax_2", + "static": "Si_lobster_uniform/static", + "non-scf uniform": "Si_lobster_uniform/non-scf_uniform", + } +ref_paths_lobster = { + "lobster_run_0": "Si_lobster/lobster_0", + } + + +# We first load a structure that we want to analyze with bonding analysis. + + + +from jobflow import JobStore, run_locally +from maggma.stores import MemoryStore +from pymatgen.core import Structure + +from atomate2.vasp.flows.lobster import VaspLobsterMaker, LobsterMaker +from atomate2.vasp.powerups import update_user_incar_settings + +job_store = JobStore(MemoryStore(), additional_stores={"data": MemoryStore()}) +si_structure = Structure.from_file(TEST_DIR / "structures" / "Si.cif") + + +# Then, we initialize a workflow: + + +job = VaspLobsterMaker(lobster_maker=LobsterMaker( + task_document_kwargs={ + "analyze_outputs":False + }, + user_lobsterin_settings={ + "COHPstartEnergy": -5.0, + "COHPEndEnergy": 5.0, + "cohpGenerator": "from 0.1 to 3.0 orbitalwise", + }, + ), + delete_wavecars=True, + ).make(si_structure) +job = update_user_incar_settings(job, {"NPAR": 4}) + +# run the flow or job and ensure that it finished running successfully + + + +# We then run this workflow locally to show-case the capabilities. In real-life, you would omit the `mock*` parts. + + +with mock_vasp(ref_paths) as mf: + with mock_lobster(ref_paths_lobster) as mf2: + responses = run_locally( + job, store=job_store, create_folders=True, ensure_success=True) + diff --git a/tutorials/mock_lobster.py b/tutorials/mock_lobster.py index bbd5d3d5fd..6d907e2e28 100644 --- a/tutorials/mock_lobster.py +++ b/tutorials/mock_lobster.py @@ -28,12 +28,13 @@ def mock_lobster(ref_paths: dict) -> Generator: function: A function that mocks calls to VASP. """ for mf in monkeypatch_lobster(MonkeyPatch(), TEST_DIR / "lobster"): - fake_run_lobster_kwargs = {k: {"lobsterin_settings": ()} for k in ref_paths} + fake_run_lobster_kwargs = {k: {"check_lobster_inputs": ()} for k in ref_paths} + old_cwd = os.getcwd() new_path = tempfile.mkdtemp() os.chdir(new_path) try: - yield mf(ref_paths, fake_run_lobster_kwargs) + yield mf(ref_paths, fake_run_lobster_kwargs=fake_run_lobster_kwargs) finally: os.chdir(old_cwd) #shutil.rmtree(new_path) From cd2c59cd1b49b2f8c36da8a18cda8e843a0d4128 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 17:28:47 +0100 Subject: [PATCH 18/61] add plot --- tutorials/lobster_workflow.ipynb | 74 ++++++++++++++++++++++++------- tutorials/plots_all_bonds0.pdf | Bin 0 -> 13498 bytes 2 files changed, 59 insertions(+), 15 deletions(-) create mode 100644 tutorials/plots_all_bonds0.pdf diff --git a/tutorials/lobster_workflow.ipynb b/tutorials/lobster_workflow.ipynb index afff3c1efe..c06906791b 100644 --- a/tutorials/lobster_workflow.ipynb +++ b/tutorials/lobster_workflow.ipynb @@ -258,31 +258,75 @@ ], "execution_count": 4 }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We can also analyze the data from the database", + "id": "d9d12a73fba583e0" + }, { "metadata": { "ExecuteTime": { - "end_time": "2025-02-10T16:23:41.153939Z", - "start_time": "2025-02-10T16:23:41.152239Z" + "end_time": "2025-02-10T16:28:27.961017Z", + "start_time": "2025-02-10T16:28:27.720019Z" } }, "cell_type": "code", - "source": "", - "id": "96f7ec288b224e17", - "outputs": [], - "execution_count": null + "source": [ + "\n", + "from jobflow import SETTINGS\n", + "from pymatgen.electronic_structure.cohp import Cohp\n", + "from pymatgen.electronic_structure.plotter import CohpPlotter\n", + "\n", + "store = job_store\n", + "\n", + "result = store.query_one(\n", + " {\"name\": \"lobster_run_0\"},\n", + " properties=[\n", + " \"output.lobsterpy_data.cohp_plot_data\",\n", + " \"output.lobsterpy_data_cation_anion.cohp_plot_data\",\n", + " ],\n", + " load=True,\n", + ")\n", + "\n", + "for number, (key, cohp) in enumerate(\n", + " result[\"output\"][\"lobsterpy_data\"][\"cohp_plot_data\"][\"data\"].items()\n", + "):\n", + " plotter = CohpPlotter()\n", + " cohp = Cohp.from_dict(cohp)\n", + " plotter.add_cohp(key, cohp)\n", + " plotter.save_plot(f\"plots_all_bonds{number}.pdf\")\n", + "\n", + "for number, (key, cohp) in enumerate(\n", + " result[\"output\"][\"lobsterpy_data_cation_anion\"][\"cohp_plot_data\"][\"data\"].items()\n", + "):\n", + " plotter = CohpPlotter()\n", + " cohp = Cohp.from_dict(cohp)\n", + " plotter.add_cohp(key, cohp)\n", + " plotter.show()" + ], + "id": "2f60e44da88abca4", + "outputs": [ + { + "data": { + "text/plain": [ + "
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e73e6ddf363b6787c60458ab574bb786936c8dcc Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 17:36:55 +0100 Subject: [PATCH 19/61] fix linting --- src/atomate2/lobster/schemas.py | 4 +- src/atomate2/utils/testing/lobster.py | 4 +- tests/vasp/lobster/conftest.py | 15 +- tutorials/lobster_workflow.ipynb | 328 ++++++-------------------- tutorials/lobster_workflow.py | 51 ++-- tutorials/mock_lobster.py | 4 +- tutorials/mock_vasp.py | 1 - 7 files changed, 111 insertions(+), 296 deletions(-) diff --git a/src/atomate2/lobster/schemas.py b/src/atomate2/lobster/schemas.py index d89f2d5b3a..a5e6faa511 100644 --- a/src/atomate2/lobster/schemas.py +++ b/src/atomate2/lobster/schemas.py @@ -973,7 +973,9 @@ def from_directory( if describe_ionic is not None else None, strongest_bonds_cation_anion=sb_ionic, - calc_quality_summary=calc_quality_summary if calc_quality_summary is not None else None, + calc_quality_summary=calc_quality_summary + if calc_quality_summary is not None + else None, calc_quality_text=" ".join(calc_quality_text) if calc_quality_text is not None else None, diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py index 6e6e51da3e..1dd056028e 100644 --- a/src/atomate2/utils/testing/lobster.py +++ b/src/atomate2/utils/testing/lobster.py @@ -27,12 +27,11 @@ def lobster_test_dir(test_dir): return test_dir / "lobster" - def monkeypatch_lobster(monkeypatch, lobster_test_dir): """ This is provided as a generator and can be used as by conextmanagers and pytest.fixture ("mock_lobster"). - + It works by monkeypatching (replacing) calls to run_lobster that will work when the lobster executables are not present. @@ -98,6 +97,7 @@ def fake_run_lobster( ): """ Emulate running LOBSTER and validate LOBSTER input files. + Parameters ---------- ref_path diff --git a/tests/vasp/lobster/conftest.py b/tests/vasp/lobster/conftest.py index 9839df85f8..cf5a74a2a0 100644 --- a/tests/vasp/lobster/conftest.py +++ b/tests/vasp/lobster/conftest.py @@ -1,17 +1,14 @@ -from atomate2.utils.testing.lobster import * -from pytest import MonkeyPatch - -from typing import TYPE_CHECKING, Any, Final +from collections.abc import Callable, Generator +from typing import Any -from collections.abc import Generator, Callable +from pytest import MonkeyPatch +from atomate2.utils.testing.lobster import * @pytest.fixture def mock_lobster( monkeypatch: MonkeyPatch, lobster_test_dir: Path ) -> Generator[Callable[[Any, Any], Any], None, None]: - """ - - """ - yield from monkeypatch_lobster(monkeypatch, lobster_test_dir) \ No newline at end of file + """ """ + yield from monkeypatch_lobster(monkeypatch, lobster_test_dir) diff --git a/tutorials/lobster_workflow.ipynb b/tutorials/lobster_workflow.ipynb index c06906791b..23a3723b19 100644 --- a/tutorials/lobster_workflow.ipynb +++ b/tutorials/lobster_workflow.ipynb @@ -1,280 +1,131 @@ { "cells": [ { - "metadata": {}, "cell_type": "raw", - "source": "The first lines are needed to ensure that we can mock VASP and LOBSTER runs.", - "id": "709fcbd6cc50b3a6" + "id": "0", + "metadata": {}, + "source": [ + "The first lines are needed to ensure that we can mock VASP and LOBSTER runs." + ] }, { - "metadata": { - "collapsed": true, - "ExecuteTime": { - "end_time": "2025-02-10T16:23:35.273002Z", - "start_time": "2025-02-10T16:23:31.749277Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], "source": [ - "from mock_vasp import mock_vasp, TEST_DIR\n", "from mock_lobster import mock_lobster\n", - "\n", + "from mock_vasp import TEST_DIR, mock_vasp\n", "\n", "ref_paths = {\n", - " \"relax 1\": \"Si_lobster_uniform/relax_1\",\n", - " \"relax 2\": \"Si_lobster_uniform/relax_2\",\n", - " \"static\": \"Si_lobster_uniform/static\",\n", - " \"non-scf uniform\": \"Si_lobster_uniform/non-scf_uniform\",\n", - " }\n", + " \"relax 1\": \"Si_lobster_uniform/relax_1\",\n", + " \"relax 2\": \"Si_lobster_uniform/relax_2\",\n", + " \"static\": \"Si_lobster_uniform/static\",\n", + " \"non-scf uniform\": \"Si_lobster_uniform/non-scf_uniform\",\n", + "}\n", "ref_paths_lobster = {\n", - " \"lobster_run_0\": \"Si_lobster/lobster_0\",\n", - " }" - ], - "id": "initial_id", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "execution_count": 1 + " \"lobster_run_0\": \"Si_lobster/lobster_0\",\n", + "}" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "We first load a structure that we want to analyze with bonding analysis.", - "id": "87fa2e22363a6b0d" + "id": "2", + "metadata": {}, + "source": [ + "We first load a structure that we want to analyze with bonding analysis." + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T16:23:35.505765Z", - "start_time": "2025-02-10T16:23:35.277556Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": {}, + "outputs": [], "source": [ "from jobflow import JobStore, run_locally\n", "from maggma.stores import MemoryStore\n", "from pymatgen.core import Structure\n", "\n", - "from atomate2.vasp.flows.lobster import VaspLobsterMaker, LobsterMaker\n", + "from atomate2.vasp.flows.lobster import LobsterMaker, VaspLobsterMaker\n", "from atomate2.vasp.powerups import update_user_incar_settings\n", "\n", "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")" - ], - "id": "edefa4c433c5fed1", - "outputs": [], - "execution_count": 2 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Then, we initialize a workflow:", - "id": "168042f064c6aaa8" + "id": "4", + "metadata": {}, + "source": [ + "Then, we initialize a workflow:" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T16:23:35.796368Z", - "start_time": "2025-02-10T16:23:35.619909Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], "source": [ - "job = VaspLobsterMaker(lobster_maker=LobsterMaker(\n", - " task_document_kwargs={\n", - " \"calc_quality_kwargs\": {\"potcar_symbols\": [\"Si\"], \"n_bins\": 10},\n", - " \"add_coxxcar_to_task_document\": True,\n", - " },\n", - " user_lobsterin_settings={\n", - " \"COHPstartEnergy\": -5.0,\n", - " \"COHPEndEnergy\": 5.0,\n", - " \"cohpGenerator\": \"from 0.1 to 3.0 orbitalwise\",\n", - " },\n", - " ),\n", - " delete_wavecars=True,\n", - " ).make(si_structure)\n", + "job = VaspLobsterMaker(\n", + " lobster_maker=LobsterMaker(\n", + " task_document_kwargs={\n", + " \"calc_quality_kwargs\": {\"potcar_symbols\": [\"Si\"], \"n_bins\": 10},\n", + " \"add_coxxcar_to_task_document\": True,\n", + " },\n", + " user_lobsterin_settings={\n", + " \"COHPstartEnergy\": -5.0,\n", + " \"COHPEndEnergy\": 5.0,\n", + " \"cohpGenerator\": \"from 0.1 to 3.0 orbitalwise\",\n", + " },\n", + " ),\n", + " delete_wavecars=True,\n", + ").make(si_structure)\n", "job = update_user_incar_settings(job, {\"NPAR\": 4})\n", "\n", - "# run the flow or job and ensure that it finished running successfully\n", - "\n" - ], - "id": "423ed97c01c1eb4a", - "outputs": [], - "execution_count": 3 + "# run the flow or job and ensure that it finished running successfully" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "We then run this workflow locally to show-case the capabilities. In real-life, you would omit the `mock*` parts.", - "id": "59e2c8121196a5a0" + "id": "6", + "metadata": {}, + "source": [ + "We then run this workflow locally to show-case the capabilities. In real-life, you would omit the `mock*` parts." + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T16:23:41.141393Z", - "start_time": "2025-02-10T16:23:35.803301Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], "source": [ "with mock_vasp(ref_paths) as mf:\n", " with mock_lobster(ref_paths_lobster) as mf2:\n", " responses = run_locally(\n", - " job, store=job_store, create_folders=True, ensure_success=True)" - ], - "id": "81ea06138ad6e7dc", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 17:23:35,816 INFO Started executing jobs locally\n", - "2025-02-10 17:23:35,824 INFO Starting job - relax 1 (cf0e7b23-c5f8-4ac3-8a2d-4762f2cf5e38)\n", - "2025-02-10 17:23:36,015 INFO Finished job - relax 1 (cf0e7b23-c5f8-4ac3-8a2d-4762f2cf5e38)\n", - "2025-02-10 17:23:36,015 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 17:23:36,016 INFO Starting job - relax 2 (b8a483e9-ea94-4b3a-9e35-6d54310ccd0f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpdzzrblpr/job_2025-02-10-16-23-36-016113-19978/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 17:23:36,276 INFO Finished job - relax 2 (b8a483e9-ea94-4b3a-9e35-6d54310ccd0f)\n", - "2025-02-10 17:23:36,277 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 17:23:36,277 INFO Starting job - get_basis_infos (9ca5ae0e-5139-4396-8578-432628fe0efe)\n", - "2025-02-10 17:23:36,327 INFO Finished job - get_basis_infos (9ca5ae0e-5139-4396-8578-432628fe0efe)\n", - "2025-02-10 17:23:36,328 INFO Starting job - update_user_incar_settings_maker (28e68e55-6a8f-495c-a21e-915c4f257a52)\n", - "2025-02-10 17:23:36,469 INFO Finished job - update_user_incar_settings_maker (28e68e55-6a8f-495c-a21e-915c4f257a52)\n", - "2025-02-10 17:23:36,471 INFO Starting job - static (50c6b38c-3d3b-48e5-ab9f-41d0d2e50487)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpdzzrblpr/job_2025-02-10-16-23-36-471242-51151/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 17:23:36,711 INFO Finished job - static (50c6b38c-3d3b-48e5-ab9f-41d0d2e50487)\n", - "2025-02-10 17:23:36,714 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 17:23:36,715 INFO Starting job - non-scf uniform (3d72d6fa-880f-433d-afbe-b76fb5c669c5)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpdzzrblpr/job_2025-02-10-16-23-36-715060-90187/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 17:23:37,457 INFO Finished job - non-scf uniform (3d72d6fa-880f-433d-afbe-b76fb5c669c5)\n", - "2025-02-10 17:23:37,457 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 17:23:37,458 INFO Starting job - store_inputs (28e68e55-6a8f-495c-a21e-915c4f257a52, 2)\n", - "2025-02-10 17:23:37,459 INFO Finished job - store_inputs (28e68e55-6a8f-495c-a21e-915c4f257a52, 2)\n", - "2025-02-10 17:23:37,459 INFO Starting job - get_lobster_jobs (588c1175-8b0b-4436-af11-2e4137f64a37)\n", - "2025-02-10 17:23:37,498 INFO Finished job - get_lobster_jobs (588c1175-8b0b-4436-af11-2e4137f64a37)\n", - "2025-02-10 17:23:37,500 INFO Starting job - lobster_run_0 (fd14a831-a6f6-4079-a6df-f3325e2480e6)\n", - "{'calc_quality_kwargs': {'potcar_symbols': ['Si'], 'n_bins': 10}, 'add_coxxcar_to_task_document': True}\n", - "True\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/lobster/inputs.py:699: UserWarning: Always check and test the provided basis functions. The spilling of your Lobster calculation might help\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/lobsterpy/cohp/analyze.py:870: UserWarning: The bonding, antibonding integral/percent values are numerical estimate. These values are sensitive to COHPstartEnergy parameter. If COHPstartEnergy value does not cover entire range of VASP calculations then absolute value of ICOHP_sum might not be equivalent to (bonding- antibonding) integral values.\n", - " ) = self._integrate_antbdstates_below_efermi(cohp, start=self.start)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'e_range': [-20, 0], 'dos_comparison': True, 'n_bins': 10, 'bva_comp': True, 'potcar_symbols': ['Si']}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Oxidation states from BVA analyzer cannot be determined. Thus BVA charge comparison will be skipped\n", - " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Consider using DOSCAR.LSO.lobster, as non LSO DOS from LOBSTER can have negative DOS values\n", - " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Minimum energy range requested for DOS comparisons is not available in VASP or LOBSTER calculation. Thus, setting min_e to -5 eV\n", - " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 17:23:40,414 INFO Finished job - lobster_run_0 (fd14a831-a6f6-4079-a6df-f3325e2480e6)\n", - "2025-02-10 17:23:40,416 INFO Starting job - store_inputs (588c1175-8b0b-4436-af11-2e4137f64a37, 2)\n", - "2025-02-10 17:23:40,417 INFO Finished job - store_inputs (588c1175-8b0b-4436-af11-2e4137f64a37, 2)\n", - "2025-02-10 17:23:40,418 INFO Starting job - delete_lobster_wavecar (d1fbf5bd-91e4-454b-af3a-21deff67d474)\n", - "2025-02-10 17:23:41,137 INFO Finished job - delete_lobster_wavecar (d1fbf5bd-91e4-454b-af3a-21deff67d474)\n", - "2025-02-10 17:23:41,137 INFO Finished executing jobs locally\n" - ] - } - ], - "execution_count": 4 + " job, store=job_store, create_folders=True, ensure_success=True\n", + " )" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "We can also analyze the data from the database", - "id": "d9d12a73fba583e0" + "id": "8", + "metadata": {}, + "source": [ + "We can also analyze the data from the database" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T16:28:27.961017Z", - "start_time": "2025-02-10T16:28:27.720019Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], "source": [ - "\n", - "from jobflow import SETTINGS\n", "from pymatgen.electronic_structure.cohp import Cohp\n", "from pymatgen.electronic_structure.plotter import CohpPlotter\n", "\n", @@ -304,37 +155,10 @@ " cohp = Cohp.from_dict(cohp)\n", " plotter.add_cohp(key, cohp)\n", " plotter.show()" - ], - "id": "2f60e44da88abca4", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 6 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "c0ab5a735c8f308f" + ] } ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", diff --git a/tutorials/lobster_workflow.py b/tutorials/lobster_workflow.py index 503ebe4552..c5d5765ee8 100644 --- a/tutorials/lobster_workflow.py +++ b/tutorials/lobster_workflow.py @@ -1,32 +1,29 @@ #!/usr/bin/env python -# coding: utf-8 -#The first lines are needed to ensure that we can mock VASP and LOBSTER runs. +# The first lines are needed to ensure that we can mock VASP and LOBSTER runs. -from mock_vasp import mock_vasp, TEST_DIR from mock_lobster import mock_lobster - +from mock_vasp import TEST_DIR, mock_vasp ref_paths = { - "relax 1": "Si_lobster_uniform/relax_1", - "relax 2": "Si_lobster_uniform/relax_2", - "static": "Si_lobster_uniform/static", - "non-scf uniform": "Si_lobster_uniform/non-scf_uniform", - } + "relax 1": "Si_lobster_uniform/relax_1", + "relax 2": "Si_lobster_uniform/relax_2", + "static": "Si_lobster_uniform/static", + "non-scf uniform": "Si_lobster_uniform/non-scf_uniform", +} ref_paths_lobster = { - "lobster_run_0": "Si_lobster/lobster_0", - } + "lobster_run_0": "Si_lobster/lobster_0", +} # We first load a structure that we want to analyze with bonding analysis. - from jobflow import JobStore, run_locally from maggma.stores import MemoryStore from pymatgen.core import Structure -from atomate2.vasp.flows.lobster import VaspLobsterMaker, LobsterMaker +from atomate2.vasp.flows.lobster import LobsterMaker, VaspLobsterMaker from atomate2.vasp.powerups import update_user_incar_settings job_store = JobStore(MemoryStore(), additional_stores={"data": MemoryStore()}) @@ -36,29 +33,27 @@ # Then, we initialize a workflow: -job = VaspLobsterMaker(lobster_maker=LobsterMaker( - task_document_kwargs={ - "analyze_outputs":False - }, - user_lobsterin_settings={ - "COHPstartEnergy": -5.0, - "COHPEndEnergy": 5.0, - "cohpGenerator": "from 0.1 to 3.0 orbitalwise", - }, - ), - delete_wavecars=True, - ).make(si_structure) +job = VaspLobsterMaker( + lobster_maker=LobsterMaker( + task_document_kwargs={"analyze_outputs": False}, + user_lobsterin_settings={ + "COHPstartEnergy": -5.0, + "COHPEndEnergy": 5.0, + "cohpGenerator": "from 0.1 to 3.0 orbitalwise", + }, + ), + delete_wavecars=True, +).make(si_structure) job = update_user_incar_settings(job, {"NPAR": 4}) # run the flow or job and ensure that it finished running successfully - # We then run this workflow locally to show-case the capabilities. In real-life, you would omit the `mock*` parts. with mock_vasp(ref_paths) as mf: with mock_lobster(ref_paths_lobster) as mf2: responses = run_locally( - job, store=job_store, create_folders=True, ensure_success=True) - + job, store=job_store, create_folders=True, ensure_success=True + ) diff --git a/tutorials/mock_lobster.py b/tutorials/mock_lobster.py index 6d907e2e28..4472ade933 100644 --- a/tutorials/mock_lobster.py +++ b/tutorials/mock_lobster.py @@ -2,7 +2,6 @@ import contextlib import os -import shutil import tempfile from collections.abc import Generator from pathlib import Path @@ -37,5 +36,4 @@ def mock_lobster(ref_paths: dict) -> Generator: yield mf(ref_paths, fake_run_lobster_kwargs=fake_run_lobster_kwargs) finally: os.chdir(old_cwd) - #shutil.rmtree(new_path) - + # shutil.rmtree(new_path) diff --git a/tutorials/mock_vasp.py b/tutorials/mock_vasp.py index 3e1153b8cf..cc99676163 100644 --- a/tutorials/mock_vasp.py +++ b/tutorials/mock_vasp.py @@ -37,4 +37,3 @@ def mock_vasp(ref_paths: dict) -> Generator: finally: os.chdir(old_cwd) shutil.rmtree(new_path) - From b69a0b6de479cc510991f2383dbc8edddc2b065c Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 18:08:58 +0100 Subject: [PATCH 20/61] fix linting --- src/atomate2/utils/testing/lobster.py | 18 +++++++++++------- tutorials/lobster_workflow.ipynb | 2 +- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py index 1dd056028e..598cbffcfe 100644 --- a/src/atomate2/utils/testing/lobster.py +++ b/src/atomate2/utils/testing/lobster.py @@ -1,9 +1,11 @@ +"""Utilities for testing LOBSTER calculations.""" + from __future__ import annotations import logging import shutil from pathlib import Path -from typing import TYPE_CHECKING, Literal +from typing import TYPE_CHECKING import pytest from pymatgen.io.lobster import Lobsterin @@ -18,8 +20,8 @@ _LFILES = "lobsterin" _DFT_FILES = ("WAVECAR", "POSCAR", "INCAR", "KPOINTS", "POTCAR") -_LOBS_REF_PATHS = {} -_FAKE_RUN_LOBSTER_KWARGS = {} +_LOBS_REF_PATHS: dict[str, str | Path] = {} +_FAKE_RUN_LOBSTER_KWARGS: dict[str, dict[str, Sequence]] = {} @pytest.fixture(scope="session") @@ -91,8 +93,8 @@ def _run(ref_paths, fake_run_lobster_kwargs): def fake_run_lobster( ref_path: str | Path, - check_lobster_inputs: Sequence[Literal["lobsterin"]] = _LFILES, - check_dft_inputs: Sequence[Literal["WAVECAR", "POSCAR"]] = _DFT_FILES, + check_lobster_inputs: Sequence[str] = _LFILES, + check_dft_inputs: Sequence[str] = _DFT_FILES, lobsterin_settings: Sequence[str] = (), ): """ @@ -105,6 +107,8 @@ def fake_run_lobster( and output files in the folder named 'outputs'. check_lobster_inputs A list of lobster input files to check. Supported options are "lobsterin.gz". + check_dft_inputs + A list of VASP files that need to be copied to start the LOBSTER runs. lobsterin_settings A list of LOBSTER settings to check. """ @@ -131,7 +135,7 @@ def verify_inputs(ref_path: str | Path, lobsterin_settings: Sequence[str]): user = Lobsterin.from_file("lobsterin") # Check lobsterin - ref = Lobsterin.from_file(ref_path / "inputs" / "lobsterin") + ref = Lobsterin.from_file(Path(ref_path) / "inputs" / "lobsterin") for key in lobsterin_settings: if user.get(key) != ref.get(key): @@ -139,7 +143,7 @@ def verify_inputs(ref_path: str | Path, lobsterin_settings: Sequence[str]): def copy_lobster_outputs(ref_path: str | Path): - output_path = ref_path / "outputs" + output_path = Path(ref_path) / "outputs" for output_file in output_path.iterdir(): if output_file.is_file(): shutil.copy(output_file, ".") diff --git a/tutorials/lobster_workflow.ipynb b/tutorials/lobster_workflow.ipynb index 23a3723b19..a2f4db6c10 100644 --- a/tutorials/lobster_workflow.ipynb +++ b/tutorials/lobster_workflow.ipynb @@ -5,7 +5,7 @@ "id": "0", "metadata": {}, "source": [ - "The first lines are needed to ensure that we can mock VASP and LOBSTER runs." + "The first lines are needed to ensure that we can mock VASP and LOBSTER runs. The test files here might not belong to the same calculation but are good enough for testing." ] }, { From 17c5394b27e1434f459d20d5e10bbe3f9d813145 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 18:56:07 +0100 Subject: [PATCH 21/61] more type hints --- src/atomate2/utils/testing/lobster.py | 91 +++++++++++++++------------ tutorials/lobster_workflow.py | 59 ----------------- 2 files changed, 50 insertions(+), 100 deletions(-) delete mode 100644 tutorials/lobster_workflow.py diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py index 598cbffcfe..f2d4a93675 100644 --- a/src/atomate2/utils/testing/lobster.py +++ b/src/atomate2/utils/testing/lobster.py @@ -29,46 +29,41 @@ def lobster_test_dir(test_dir): return test_dir / "lobster" -def monkeypatch_lobster(monkeypatch, lobster_test_dir): - """ - This is provided as a generator and can be used as by conextmanagers and - pytest.fixture ("mock_lobster"). +def monkeypatch_lobster(monkeypatch: pytest.MonkeyPatch, lobster_test_dir: Path): + """Monkeypatch LOBSTER run calls for testing purposes. + + This generator can be used as a context manager or pytest fixture ("mock_lobster"). + It replaces calls to run_lobster with a mock function that copies reference files + instead of running LOBSTER. - It works by monkeypatching (replacing) calls to run_lobster that will - work when the lobster executables - are not present. The primary idea is that instead of running LOBSTER to generate the output files, - reference files will be copied into the directory instead. As we do not want to - test whether LOBSTER is giving the correct output rather that the calculation inputs - are generated correctly and that the outputs are parsed properly, this should be - sufficient for our needs. - To use the fixture successfully, the following steps must be followed: - 1. "mock_lobster" should be included as an argument to any test that would - like to use its functionally. - 2. For each job in your workflow, you should prepare a reference directory - containing two folders "inputs" (containing the reference input files - expected to be produced by Lobsterin.standard_calculations_from_vasp_files - and "outputs" (containing the expected - output files to be produced by run_lobster). These files should reside in a - subdirectory of "tests/test_data/lobster". - 3. Create a dictionary mapping each job name to its reference directory. - Note that you should supply the reference directory relative to the - "tests/test_data/lobster" folder. For example, if your calculation - has one job named "lobster_run_0" and the reference files are present in - "tests/test_data/lobster/Si_lobster_run_0", the dictionary - would look like: ``{"lobster_run_0": "Si_lobster_run_0"}``. - 4. Optional: create a dictionary mapping each job name to custom - keyword arguments that will be supplied to fake_run_lobster. - This way you can configure which lobsterin settings are expected for each job. - For example, if your calculation has one job named "lobster_run_0" - and you wish to validate that "basisfunctions" is set correctly - in the lobsterin, your dictionary would look like - ``{"lobster_run_0": {"lobsterin_settings": {"basisfunctions": Ba 5p 5s 6s}}``. - 5. Inside the test function, call `mock_lobster(ref_paths, fake_lobster_kwargs)`, - where ref_paths is the dictionary created in step 3 - and fake_lobster_kwargs is the - dictionary created in step 4. - 6. Run your lobster job after calling `mock_lobster`. + reference files will be copied into the directory instead. This ensures that the + calculation inputs are generated correctly and that the outputs are parsed properly. + + To use the fixture successfully, follow these steps: + 1. Include "mock_lobster" as an argument to any test that would like to use its functionality. + 2. For each job in your workflow, prepare a reference directory containing two folders: + "inputs" (containing the reference input files expected to be produced by + Lobsterin.standard_calculations_from_vasp_files) and "outputs" (containing the expected + output files to be produced by run_lobster). These files should reside in a subdirectory + of "tests/test_data/lobster". + 3. Create a dictionary mapping each job name to its reference directory. Note that you should + supply the reference directory relative to the "tests/test_data/lobster" folder. For example, + if your calculation has one job named "lobster_run_0" and the reference files are present in + "tests/test_data/lobster/Si_lobster_run_0", the dictionary would look like: + {"lobster_run_0": "Si_lobster_run_0"}. + 4. Optionally, create a dictionary mapping each job name to custom keyword arguments that will be + supplied to fake_run_lobster. This way you can configure which lobsterin settings are expected + for each job. For example, if your calculation has one job named "lobster_run_0" and you wish + to validate that "basisfunctions" is set correctly in the lobsterin, your dictionary would look like: + {"lobster_run_0": {"lobsterin_settings": {"basisfunctions": Ba 5p 5s 6s}}. + 5. Inside the test function, call `mock_lobster(ref_paths, fake_lobster_kwargs)`, where ref_paths is the + dictionary created in step 3 and fake_lobster_kwargs is the dictionary created in step 4. + 6. Run your LOBSTER job after calling `mock_lobster`. + + Args: + monkeypatch (pytest.MonkeyPatch): The pytest monkeypatch fixture. + lobster_test_dir (Path): The directory containing reference files for LOBSTER tests. """ def mock_run_lobster(*args, **kwargs): @@ -81,7 +76,10 @@ def mock_run_lobster(*args, **kwargs): monkeypatch.setattr(atomate2.lobster.run, "run_lobster", mock_run_lobster) monkeypatch.setattr(atomate2.lobster.jobs, "run_lobster", mock_run_lobster) - def _run(ref_paths, fake_run_lobster_kwargs): + def _run( + ref_paths: dict[str, str | Path], + fake_run_lobster_kwargs: dict[str, dict[str, Sequence]], + ): _LOBS_REF_PATHS.update(ref_paths) _FAKE_RUN_LOBSTER_KWARGS.update(fake_run_lobster_kwargs) @@ -131,7 +129,13 @@ def fake_run_lobster( logger.info("ran fake LOBSTER, generated outputs") -def verify_inputs(ref_path: str | Path, lobsterin_settings: Sequence[str]): +def verify_inputs(ref_path: str | Path, lobsterin_settings: Sequence[str]) -> None: + """Verify LOBSTER input files against reference settings. + + Args: + ref_path (str | Path): Path to the reference directory containing input files. + lobsterin_settings (Sequence[str]): A list of LOBSTER settings to check. + """ user = Lobsterin.from_file("lobsterin") # Check lobsterin @@ -142,7 +146,12 @@ def verify_inputs(ref_path: str | Path, lobsterin_settings: Sequence[str]): raise ValueError(f"lobsterin value of {key} is inconsistent!") -def copy_lobster_outputs(ref_path: str | Path): +def copy_lobster_outputs(ref_path: str | Path) -> None: + """Copy LOBSTER output files from the reference directory to the current directory. + + Args: + ref_path (str | Path): Path to the reference directory containing output files. + """ output_path = Path(ref_path) / "outputs" for output_file in output_path.iterdir(): if output_file.is_file(): diff --git a/tutorials/lobster_workflow.py b/tutorials/lobster_workflow.py deleted file mode 100644 index c5d5765ee8..0000000000 --- a/tutorials/lobster_workflow.py +++ /dev/null @@ -1,59 +0,0 @@ -#!/usr/bin/env python -# The first lines are needed to ensure that we can mock VASP and LOBSTER runs. - - -from mock_lobster import mock_lobster -from mock_vasp import TEST_DIR, mock_vasp - -ref_paths = { - "relax 1": "Si_lobster_uniform/relax_1", - "relax 2": "Si_lobster_uniform/relax_2", - "static": "Si_lobster_uniform/static", - "non-scf uniform": "Si_lobster_uniform/non-scf_uniform", -} -ref_paths_lobster = { - "lobster_run_0": "Si_lobster/lobster_0", -} - - -# We first load a structure that we want to analyze with bonding analysis. - - -from jobflow import JobStore, run_locally -from maggma.stores import MemoryStore -from pymatgen.core import Structure - -from atomate2.vasp.flows.lobster import LobsterMaker, VaspLobsterMaker -from atomate2.vasp.powerups import update_user_incar_settings - -job_store = JobStore(MemoryStore(), additional_stores={"data": MemoryStore()}) -si_structure = Structure.from_file(TEST_DIR / "structures" / "Si.cif") - - -# Then, we initialize a workflow: - - -job = VaspLobsterMaker( - lobster_maker=LobsterMaker( - task_document_kwargs={"analyze_outputs": False}, - user_lobsterin_settings={ - "COHPstartEnergy": -5.0, - "COHPEndEnergy": 5.0, - "cohpGenerator": "from 0.1 to 3.0 orbitalwise", - }, - ), - delete_wavecars=True, -).make(si_structure) -job = update_user_incar_settings(job, {"NPAR": 4}) - -# run the flow or job and ensure that it finished running successfully - - -# We then run this workflow locally to show-case the capabilities. In real-life, you would omit the `mock*` parts. - - -with mock_vasp(ref_paths) as mf: - with mock_lobster(ref_paths_lobster) as mf2: - responses = run_locally( - job, store=job_store, create_folders=True, ensure_success=True - ) From 612ab6f2bca359f357f4035a2916ff261ed4b44c Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 19:14:31 +0100 Subject: [PATCH 22/61] fix linting --- src/atomate2/utils/testing/lobster.py | 4 +- tests/conftest.py | 2 +- tests/vasp/lobster/conftest.py | 4 +- tutorials/force_fields/__init__.py | 1 + tutorials/lobster_workflow.ipynb | 210 +++++++++++++++++++++++--- tutorials/plots_all_bonds0.pdf | Bin 13498 -> 13498 bytes 6 files changed, 195 insertions(+), 26 deletions(-) diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py index f2d4a93675..8f26b7731f 100644 --- a/src/atomate2/utils/testing/lobster.py +++ b/src/atomate2/utils/testing/lobster.py @@ -66,7 +66,7 @@ def monkeypatch_lobster(monkeypatch: pytest.MonkeyPatch, lobster_test_dir: Path) lobster_test_dir (Path): The directory containing reference files for LOBSTER tests. """ - def mock_run_lobster(*args, **kwargs): + def mock_run_lobster(*args, **kwargs) -> None: from jobflow import CURRENT_JOB name = CURRENT_JOB.job.name @@ -79,7 +79,7 @@ def mock_run_lobster(*args, **kwargs): def _run( ref_paths: dict[str, str | Path], fake_run_lobster_kwargs: dict[str, dict[str, Sequence]], - ): + ) -> None: _LOBS_REF_PATHS.update(ref_paths) _FAKE_RUN_LOBSTER_KWARGS.update(fake_run_lobster_kwargs) diff --git a/tests/conftest.py b/tests/conftest.py index 1edbd41ce8..7def0a6906 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -64,7 +64,7 @@ def tmp_dir(): @pytest.fixture(scope="session") def debug_mode(): - return True + return False @pytest.fixture(scope="session") diff --git a/tests/vasp/lobster/conftest.py b/tests/vasp/lobster/conftest.py index cf5a74a2a0..e7ef4fbce1 100644 --- a/tests/vasp/lobster/conftest.py +++ b/tests/vasp/lobster/conftest.py @@ -1,9 +1,11 @@ from collections.abc import Callable, Generator +from pathlib import Path from typing import Any +import pytest from pytest import MonkeyPatch -from atomate2.utils.testing.lobster import * +from atomate2.utils.testing.lobster import monkeypatch_lobster @pytest.fixture diff --git a/tutorials/force_fields/__init__.py b/tutorials/force_fields/__init__.py index e69de29bb2..90db5c4b7a 100644 --- a/tutorials/force_fields/__init__.py +++ b/tutorials/force_fields/__init__.py @@ -0,0 +1 @@ +"""Forcefield-based tutorials""" diff --git a/tutorials/lobster_workflow.ipynb b/tutorials/lobster_workflow.ipynb index a2f4db6c10..20c0d5c8e1 100644 --- a/tutorials/lobster_workflow.ipynb +++ b/tutorials/lobster_workflow.ipynb @@ -10,10 +10,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T18:13:32.703013Z", + "start_time": "2025-02-10T18:13:29.131593Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "from mock_lobster import mock_lobster\n", "from mock_vasp import TEST_DIR, mock_vasp\n", @@ -39,9 +53,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "3", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T18:13:32.921304Z", + "start_time": "2025-02-10T18:13:32.706995Z" + } + }, "outputs": [], "source": [ "from jobflow import JobStore, run_locally\n", @@ -65,9 +84,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "5", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T18:13:33.145104Z", + "start_time": "2025-02-10T18:13:32.967432Z" + } + }, "outputs": [], "source": [ "job = VaspLobsterMaker(\n", @@ -99,16 +123,137 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "7", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T18:13:38.143242Z", + "start_time": "2025-02-10T18:13:33.150702Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 19:13:33,162 INFO Started executing jobs locally\n", + "2025-02-10 19:13:33,171 INFO Starting job - relax 1 (4302e82f-21d6-4811-a746-097935292cd4)\n", + "2025-02-10 19:13:33,356 INFO Finished job - relax 1 (4302e82f-21d6-4811-a746-097935292cd4)\n", + "2025-02-10 19:13:33,357 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 19:13:33,358 INFO Starting job - relax 2 (3eb620c7-4cf9-41ec-999b-e3c9a9dc6342)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpy9qwme0j/job_2025-02-10-18-13-33-357854-58396/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 19:13:33,611 INFO Finished job - relax 2 (3eb620c7-4cf9-41ec-999b-e3c9a9dc6342)\n", + "2025-02-10 19:13:33,611 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 19:13:33,612 INFO Starting job - get_basis_infos (b7182908-752b-4214-a407-b2a9b8c8dee6)\n", + "2025-02-10 19:13:33,663 INFO Finished job - get_basis_infos (b7182908-752b-4214-a407-b2a9b8c8dee6)\n", + "2025-02-10 19:13:33,663 INFO Starting job - update_user_incar_settings_maker (6bcaf71b-a71b-4b5e-96f8-397876885327)\n", + "2025-02-10 19:13:33,808 INFO Finished job - update_user_incar_settings_maker (6bcaf71b-a71b-4b5e-96f8-397876885327)\n", + "2025-02-10 19:13:33,810 INFO Starting job - static (3c51a3a5-3d1f-4116-9228-e3a55578b254)\n", + "2025-02-10 19:13:34,040 INFO Finished job - static (3c51a3a5-3d1f-4116-9228-e3a55578b254)\n", + "2025-02-10 19:13:34,041 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 19:13:34,042 INFO Starting job - non-scf uniform (edc411e6-df6b-43fc-a71b-b9571531d68c)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpy9qwme0j/job_2025-02-10-18-13-33-810541-30881/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpy9qwme0j/job_2025-02-10-18-13-34-042249-10626/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 19:13:34,748 INFO Finished job - non-scf uniform (edc411e6-df6b-43fc-a71b-b9571531d68c)\n", + "2025-02-10 19:13:34,749 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-10 19:13:34,750 INFO Starting job - store_inputs (6bcaf71b-a71b-4b5e-96f8-397876885327, 2)\n", + "2025-02-10 19:13:34,751 INFO Finished job - store_inputs (6bcaf71b-a71b-4b5e-96f8-397876885327, 2)\n", + "2025-02-10 19:13:34,751 INFO Starting job - get_lobster_jobs (2775346f-edda-4b61-ad79-04355381b24e)\n", + "2025-02-10 19:13:34,791 INFO Finished job - get_lobster_jobs (2775346f-edda-4b61-ad79-04355381b24e)\n", + "2025-02-10 19:13:34,792 INFO Starting job - lobster_run_0 (7643f7a2-506a-48a1-b164-cb790592556c)\n", + "{'calc_quality_kwargs': {'potcar_symbols': ['Si'], 'n_bins': 10}, 'add_coxxcar_to_task_document': True}\n", + "True\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/lobster/inputs.py:699: UserWarning: Always check and test the provided basis functions. The spilling of your Lobster calculation might help\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/lobsterpy/cohp/analyze.py:870: UserWarning: The bonding, antibonding integral/percent values are numerical estimate. These values are sensitive to COHPstartEnergy parameter. If COHPstartEnergy value does not cover entire range of VASP calculations then absolute value of ICOHP_sum might not be equivalent to (bonding- antibonding) integral values.\n", + " ) = self._integrate_antbdstates_below_efermi(cohp, start=self.start)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'e_range': [-20, 0], 'dos_comparison': True, 'n_bins': 10, 'bva_comp': True, 'potcar_symbols': ['Si']}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Oxidation states from BVA analyzer cannot be determined. Thus BVA charge comparison will be skipped\n", + " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Consider using DOSCAR.LSO.lobster, as non LSO DOS from LOBSTER can have negative DOS values\n", + " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Minimum energy range requested for DOS comparisons is not available in VASP or LOBSTER calculation. Thus, setting min_e to -5 eV\n", + " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-10 19:13:37,412 INFO Finished job - lobster_run_0 (7643f7a2-506a-48a1-b164-cb790592556c)\n", + "2025-02-10 19:13:37,414 INFO Starting job - store_inputs (2775346f-edda-4b61-ad79-04355381b24e, 2)\n", + "2025-02-10 19:13:37,416 INFO Finished job - store_inputs (2775346f-edda-4b61-ad79-04355381b24e, 2)\n", + "2025-02-10 19:13:37,416 INFO Starting job - delete_lobster_wavecar (743e0cb0-aa18-40f6-862a-54f59ab34404)\n", + "2025-02-10 19:13:38,138 INFO Finished job - delete_lobster_wavecar (743e0cb0-aa18-40f6-862a-54f59ab34404)\n", + "2025-02-10 19:13:38,139 INFO Finished executing jobs locally\n" + ] + } + ], "source": [ - "with mock_vasp(ref_paths) as mf:\n", - " with mock_lobster(ref_paths_lobster) as mf2:\n", - " responses = run_locally(\n", - " job, store=job_store, create_folders=True, ensure_success=True\n", - " )" + "with mock_vasp(ref_paths) as mf, mock_lobster(ref_paths_lobster) as mf2:\n", + " responses = run_locally(\n", + " job,\n", + " store=job_store,\n", + " create_folders=True,\n", + " ensure_success=True,\n", + " raise_immediately=True,\n", + " )" ] }, { @@ -121,10 +266,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "9", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-10T18:13:38.359855Z", + "start_time": "2025-02-10T18:13:38.154159Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from pymatgen.electronic_structure.cohp import Cohp\n", "from pymatgen.electronic_structure.plotter import CohpPlotter\n", @@ -144,21 +305,26 @@ " result[\"output\"][\"lobsterpy_data\"][\"cohp_plot_data\"][\"data\"].items()\n", "):\n", " plotter = CohpPlotter()\n", - " cohp = Cohp.from_dict(cohp)\n", - " plotter.add_cohp(key, cohp)\n", + " cohp_obj = Cohp.from_dict(cohp)\n", + " plotter.add_cohp(key, cohp_obj)\n", " plotter.save_plot(f\"plots_all_bonds{number}.pdf\")\n", "\n", "for number, (key, cohp) in enumerate(\n", " result[\"output\"][\"lobsterpy_data_cation_anion\"][\"cohp_plot_data\"][\"data\"].items()\n", "):\n", " plotter = CohpPlotter()\n", - " cohp = Cohp.from_dict(cohp)\n", - " plotter.add_cohp(key, cohp)\n", + " cohp_obj = Cohp.from_dict(cohp)\n", + " plotter.add_cohp(key, cohp_obj)\n", " plotter.show()" ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", diff --git a/tutorials/plots_all_bonds0.pdf b/tutorials/plots_all_bonds0.pdf index c1e7ea9ba5c6ea61cfd66f1a94a9667045d39b14..a767c639d3c6f8841e2b560e03498d5779c06bbd 100644 GIT binary patch delta 18 acmdm$xhr$SHv?8nLt|r$&A$!yGXnrnHwVf9 delta 18 acmdm$xhr$SHv?94BMT$*&A$!yGXnrnM+eLR From a9a03f38e91faf839657d73ce95a8938d2c52feb Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 19:17:26 +0100 Subject: [PATCH 23/61] fix linting --- tutorials/lobster_workflow.ipynb | 189 +++---------------------------- 1 file changed, 13 insertions(+), 176 deletions(-) diff --git a/tutorials/lobster_workflow.ipynb b/tutorials/lobster_workflow.ipynb index 20c0d5c8e1..509f0a5daa 100644 --- a/tutorials/lobster_workflow.ipynb +++ b/tutorials/lobster_workflow.ipynb @@ -10,24 +10,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T18:13:32.703013Z", - "start_time": "2025-02-10T18:13:29.131593Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "from mock_lobster import mock_lobster\n", "from mock_vasp import TEST_DIR, mock_vasp\n", @@ -53,14 +39,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "3", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T18:13:32.921304Z", - "start_time": "2025-02-10T18:13:32.706995Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "from jobflow import JobStore, run_locally\n", @@ -84,14 +65,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "5", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T18:13:33.145104Z", - "start_time": "2025-02-10T18:13:32.967432Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "job = VaspLobsterMaker(\n", @@ -123,128 +99,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "7", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T18:13:38.143242Z", - "start_time": "2025-02-10T18:13:33.150702Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 19:13:33,162 INFO Started executing jobs locally\n", - "2025-02-10 19:13:33,171 INFO Starting job - relax 1 (4302e82f-21d6-4811-a746-097935292cd4)\n", - "2025-02-10 19:13:33,356 INFO Finished job - relax 1 (4302e82f-21d6-4811-a746-097935292cd4)\n", - "2025-02-10 19:13:33,357 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 19:13:33,358 INFO Starting job - relax 2 (3eb620c7-4cf9-41ec-999b-e3c9a9dc6342)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpy9qwme0j/job_2025-02-10-18-13-33-357854-58396/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 19:13:33,611 INFO Finished job - relax 2 (3eb620c7-4cf9-41ec-999b-e3c9a9dc6342)\n", - "2025-02-10 19:13:33,611 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 19:13:33,612 INFO Starting job - get_basis_infos (b7182908-752b-4214-a407-b2a9b8c8dee6)\n", - "2025-02-10 19:13:33,663 INFO Finished job - get_basis_infos (b7182908-752b-4214-a407-b2a9b8c8dee6)\n", - "2025-02-10 19:13:33,663 INFO Starting job - update_user_incar_settings_maker (6bcaf71b-a71b-4b5e-96f8-397876885327)\n", - "2025-02-10 19:13:33,808 INFO Finished job - update_user_incar_settings_maker (6bcaf71b-a71b-4b5e-96f8-397876885327)\n", - "2025-02-10 19:13:33,810 INFO Starting job - static (3c51a3a5-3d1f-4116-9228-e3a55578b254)\n", - "2025-02-10 19:13:34,040 INFO Finished job - static (3c51a3a5-3d1f-4116-9228-e3a55578b254)\n", - "2025-02-10 19:13:34,041 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 19:13:34,042 INFO Starting job - non-scf uniform (edc411e6-df6b-43fc-a71b-b9571531d68c)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpy9qwme0j/job_2025-02-10-18-13-33-810541-30881/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpy9qwme0j/job_2025-02-10-18-13-34-042249-10626/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 19:13:34,748 INFO Finished job - non-scf uniform (edc411e6-df6b-43fc-a71b-b9571531d68c)\n", - "2025-02-10 19:13:34,749 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-10 19:13:34,750 INFO Starting job - store_inputs (6bcaf71b-a71b-4b5e-96f8-397876885327, 2)\n", - "2025-02-10 19:13:34,751 INFO Finished job - store_inputs (6bcaf71b-a71b-4b5e-96f8-397876885327, 2)\n", - "2025-02-10 19:13:34,751 INFO Starting job - get_lobster_jobs (2775346f-edda-4b61-ad79-04355381b24e)\n", - "2025-02-10 19:13:34,791 INFO Finished job - get_lobster_jobs (2775346f-edda-4b61-ad79-04355381b24e)\n", - "2025-02-10 19:13:34,792 INFO Starting job - lobster_run_0 (7643f7a2-506a-48a1-b164-cb790592556c)\n", - "{'calc_quality_kwargs': {'potcar_symbols': ['Si'], 'n_bins': 10}, 'add_coxxcar_to_task_document': True}\n", - "True\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/lobster/inputs.py:699: UserWarning: Always check and test the provided basis functions. The spilling of your Lobster calculation might help\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/lobsterpy/cohp/analyze.py:870: UserWarning: The bonding, antibonding integral/percent values are numerical estimate. These values are sensitive to COHPstartEnergy parameter. If COHPstartEnergy value does not cover entire range of VASP calculations then absolute value of ICOHP_sum might not be equivalent to (bonding- antibonding) integral values.\n", - " ) = self._integrate_antbdstates_below_efermi(cohp, start=self.start)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'e_range': [-20, 0], 'dos_comparison': True, 'n_bins': 10, 'bva_comp': True, 'potcar_symbols': ['Si']}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Oxidation states from BVA analyzer cannot be determined. Thus BVA charge comparison will be skipped\n", - " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Consider using DOSCAR.LSO.lobster, as non LSO DOS from LOBSTER can have negative DOS values\n", - " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/lobster/schemas.py:601: UserWarning: Minimum energy range requested for DOS comparisons is not available in VASP or LOBSTER calculation. Thus, setting min_e to -5 eV\n", - " cal_quality_dict = Analysis.get_lobster_calc_quality_summary(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-10 19:13:37,412 INFO Finished job - lobster_run_0 (7643f7a2-506a-48a1-b164-cb790592556c)\n", - "2025-02-10 19:13:37,414 INFO Starting job - store_inputs (2775346f-edda-4b61-ad79-04355381b24e, 2)\n", - "2025-02-10 19:13:37,416 INFO Finished job - store_inputs (2775346f-edda-4b61-ad79-04355381b24e, 2)\n", - "2025-02-10 19:13:37,416 INFO Starting job - delete_lobster_wavecar (743e0cb0-aa18-40f6-862a-54f59ab34404)\n", - "2025-02-10 19:13:38,138 INFO Finished job - delete_lobster_wavecar (743e0cb0-aa18-40f6-862a-54f59ab34404)\n", - "2025-02-10 19:13:38,139 INFO Finished executing jobs locally\n" - ] - } - ], + "metadata": {}, + "outputs": [], "source": [ "with mock_vasp(ref_paths) as mf, mock_lobster(ref_paths_lobster) as mf2:\n", " responses = run_locally(\n", @@ -266,26 +124,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "9", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-10T18:13:38.359855Z", - "start_time": "2025-02-10T18:13:38.154159Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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bbtTXhAkTduu1tqU53lO0UUoBAAC0Ud7Fi6WqqrCxpDP+6FAaAO3Ztm5Bq6ysjH0QRN26des0adKksLHnnnsu7Nax2bNn629/+1usozVZ7969w7bbwp/Rlv6eePoeAABAG1X+zLMRY8nn/8mBJADqsy1LVnGxoxnMzEwZZuzmKFRUVESM7exR9u3RgAED9Pnnn+/8wAZefPFFvfTSS6Hto48+WjfccENo2+PxRCVfQ36/X+ecc46K6/15njRpkiZOnKhRo0Zp9OjRoXWm7rvvPh155JE66qijmiVLNDT8c9qxY0eHkkRPS39PlFIAAABtlPfH2WHbZrduMpOSHEoDoI5VXKyNw/ZxNEOXnxfKFcNSaM6cOeGv36WL4uLiYvb6rUVKSoqOPPLIJp/XcJ2grl277tJ1muq2227Td999F9oePHiwHn44uJbh0KFD9cADD+jyy4PrGNq2rT/96U9atGiROnfu3OzZmqq0tFQrVqwIG2v4BLvWpjW8J27fAwAAaIMsy1IgLy9sLP6A/R1KA6A1Kygo0CuvvCLLsnbpfK/Xq0ceeSRs7JhjjolGNDjoiy++0D333BPaTkhI0Guvvaakej/8uOyyy3TaaaeFtjdt2qQ//elPYYu5R8vLL7+sgoKCXT7/0UcfVU1NTWg7MTFRhxxySDSi7bK2+J4aopQCAABog7zffy81+Atk4imnOJQGQGtWXl6uc889V0OHDtXLL7+sqgZr1e1IdXW1zjvvPC1dujRs/Pzzz492zJiYMGFC2FPtmmNx6tYgPz9f5513XlhR+eCDD2ro0KERxz777LNh6xp9/vnnmjx5ctQzPfvss+rbt69uueUWrV27tknnvvrqq7r99tvDxk4//XQlJiZGMWHTtcX31BC37wEAALRBVf/7NHzAMBR/aMv66SiA1mXZsmX605/+pCuuuEJnnHGGjj76aB100EERT/eSpA0bNujdd9/VfffdpzVr1oTtO/XUU3X44YdHNdt33323zbJs0aJFYdvV1dWaMWPGNq/RrVs3DR48OKq52iLbtnX++edr48aNobHTTjtNl1566TaPz8jI0CuvvKJDDz009AS+W2+9VWPHjtWYMWOimq28vFz33HOP7r33Xo0dO1annnqqDjroIA0bNkxmgzXUqqqq9NVXX+nxxx/XBx98ELYvJSUlbBaYk9rie6qPUgoAAKAN8i36OWzbzMyM+OYVgDPMzEx1+Xmh4xl2VWlpqZ599lk9+2zwYQodOnRQx44dlZGRoerqauXl5Sk/P3+b544ZM0bPP//8Lr/29px77rmNmkmyadOm7S60fcEFF2jq1KlRTtb23Hffffr0060/+Ojdu3foz8L2HHjggbrjjjtCT+Dz+/0666yztHDhQqWnp0c9o23b+vLLL/Xll19KCt621rlz59Ai38XFxVqzZo0CgUDEuUlJSXr//ffVrVu3qOfaHW3xPUmUUgAAAG2SXVIStu3axuPYATjDMM2YLjK+u1JSUjR8+PCIWUd1CgsLVVhYuMNrmKapiy++WJMnT1ZqampzxEQMzJ49W7feemto2+1269VXX1VGRsZOz7355ps1c+ZMffHFF5KkNWvW6OKLL9brr78elWwjR47U7NmzQ0/7q6+qqkpr1qyJmLXX0L777qunn35aI0eOjEqm3dUW31ND/LgMAACgDbIb/KQ08YQTHEoCoLXr2LGjFi5cqFWrVumBBx7QiSeeqMxGzrTq2rWrrr76ai1cuFBPPPEEhVQrVlJSorPPPls+ny809s9//lMHHHBAo843TVMvv/yyOnXqFBqbPn26nn766ajku//++7V582ZNnz5dF154ofbcc89GnRcfH69x48Zp+vTpmjNnTosqb9rie2rIsJtj2Xu0GaWlpUpPT1dJSYnS0tKcjgMAABrBtixt6DdAqvcXh+z331PcfiMcTIWWpkePHsrNzVX37t2Vk5PjdJw2r7q6WqtXr1bfvn2VkJDgdJzdZtu21q5dqxUrVmjdunUqKSlRVVWVkpKSlJqaqm7dumn48OHq0aOH01HRjm3ZskW//vqrfvvtNxUWFqqsrEwul0tpaWnKysrS4MGDNWjQIHk8HqejNlpLek87+lxrbJfA7XsAAABtjLVpU1ghJUmunvzFEED0GIahPn36qA+3BqMFy8jI0JgxY6K+oLqT2tp74vY9AACANiZQ74lIkqS4OJm1C6ECAAC0FJRSAAAAbYzVYJFzMzNDBk/eAwAALQzfnQAAALQxEaVUeoYzQQAAAHaAUgoAAKCN8a9cFbZtW5ZDSQAAALaPUgoAAKCN8a9cEbZt5ec7lAQAAGD7KKUAAADamIiZUYbhTBAAAIAdoJQCAABo62zb6QQAAAARKKUAAADaGCM5JXzA53MmCAAAwA5QSgEAALQxZnpa2Lbt9zuUBAAAYPsopQAAANoYMzMzfIBSCmgxbG6nBdBGROPzjFIKAACgjXH36xc+YNuyKiqcCQNAkuRyuSRJXq/X4SQAEB2VlZWSJI/Hs8vXcEcrDAAAAFoGz96DI8Z8y39R/Mj9HEgDQJLcbreSkpK0efNmeTwemSbzAwC0TrZtq7KyUvn5+crIyAiV7ruCUgoAAKCNcfXpEzHmXbCAUgpwkGEY6tq1q1avXq21a9c6HQcAdltGRoa6dOmyW9eglAIAAGhjTJdLio+XampCY775CxxMBECS4uLitMcee3ALH4BWz+Px7NYMqTqUUgAAAG2Qq2NHBXJzQ9u+FSscTAOgjmmaSkhIcDoGALQI3MgMAADQBrn79g3bDqxf71ASAACAbaOUAgAAaIPiDjggbNuuqJBVVuZQGgAAgEiUUgAAAG1Q0qmnRIxVvve+A0kAAAC2jVIKAACgDXL36inFxYWN+VlXCgAAtCCUUgAAAG1UwpFHhG37Fi5yKAkAAEAkSikAAIA2Kvncc8K2vQsWKFBU7FAaAACAcJRSAAAAbVT8/vvLqP/oectS9eefORcIAACgHkopAACANspISFD8oYeEjVW9/4FDaQAAAMJRSgEAALRhiSedGLZd8823ChQWOpQGAABgK0opAACANizhqKPCb+ELBFT2nwecCwQAAFCLUgoAAKANM5OTFX/UkWFjFa++JsuyHEoEAAAQRCkFAADQxsUNHx4+4PWq8qWXnAkDAABQi1IKAACgjUu++CLJ7Q4bK3v4EYfSAAAABFFKAQAAtHGmy6WEsWPDxqyNm1T99dfOBAIAABClVJty9tlnyzCMsK8+ffo4HQsAALQA6ff8O2Ks5B+3xz4IAABALUqpNuKDDz7Qa6+95nQMAADQQrm7dpWnwdpS/pUrVfPTTw4lAgAA7R2lVBtQUlKiyy67zOkYAACghcvYxmyp4uv+6kASAAAASqk24YYbblBubq4kKTk52eE0AACgpYobNkyevfcOGwusXq2qmTMdSgQAANozSqlWbtasWXr22WclSaZp6rbbbnM4EQAAaMkyHv5vxNiWa6+LfRAAANDuUUq1YlVVVbrwwgtl27Yk6S9/+YtGjRrlcCoAANCSxQ0cKM9+I8LGrIJClT38iEOJAABAe0Up1Yr9/e9/16pVqyRJvXr10p133ulwIgAA0BpkPf2UZBhhY6UPPCirvNyhRAAAoD2ilGql5syZo//+97+h7ccee0wpKSnOBQIAAK2Gu0sXJZ5+evigz6fCSRc5EwgAALRLlFKtkM/n06RJkxQIBCRJf/zjHzVu3DiHUwEAgNYk4757pcTEsDHvt9+qZvZshxIBAID2hlKqFbr77ru1ePFiSVJGRoYefvhhhxMBAIDWxvR4lHHPPRHjpXffK7v2B18AAADNiVKqlVm2bJnuuuuu0Pa9996rLl26OJgIAAC0VsmnnyrP8OFhY945c1T+9DMOJQIAAO0JpVQrYlmWJk2aJK/XK0k6+OCDddFFrP0AAAB2XYc3p8vVu1fYWOnk++RbttyhRAAAoL2glGpFHn74Yf3444+SpLi4OD399NMyGjw5BwAAoClcSUnKfPCB8Kfxeb0q+stfZFdVORcMAAC0eZRSrcTq1at16623hrZvueUWDRw40MFEAACgrYgfM0Ypl10aNub/5Vdtue0OhxIBAID2gFKqlbj44otVUVEhSRo4cKD+7//+r1lep6amRqWlpWFfAACg7Uv76/VyDxoUNlY5bZq23H67M4EAAECbRynVCjz33HOaMWOGJMkwDD399NOKi4trlte6++67lZ6eHvrq2bNns7wOAABoWYz4eGU98ZiMxMSw8YpnnlPVl7OcCQUAANo0SqkWLi8vT3/9619D2xdeeKEOPvjgZnu9W265RSUlJaGv9evXN9trAQCAlsWzxx5Ku+nGiPGiP0+Uf8MGBxIBAIC2jFKqhbviiiu0ZcsWSVKXLl00efLkZn29+Ph4paWlhX0BAID2I/nCSXL17h0+6PNp83EnyPL5nAkFAADaJEqpFuyNN97QO++8E9p+6KGHlJGR4VwgAADQ5hmGoewP3pMSEsLGrYICFZx6mkOpAABAW0Qp1YLdcMMNod+fcMIJOuOMMxxMAwAA2gtXhw7q+OqrkmGEjfvmL1DRVdc4EwoAALQ5lFItWN1te5L00UcfyTCMnX4ddthhYddYu3ZtxDELFy6M7RsBAACtTvzokUq7/R8R41VvvaXShx5xIBEAAGhrKKUAAACwTakXXqjE00+NGC+bPFmVb7+zjTMAAAAaj1IKAAAA25X10EPy7DciYrz4qqtVNesrBxIBAIC2wu10AGzfe++9J18Tn3KzaNEi/fWvfw1td+7cWS+//HLYMQMGDIhKPgAA0D50fOdt5R/wBwVyc7cO2raKzr9AHd97R/H77utcOAAA0GpRSrVghx56aJPPcbvD/5UmJCToyCOPjFYkAADQDpkulzrN+Ewb9z9QdknJ1h2BgApOOU3Zn3ykuEGDnAsIAABaJW7fAwAAwE6ZaWnq9OUXUmJi+A6fTwXjT1Fg82ZnggEAgFaLUgoAAACN4u7cWdmffCx5PGHjdkWFCs48S4HCQoeSAQCA1ohSCgAAAI0Wt8cAZb/7ttRgyQD/ryuCxRQzpgAAQCNRSgEAAKBJ4vbZRx3felNmj+5h4/7lv6jgtD8qsCHPoWQAAKA1oZQCAABAk8WP3E/Zb78lV8+eYeP+Vau0+bTT5V+3zqFkAACgtaCUAgAAwC5xd++ujm+8LlfvXmHjgXXrtOmIo1T1xRcOJQMAAK0BpVQbM3bsWNm2Hfpas2aN05EAAEAb5u7ZU9lvvSn3gAHhOyorVXTBn1X+6mvOBAMAAC0epRQAAAB2i6trV3V86w25Bg4M32HbKvnrDSr970POBAMAAC0apRQAAAB2m6tjR3WcOkVGUlLEvrL77lfxX29wIBUAAGjJKKUAAAAQFe6ePdXpu29kZGZG7Kt89TXln3yqrEDAgWQAAKAlopQCAABA1Lg7dVKXH7+Xq0ePiH2+OXOUf+BBskpLHUgGAABaGkopAAAARJWZkqJO330jz7BhEfsCOTnaOGqMfL+ucCAZAABoSSilAAAAEHWm261On3ykxPEnReyzy8uVf9TRqnz/AweSAQCAloJSCgAAAM0m6/HHlHr9dZE7AgEVX3a5Sv55Z+xDAQCAFoFSCgAAAM0q7bprlfnE45LLFbGv/KmnlD/+FFk1NQ4kAwAATqKUAgAAQLNLOulEZX/2PxkpyRH7fHPnquDscxXIz3cgGQAAcAqlFAAAAGIibuBAdZk7R65+/SL2+WbPVv4xx6nmxx8dSAYAAJxAKQUAAICYMVNT1emrL5Vw/HER+6z8fBWccZbKHn9Ctm07kA4AAMQSpRQAAABiyjRNdXjmaWU996xc3bqF7wwEVHrXv1U0cZKsLVscyQcAAGKDUgoAAACOSDz2GGV/+onixx4asa/6s8+VN3K0Kt97z4FkAAAgFiilAAAA4BhXVpY6vPSiUv96vWQY4TurqlR8+ZXacvMtzoQDAADNilIKAAAAjjJMU2nXXqMOr7wsMysrYn/FSy8r/6Txsnw+B9IBAIDmQikFAACAFiHhkEOU/b9PZKSkROzzzZuvTaPHyJ+f70AyAADQHCilAAAA0GK4u3dTl/lz5R44MGKflb9Z+X84WFZFhQPJAABAtFFKAQAAoEUxk5PV+YvPlXjKyRH77MpKbbnxptiHAgAAUUcpBQAAgBYp69FHlPaPv0csgF795SxnAgEAgKiilAIAAECLlXrJxUq+6MKwMbukRL7Vqx1KBAAAooVSCgAAAC1ayuWXRYyVP/W0A0kAAEA0UUoBAACgRXNnZ8vM7hg2VvPNtw6lAQAA0UIpBQAAgBYvbtTosO3AunWyLMuhNAAAIBoopQAAANDiJf3x9PABy1LN5587EwYAAEQFpRQAAABavPgjj5DM8G9dy59+xqE0AAAgGiilAAAA0OKZpinPoIFhY74lS2XbtkOJAADA7qKUAgAAQKuQPOGCsG27vFzeefMdSgMAAHYXpRQAAABahaSzz5arT++wsYoXXnAoDQAA2F2UUgAAAGgVDMNQ8rnnho1VffChAps3O5QIAADsDkopAAAAtBpJZ50lJcRvHfD5VP7cFOcCAQCAXUYpBQAAgFbDlZWppPHjw8Yqnp+qQFGxQ4kAAMCuopQCAABAq5Jy+WWSYYS27fJyldx6q4OJAADArqCUAgAAQKviGTBAiSeHz5aqeu99eRcvdigRAADYFZRSAAAAaHWS//zniLGiiy91IAkAANhVlFIAAABodeL3GyFXv35hY4F161Q+bZpDiQAAQFNRSgEAAKBVynr26Yixkr/fJquszIE0AACgqSilAAAA0CrF7bWXEo46KnywpkaFF0Te2gcAAFoeSikAAAC0WpmPPSIlJISNeWfPVuV77zmUCAAANBalFAAAAFotMzlZmf+5P2K8+Opr5d+82YFEAACgsSilAAAA0KolnTxecWNGhw/6fCoYf4ps23YmFAAA2ClKKQAAALR6HV6YKiWG38YXWLtWW264wZlAAABgpyilAAAA0OqZqanqMPX5iPHKV19XxfTpDiQCAAA7QykFAACANiHhoIOUPGlixPiW62+Qd/ESBxIBAIAdoZQCAABAm5HxzzvkHjgwfNCyVPDHP8quqnImFAAA2CZKKQAAALQp2R+8JyMtLWzMLitX0eVXyPb7HUoFAAAaopQCAABAm2ImJSn7ww8klytsvPqzz7Xl5lt4Ih8AAC0EpRQAAADaHE//furw0otSamrYeOWrr6n03skOpQIAAPVRSgEAAKBNSjj0EHV84XkpPj5svPyRR1X634ccSgUAAOpQSgEAAKDNih8zRlmPPyqZ4d/2lt13v4quvc6hVAAAQKKUAgAAQBuXeOyxyrj3nojxqulvqGDCnx1IBAAAJEopAAAAtAPJ55yt9Lv+FTFe8/kMbf7jGbIsy4FUAAC0b5RSAAAAaBdSJkxQ0jlnR4x7v/9Bm48/QVYg4EAqAADaL0opAAAAtBuZ901W/JFHRIz7Fy9R/qGHyaqudiAVAADtE6UUAAAA2pWOL0xV4kknRYwHVq/WplFj5N+0yYFUAAC0P5RSAAAAaHeynnhMSef/KWLcKirSpgMPknfxEgdSAQDQvlBKAQAAoF3KvPvfSvnLlZE7qqu1+YRxqvr449iHAgCgHaGUAgAAQLuVfvNNSr/nbskwwncEAiq66BKV/PtuZ4IBANAOUEoBAACgXUv503nq8PLLktsdsa/8sce1+YwzeTIfAADNgFIKAAAA7V7C2EPUacZnMpKTI/Z5v/tem484UlZZmQPJAABouyilAAAAAEmePfZQlzmz5erdO2Kff+Vv2nz8OPmWL3cgGQAAbROlFAAAAFDLTE9Xp2+/VsLRR0Xs8//+uzaPO0mVb77lQDIAANoeSikAAACgHtM01eH5KUq96UYpKSlsn11dreKrr1HxjTfJqqpyKCEAAG0DpRQAAACwDWlX/UWdv/hcnqFDI/ZVTntF+Ucdo6qPP3YgGQAAbQOlFAAAALAd7l69lP3u20r+03kR+wKrV6vooktUeOllsizLgXQAALRulFIAAADADhgJCcq4525lPvyQjMTEiP3VH3yoTSNGyrdihQPpAABovSilAAAAgEZIOu1UZf/vY7n69YvYZ23erPzDj1TJXf92IBkAAK0TpRQAAADQSJ4BA9Tpow/kHrJ35E7bVvnjT2jjAX+Qf/362IcDAKCVoZQCAAAAmsBMS1PnT/+n1L9eLxlGxP7AunXadOBBKv3vQw6kAwCg9aCUAgAAAHZB2rXXKPuLz2V26hS507JUdt/92njAgfKtWhX7cAAAtAKUUgAAAMAuittrL3WeN0dJ556zzf2BdeuVf+hh2vK3W3lCHwAADVBKAQAAALvBNE1lTr5X2R+8LyMjI/IA21bF1Be0aZ8Rqpk/P+b5AABoqSilAAAAgCiIG7Gvuvy8UImnn77N/VZhoQpO+6PKn58qOxCIcToAAFoeSikAAAAgSkyXS1kPPajsTz+R2blz5AFer0pu/bsKTjlNvhUrYh8QAIAWhFIKAAAAiLK4IUPUdf5cpVx2qWRGfsvtnTdP+Ucfq9IH/yvb73cgIQAAzqOUAgAAAJpJ+q1/U+fvvlH84YdH7vT5VHb/f1Rw+hny/fZb7MMBAOAwSikAAACgGbl79VLHl15Qx+mvy9WnT8R+75w5yh97uEru+GfswwEA4CBKKQAAACAG4v9woDrP+EwpV1wuuVzhO21b5U8/o02HHiarpMSZgAAAxBilFAAAABAjRmKi0v/vFmW/+47Mnj0j9vt/+00bxxwg3+o1sQ8HAECMUUoBAAAAMRY3Yl91+vB9ubp3j9hnl5Up/7DDVfPDDw4kAwAgdiilAAAAAAe4OnZUpx+/V+LZZ0Xu9PlU8MczVfH69NgHAwAgRiilAAAAAIeYpqms++9T5hNPSGaDb81tW1uuu15VH3zoTDgAAJoZpRQAAADgsKSTxin7vfek+PiIfUWXX8GtfACANolSCgAAAGgB4kbso87ffi0jIyN8h2Wp4Kxz5P31V0dyAQDQXCilAAAAgBbC3a2bunz7tYzU1PAdfr8Kxp8iy+93JhgAAM2AUgoAAABoQczMTHWa8VnErXx2WZmKL77EoVQAAEQfpRQAAADQwrh79FD2++9JLlfYePWnn6lqxhcOpQIAILoopQAAAIAWKG7I3kr7+98ixkvvvVe2bTuQCACA6KKUAgAAAFqo1IsuknvI3mFj/mXL5Z0716FEAABEj9vpAGga27a1Zs0aLV68WDk5OdqyZYvi4+OVmZmpPfbYQ6NGjVJCQoLTMQEAABAl2a+9qk2HHiarsDA0Vv7U04ofNcrBVAAA7D5KqVaguLhY7777rv73v/9p5syZKigo2O6xHo9HJ5xwgq655hodeuihMUwJAACA5mBmZirlistV+s9/hcaq//ep/OvWyd2rl4PJAADYPdy+18JdccUV6tKliyZOnKjp06fvsJCSJJ/Pp3fffVdjx47VBRdcoNLS0hglBQAAQHNJPudsGampWwdsWzVffe1cIAAAooBSqoWbPXu2vF5vxLjL5VKPHj203377adiwYUpPT4845sUXX9RRRx2l8vLyWEQFAABAMzFTU5Vw5BFhY9758x1KAwBAdFBKtSIZGRm6/PLL9dFHH6m4uFjr16/X3LlztWjRIhUWFurLL7/UwQcfHHbOTz/9pAkTJjgTGAAAAFETN2JE2LZ3HqUUAKB1o5RqBfr06aNnn31WGzZs0GOPPabjjz9eqfWnbys4c2rs2LH68ssvdfHFF4fte+utt/Tll1/GMjIAAACizNW3b9i2f/Vqh5IAABAdlFIt3B133KFff/1VkyZNUmJi4k6Pd7lcevzxxzVy5Miw8Weffba5IgIAACAGrJKSBgOWM0EAAIgSSqkW7oQTTlBcXFyTznG5XLrxxhvDxj799NNoxgIAAECs+XxOJwAAIKoopdqohmtLFRYWqrKy0qE0AAAA2F2BtWudjgAAQFS5Y/2Ctm0rLy9PZWVlqqiokN/vV1JSkpKTk9WpUyclJyfHOlKblJmZGTFWUlKipKQkB9IAAABgd1XPmhW2baSkOBMEAIAoadZSasuWLfrmm280Z84czZkzRytWrFBOTo78fv92z8nMzFTfvn01fPhwjRo1SmPGjNE+++zTnDHbpNzc3IixDh06OJAEAAAA0eD/dUXYtmfQQIeSAAAQHVEvpdasWaNXX31VH330kX766ScFAoHQPtu2d3p+UVGRioqKNH/+fD3//POSpE6dOunYY4/V+PHjNW7cOLndMZ/g1ep88803Ydu9e/du8tpUAAAAaBkq3n5HdoOlGOKPPNKhNAAAREdU2h2v16tXXnlFzz//vL777rtQ+dSwhDIMo9HXrH/upk2b9OKLL+rFF19UVlaWzjrrLF188cUaOnRoNOK3SVOmTAnbPv744x1KAgAAgN1Vete/wwcMQyl/Os+ZMAAARMluLXReUFCgO+64Q7169dKkSZP07bffyrKsUKFkGEbYl23bjfra0bmFhYV6/PHHtc8+++iYY47hqXLb8PHHH+vrr78OG5swYYIzYQAAALBbKt59V9bGjWFj8YccLDM93aFEAABExy7NlCotLdXkyZP18MMPq6KiIqJIkiJnSWVlZalXr17q0aOHunbtqqSkJCUmJsrtdquqqkpVVVUqKipSTk6OcnNztW7durBb/+rPsqq79owZMzRjxgyNHj1ad911lw4//PBdeTttSlFRkS655JKwsZNPPlmjR49u1Pk1NTWqqakJbZeWlkY1HwAAABovUFysLddeHzGecd9kB9IAABBdTSqlAoGAHn74Yd15553asmVLWBklbS2L4uPjtf/+++uwww7TqFGjNHz4cHXr1q1Jwaqrq7VkyRItWrRI33zzjb788kutX78+tL/+a86ePVtHHXWUDj/8cD300EMaPHhwk16rrbAsS+edd55ycnJCY+np6Xr44YcbfY27775bd9xxR3PEAwAAQBMVnP5HyesNG4s74AC5u3d3KBEAANFj2I1ZfVzSzJkzddVVV2n58uVhZVTd7zMyMnTiiSfq9NNP11FHHaWEhISoh125cqXeeustvfXWW5o3b14og7S1EHO73frLX/6i22+/XampqVHP0JJdf/31euCBB8LGXnvtNZ155pmNvsa2Zkr17NlTJSUlSktLi1pWAAAA7FjxjTepctor4YMJCeq6aIHMlJTdvn6PHj2Um5ur7t27h/1QEwCA3VVaWqr09PSddgmNLqVM0wyVUPXLqLFjx+qiiy7SaaedFtOnu61cuVJPPfWUXnzxRRUUFISVU4Zh6Pbbb9ff//73mOVx2sMPP6yrr746bOzGG2/Uvffeu1vXbewfJAAAAERPyb33qvzhRyPGs158QYlHRGfJCkopAEBzaWyXsEsLnbvdbk2YMEHLli3TzJkzdfbZZ8e0kJKkPfbYQ/fff79yc3P17LPPas899wxbx6qRXVub8Morr+iaa64JG5swYYLuueceZwIBAABgl1V//bXKn3gqYjzx5JOjVkgBANASNKmUcrlcuuyyy7Rq1SpNmTJFAwcObK5cjebxeDRx4kQtW7ZM06dP11577dWuCqkPP/xQF1xwQdh7PvXUU/Xss8+GLQ4PAACAlq/qw49U+OeJks8XNu4ZPkwZjzzkUCoAAJpHoxc6P/300/Xvf/9bAwYMaM48u8wwDJ1++uk69dRTNWXKlHZRTH355Zf64x//KL/fHxo76qij9Oqrr8rlcjmYDAAAAE1h27bKH3tcpXdHznT37LOPOn7wnkxzl25yAACgxWp0KTV9+vTmzBE1pmnqwgsvdDpGs5s9e7ZOOukkVVdXh8YOPPBAvfPOOzG/lRIAAAC7zvZ6teWW/1Pla69H7Escf5IyH3lYBoUUAKANanQphZbj559/1nHHHafy8vLQ2L777quPP/5YycnJDiYDAABAU1S+/4G2XHe97KqqiH1J556rjLv+JYMZ8ACANopSqpX59ddfddRRR6m4uDg0NmjQIH366adKT093MBkAAAAay6qsVOGEP8v73feROw1Dabf+TSmXXMwaoQCANo1SqhVZu3atjjzySOXn54fG+vbtq88//1zZ2dkOJgMAAEBjVX38sYqvumabs6OMxERlPvqwEo891oFkAADEVpNvTr/00ks1d+7c5siCHcjLy9MRRxyhnJyc0Fj37t31xRdfqHv37g4mAwAAQGP4c3K06ehjVXTRJdsspJSQoI7vvE0hBQBoN5pcSj399NMaM2aMhg0bpocffliFhYXNkQv1FBUV6aijjtKqVatCY9nZ2fr888/Vt29fB5MBAABgZyy/X8V//as27X+g/EuXbvOYuJEj1XXhfMUNHRLjdAAAOGeXHuNh27aWLFmia6+9Vt27d9eZZ56pTz/9NNrZIKmsrEzHHnusltb7BiYjI0OfffaZBg0a5GAyAAAA7EzFG29o46C9Vfnq65JtRx4QF6eMB/+j7PfekZmaGvuAAAA4aJfWlDIMQ7Zty7Zteb1evfnmm3rzzTfVo0cPTZgwQX/+85/Vp0+fKEdtn0466STNmTMnbOy6665TQUGBZsyY0aRr7bfffsrMzIxmPAAAAGxDzeyfVHzV1QrUW3qhobhRo9Thhedl8rAaAEA7Zdj2tn5ks32maUY8BaT+JQzDkGEYGjt2rC688EKdeuqpiouLi07adiiaT1z58ssvNXbs2CadU1paqvT0dJWUlCgtLS1qWQAAANoif26uii66WL5FP2/3GDO7ozIfe0wJfzgwhski9ejRQ7m5uerevXvYuqUAAOyuxnYJTZ4pdcMNN+jll19WXl6epK0lVJ26GVRffvmlvvzyS2VkZOicc87RxIkTte++++7CWwEAAABix7IsqaJCVlmZrNJS2WVlssrKZZeXy66okF1RIauiQq4e3WW4PbJraqTqagXy81U2ZYpUUrrtC7vdSr36aqVdd01M3w8AAC1Vk2dKScH/UX/yySd67rnn9NFHH8nn8wUv1qCcCr1I7fjw4cN14YUX6pxzzlFGRsZuRm8fmCkFAADaIzsQkF1TI7u6RnZpifw5OcFyqHxrKWRXVkqVlbKqqmRXVcmuqpZdXSW7ujpYFNV4ZXu9cu+1pwyXK3Q91dTIrqmRVVoq/5o1UiAQXO+p7qsZxB92mLIef1RmC/p+iplSAIDm0tguYZdKqfoKCgr00ksvaerUqVq8eHHwoju5vS8+Pl6nnHKKJk6cqCOOOGJ3Xh7NjFIKAID2ybIsqXamkFVWJrusXFZ5cLaQq1s3yeerLX+CBZBdU6PAhjz5fl4cHKsOjtneGtler+T1Bc/x+WT7/cEiKOCXHbCCv7csGWlpwWNqaiS/3+l/BFHhHjxYWU8+Lk///k5HiUApBQBoLjErpeqbO3eupkyZotdee01btmwJvsBOZk/17t1bEydO1AUXXKCePXtGKwqihFIKAIDosiwrWLhUVcmqrpa8XhmJicEyJhAI/ur3S36/Aps3K7AhL1js1M76kbf+rz7ZPq9sX23hU1f8eGtk+3yKGz68dsZQbUFUO4MokJcn/+o1UiAQfM3aUki2HfwVuy3+oIOUcuXlSjj4YKejbBelFACguThSStWpqanRW2+9palTp2rmzJmyLGuns6dM09QRRxyhCy+8UOPHj5fH44l2LOwCSikAgBMsy5J8Pqm6WlZVdfDXmhqpuio0K6fu9iyrrnSpLWuMOI/cPXrKDvglnz9U8Nh+n/wrf5M/J7de8ROcuSN/QLbfFzwuEAiWRv56hU0gIMXFybPnHrL9gdrz/MGZPj6/Aps2yioo3FrqNLwVrJluCYNDDEMyDJkdOsjMypQRHy8jPkFGfLxcPXso+eyzFbffCKdT7hSlFACguThaStW3fv16TZkyRS+++KJWr14dfNHtFFR141lZWTrvvPM0ceJEDR06tDnjYScopQCg5bIsS/J6g4VNdY0M2ZLLVVvA1BYytcWMb9Wq4CLN9cqbrTNufLJ9NbJ9ftm+um1/bXHjk6tDR7l69th6Xb8/VMh4f14sq7hIqr0Fyw4EJCsgBSzZVu1tWbYlWcGyxq5X2Bjx8TJTU2X7/cFiqMEsIWCHTDNYDpmm5HLJcLmCv7rdksctw+2R4jwyPHHyDBsqV5cuMhISaguk4Jfcbvl/+UVKSpKRlCQzOUlGcrLM5BQZKSkyUlNlpqUGf01Pl5mQ4PS7jipKKQBAc2kxpVR9X375pZ577jm98847qqqqCgbYye19++23ny688EKdffbZSk1NjVVU1KKUAtBeWIGAVFoqq6R2/ZzyMikxMbg4ct3CyLW3PllVVar59jvZ1VW1M3F8YWXK1l9rZ9wEwmfcmF27ytUhK1js+H1hM2/8K1cGCxpm3KAlS0yUmZgoIyFBio+XkVBb8liW/OvWy3C7ZXg8kqe2GIqLlxEfF/w1ITirSAkJMpISgtdJDJZBnr2HyJWVGXZNIz5e8nhkmKaUkiLT5XL63bcZlFIAgObSIkupOmVlZXrllVc0depUzZ49OxhkJ7f3JSYm6rTTTtPEiRN16KGHxjRve0YpBSBWbNuWXV0dfPx6aWnw8etlZbLLaxdXrve0reBX3dO2quQe0F9GfHxozZy6AskqL5d33vzQbVmh27Esa+sXJQ9aqrpZQIHA1u1tfZlm8Pso0wx9GW635HYrbvQomcnJW2cH1ZZIdk21/L+vDs4cqi2Yts4WSpaRkiIzJVlGSqrM1BQZqWky0lJlpqS0udlC7RmlFACgubToUqq+5cuX67nnntO0adO0adOmYKidzJ7q16+fJk2apPPPP1/dunWLbeB2hlIKaF/qnrZl194WVn/tHru6RlZRkXxLlsquDJZDVugR7NVS3RO4quuetuULXsPnC80ecvfsKRkKu6ZqZx6putrpt4+2wDAkt1uu7OzgLVyuerdyuV2yirfIKi7eWt64XBG3f8llBs9zuySXW4bbJbndMpOSFTdmdHAGkNsdKn4Mjye4IPnGjTI8HhlxcVJcnIw4j+SJkxkfX7sdJyXEy4yLD84EqjcbyNWtW/D1PZ6t12VGEJoZpRQAoLm0mlKqTiAQ0Icffqjnn39eH3/8sfx+/05nT7lcLnm93lhHbVcopYCWKVBUJN+iRbL9AVn5+WFP1bKrqxXIy5Nv8ZLg+kB1j2D3+SNvJeNpW+1D3f9P6/4/GjbTRpJhbnfGjUxTrg4dgrPR3B7J4w4WNZ5gKeNb9bus0tJQcVNX/gSLG48Mjzv4a1xtkVNbzhgej4ysLMUNGhi6ntye4HU8HlnFW4KLlte/jSshIVjsJCYGZ+skJMiMi3PsHyvQ2lFKAQCaS2O7BHcMM+2Qy+XS+PHjNX78eOXn5+vFF1/U1KlTtWzZstAxDWdQBeqmswNAG2NVV8u36Gd5Fy6Ub/kvCqxZrUDeRlnFxbIrK7nlLJY8nq2FSN36NvHxCuTmSLYiZtxsnW1Tt+hy3e/rZta45O7bV+4+fbY548a7dJlkBWqLm7ja9XjiJI8nOOMmPj543jZm3Jjp6TKzsphxAwAAgFahxcyU2p6ffvpJU6ZM0UsvvaTqerd22LYtwzAoppoZM6WA5mHbtqyiInnnz1fN11/Lv+p3+XNzZW0ukF1RwZPHtqXe+jlhRZDbLXffvnJ16xpRHhnx8apZsDC4wHJCQnAx5cQEmYlJMpISZSQly0ip97St2idtmampMtLSgtsej9PvHACaBTOlAADNpdXNlNqW8vJyLVq0SIsWLVJVVZUMwwiVUQDQWlR9/rlqvv5W/twc2cXFChQUKJC3Uap9CmmrUDtbSG637NLSemvwmFLt2jtbn7YVvDVLcXG1xVBtIRSfIM/wYbWPZW8w88jtUSAvL7i4cmqKjLQ0mampMtPSpKQkmabp9D8BAAAAAFHWIkupWbNm6fnnn9dbb72lqnp/aaOQAtDaFF5+harfe7/Zrm+kpcndr29wxk9CQmjtHdvvl3/FyuBtX/FxW2cQ1T7CPeJpWykpwdlCtU/bcvfsEXzaVt1aPtz+BQAAACDKWkwptX79ek2dOlUvvPCCVq9eLWnbT96rP96nT5+YZgSApqr+5H/Nen27tFS+RT8H1yxyu7eWT3VFU1qqzIwMmZmZMjt0kKtjR7k6d5LZpatcPbrL1akTZT8AAAAARzhaSnm9Xr399tuaMmWKZs6cKdu2d1pExcfH65RTTtGkSZN0xBFHxDwzADSFmZUla+PG5n0R2w6uQeX3B5/AV1IiSdrlFfc8bklG8Lqu4FPUzA4dlPngfxQ/Zky0UgMAAABo5xwppebOnavnn39er732mrZs2SJpa+nU8Cf2dePDhw/XpEmTdO655yozMzOmeQHsGsuyJK9X8npl+/2yvT7J55Xt88v2+SSfTwoEZHbIkgIBKWDJtgKh3weKixTYkBcsW/x+ye+T7Q9IPr/sgH/reMAv+QPB7UDwfNvvl2fffWTYCm4HApJlSYGA/Js3y//LL7WvE5AdCI7Lqv29VZdl6+9l1W1b8gwdGnyiWqD+8QEFSkrlX7ky+Dq2LdmW7Krqnf5zanF89RZZ9/tl19QoUF6uglNPV/KfL1DGnXc6lw0AAABAmxGzUqqgoEAvvfSSnn/+eS1dulTSzm/PS09P1znnnKNJkyZpxIgRsYqKdsiyrGBB4vVKfr+susLE55PtcgUfwx4IBEuJekVGYMMG2WVlwcLF7wuWJbUzVuoKk9B2IBAqUuQPyEhJkbtvn2D5Ua8wsQMB+VaskLVpU23BUu816xU2dqgsqStGgr83kpLlGbhXRGFiByz5162VtXFTsDCxLNm2JVl2bYFi1ytTamct1v9yueTq3CmUJ/T6gYCsqiqpugWWL9NeaZbLBtasaZbrtgaV09+klAIAAAAQFc1aSlmWpY8//lhTpkzRRx99JL/fv90iStq6kPnYsWM1adIknXbaaUpISGjOiGikkgcflG2YCv7bs+WdMzdYSKh+caHIQkN2cJ0bjydUpLh79pSrUyfZAStY5AQCsi1bhm3Ju2CBVF0TLH9ChYkl2wovTLZVnphpaTLT02sLnkBYeWIVFQfLINVmbON8CxY0w0V9CqxdF/3rolUxs7KcjgAAAACgjWiWUuqXX37R888/r5deekmbNm2StPNZUd26ddOECRM0ceJE9evXrzliYTdUPPl01B7J7l+yNCrXacjavFnW5s3Ncm2gzaj779iymnyqe489lDXluSgHAgAAANBeRa2UKi8v16uvvqrnn39es2fPlrT9Iqpun8fj0bhx4zRp0iQde+yxUSs9ALRzpikZhozMTJnx8bI9HhlulwxPnOTxSH6/Ahs2yHC5gsfWfhmmUXtu7ZjLlGEEf5UZPNZwmYrbbz8ZycnB81114y5ZVdXyLV2yddzlqv29W3LXbbuD61G5XDLcdeNuGW633AP3kpmaVnuOGTzGdMm2AvKvXy/D7Qme43EHz/HUzkL01I17ZNR+qfbL9HikhAQZcXHBnDxpDwAAAEALsdul1KxZszRlyhS9/fbbqqqqkrTzRcsHDhyoSZMm6fzzz1d2dvbuRgCAcLWzgOyCgh0+gW6HN3LWfX4ZhsysLBmJiaFyqua774O/D1gKrF8fPNYwQmVY3e+Nuu1Q6bW1AJPLlGfgQJkdOspwmbJdLnnnzJXhcsmW5J07L1RuhUoud7CkUv1Sy+UK3iIb2nbL3b+fXJ07yfDEyUhPl5mZITMjQ2Z6erCsAgAAAIAWYJdKqfXr12vq1KmaOnWq1tQu+Luz2/NSUlJ0xhlnaNKkSTrggAN2IzIcERe3dT0naZdu/QFalbo/67Ytq6Bg1y6xk/2B1Wt26bq7pa4o83hkJCTITEqUkZIiIy1NZkamzI5ZcmVny9W5s1xdu8nVs4fcPXvKTEuLfVYAAAAAbVqTS6mjjz5aM2fOlF23wHSt7c2KOuCAAzRp0iSdeeaZSk5O3s24cEq3xYuU1si/lFqWFXyKXU2NLJ9PhmEEZ38EAvWeImcFb6HK3RB8cpvPF3zqndcr+X2SLyA7UPv0u2080U7+gMyMdLl69pD8gYjFzb2LF8sqKt467vdvfZKdFQhew7LCn2JX+wQ8Iz1dnv79ZPsDYU+ukxWQ/7dVsoqLwhder78Au20H35vCF36XHVzw3cxIr5e33lP1vF6KPsRG3Z+zQEB2dbUCW7Y0+lQjNVXunj1lZNTOvKqbgZWRIf/6HJnJSTI7d5a7a1e5uneXq3cvuVgYHQAAAMB2NLmUmjFjRuj32yuisrOzdf7552vSpEkaOHDgbkZEa2OappSQICUkaGerhLl7926WDK21/rRqyzrV1Mj2+2XXeCWfV7bPJ9vnk7xe2T5/bVnnle31SYbk7tKlXulnhcq4wIY8+TdskAJ+aRvlXl3BZ1vBX4OFmT94HRmK22/f4HhdkVj7Gv7cXPl/+622rNta6NmWtbUctKytY7YdPKa2tPPsvXftOeF5rS0lCmzcGF7m1ZuxFPZ7xJxdVibfsmVNP9E0JY9bRly8jMREGcnJMtNSg4VWVpY8gwbJM2yYzMzM4FdWpszExOi/AQAAAAAtyi7dvretMsrlcumYY47RpEmTdOKJJ8rtbpYH+wFtmmmawVsl4+KcjtIqWYGAjEBkiWZVV8sqLKot83yyvd7aQq7ebDyfN7y48/mDxwb88vTrF1yLqWGJVlom76JFwRKuruSzAltnwwUCUqDeLL1A+Kw+9157yUxJ3mZe3/wF9Yq+2oLP3t4MPSs4e6922+zQQUZ8vOT1yiopkV1R4fC/GEuq8cqu8couK5OksLW+qt55N+IUIyFBZmamrIqK4LpeSUkyUlNkpqfLzMyS2bGjXJ07ydWtm1w9usvdu7fM7t1l8v8eAAAAoNXY5e/e62ZF9evXTxMnTtSECRPUrVu3qAUDgKYy6xYEl1S/OjclqWvXZnrVPzXTdaPH9nplbdmiQH6+vHPnKbBpkwL5+bIKi2RvKZZVUiq7vFxWZaXsmmrJ66udLedg5upqBfLyQtuBkhIpbwcn1DGM4MLvcXFy9eypuGFDZWZlbZ2FlZkpMzNDdk2NzI7ZcvfpLTMlpfneCAAAAIDt2qVSKj4+XqeddpomTZqksWPHRjkSACCajLg4uTp1kqtTJ8UNGdLo86zKSgVyc+Vfn6PAhg3B8sbvl7VlS9hXoKBAvnnzg2uj1d6m6RjbDs2I8//yi/y//NK481wuKc4jIyFRZnKSjNRUmekZMjtkydWxo8zadbLcvXvJ3b+/XBkZzfo2AAAAgPagyaXUY489pnPOOUfp6enNkQcA0EKYSUky99hDnj32aNJ5ltcra8MG+devVyB3gwIbN8rKz1egsFBWUXHwlsKyMtkVFbKrq2WkpgYfjFBS4tyC/4GAVBWQXVWtQHHxTg83EhODM7CysmR2qP01M0vWpo2SxyNXp85ydQ8+vdDVu4/cffvI5LZcAAAAIEyTS6nLLrusOXIAANoIMy5OZp8+cvfp06TzbMuSXVIiq3iLrOJiBTZvVvUXMxUo2Bx8mmZJiayyMtmVlbJraoJP7XSoxLKrqhTIzVUgN7fxJ9XdWhgfLyMpUUbK1sXeXdnZ8gzcS3H7jQiVXUZKSsQajgAAAEBbwoqwAIAWwTBNGbXrPkl9JUmJxx6z0/MCRUXyr1mjwPr18ufkytq4SYHNm+XqlC3JkFVcLKu4qPbXYgWKiqXaBddjqt6thXZ5uZS/WTtcuSsuLvgkwsws2aWlkm3LSEutXei9g1zZneTq3k3unj3l6tdX7r59ZSYkxOrdAAAAALvN0VIqEAioqKhIVVVVkqRevXo5GQcA0Aq5srLkysqSRoxo9DlWdbX8a9cpsH6dAjm5CmzMU2BTvqyCwmCBVbv4u11VtXVWVqzXyvJ6ZW3cJGvjpq1jG3ZyTt1srIQEGcnJMtPT5B4wQPEHHhhcG6tD8MmFZoeOMjPSZZhms74FAAAAYEdiWkotXrxY7777rmbNmqUFCxaopKQktM8wDPn9/u2eW1JSokC9p0GlpKQojvU5AAC7wExIUNxee0p77dnocyy/X9aGDbLLK2RVVsoqKpRVVBRcJ6uwUIGCAtXM+ip4e6HXK+3g/2nNpv5srLIyWRs3yv/rClV/9HHksS6X5A5+G2AkJMhMSZGRni4zKzO4MH7XLnL16CF37z5y9+8ns1s3mZRYAAAAiKKYlFILFy7Urbfeqk8++SQ0ZjfxJ85XXXWVXn755dD2RRddpCeffDJqGQEA2BHT7ZbZxBm9Vnm5/GvXKrBmrfw5OQpsyFNg0yZZhYWyiotll5bKqlvwPSFBdmWl5PU20ztoIBAIfkmya2oUKCmRdrZGlmlKcXEyEhPl7tVL8WNGy+zQQWbHDjI7dJSrY4fgTKyMDJmpqTF4EwAAAGjNDLup7VATPfXUU7rmmmvk9XpDRVTDhVtt25ZhGGEzoRpasmSJhg8fHrpGRkaG8vLyFB8f33zhodLSUqWnp6ukpERpaWlOxwGANs22bdkVFbUzsIKzsAKbN6vqk/8FZ2ZtqX1yYd1i735/7G8rbIq6WwlTUmRmpMvM6iBX585y9egmd6/ecvfvJ/ceewRvvwQQcz169FBubq66d++unJwcp+MAANqQxnYJzTpT6s4779Rtt90WUUbV78Ea+2ShIUOG6IgjjtCMGTMkBW/n+/jjj3XKKadEOTUAAM4wDCNY4KSkSPVmZSWfecZ2z7Gqq4MLva/6Xf516xTYkKvAxk2yNhfIKi6W2SFLClgKFBbKKiwMLpoeK35/cG2u8nJZGzfu+NjaAsvVo0dwBlZ2dnAdrE7ZcnXMlpndUUZGhlz8gAQAAKDNaLZS6q233goVUvXLqD322EPHHXec+vbtqwceeKBJP5U588wzNWPGjND1Pv30U0opAEC7ZiYkKG7gQGngwEYdb9fUyCoqkj8/XzVffa3Ahg3BEqvulsL6M7F2MIM56moLLP8vv8j/yy87PjZsMff04NMIO3eWq1s3uXr1kmdAf3kGDZKZnh6b7AAAANglzXL7XmVlpfr3769NmzbJMAzZtq20tDQ9/vjjOuecc0LH7bvvvvr5558bdfueJBUVFalz586yLEu2bat///5auXJltOOjHm7fA4D2LVBUJP+qVfKvXq3A+hz5N2yQlZ8vs0MHyZaswoJgoVUQXOw9ZmtiNVbdGli9eyv+wANkduokV+dOcnXqHPq9kZLS6JnbQFvC7XsAgObi6O17jz76aFghlZqaqq+//lrDhg3bretmZWVp0KBBWrp0qSTp999/V2lpKWUJAADNxJWVJVdWluJHjdrpsbZty9qyRd4FCxVYvVqB9evl35AnK3+TAkXFsktKYj8Ly+uV7fXK9/PP8v3887aPMU3JNINPIUxNlZGZKVd2tsxuXeXu1Uue/v3k3msvufr0kemO6YOLAQAA2rRm+c7qpZdeChVShmHowQcf3O1Cqs5+++2nJUuWhLaXL1+uMWPGROXaAABg1xmGIVdmphIPP0zSYTs81rIsWXl58q38TYFVq4IFVl6ezMxMKRCQtXmzAgUFsjYXKLB5s1RT03zBLUuyLNnl5QqUl0t5efJv71jTlJEQLyM5RWZGhtwD91LCQQdtnYHVubPM7GwZLlfz5QUAAGgjol5Kbdy4UUuXLg1Ng+/Vq5cmTJgQtesPHjw4bHvVqlWUUgAAtDKmacrs3l3u7t2lsYfu8FjbtmWVlMi7cJECq39XYN360G2EgaIi2SWlsisqYjMDy7JkV1bJrqyStXmz/CtXqvqDD8OPcbnk6tRJtt8vmabMDlm1Tx3sKXe/vnLvuZfihu4tV4cOzZsVAACghYt6KTVnzpzQ7w3D0HHHHSfTNKN2/czMzLDtLVu2RO3aAACg5TEMQ66MDCWOPXSnBZZlWbJyc+X79Vf5f1sl/9q1CmzYIFdWlmyvV4FN+cEyKz9fdllZ8wQOBBTIy9uaadMm+Zct3/axcXEykpNkpmdsvWWwT2/FDRuuuBH7yuzYUUYUv48CAABoSaJeSuXn50tS6Na9fffdN6rXz8jIkKTQTKyy5vqGEgAAtDqmacrs2VPunj2lI4/c4bFWVZUCubmq+fY7BdaskT83d+uTCEtLZVdWBhduj/4zYbaqXfMqULxFgTVrIve73cFZVl26yOzSRa4uXWQVF8tMTZG7Xz+599pTccOG8aRBAADQKkW9lCooKAjbzsrKiur1axqsKRHNWVgAAKD9MBMTZQ4YIM+AATs8zqqokO/XFfKvXFn7FML1CmzcKKugUGZWluzqKgXy82VtLoj+7YN+vwK5uQrk5u74OMOQkZAgIy1NZseOcnXrKk+fPnLvtZc8Q/aWe9AgFmkHAAAtTtS/O4mPjw/brq6ujur1i4qKJG2didWB9RgAAEAzMpOTFT9iX8WP2PHsbzsQkFVQoMDGjfKvXauqT/6nwIY8WQWbZW0p2TrzqjnYtuyqKtlVVcHbBZcuVcTS8C6XXD26K26//eTq1k2ubt3k7t49+Pvu3WSkpYVmogMAAMRC1Eup7OzssO2GM6d219KlS8O2KaUAAEBLYLhcwVvtOndW3PDhSjrppG0e58/Pl2/JUvlXrAjOvMrJCd42WFwku6xcdnV18JbBaN82GAgosHadqtau2/Z+05TcbhkpyTKzsuTq3EXuXr3k3mOAPIMHKW6ffWSmpkY3EwAAaNeiXkp17txZ0tY1nxYsWBDV68+aNUuGYciu/Uatf//+Ub0+AABAc3J36iT34Z2kww/b7jG23y9r82YFNm6s/doUnIG1eo1qvv02OOvK54tuMMsKrnFV5FWgqFiB31bJ+9134ceYpoykJJmZmXJ17SpXn97yDByouDGjFbf33jI8nuhmAgAAbVrUS6lRo0bJ5XLJsizZtq0vvvgidKvd7vr222+1YsWK0LWysrI0ZMiQ3b4uAABAS2K43cHSp2vXHR7nX79evp8XB582uGqV/Dk5svI3y9pSLLuySvL7oxvMsmSXlytQXq7A+vXSTz+pqm6fyxXM3LOH3D17ylW74LztrZF74EB5hg6VGRcX3TwAAKBVi3oplZ6ertGjR+uHH36QJG3YsEHvvPOOTj311N2+9j/+8Q9JW9eTGjt27G5fEwAAoLVy1xY/iSccv839lt8v/6+/yrd4ifwrVsjMyFCgsFCBDXkKbMgNrnlV++Tk3RYIBG9FzMmR94cftxPYLSM5WWaHDnJ36yZXv77yDBqouH33lXvvvXmADQAA7UyzPIbltNNO0w8//BC6ze7aa6/VUUcdpdTdWIfg9ttvj7h1789//nO0IgMAALQ5ptutuL33Vtzee2/3GLumRv7Vq1X9zbfy//abAmvXBte4KiqUVV4u1URxcXa/X3ZJiQIlJQr8/rv07bfh++PjZWZkyNW1i9z9+inh0EMVd8D+cnXtKoPCCgCANsew7Wivohl84l7//v21cePG0KymAw88UB999JHS0tJCx+277776+eefQ8cEtvEYZcuydP311+vhhx+WtHWW1PDhwzV//vxoR0cDpaWlSk9PV0lJSdi/OwAA0H7416+Xd/4C+ZYulf+33+RfnyNrc76s0jKpJuI5f9EXHx9cdL1Pb7n69JG7Tx/ZlZXy7DNccSNGyExIaP4MbVCPHj2Um5ur7t27Kycnx+k4AIA2pLFdQrPMlEpISNAdd9yhiy++ODSz6fvvv9fee++tu+++W2eccYbidrKmwObNm/X222/r/vvv1++//x62LpVhGLrnnnuaIzoAAAAaqLtNUOMjnyhoWZas4i2ycnMUWJ8j//p1wV/XrZdvxQpZ0Sg7amrkX7lS/pUrtxPQLTM9TWbnznL36SPPoEHyjBih+NGjZCYl7f7rAwCAZtEsM6XqXHjhhZoyZUrYLXeGYSg1NVUjRozQokWLVFxcHBo/88wzVVRUpLVr12rFihWSFHZeXTH1t7/9Tf/85z+bKzbqYaYUAADYHZZlyb/yN/kWLJBv2TL5f/9dgZxcBQoKZJeXR/8pgg15PMGnBfboEXxS4H4jlHD4YXJ16tS8r9sKMFMKANBcGtslNGsp5ff7NX78eH3yySehWU71S6b62zsbqxs/++yzNW3atOaKjAYopQAAQHOy/H75Fy+Rd948eZcslX/VKgU2bJBdUiLbMKTKymZ5XbNLZ3kG7CH3gP5y7zFA7v4D5B7QX2bnzu1mwXVKKQBAc2kRpZQULJJuv/123XXXXbIsK6Jk2maoesfUHWcYhm677bbQE/gQG5RSAADAKbZtyyookH/NGvlXr1FgzRr516yRd/4CBdavb7bXNZKSZGZny923jzx77624/fdX/P5j2tytgJRSAIDm0mJKqTo//PCD7rzzTn3yySdbX7xB+VRf/Vhjx47VnXfeqQMPPLBZMyISpRQAAGiJLMtSYM1aeWfPlvfnn+VfsVKB3FxZhYWyq6qk5vgWNyFBrs6d5d5zD8WNGqWEI49U3F57Rv91YoRSCgDQXFpcKVVn6dKlevPNN/XVV1/pxx9/VHV1dWSo2qfrHXXUURo/fjxllIMopQAAQGvkX7tWNd9+r5r5c+X/dYUC63Nkbdki+f3RfSHDkJGRLnevXko680zFjxwp9x4DZOzkoT4tAaUUAKC5tNhSqj7LslRYWKjCwkIVFxcrMTFRHTt2VHZ2tuLj452KhXoopQAAQFtiVVUpsG5dcPH1336Tf9Uq+Vf+Jv9vvwVnWEWD2y33HgOCTwEcNEiu3r3k7t1b7sGDW9R6VZRSAIDm0tguwR3DTBFM01R2drays7OdjAEAAIB2wkxMlLnXXvLstZcS643bliXv4sWq+fob+RYtCi64vnGT7LKypt8K6PfLv/wX+Zf/oiq9U+/FTZkdsuTeay/FH3SQkk46Ue7evaPyvgAAaI0cLaUAAACAlsAwTcUPH6744cMj9vnXrFH1FzNVM/sn+X75RVZenuxdeSqgZcnaXCDv5gJ5v/1OZffcK7ndcnXtKs+QvRU/dqySTjheZmZmFN4RAAAtn6O376Hl4/Y9AACASJbXK+/3P6rmq1nyLloku7paVvEWBdat2/2LJyTI3aunPCP2VeLRxyjhyCNkuFy7f90GuH0PANBcWsXtewAAAEBrZMbFKWHsIUoYe0jYuFVaKt+vv8q3bLn8y5fLt/wXeefNa9otgNXV8q9YKf+Klap6bbqMlBTFjR6t+D8coPgDDpBnyJBmKakAAIg1SikAAAAgSsy0NMWPGqX4UaNCY5bfL+9336v6009VM3eeAmvWyK6oaPQ17fJy1cycqZqZMyVJRmqq4seMkdm9u+JG7qfEcSfIbAVP+wMAoKFG3763bNkyDR48uLnzRIXf79eaNWs0YMAAp6O0ety+BwAAEH1WZaWqPv1MNTO+kHfRIgU25Eo13l2+npmdrbgR+ypx/HglnHC8TPfOf/bM7XsAgObS2C6h0c+kHT58uC655BJt3LgxKgGby/Tp0zVo0CC98sorTkcBAAAAtslMSlLyKScr67FH1OXbr9X991XqPG+u0u+4XfFHHC6zSxepCbfoWZs3q/rTz1R8+RXK69NPGw8+RKX/fUhWeXkzvgsAAHZPo0upQCCgZ599VgMGDNB1112n3Nzc5szVZG+++ab2228/nX322fr999+djgMAAAA0ibtLZ6VcOEkdX3xBXefNUfd1a9RlwTxlPv6Ykv90ntz9+zfuQratwO+rVXbf/crba5A2HniQSu67X1ZpafO+AQAAmqjRpVSdyspKPfTQQ+rfv78uuugizZ8/vzlyNUpZWZkef/xxDRw4UGeeeaYWLlwoHiYIAACAtsLVqZOSxp+kjHvuVuevZ6nLvDnKfOwRxR18kNSIW/QkKbB2rcr/+5DyBu2tjfsfqJJ/3y2ruLiZkwMAsHONXlPq4IMP1nfffSfDMCRJtm2Hfj9ixAhdeOGFOvXUU5Wdnd18aSVZlqVZs2Zp2rRpmj59uiorK0NFlGEYsm1bycnJeuWVV3TiiSc2a5b2gDWlAAAAWi7vgoWqmD5dNd9+p8C6dZLf37gTExI0uqhAG7ZsYU0pAEDUNbZLaHQpJUkvvPCCbr75Zm3atCmsnJKChZBpmjrooIM0btw4jR07ViNGjAgdtzs2bdqkL7/8UjNmzND777+vwsLCiNeu+/1pp52mBx98UD169Njt1wWlFAAAQGtS8+OPKnviKXl/+GGnT/gbuTFPG60ApRQAIOqapZSqu/A999yjRx55RBUVFdssp+qkpaVpn3320bBhwzRkyBD17t1b3bt3V5cuXZScnKyEhARJwaflVVVVqaioSDk5OcrJydGvv/6qxYsXa9GiRVq1alXomvXj1i+jxowZo7vvvltjx45tytvBTlBKAQAAtE7e+QtV9vhjqvn2W9llkQueh0qpbt2U08LWiwUAtG7NVkrVyc/P11133aXnnntOlZWVYWVUw+JoR+oXS9vScF/DEmzEiBH6xz/+oZNOOqnJ7wE7RykFAADQ+nmXLFHZo4+pZtZXssvKJG0tpbqlpiqXRdABAFHU2C6hyQud1+nUqZMeeughrVu3Tv/617/UuXNn2bYdWmuq7ktSaHxbX5Zl7XB//WvVL7DGjRunmTNnau7cuRRSAAAAwA7EDRmiDk8+oa7LlijugP3D9lmVlbJqiyoAAGJpl0upOllZWfrb3/6m9evX6/3339fpp5+u+Pj4UKkkKaJYaspX/YJq4MCBuuuuu7RmzRq9//773KoHAAAANIFhmsqcPFky6/01wJb8K1Y6FwoA0G417jmyjeByuTRu3DiNGzdOlZWV+uKLL/TJJ5/oq6++0q+//irLspp8zaysLI0ZM0ZHH320jj/+eO2xxx7RigsAAAC0S+5+fWUkJjodAwCA6JVS9SUlJenEE0/UiSeeKEkqLy/X/PnztWLFCq1Zs0Y5OTkqLS1VZWWlAoGAEhMTlZycrE6dOql3797q16+fhg0bpn79+jVHPAAAAKBds/3+sG2rPHIhdAAAmluzlFINpaSk6JBDDtEhhxwSi5cDAAAAsCM1NWGbgRyevgcAiL3dXlMKAAAAAAAAaCpKKQAAAAAAAMQcpRQAAAAAAABijlIKAAAAAAAAMUcpBQAAAAAAgJijlAIAAAAAAEDMUUoBAAAAAAAg5iilAAAAAAAAEHOUUgAAAAAAAIg5SikAAAAAAADEHKUUAAAAAAAAYo5SCgAAAAAAADFHKQUAAAAAAICYczsdALtu1apV+umnn5STkyOv16vMzEwNHDhQBx54oBISEpyOBwAAAAAAsF2UUq3Qu+++q3/961+aP3/+NvenpKRowoQJuu2229SxY8cYpwMAAECLl5gYtunu38+hIACA9ozb91qRmpoanXfeeTrllFO2W0hJUnl5uR599FENHjxYX3/9dQwTAgAAoDUwzPC/Bhgej0NJAADtmSOl1Pnnn68HHnhAc+fOdeLlWyXLsnTmmWdq2rRpYeMul0t9+/bVPvvso/T09LB9mzdv1nHHHacffvghllEBAAAAAAB2ypFS6uWXX9YNN9ygf/zjH068fKt033336b333gsbu/TSS7Vu3Tr9/vvvWrBggYqKivT222+rV69eoWMqKyt1xhlnqKSkJNaRAQAAAAAAtqvF3b735ptvavny5QoEAk5HaTEKCwt11113hY3dfffdeuKJJ9StW7fQmGmaOuWUU/T999+rT58+ofGcnBw98MADsYoLAACAVsa2bacjAADaIUdKKZfLtd19Z5xxhoYMGaLx48fHMFHLNnnyZJWVlYW2DznkEN10003bPb579+569tlnw8YefPBBFRYWNltGAAAAtCKGEb5NKQUAcIAjpVRqaqok7bAksSwrVnFaNMuy9Pzzz4eN3X777TIafiPRwBFHHKGDDz44tF1WVqbp06c3S0YAAAC0LnaD77WtqiqHkgAA2jNHSqk+ffrItm2tWLFCVfwPcIe+//57bd68ObTdr18/jR07tlHnTpo0KWz73XffjWIyAAAAtFqVlWGbgbVrHQoCAGjPHCml9t9/f0lSaWmprr76avn9fiditAofffRR2PZRRx2101lS9Y+tb9asWaqoqIhaNgAAALQRNncpAABiz5FSasKECaHfP/fcc+rWrZsuvfRSTZs2zYk4LdrChQvDtg888MBGn9utW7ewBc+9Xq+WLVsWpWQAAABoMyzWlAIAxJ4jpdTo0aN19dVXh57yUVBQoGeeeUbnn3++pODTP3766SdNmDBB//3vfzVr1ixt2bLFiaiOW758edj24MGDm3R+w+MbXg8AAACQKKUAALHnduqFH3zwQfXv31933HGHCgsLIx5DW1xcrJdeekkvvfRSaKxXr17aZ599wr569+4d6+gxU1VVpXXr1oWN9ezZs0nXaHj8r7/+utu5AAAA0Mo1XA6CmVIAAAc4VkpJ0pVXXqmLL75YH374oWbMmKHvv/9eP//8swzDiCipJGndunVat26d3n///dBYenp6RFE1ePBgud2OvrWoKCgoCPvn4PF41KlTpyZdo3v37mHb+fn5UckGAACANoQ1pQAADnC8uYmLi9Opp56qU089VZJkmsE7CocOHaqTTz5ZCxcu1IIFC5STk7PNomrLli366quv9NVXX4XGPB6PBg8erH333VfPPfdcbN5IMygvLw/bTkpKavQi53WSk5N3eE0AAABAdFIAAAc4XkptT/fu3XXHHXeEtouKirRgwQItXLgwVFT9+uuvCgQCEWWV1+vVwoULtWjRojZVSiUkJDT5GomJiTu8ZkM1NTWqqakJbZeWlkqSBg4cGCoMt2fEiBFhs9gk6aSTTtL8+fN3mvO6667TddddF9ouKyvToEGDdnqeJL333nvab7/9QtsffvihLr300p2el5KSol9++SVs7IYbbtCrr76603NPOOEEPfXUU2FjI0eO1MaNG3d67uTJk3XOOeeEtn/99VcdccQROz1PkubMmaOuXbuGtp9++mn985//3Ol5e+65p2bOnBk2du6554aVudtz0UUX6bbbbgsb69GjR6Pyvvzyyxo7dmxoe9asWTrvvPMadW5OTk7Y9h133KFnnnlmp+cdeuihEQ9NOPzww7VixYqdnvuPf/xDF198cWg7Ly9Po0aNalTeL774QnvttVdo+5VXXtGNN9640/O6dOmiuXPnho1dcsklEU/e3Jazzz5b9913X9jYwIEDG1U+P/nkkxo3blxoe968eRo/fvxOz5OCa9OlpqaGth944AE98MADOz2Pzwg+IxriM4LPiPr4jIj9Z0Rl7eyofCugvW78q4w7btvmeXxG8BnREJ8R7eMzgu8jIvEZ0fjPiEMOOWSnx0ktsJRyuVwKBAIR41lZWTriiCPC/oOqqanR4sWLQ2XVggULtHjxYlVUVMQycrOprq4O246Li2vyNeLj48O2q6qqdnj83XffHVYG1snLy9vpa21rvavNmzcrNzd3p+fWlV91bNtu1HlSsISsr6qqqlHn1v+fYZ3i4uJGnVtUVBQxtnHjxkadW1lZGbbt9/sb/V4b/rdRXl7eqHPT09MjxgoKChp1bklJScRYY/PWLzjrtht77rZyNObcgoKCiLFNmzY16tyGH7CBQKDRef1+f9h2ZWXlLr/XoqKiRp1bXFwcMbZhwwaVlZXt9NyGnwVer7fReRv+IKC0tLRR5/IZwWdEQ3xG8BlRH58RMf6MMIzQ0uaWpA1btkjbebAQnxF8RjTEZ0Q7+IwQ30dsC58Ru/YZsSMtrpSqqqrSypUrG/VG4+PjNXLkSI0cOTI0Ztu2Vq5cGSqqWrOGM6MafiA2RsP/UHc22+qWW24J+ylCaWmpevbsqa5du+50plR2dvY2xxqua7UtaWlpYduGYTTqPCmyrEtMTGzUuSkpKRFjmZmZjTo3KysrYqxLly47PU8K3oZZn9vtbvR7dblcYdspKSmNOrdz584RYx07dmzUudv6n0xj8zYsRePj4xt97rZyNObcjh07Rox17tx5m//Da6jhnwmXy9XovA3XsUtKSmrUudv6c5OVldWoczMzMyPGunXr1qifXjScRRkXF9fo99rwNuK0tLRGnctnBJ8RDfEZwWdEfXxGxPgzwuVS3b8pU1KX1FQZDf451uEzgs+IhviMaAefEeL7iG3hM2LXPiN2xLC3tVATWoTly5dr8ODBoe309HRt2c5PsLbngQce0PXXXx/aPvPMM/Xaa681+vzS0lKlp6erpKQk4sMcAAAArdOGvQZpv99WaqMVUBfTpSUXXaQOTz7hdCwAQBvR2C5hx1Nf4KiGTWplZeU2F3vfkYa3Mm6rsQcAAED7YjR4GE5gwwaHkgAA2rMml1JTpkzR0qVLm1yOoOk6duwYNr3V5/MpPz+/SddoeK9op06dopINAAAArZerwe02gVxKKQBA7DW5lLrwwgs1bNgwpaena9KkSc2RCbUSExPVq1evsLF169Y16RoNjx84cOBu5wIAAEDr5h7QP2zb2saCtgAANLddun3Ptm2Vl5drwYIF0c6DBhqWSMuWLWvS+cuXL9/h9QAAAND+eIYNCx/wemU14WlJAABEwy6VUg2fmIDms88++4Rtf//9940+Ny8vT2vWrAltezyesIXTAQAA0D4lHn9c+IBty7e0aT/8BABgd8V0ofOJEyfqqquu0rRp07Ry5cpYvnSrNW7cuLDtGTNmNHo9r88++yxs+7DDDmOhcwAAAMjdvbsMT/gjxr3z5jmUBgDQXsW0lFqwYIEee+wxnX/++Ro0aFAsX7rVOvDAA9WxY8fQ9u+//65Zs2Y16tznnnsubHv8+PHRjAYAAIDWzOMJ26z59luHggAA2quYllJScD2quq9dcf/99+uFF15o8tpKrZVpmpowYULY2B133LHTf35ffPGFvvnmm9B2amqqzjjjjOaICAAAgFbIiI8P26757ntZlZUOpQEAtEcxL6V2dz2qadOmaeLEiRo6dKgyMjKiE6qFu+mmm8Juu/vqq6907733bvf43NxcXXjhhWFjV199ddiMKwAAALRvDUsp1dSopt4PNQEAaG4xL6WioW6mVVlZmdNRYqJjx476v//7v7CxW265RZdffrk2bNgQGrMsS++++64OPPDAsAXOu3Xrpuuvvz5WcQEAANAabOOHxZVvvuVAEABAe9UqS6n2+PS/m266KWLR8yeeeEK9evVS//79NWLECHXo0EGnnHKK1q1bFzomMTFR06dPbzezygAAALDrqj/+RP6cHKdjAADaiVZZSrVHpmnqjTfe0FlnnRU2HggE9Pvvv2vBggXasmVL2L4OHTro448/1h/+8IcYJgUAAEBrVnrPZKcjAADaCUqpViQhIUGvvvqq3nzzTe2zzz7bPS45OVmXX365li1bprFjx8YsHwAAAFofwxX+V4Kqjz6SZVkOpQEAtCdupwOg6U477TSddtpp+u233zR79mzl5ubK6/UqIyNDgwYN0h/+8AclJCQ4HRMAAACtgJGUFD7g9ar88ceVduWVzgQCALQblFKt2IABAzRgwACnYwAAAKAVM1JSJLdH8vlCY+VPPkUpBQBodty+BwAAALRziSeGP1DHLt6i8hdfdCgNAKC9oJQCAAAA2rmMf94hNXjCdend97C2FACgWVFKAQAAAO2cmZmphOOODRuzS8tU/uijDiUCALQHlFIAAAAAlPmf+yWXK2ys7IH/yiovdygRAKCto5QCAAAAIDMtTYmnnRo+6POp6AoWPAcANI/dKqXy8vL0xBNP6Pvvv1dFRUW0MgEAAABwQMZ9k2UkJoaN1cz4QjXzFziUCADQlrl35+T8/HxdWfuoWMMw1LdvXw0fPjzsq3fv3lEJCgAAAKB5mW630u/8p7Zcf0PYeOH5F6jLzwtlmtxoAQCInl0upQzDkG3boW3btrVq1Sr9/vvveuedd0LjaWlpGjZsmPbZZx/l5+fvXloAAAAAzSr5rLNU9vgTCqz6PTRmFxdry7XXKeuh/zoXDADQ5uzWjzoMw4j4sm077KukpETffvutHn30UW3cuDHs/Jtuukmvv/66VqxYsVtvAgAAAED0dHz5JanBrKiqjz6Wf+1ahxIBANqiJs+UOvvsszV//nytWLEibKaUYRhhv9ZX/7j6Y/fff39oOzk5WcOGDdO+++4b+ho6dKjc7t26wxAAAABAE7l79VLqNVer7IEHtw5WVano8iuU/c7bMuLinAsHAGgzmtz4TJs2TZJUUVGhBQsWaN68eZo/f77mzZunX3/9VYFAIOz4uhlUDdXNpKpTXl6uH374QT/88ENozOPxaPDgwWFFldfrbWpkAAAAAE2Udv118q1cqeoPPgyN+RYuUuk99yr9H393MBkAoK0w7G1NY9pFVVVVWrhwYaikmjdvnpYvXy6/3x/+otsoqaRtz6ja3vG2bcswjIgSDNFVWlqq9PR0lZSUKC0tzek4AAAAiJIePXooNzdX3bt3V05OzjaPscrLlX/s8QqsXh02nvXs00o87rhYxAQAtEKN7RKiem9cYmKiDjjgAB1wwAGhsZqaGi1atChUVM2fP19LliyRz+cLO7exM6rqjgUAAADQvMyUFGU9+YQ2nzReqqkJjRdffa3cffvKM3Cgg+kAAK1dsy/YFB8fr9GjR2v06NGhMZ/Pp8WLF4dmU82fP1+LFy9WTb3/0UnbL6oAAAAAxEbckL2V/o+/q+Rvt4bG7IoKbT7pZGX/7xN5+vV1MB0AoDWL6u17u8Pv92vp0qVha1T9/PPPqqqqCjuurqTi9r3Y4PY9AACAtqkxt+/VsW1bW669TpVvvBk2bqSlqsvsH2XyfSIAoJ7GdgktppTaFsuytGzZsrCiatGiRaqoqJAkSqkYoJQCAABom5pSSkmSXV2tTcccp8Bvv4WNu7p1U6cfvpPJU7MBALUcWVMq2kzT1JAhQzRkyBBdcMEFkoI/pfnll19CJRUAAACA5mckJCjr0Ye1+fhxkmWFxgMbNqhg3Enq9L+PHUwHAGiNTKcDNJVhGBo0aJDOPfdcPfDAA07HAQAAANqNuKFDlfHIQxHjvsWLVXDueQ4kAgC0Zq2ulAIAAADgnOSTT1bqLTdHjNfM+kqFky5yIBEAoLWilAIAAADQJGlXXqGk886NGK/+3/9U9JerHEgEAGiNKKUAAAAANFnmvfco4bhjI8ar3n5HRVddE/tAAIBWh1IKAAAAwC7p8Owzih87NmK86q23VHjxpbEPBABoVSilAAAAAOyyjtNeUtz+YyLGqz/6SAV/usCBRACA1oJSCgAAAMBu6fDGdMWNHh0xXjNzpvJPGi/LshxIBQBo6SilAAAAAOwW0zSV/c5bij/kkIh9vnnzteW662X7fA4kAwC0ZJRSAAAAAKKi46vTlHDsMRHjVW+8qcKJk2RVVjqQCgDQUlFKAQAAAIiaDs89q8Qz/hgxXjPzSxWcdroCGzc6kAoA0BJRSgEAAACIqqwHH1Dmc8/KSEgIG/f9vFj5J5wo75IlDiUDALQklFIAAAAAoi7p2GPUcfrrMjIywsatjRu1+aSTVXLPPc4EAwC0GJRSAAAAAJpF3H4jlP3eu3L16R2+o6ZG5Y88ps1nnCnL73cmHADAcZRSAAAAAJqNZ0B/ZX/wgeL2HxOxz/vd99o0crT8ubkOJAMAOI1SCgAAAECzcmVlquOrryhu9OiIfdbmzdr0h4NV9fnnDiQDADjJ7dQLT5w4MWz77rvvVufOnR1KAwAAAKA5GXFx6vDWGyr680TVzPgifKfPp6IJE5V8wQXK+PedzgQEAMScYdu27cQLm6YpwzBk27YMw9Dy5cu15557OhEFO1BaWqr09HSVlJQoLS3N6TgAAACIkh49eig3N1fdu3dXTk5OTF+77PEnVfrvf0vb+KuIe8AAZb/7tszMzJhmAgBET2O7BG7fAwAAABBTqZdfqo7vvCUlJETs8//2m/L2G6WqmTMdSAYAiCVKKQAAAAAxFz9qlLrO/Umufv0id9bUqOhPF6jo2utkWVbswwEAYoJSCgAAAIAjzMxMdfnmKyWdd94291dNf0ObRo2Wb/Wa2AYDAMQEpRQAAAAAR2Xee7eyXpgqxcdH7LM2blL+IYeq6tPPYh8MANCsKKUAAAAAOC7xyCPUZe5Pcg8YELnTslQ0cZIKJ06Sd8mS2IcDADQLSikAAAAALYIrK0udv/pSKVdeIRlGxP7qTz/T5mOOo5wCgDaCUgoAAABAi5J+y83K/t/HMjt02Ob+unJq8xlnquKNN1gMHQBaKUopAAAAAC1O3JAh6rJwvtLv/JfMzMxtHuP97nttueY65fUboM2nnKqqjz6moAKAVoRSCgAAAECLZJimUv48QZ1//F5pt9y83XJKPp+8P81R0cWXbC2oPvyIggoAWjhKKQAAAAAtmpmSotQrr9h5OSVtLaguuTRYUJ18iio/+JCCCgBaIEopAAAAAK1CXTnV6YfvFD/2UMncyV9nfD5558xV8aWXBQuq8aeo8v0PKKgAoIWglAIAAADQqrhSU9Vx2svquuZ3Zdw3WZ6hQySXa8cn+Xzyzp2r4ssuV16/ASq569/yLV0m27ZjExoAEIFSCgAAAECrZLpcSj7nbHX63yfqunpVsKAaNrRRBVX5408o/+hjtOngQ1Vyz70UVADgAEopAAAAAK1eqKD65OPgDKr775Nn2LCdFlSB1atV/sijyj/6GOUfdoSKrrte1d98E6PUANC+UUoBAAAAaFNM01Ty2Wep0ycfBQuq/9wvz/CdF1T+lStV9fp0FZ51jjbssacKJ14o75IlMUoNAO2P2+kAAAAAANBcTNNU8llnKvmsM2VZlqo//FC+RT+r6sOPFMjJ2e55dmWVqj/9VNWffiojPV0JRx+ltOuulbtXrximB4C2jZlSAAAAANoF0zSVdNJJSv/7rer84/fK/ugDpVx6iVw9euzwPLukRFVvvKlNB/xBeSNHqWTyfbIqKmKUGgDaLkopAAAAAO2OYRiK22efUEHV8d135NlnuGQYOzzPytuo8oceVt5eg7TpyKNVMX26LMuKUWoAaFsopQAAAAC0a4ZhKH7USHX66EN1XfmrUm+6Qa7evXd8km3Lv3y5tlx7vfL6DVDBOefKl5Mbm8AA0EZQSgEAAABALTMxUWlXXaUu33+rrkt+VsrFF8ns3GnHJ/l8qvnqa+Xvf4AKzjlXle+9J7u6OjaBAaAVo5QCAAAAgG0wMzOVfts/1HX+PHX69lslnjxeRnLy9k+wbdV89bWKL79SeSNGasvfbpX3559l23bsQgNAK0IpBQAAAAA74enbW1mPPapuK35Rh9deVdz++0vu7T/M3C4pUcXUF7T5uBOUt9cgFU6YKO+KlTFMDAAtH6UUAAAAADRBwsEHKfutN9R19Sql332X4vYfIyM1dbvH2xUVqv78c20+7HDljRytsscelxUIxDAxALRMlFIAAAAAsAtM01TK+ecr+6031WXBPGU+8rDi//CHHZ5j5eWp9N93BxdHP/c8eZcvj1FaAGh5KKUAAAAAYDeZiYlKOvUUdZz+mjr/8J1Sr7tWZteu2z/B71fNrK+0+cijlbffKJU+/Kgsny92gQGgBaCUAgAAAIAocvfqpbTrr1PnH79X2t//LlfPnjs83tq4UWX33qu8AXuq8IIJ8q9ZE5ugAOAwSikAAAAAaAam263USy9Wlx+/V+fvv1XCCcdLcXHbP8HvV/WML7TpDwdr48GHqOrDj2IXFgAcQCkFAAAAAM3M3bu3Ojz9lLquWqn0yffK1afPDo8P/L5aRZdcqs0nn6rKd9+V7fXGJigAxBClFAAAAADEiGmaSjn3HHX57ht1nv2DEk48UYqP3+7x3jlzVHzFX7RxzAEqffC/ChQWxjAtADQvt1MvfOqpp8owjNB2WlqaU1EAAAAAIObcPXqow5OPy7IsVb70ksofe0KB3NxtHmvl56vs/v+o7JFHlXDUkUo8/jgljR8f48QAEF2Gbdu20yHQcpWWlio9PV0lJSUUhwAAAG1Ijx49lJubq+7duysnJ8fpOKjlXbZMJXf8U77lv8jeyawoIy1NyRecr9Trr5Pp8cQoIQDsXGO7BG7fAwAAAIAWIm7wYGW//pq6zpujrCefUNz+Y7Z7rF1aqvJHHlXegD1VdOVfFNiyJXZBASAKKKUAAAAAoIUxPB4lnjhO2W+9qexP/6ekP56+/Sf3+f2qeuddbRwyTAVnnyP/2nWxDQsAu4hSCgAAAABasLgheyvzvw+qy+wfFH/YYVK9tXnD2LZqvv5Gmw78g/KPPV418+fHNigANBGlFAAAAAC0Aq5OndTx5RfVddkSJZ1ztpSw/af2+RYvVsGJ47XxoENU/e23MUwJAI1HKQUAAAAArYiZlqbM+yar68oVSrvlZhkZGds9NrB6tQrPPFsb9z9Q1V98EbuQANAIlFIAAAAA0AqZpqnUK69Qt6WLlfnIQzK7ddvusYH161V4/gQV/eUq+VaujGFKANg+SikAAAAAaOWSTj1VXefMVsc3pss9cOB2j6t6+x3lH3aEii6/Qr5Vv8cwIQBEopQCAAAAgDYi/sAD1PmLz9VpxmfyDB++7YNsW1Xvva/8sYep+Lrr5V+/PrYhAaAWpRQAAAAAtDGeQYPU6eMP1enrWYobNVJKSIg8yLJU+fp0bfrDwdp0yFjVzJ0b+6AA2jVKKQAAAABoozz9+yv73XfUZc5PSr36KhmpqZEHBQLyr1qlgvGnKP+YY+X7ndv6AMQGpRQAAAAAtHGurEyl3XiDuvzwnVKuvEJGYuI2j/MtWar8gw9VwZ8uUKCkJMYpAbQ3lFIAAAAA0E6YmZlKv+Vmdf7xeyVdOEkyjG0eVzNzpjYOHa4tN/+fLL8/xikBtBeUUgAAAADQzrg6dlTmHber06f/k3vwoG0fFAio4qWXtHHgYJW/+FJsAwJoF5pcSk2ZMkVLly6VbdvNkQcAAAAAECOevQer8+efqcMbb8js3n2bx9hVVSq55f+0cf8D5V28OMYJAbRlTS6lLrzwQg0bNkzp6emaNGlSc2QCAAAAAMRQwoH7q+tPPyrjgf9sezF0SYH167X52ONVcN75ssrKYpwQQFu0S7fv2bat8vJyLViwINp5AAAAAAAOST7zDHVdvlSpV18leTzbPKbmyy+VN3SYKqZPj3E6AG3NLpVSxnYWwwMAAAAAtG6GYSjtxhvUddkSJRxzzLYP8vm15drrVXzjzbJ4Sh+AXRTThc4nTpyoq666StOmTdPKlStj+dIAAAAAgCYwk5LUYcqz6vTF53L167fNYyqnTdOmQw9T5Xvvs+4wgCaLaSm1YMECPfbYYzr//PM1aNB2nvAAAAAAAGgxPAMHqss3Xynjwf/ISEqK2G9t3qziy69Q4QV/ViAvz4GEAFqrmJZSUnA9qrqvXXH//ffrhRde0LJly6KcDAAAAACwPclnnKEuy5Yo7Zabt1lO1XzxhTYedoSKr71OViDgQEIArU3MS6ndXY9q2rRpmjhxooYOHaqMjIzohAIAAAAA7JTp8Sj1yivUadZMJRx5ZOQBZWWqnP6GNu07QjWLfo59QACtSsxLqWiom2lVxmNIAQAAACDm3N27K2vqFGU99aTMTp0i9luFRSo4/gRt+fs/WGsKwHa5nQ6wKwzDaJcfbJZl6bffftPixYuVl5en0tJSJSYmKisrS4MGDdK+++4rz3Ye2woAAAAA0WQYhhLHnaC4Aw/UlptvVvVHH0ccUzHleVV/9bWy33lLrg4dHEgJoCVrlaVUe7Jx40a99dZb+uyzzzRr1iyVlpZu99jExESdfvrpuvbaa7XvvvvGMCUAAACA9sqVlakOTz+l0vv/o7KHHpYsK2x/YNUqbRw5WlmPP6bE4451KCWAlqhV3r7XXowfP17du3fXlVdeqffff3+HhZQkVVVV6aWXXtLIkSN1ww03yOv1xigpAAAAgPYu7a/Xq9PXs+Tq3j1yp9erogsvUtG117XLu14AbBulVAv23XffyWrwUwZJ8ng86t27t0aOHKkhQ4YoqcGTLyzL0v33368//vGP8vv9sYoLAAAAoJ3z9O2rzrN/UNI5Z29zf9X0N5R/5FGyKipinAxAS0Qp1Up07txZN9xwg7744guVlpZqzZo1mjNnjhYvXqwtW7bo/fff19ChQ8POef/993XzzTc7lBgAAABAe2QYhjLvm6wOL70oxcdH7Pf/8qs27jdKvuW/OJAOQEtCKdXCDRkyRG+88YZycnI0efJkHX744UpISAg7xuPx6MQTT9RPP/2kcePGhe17+OGHtWLFilhGBgAAAAAlHH6YuiyYJ/fAvSL22WVlyj/6GFW+844DyQC0FJRSLdhzzz2nRYsW6fTTT5fbvfM16RMSEvTaa6+pR48eoTGfz6cXXnihOWMCAAAAwDa50tPV+YsZSjrvvMidlqXiK69S2VNPxT4YgBaBUqoFGz9+vEyzaf+KkpOTddVVV4WNffrpp9GMBQAAAABNknnv3cp46EHJ5YrYV3rPZFV99LEDqQA4jVKqDTr44IPDttetW+dQEgAAAAAISj79dHX67FMZqSnhO7xeFV1yqcqnPO9MMACOoZRqgzIzM8O2S0pKHEoCAAAAAFt5Bu6lzj/NlqvekiOSJNtWyd//obInnnQmGABH7FYplZeXpyeeeELff/+9KnikZ4uRm5sbtt2hQweHkgAAAABAOFdamjp/980215kqvfMulT3yqAOpADhh56tn70B+fr6uvPJKScHHfvbt21fDhw8P++rdu3dUgqLxvvnmm7DtPffc06EkAAAAABDJcLuVcc+/5eraRWX33R+2r/See+Vd/ouyHn1YRhPX2AXQuuxyKWUYhmzbDm3btq1Vq1bp999/1zv1HuuZlpb2/+3dd5hU5f3//9c507axBXbp1aARIgQV0FjAhhrxJ/lETTEa8UNiAS9jsMf4AWOKfm0xRUXsxhJjQ9GIiBI1Go0gTRCU3hEWdrZPOef3x8wOHGaB3WVnzs7M83Fdk5n7Pae8Z3Gz7Iv73EdDhw7VsGHDtG3btoPrFgcUjUb15JNPOmpnnXWWS90AAAAAQPMMw1Dx1b+QWVCgqlt/43ivYcYMba/coYrnnnWpOwDpcFCxs2EYSQ/bth2PqqoqffDBB/rLX/6iLVu2OPa/4YYb9Pe//10rVqw4qA+B3aZNm6ZVq1Ylxj6fTxdccIGLHQEAAADAvhVd+nOV/ObWpHro/Q+085eTXegIQLq0eqbUj3/8Y82fP18rVqxwzJQyDMPxvKc9t9uzdtddu6dpFhYWaujQoTryyCMTjyFDhsjrPagrDHPKypUrdeONNzpqkyZNUu+9FxEEAAAAgA6kaML/KlpZqZo/3ueo1z3/D5k9e6jkuutc6gxAKrU68Xn66aclSbW1tfrss880b948zZ8/X/PmzdPy5csVjUYd2zfNoNpb00yqJjU1Nfroo4/00UcfJWo+n0+DBw92BFWhUKi1LeeEuro6nXfeeaqurk7U+vXrp9/85jf72StZY2OjGhsbE+NgMNhuPQIAAADAvpRcd60iy75Qw6xZjnrNH/8k/7Bhyh8zxqXOAKSKYTc3jamN6uvrtWDBgkRINW/ePC1btkyRSMR50mZCKqn5GVX72t62bRmGkRSC5SLbtvWDH/xAL7zwQqLm9Xo1d+5cHX/88a061tSpU3XrrclTZ6uqqlRcXHzQvQIAAKBj6N27tzZu3KhevXppw4YNbrcDSIr9bvP12ecovGCB8w2fT93+/b68vXq50heA1gkGgyopKTlgltCuoVRzGhsbtXDhwkRQNX/+fC1ZskThcNjZSCuCqqa1q1IZSl199dW67777DrzhQZoyZYqmTp16UMeYPHmy7r33Xkftr3/9qyZOnNjqYzU3U6pPnz6EUgAAAFmGUAodlRUOa9sJoxTd+79L05R8PqnpahzDkExTZufO8vTuLcPrkTweyfTI8JiS16vIVytl19bE9jU98Wcz9n58bHjMPfbzSPGxp6KrfN8aHNvG641vY8rweBRes1rWrioZHm/svF5f/Nkrebwy/F7J65XhiT/7/bFjeL0yOnWSr3//xHkM0yPFe7dra2VHozJ8Phlen+T3SYGATJ8v9tn9fpkejyt/LkBrtDSUSvmCTYFAQCNHjtTIkSMTtXA4rMWLFydmU82fP1+LFy92hCHSvi/9w2633357UiA1ZcqUNgVSUuzPKxAItEdrAAAAANBqps+nijdmasvIY6WGht1vWJYU/51xz6kL0ZoaRdetS0kv9a+8kpLjtjvDkDwemZ07x4O1eLhmemR4vbKCVbJ2VcUCPdOMbW8YkmlIhpl4NuKhXWI705QCAQWGHx0/pnePEM+UtX27ImvXxYM7z+6QzRML2nb3En/t8+1+7fXKf8wxMr3e3QGdJ/baqq5RdNMmGT5fLMiLP8vnk+mLPcvrk+H3xd/zyQj4pXiYZ3YuiwWBppk4v0yTfKEDcmUVcZ/Pp6OOOkpHHXWUfv7zn0uSIpGIPv/8c8caVYsWLVJ9fb1jX/4j2m3atGm66aabHLWrrrrqoGdeAQAAAICbPF26qMtTT2jH+T90u5XMYNtSJCJr27YDb9qG9+r2uMN7e6p78qmUHHefTDMWbu6pKWPY87mZh2EY8gwYILOgIB6ieRMz6+zGkCJffRmbfWcY8Zl4ZizwawoHzT1eN83U88Re+4YOlVlWlhTi2dGoQgsWJGbZNdWNpnCvqe71xV97YjPsfF4ZPq88ffvJU1YaC/bMeFDp9UqGqeiWLbEZeb74jDyfPxb4BQLx8C82M0+BQEpn56X88r2DYVmWli5d6giqFi5cqNraWklK6eV7s2fP1scff5ySY+9p1KhRGjVqVKv3e+aZZ3TRRRfJ2uMb6uKLL9Zjjz3WrsFdS6fcAQAAILNw+R4yQfVf/6rg7293uw0AewoEZOTnxS899SYua5XHk5i1WG1ZGrRlk/trSrU327b1xRdfJEKqe+65x+2W0m7GjBk677zzHAvIn3vuufr73/8uTzsnmIRSAAAA2YlQCpkitHix6mfOlF3fIEUisqNR2ZGIFI1KkajMigp5e/eSolHZVlSKWrHtLEuhzxbIDlbJjkZj70et2EyZaFSyLNmWJTXtY1tSNF6zLZklpfL07h17PxKNHSO+bWTtWtk1NbE1kPf1kJKfpd2zb/aesQNkkawNpXLd22+/rbPPPtux/tYZZ5yhV199VX6/v93PRygFAACQnQilAHfZth0LpuIBmqJRWdXVsuvqZIfDUjgsOxyRwiHZoVDsdSQshSOywiEpHIntGwlLobDk88nbv78UjcTDtWgiuAuvXavoxo3x7eOBXjQqOxJNBH27w7fY/rJi7xs+r/zf/nYi2Nsd7kUVXb9ekXXrY5/DisZDvvjnsq3dY7vp2Y7XYzcu8/TtE+vVcf5o7GtQXd30hXL1zwlt09JQypU1pdA2//73vzVu3DhHIHXiiSfq5ZdfTkkgBQAAAABIDSO+OLk8HjUtwGIWFaXkXPkpOap7rGhUCoWkUEhWPMBTY0h2OCQ7HJZZWrp7RlxTQBeJyApWK7JxYyzoi4d6isTCPzsSjoVi4YgUjYV/diQqReNBXiQi3xFHyPB69wroorKqqhResHB33YrPrItazhl8luV43fTwHHqozPy83YFfPBi06usVWb48FsxZlmTb+56dJzlfS7HL7Awj1kso5Mqf1YEQSmWI+fPna+zYsaqrq0vUhg8frpkzZyo/P9v+LwYAAAAAgOaZHo+Uny/l58ts5b4BjUhJT21lRSKyd+2S1fSoqpJdFZQVDMqqDso+ZqTsmlpZtTWya+tk19fJrquXp19fGYE82fX1shsaYs/19bLqahX5YnksHAuFYrPVOjBCqQywdOlSnXHGGaqqqkrUjjjiCL355ptcUgcAAAAAQDuzLUt2Q4Os6mpFN22SVRWUXVUlKxiUXR2MXWpZUyOrpk52XW3sksO6etkN8ZCoMSSFGuXp20+GacqKh0axEKleVm2d1HSJYlt88kn7fdh2YuTnK+/MM1R8w/XqVFIilZQccB9CqQ5u9erVGjNmjLZv356oDRw4ULNnz1aXLl1c7AwAAAAAAHck1p6KP6KVOxVesWKP4KhaVk1tbEH6ulpZtXWyG+qlhsZYaBRqlB2KX/oXicisqIhd4hcPjdTQeOAmWiC6cVO7HKfDMgz5hgxRp6uuUt6Zp8cuS5WkYLBFuxNKdWCbNm3Saaedpk2bdv9H3LdvX82ZM0fdu3d3sTMAAAAAAPataaZRIjiqr4+FR1u2KrR0qexgUHZtbSw4qq2NzTaqb4jPNGqU3dgYu/wsHhrZkUhsXaXCAqkxJDW2T2jUJLp+fbseL2M03Q3SNCWPR54ePeTt20dGfr7zkZen8LIvJNkyCgpkFBTI983DVHDRRfIcxFpohFIdVF1dnU4//XStWrUqUfN4PLrxxhu1YsUKrVixolXHO+GEE5SXl9febQIAAAAAMpBt27EwKBSOrVMUD42aLkOLrFun8LIvZNdUy6rd8/K0BtkNDVJjY/yugPE7BTbd1S++IHfKBA/ikrdMY5qJsMjweCSvV4bPJ/n98h06UN5DDnGERk2vw8uWyQgEZHQqktmpWEZxJ5nFJTJLSmWUlspTViKjtFSmz+f2JySU6qi2bdumzz//3FGLRqOaOHFim463evVq9e/fvx06AwAAAACki2VZsoPVsbvE1dbKrq2LhUT1dbJraxVZuUrhZcviM45qYsFRff3uGUehUHJo1MEXv+7wzPjMItMjw+uV4g/D7489An4ZgTwZ+XnyDRkib79+e8w62h0eRdatk1lcLKOkRGZpqcyyMpklJTJzaEIJoRQAAAAAAO3Aqq+XGhpk1dXFA6Ra2XX1smprFf5yhSIrvowvjh2feVRfF79krSFxqVrT5WqER220x6Vohscj+XyJ2UVGIBCbUdQ0q6igQGZRoYyCQvmHfVuenj0doZGRny/l5cXWnCotjdWa1kxqB4Fjjmm3Y2UqQikAAAAAQE6xIhGpvj62plFt/O5p8QApvOwLRVavlFVdE1sku7YuFjI1LZIdv2xNkbDsSDQ9l6xli7yADH9Adk2N5PFI3vhMI59Phm/P0CggIz+2bpFRWCCjsFBmUZHM4mL5hg6Vp1vX2HtNwVI8PGrPwAjpQSjVQfXv3182/6cGAAAAALEZRDU1scvWampkVdcovGypomvWyQrukhWs3n23tT0XzG7c49K1ptlH/J61bz7f7rDH41F0x47ds43il6YpL09GICAzHggZhQUyiopiwVGnTjKLi2WWlMg3eJDMzp0Ti2Ib+fkyTNPtT4gOhlAKAAAAANCubMuKzTyqqVF49RpFN26QtWOnrF07Ze2qkhWskh2fidS0PpLqGxKzkOxwODYDyTRjl2O1853WMpphxMOgQhkFBZJhyPp6m+RrWs8oEL8ELRYEmUWFMgqLZBQVyexUJKO4WGZxiYySYnlKS+UdOFBmSXEsNOoAC18jtxBKAQAAAECOs21bdlVQ0a+3yfr6a0W375BVuUNW5U5Zu3bJCgZlB4OyampkN62H1BC/E1tjKHa5VWFRIoiy6+rc/kgdgpGXlwiPbNuSXblz96yjQEDKy5OZnyejoDB+mVqRzOJOMjt12r34dWmZzC5lsfCouDh2TC5TQ5YglAIAAACADGVZluydOxXdtFnRbVtlfb1d0e3bZVdWKrozNivJrg7G1keqrZVRVCRPWWlivOclcYpG29yHHZSkr9vtc6Wd1yujqFBmQaHsaFRWMCij6W5qgYAUCMRmHRXkx0KmwqLY9vFL1czSEhnxAMk36HCZRUWx2Uwej9ufDOjQCKUAAAAAwAVWQ4OsLVsU3bpV0S3bZG3/WtEdO2Tt3CmjoECe0tLYzKRgUFZ1tezqGlnVQYWXfSG7trbN6yNFUvBZ0s0oLJRMQ3Z9QyxQ8vnii2QHpLx8mQW7F8c2ijvJLO4ko6hYZml89lGXzvIN/tbukMnvd/sjATmJUAoAAAAAWsGyLFmVlbK2bJUdapQhQ1ZNtexg9e7n6mo1vPe+rB07ZNfHLnPTnmslWZbbHyP9DCO2RpTXG7vj2l7rH5nxBbPNTsUySmIBkqe8Qv4Rw2OXtRUVxhbULihgwWwgSxBKAQAAAIBi6yqFv/xKofffU2j+AoW//FLW1q2Jxbdz8u5tpil5PLEFsBOzkfJiQVJhoczCQhmdOsmsqFDe8cfLiC+q3RQgmYWFUkGBzEDA7U8CoAMilAIAAACQU2zbVnTTJkW+/FLh5V+q9plnZG3ZErskLhsCJ8NwzkbKy4vdWa2wQJ5u3RQ4/vjYQtpFhTLjayMZRUWx0C0QkNm1q8yyMpnMRgKQYoRSAAAAALKSVVenxg8/Uug/H8luaJRVFVTkqy8V+Wplx707nMcjeb0yS0rkGzIkdie2oiIZxcWJ9ZGsyp2yQyGZXbrILC+Xp2tXebp1k6dH99jMJADIEIRSAAAAALJG9UPTVTN9uqyvt0vhcFrOaeTlxS5h69Qptqh2UezZ2rZNVn3D7sW2S0pklpbJ7NJZni5dZHatkKdrV5ndu8usqGBmEoCcQygFAAAAICvUvzlLwVt/0z4HCwTk6dJFnj59ZHatiAVKnWNhklHeJT47qbs83brKLCpqn3MCQI4hlAIAAACQFRrmvNOq7Y2iInkPHSizuFjyeOQbMkSB474j/8iRMv3+FHUJAGhCKAUAAAAgKxRefJHqnnmm2feMkhLln/P/yXfYYfIOHCjfoQNldu8uwzDS3CUAoAmhFAAAAICs4D/iCBX+9KeqffJJ5xumqYoZL8t36KHuNAYAaBYr6QEAAADIGqV/+J18Q4Y4i5alr79/nqxIxJ2mAADNIpQCAAAAkFXKX35RRkmJo2ZXVqpy/CUudQQAaA6hFAAAAICsYubnq/zFFyTT+etO47tzVfP4Ey51BQDYG6EUAAAAgKzjH3S4in/9q6R61S3/p/DKlS50BADYG6EUAAAAgKzU6bLL5D/uOGfRsrRz4pWyo1F3mgIAJBBKAQAAAMhaXZ5+Kml9qfCSJar56/0udQQAaEIoBQAAACBrmX6/yl/4h1RY6KgH77lX4c+XutQVAEAilAIAAACQ5fyDB6n8b086Fz4Ph1X5i6tlh0LuNQYAOY5QCgAAAEDWC4wcqaKJVzhqkWXLVH3vH91pCABAKAUAAAAgNxRP/qW8gw531Kr/er8a//tflzoCgNxGKAUAAAAgJxiBgMr+eK/k9e4uRqPa/qMLZFVXu9cYAOQoQikAAAAAOcN/xBHqdPUvnMWGBu0Yf4k7DQFADiOUAgAAAJBTiiZeIeXlOWqh/3yshn/9y6WOACA3EUoBAAAAyClmIKCyu+9KqldOvFK2bbvQEQDkJkIpAAAAADmn4Hvj5Bs6xFGzd+1S1ZRbXeoIAHIPoRQAAACAnNTlsUcl0/krUe1jjym6ebNLHQFAbiGUAgAAAJCTPN27q3DCBGfRsrTjkgnN7wAAaFeEUgAAAAByVsmUW2SUlTlq4cWLVffGGy51BAC5g1AKAAAAQM4yDEOd7/9LUr3qxl+x6DkApBihFAAAAICcljdqlPwjRzhq1o4dqn30MZc6AoDcQCgFAAAAIOd1fuB+yTActeDtd8iKRl3qCACyH6EUAAAAgJzn6d5deWPPctTsujpV336HSx0BQPYjlAIAAAAASWX33C15vY5azfSHZdXXu9QRAGQ3QikAAAAAkGQWFqrwpxc5i+Gwah6a7k5DAJDlCKUAAAAAIK54yv/JyM9z1GqffEp2KORSRwCQvQilAAAAACDO9HpVet8fHTVryxbVvfSSOw0BQBYjlAIAAACAPRSMHSv/scc4ajX3PyibO/EBQLsilAIAAACAvXS6cpJjHFm5Ug2z3nKpGwDIToRSAAAAALCXwEknyTd4sKNW/de/yrZtlzoCgOxDKAUAAAAAezEMQ0VXTnTUwgsWqu4fL7jUEQBkH0IpAAAAAGhG/tixMvv2cdSq77nHpW4AIPsQSgEAAABAMwyvV/5vD3PUous3KLxqlTsNAUCWIZQCAAAAgH0ovnZyUi3429+70AkAZB9CKQAAAADYB9/AgfL06uWoNbzzjmzLcqkjAMgehFIAAAAAsB+FP/tfZyEcVu2TT7rTDABkEUIpAAAAANiPwv/9X8nrddRqpj3kUjcAkD0IpQAAAABgP0yvV4FRoxy16Lr1in693aWOACA7EEoBAAAAwAGU3HxTUq3mgQdc6AQAsgehFAAAAAAcgO/ww2WUlDhq9a/NdKkbAMgOhFIAAAAA0AJ5J5/kGEc3bVK0stKVXgAgGxBKAQAAAEALFE2alFSreXCaC50AQHYglAIAAACAFvAPHiSjuNhRq5/xqkvdAEDmI5QCAAAAgBZKugvfhg2yGhpc6gYAMhuhFAAAAAC0UNHllyXV6me85kInAJD5CKUAAAAAoIX8w74t+XyOWnjBAneaAYAMRygFAAAAAC1kGIbyz/quoxZetsylbgAgsxFKAQAAAEArFJx/nmMc+uwzWbW1LnUDAJmLUAoAAAAAWsE/cqTk9e4uRCIKffyJew0BQIYilAIAAACAVjALC+U/6khHrfE//3GpGwDIXIRSAAAAANBK/pEjHePwkiUudQIAmYtQCgAAAABayX/EEY5xeMnnsm3bpW4AIDMRSgEAAABAK3m/8Q3H2NqxQ9H1613qBgAyE6EUAAAAALSS2ad3Uq1h1mwXOgGAzEUoBQAAAACt5OnUScrLc9Qa//ORS90AQGYilAIAAACANvB07+4Yh79Y7lInAJCZCKUAAAAAoA283zjEMbYqK13qBAAyE6EUAAAAALSB9xBnKGXX1rrUCQBkJkIpAAAAAGgD3zcPcxaiUdkNDe40AwAZiFAKAAAAANrAN3RoUi3EulIA0GKEUgAAAADQBr6BA5Nq4cVLXOgEADIToRQAAAAAtIERCEher6MW+ZKZUgDQUoRSAAAAANBGRiDgGEe373CpEwDIPIRSAAAAANBWfr9jaFfXuNQIAGQeQikAAAAAaKO9Z0rZtYRSANBShFIAAAAA0EZGXp5jbNXWudQJAGQeQikAAAAAaCMjP98xtusJpQCgpQilAAAAAKCNPN27OcbmXjOnAAD7RigFAAAAAG0UOPZYx9js3sOlTgAg8xBKAQAAAEAb2eGwY2z4vC51AgCZh1AKAAAAANoqEnEMDZ/PpUYAIPMQSgEAAABAG+09U0qEUgDQYoRSAAAAANBWe1++5+XyPQBoKUIpAAAAAGgje6/L95gpBQAtRygFAAAAAG3UMPdfjrFdW+tSJwCQeQilAAAAAKCNrG1bHeOkmVMAgH0ilMoClmXp+OOPl2EYjsdJJ53kdmsAAABAVrMbGh1jT48eLnUCAJmHUCoL/OUvf9GHH37odhsAAABATrEtS9prZpS3bx+XugGAzEMoleHWrl2rm2++2e02AAAAgJwT3bEjqeYZcIgLnQBAZiKUynCXXnqpampqJEmFhYUudwMAAADkjshXK5NqvkMHutAJAGQmQqkM9vjjj+utt96SJBUVFen66693uSMAAAAgd4TmzUuqebp3d6ETAMhMXrcbQNts3bpVkydPToxvu+02lZaWutcQAAAAkGPCCxY6C/n5Mrz8igUALcVMqQw1adIk7dy5U5I0fPhwXXXVVS53BAAAAOSWyFdfOcae8nKXOgGAzEQolYFefvllvfjii5Ikr9er6dOnyzT5owQAAADSKbpli2PsPWSAS50AQGYiycgwu3bt0qRJkxLjX/7ylxo2bJh7DQEAAAA5yLZt2fEbDjXxDTnCpW4AIDMRSmWYyZMna/PmzZKkAQMGaOrUqe42BAAAAOSgyKpVkm07av5jvuNSNwCQmQilMsjbb7+txx57LDF+8MEHVVBQ4GJHAAAAQG6qf2VGUi1w7EgXOgGAzEUolSFqa2t16aWXJsY/+clPdPrpp7vYEQAAAJC7Gv71nmNsdCqSyT8YA0CrEEpliJtvvlmrV6+WJHXp0kX33nuvyx0BAAAAuSuyYoVj7D30MJc6AYDMRSiVAf7zn//oz3/+c2J81113qaKiwsWOAAAAgNxlW5bsaNRRyxt1okvdAEDm8rrdAPYvFAppwoQJsixLknTKKado/PjxKTtfY2OjGhsbE+NgMJiycwEAAACZKLJ8uVRX56gV/OQCl7oBgMzFTKkO7rbbbtPSpUslSXl5eZo2bVpKz/eHP/xBJSUliUefPn1Sej4AAAAg0zR+/Ilj7OndW96ePV3qBgAyF6HUPlx99dUyDCPlj6lTp+6zh0WLFumOO+5IjG+55RYNHDgwpZ/7pptuUlVVVeKxfv36lJ4PAAAAyDQN77zrGPtHjnCpEwDIbFy+10FFo1FNmDBB4XBYkjRkyBBdd911KT9vIBBQIBBI+XkAAACATGTV16vx3x84anmnnOxSNwCQ2Zgp1UHdc889+vTTTyVJpmnqoYceks/nc7krAAAAILc1vv++1LB7DVZ5PMo7mVAKANqCmVL7MHbsWJWXl6f8PKNGjUqq1dfXa8qUKYnxFVdcoWOPPTblvQAAAADYv5qHHnaM/SNHyCwtdacZAMhwhm3btttNwGnXrl0qKytLybF37typ0lb80AwGgyopKVFVVZWKi4tT0hMAAADSr3fv3tq4caN69eqlDRs2uN1ORrAaGrR54GHSHr9CFV05SSU33ehiVwDQ8bQ0S+DyPQAAAABogbq/Pe0IpCQp77TTXOoGADIfoRQAAAAAtEDNE086C4GAAiOGu9MMAGQB1pTqgIqKijR79uxW7/fWW2/pzjvvTIyHDh2qu+++O+nYAAAAAFonWlmp6KpVjlrgO6z7CgAHg1CqA/J6vTqtDdOA914LoKysrE3HAQAAAOBUffc9SbXia691oRMAyB5cvgcAAAAAB1A/41XH2Cgtkf/IYe40AwBZglAKAAAAAPYjtGSJrJ07HbX8s/8/l7oBgOxBKAUAAAAA+xH8w+1JteJrJ7vQCQBkF0IpAAAAANgHq65Oje+976h5+veTp6LCpY4AIHsQSgEAAADAPgT/cLtkWY5apysnudQNAGQXQikAAAAAaIZt26p77u+OmlFYqIIf/ciljgAguxBKZZHx48fLtu3EY+7cuW63BAAAAGSs2qefkV1X56gV/PjHMgzDpY4AILsQSgEAAABAM6r/eJ+z4PGo+Mbr3WkGALIQoRQAAAAA7CW8dq2sLVsctcBJo2Xm57vUEQBkH0IpAAAAANhLzf0PSrbtqJX89jaXugGA7EQoBQAAAAB7iKxbp7rnnnPU8s87V76+fV3qCACyE6EUAAAAAOyh+r4/SZHI7kJeQCU33eheQwCQpQilAAAAACAusnq16v7xgqNWeNFF8nTv7lJHAJC9vG43AAAAAAAdRfDe+6Ro1FGrfepvqnvu75JpSqYpI/4sj0eGx5RMj+TxxMce+b71LZnlXWT4fLGa1yt5vZJhKDR/vgyvT/J5Hc+G3xfbPv4w/P7Yw+eXAj4Z/oA8vXvJ07mL5PVIHq8Mnzf27PXIqq2V4fdLeXky/X4pP1+mz+fSVxEAWoZQCgAAAAAkWVVVqn/55eQ3GhpkNzQkhnbyFg6RFSvat7H2Yhi7n/d4mOVdYmGXzyvD45W8nlhg5vUovHyFZEUls/kATl5PYp9YQLZ7f0+/vvIdcojjeIbHK/m8iqz4UnYkLPn88UAuFsIpEA/i/H6ZgYDkjwVyRl4gtm1eQEZxsTzFxZLXG+vB54sFhQAyDqEUAAAAAEiKbt0qWZbbbaRO090E97qroLV5i6zNW1xoqB0ZRmw2WjQa+zPcRwAn05CM+Gw3w0jMfpNpyiwrk+/QgUmz0OTzKbJ6taxdVXsFcXsGePFAzueNzXjzxp4Nv09mSal8Q47YfTyvL/7slVW5U1ZtbWx2W14gFs7lBWT4A7GALpAXC+QCgVhIl58v08uv8cge/NcMAAAAAJK8hx6qwKmnqnHOHLdbQWvZthQOO8d7Pu+9eTO16M6diq5a1f69pZLHsztwk3Y/h0Kxt7/xDXWe9oD8gwa51CCwf4RSAAAAACDJMAx1eeIxNfzrPTXOeVt2OCI7HJbCYdnhiBSJP0cjUjgiOxJ7bUciUiQqOxqVIhF5v/lNmQX5u7eNRGVHwrLr6hVasiQ2m8e2JMuWLEu2ZcXCEzs2TryWnK8NY58hC3LUXuufJb29cqV2XHiResz7NE0NAa1DKAUAAAAgbWzbjv0iHY3GwphIRIpGZUWjsrZtk0KhWMgTDstu3ON1Uz0SjgVC4bAUiT3bkYg83brJLCuLrX/UFBDFXzd++qnshsbY9pHIHttEdvcSjdVkRWVHrUTN06unvD16yI6/p2g08Tq8cJGsurpYkBQPk2zLUnTLlniYFA+e4sGSbVmSbMmWjIICmWXF0h7nanq2q6ubv4yQQAptYO3c5XYLwD4RSgEAAAA4IKu6WsE/3K7GefMU+Wrl7hk8ez+kA146lUkiS5aoMQXHtUMhRXftSsGRAaf8s892uwVgnwilAAAAABxQ1ZSpqvv78263Abgnvl6TUVEus2mhc48pI35HQquxQda2r2U4FlY3Hc/GHguryzB239HQNOU/cpiUlydDTQuyxx52TY3Cny+VDCn+P7F9A36ZXbvF70Dojd0hMX4XRGvrVtn19eo0+ZfKG3WiW18x4IAIpQAAAAAcUGjRYrdbQFuYpuTxyPB44pc/RpIXxd77DnXxx57hilFYIO83BsbuMBcPUmIBiEfRTZtlbf86Ea7EzmnuDmA8nnhws+fr2MMsLJB/xAjJNGPhSvxheDyKbt2q6OYt8TvceXffFc/rkTy++OvYXe8Mn1fy+WR4fJLPIyMQkO/Qw3afc49jKxSW1dgQuzuezyf5AzL88deBgOT1yjRN9/7MgBxCKAUAAADggMySYrdbSLZHqGIUFcksKXHMXJE3FoBE1q2LrR21z5krRixQ2WvmikxTnl495RswwBGWNL0Of/657PqG3XWvxzFbRZ7drw2vNx6uxMaebt3k/cYhjnCnKbiJbN4s1TdKPo/k98v0+mKzcuLBSSxI8ceO6ffLSLwOyMgLyMzP391v09cIADogQikAAAAAB+Tp2tUx9g4erMBxx+0xc8UbC0uam7ni90pen0yfT2b3bjJLS52zYkxT8nplVVfvDlf8u8MX+Xw5NXMl4HYDAJAmhFIAAAAADsjcK5TyDRqk0luntOs5PBUV7Xo8AEDHljv/3AAAAACgzTzdujnG1tfbXOoEAJAtCKUAAAAAHNDel+9FtxFKAQAODqEUAAAAgAOyo1HHOLLiS5c6AQBkC0IpAAAAAAdklpQ4C5Ylq7ranWYAAFmBUAoAAADAAfkO/2ZSLbx8uQudAACyBaEUAAAAgAMy+/ZNqkWWr3ChEwBAtiCUAgAAAHBApmlKHo+jFlm92qVuAADZgFAKAAAAQIsYeXmOcWT9epc6AQBkA0IpAAAAAC1iFBc7xtFNm13qBACQDQilAAAAALSI2bmzY2zt2OFSJwCAbEAoBQAAAKBFPN26OsZWVZVLnQAAsgGhFAAAAIAW8fTq5RjbdXUudQIAyAaEUgAAAABaxNt/gLMQCrnTCAAgKxBKAQAAAGgR72GHJtUirCsFAGgjQikAAAAALeIbPDipFlm2zIVOAADZgFAKAAAAQIt4u3dLqkVWfOlCJwCAbEAoBQAAAKDlfD7HMLJ6lUuNAAAyHaEUAAAAgBYzy8udhbx8dxoBAGQ8QikAAAAALRb4zrGOsRGNutQJACDTEUoBAAAAaDFPN+e6UtEtW1zqBACQ6QilAAAAALRYUii1datLnQAAMh2hFAAAAIAWsRoaFN2+3VGLbiGUAgC0jdftBgAAAAB0PNHKSjXMekuNH3yg8JLPFd20UXZdfdJ21tatsm1bhmG40CUAIJMRSgEAAAA5LrJmjerfeFONH3+syPLlsUvyQqEW7WuHQpJlSR5PirsEAGQbQikAAAAgh1jV1Wr86CPZwaCk2ELlW48/sc3HC4weLYNACgDQBoRSAAAAQA6Jrt+gyksmyKqtixXsNhzENOUd+A0V/PCHKrzwJ+3aHwAgdxBKAQAAADnEe+hAye9v+Q4+nzzdusp72DflP+YY5X/3DPm+8Y3UNQgAyBmEUgAAAEAOMXw++b75TWnd2uT38vNl9uwp3+DBCpxwvPLOPEPe8nIXugQA5AJCKQAAACDH+I89RsZ7c6X6ehkF+er80DQFTj5JZkGB260BAHKI6XYDAAAAANKrdOoUmZ07S5LMkhLljz2LQAoAkHaEUgAAAAAAAEg7QikAAAAAAACkHaEUAAAAAAAA0o5QCgAAAAAAAGlHKAUAAAAAAIC0I5QCAAAAAABA2hFKAQAAAAAAIO0IpQAAAAAAAJB2hFIAAAAAAABIO0IpAAAAAAAApB2hFAAAAAAAANKOUAoAAAAAAABpRygFAAAAAACAtCOUAgAAAAAAQNoRSgEAAAAAACDtCKUAAAAAAACQdoRSAAAAAAAASDtCKQAAAAAAAKQdoRQAAAAAAADSjlAKAAAAAAAAaUcoBQAAAAAAgLQjlAIAAAAAAEDaEUoBAAAAAAAg7QilAAAAAAAAkHaEUgAAAAAAAEg7QikAAAAAAACkndftBtCx2bYtSQoGgy53AgAAgPZkWVbimb/rAQDaU9PPlaZMYV8IpbBf1dXVkqQ+ffq43AkAAABSYfPmzSopKXG7DQBAFqqurt7vzxjDPlBshZxmWZY2bdqkTp06yTAMt9sBXBcMBtWnTx+tX79excXFbrcDZC2+14DU4/sMSD2+z5CrbNtWdXW1evbsKdPc98pRzJTCfpmmqd69e7vdBtDhFBcX8xcLIA34XgNSj+8zIPX4PkMuasksXBY6BwAAAAAAQNoRSgEAAAAAACDtCKUAoBUCgYCmTJmiQCDgditAVuN7DUg9vs+A1OP7DNg/FjoHAAAAAABA2jFTCgAAAAAAAGlHKAUAAAAAAIC0I5QCAAAAAABA2nndbgAAAOBgWZalr776SosXL9bmzZsVDAaVn5+vzp07a9CgQTryyCPl8/ncbhPoMFauXKlPPvlEGzZsUCgUUllZmQ4//HAdd9xxysvLc7s9ICPZtq01a9Zo8eLF2rBhg3bt2qVAIKCysjIdeuihGjFiBN9fwF4IpQDABZZl6cQTT9SHH37oqI8ePVpz5851pykgw2zZskUvvvii3nrrLc2dO1fBYHCf2+bn5+u8887TL3/5Sx155JFp7BLoWF555RXddtttmj9/frPvFxUVafz48ZoyZYrKy8vT3B2QeXbu3KlXXnlFb775pt555x1t3759n9v6fD6NHTtWV199tUaPHp3GLoGOi7vvAYAL/vSnP+kXv/hFUp1QCmiZcePGaebMmbIsq1X7maapyZMn63e/+538fn+KugM6nsbGRk2YMEFPP/10i7avqKjQCy+8oFGjRqW4MyBzTZo0SQ8//LBCoVCr9/3pT3+qP//5zyouLk5BZ0DmIJQCgDRbu3atjjjiCNXU1CS9RygFtEx5ebl27NiRVPf5fOrZs6cqKirU0NCgVatWqa6uLmm7c845Ry+++KK8XiaNI/tZlqXvf//7mjFjhqPu8XjUt29flZSUaPXq1aqqqnK8X1BQoLffflvf+c530tkukDGGDx+uefPmJdU9Ho969Oihbt26KRwOa+3atUnfX5I0cuRIzZkzR0VFReloF+iQWOgcANLs0ksvTQRShYWFLncDZL5u3brpuuuu05w5cxQMBrVmzRr997//1eLFi7Vr1y69+uqrGjJkiGOfV199VTfeeKNLHQPpdeeddyYFUpdffrnWrVunVatW6bPPPlNlZaVeeukl9e3bN7FNXV2dfvCDHzT7yzQAp9LSUk2cOFGvv/66du7cqfXr1+vTTz/VwoULtWPHDr377rs68cQTHft88sknGj9+vDsNAx0EM6UAII0ef/xxXXLJJZJi63Zcd911mjJlSuJ9ZkoBLVNeXq4ePXpoypQp+t73vnfAGU8NDQ06//zzNXPmzETN5/NpyZIlOuyww1LdLuCaHTt2aMCAAaqurk7U/vCHP+wzlN24caNOOOEErVmzJlH7v//7P916662pbhXIOMOHD9eOHTv061//WhdccIHy8/P3u300GtXEiRP10EMPOervvPOOTj755FS2CnRYzJQCgDTZunWrJk+enBjfdtttjn+RBtByjzzyiBYuXKjzzjuvRZfg5eXl6bnnnlPv3r0TtXA4rCeeeCKVbQKu+3//7/85AqlRo0bphhtu2Of2vXr10sMPP+yo3Xvvvc1eLgvkultvvVXLly/XhAkTDhhISbHL+u6//34NHz7cUd/7ew7IJYRSAJAmkyZN0s6dOyXF/mXtqquucrkjIHONGzdOptm6v8YUFhYmfd/NmjWrPdsCOhTLsvTYY485alOnTpVhGPvd79RTT3VcZlRdXa3nn38+JT0CmWzs2LGtvmmGx+PR9ddf76jxswi5jFAKANLg5Zdf1osvvihJ8nq9mj59eqt/oQZw8PZez2PdunUudQKk3ocffqivv/46MT7kkEN00kkntWjfCRMmOMavvPJKO3YG5La9fxbt2LGj2ZtyALmA34gAIMV27dqlSZMmJca//OUvNWzYMPcaAnJYWVmZY8wCzshmr7/+umM8ZsyYA86S2nPbPc2dO1e1tbXt1huQy/b+WSTx8wi5i1AKAFJs8uTJ2rx5syRpwIABmjp1qrsNATls48aNjnGXLl1c6gRIvQULFjjGxx13XIv37dmzp/r3758Yh0IhLV26tJ06A3Lb3j+LJH4eIXcRSgFACr399tuO9TwefPBBFRQUuNgRkNvef/99x5g77yGbLVu2zDEePHhwq/bfe/u9jwegbfb+WdSvX79Wr00FZAtCKQBIkdraWl166aWJ8U9+8hOdfvrpLnYE5LZoNKonn3zSUTvrrLNc6gZIrfr6+qQ10/r06dOqY+y9/fLlyw+6LwDSo48+6hjzswi5jFAKAFLk5ptv1urVqyXFpmTfe++9LncE5LZp06Zp1apVibHP59MFF1zgYkdA6mzfvl22bSfGPp9PXbt2bdUxevXq5Rhv27atXXoDctkbb7yh9957z1E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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "from pymatgen.electronic_structure.cohp import Cohp\n", "from pymatgen.electronic_structure.plotter import CohpPlotter\n", @@ -320,11 +162,6 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", From db9c883b75bd8ec557d73015cb877e43a6298572 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 19:24:13 +0100 Subject: [PATCH 24/61] fix linting --- src/atomate2/utils/testing/lobster.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py index 8f26b7731f..70416b178c 100644 --- a/src/atomate2/utils/testing/lobster.py +++ b/src/atomate2/utils/testing/lobster.py @@ -66,7 +66,7 @@ def monkeypatch_lobster(monkeypatch: pytest.MonkeyPatch, lobster_test_dir: Path) lobster_test_dir (Path): The directory containing reference files for LOBSTER tests. """ - def mock_run_lobster(*args, **kwargs) -> None: + def mock_run_lobster(*_args, **_kwargs) -> None: from jobflow import CURRENT_JOB name = CURRENT_JOB.job.name From aee1e88ba5a7e1348e8c1195db7b04626556c918 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 19:27:22 +0100 Subject: [PATCH 25/61] fix linting --- src/atomate2/lobster/schemas.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/atomate2/lobster/schemas.py b/src/atomate2/lobster/schemas.py index a5e6faa511..973949db02 100644 --- a/src/atomate2/lobster/schemas.py +++ b/src/atomate2/lobster/schemas.py @@ -597,7 +597,7 @@ def from_directory( "bva_comp": True, **calc_quality_kwargs, } - print(calc_quality_kwargs_updated) + cal_quality_dict = Analysis.get_lobster_calc_quality_summary( path_to_poscar=structure_path, path_to_vasprun=vasprun_path, @@ -845,9 +845,8 @@ def from_directory( calc_quality_text = None describe = None describe_ionic = None - print(analyze_outputs) - if analyze_outputs: - if ( + + if analyze_outputs and ( icohplist_path.exists() and cohpcar_path.exists() and charge_path.exists() From fb07b1ce60875466e7a350dde63119f44d0424a7 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 19:35:13 +0100 Subject: [PATCH 26/61] fix more linting --- src/atomate2/lobster/jobs.py | 2 - src/atomate2/lobster/schemas.py | 70 +++++++++++++-------------- src/atomate2/utils/testing/lobster.py | 20 +++++++- 3 files changed, 52 insertions(+), 40 deletions(-) diff --git a/src/atomate2/lobster/jobs.py b/src/atomate2/lobster/jobs.py index e440cd0591..aef254c64d 100644 --- a/src/atomate2/lobster/jobs.py +++ b/src/atomate2/lobster/jobs.py @@ -79,8 +79,6 @@ def make( basis_dict: dict A dict including information on the basis set """ - print(self.task_document_kwargs) - # copy previous inputs # VASP for example copy_lobster_files(wavefunction_dir) diff --git a/src/atomate2/lobster/schemas.py b/src/atomate2/lobster/schemas.py index 973949db02..d67a2782d1 100644 --- a/src/atomate2/lobster/schemas.py +++ b/src/atomate2/lobster/schemas.py @@ -597,7 +597,7 @@ def from_directory( "bva_comp": True, **calc_quality_kwargs, } - + cal_quality_dict = Analysis.get_lobster_calc_quality_summary( path_to_poscar=structure_path, path_to_vasprun=vasprun_path, @@ -845,44 +845,42 @@ def from_directory( calc_quality_text = None describe = None describe_ionic = None - + if analyze_outputs and ( - icohplist_path.exists() - and cohpcar_path.exists() - and charge_path.exists() - ): - ( - condensed_bonding_analysis, - describe, - sb_all, - ) = CondensedBondingAnalysis.from_directory( - dir_name, - save_cohp_plots=save_cohp_plots, - plot_kwargs=plot_kwargs, - lobsterpy_kwargs=lobsterpy_kwargs, - which_bonds="all", - ) - ( - condensed_bonding_analysis_ionic, - describe_ionic, - sb_ionic, - ) = CondensedBondingAnalysis.from_directory( - dir_name, - save_cohp_plots=save_cohp_plots, - plot_kwargs=plot_kwargs, - lobsterpy_kwargs=lobsterpy_kwargs, - which_bonds="cation-anion", - ) - # Get lobster calculation quality summary data + icohplist_path.exists() and cohpcar_path.exists() and charge_path.exists() + ): + ( + condensed_bonding_analysis, + describe, + sb_all, + ) = CondensedBondingAnalysis.from_directory( + dir_name, + save_cohp_plots=save_cohp_plots, + plot_kwargs=plot_kwargs, + lobsterpy_kwargs=lobsterpy_kwargs, + which_bonds="all", + ) + ( + condensed_bonding_analysis_ionic, + describe_ionic, + sb_ionic, + ) = CondensedBondingAnalysis.from_directory( + dir_name, + save_cohp_plots=save_cohp_plots, + plot_kwargs=plot_kwargs, + lobsterpy_kwargs=lobsterpy_kwargs, + which_bonds="cation-anion", + ) + # Get lobster calculation quality summary data - calc_quality_summary = CalcQualitySummary.from_directory( - dir_name, - calc_quality_kwargs=calc_quality_kwargs, - ) + calc_quality_summary = CalcQualitySummary.from_directory( + dir_name, + calc_quality_kwargs=calc_quality_kwargs, + ) - calc_quality_text = Description.get_calc_quality_description( - calc_quality_summary.model_dump() - ) + calc_quality_text = Description.get_calc_quality_description( + calc_quality_summary.model_dump() + ) # Read in charges charges = None diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py index 70416b178c..2109436e0a 100644 --- a/src/atomate2/utils/testing/lobster.py +++ b/src/atomate2/utils/testing/lobster.py @@ -4,6 +4,7 @@ import logging import shutil +from collections.abc import Callable, Generator from pathlib import Path from typing import TYPE_CHECKING @@ -25,11 +26,26 @@ @pytest.fixture(scope="session") -def lobster_test_dir(test_dir): +def lobster_test_dir(test_dir) -> Path: + """Fixture to provide the test directory for LOBSTER tests. + + Args: + test_dir: The base test directory. + + Returns + ------- + Path: The test directory for LOBSTER tests. + """ return test_dir / "lobster" -def monkeypatch_lobster(monkeypatch: pytest.MonkeyPatch, lobster_test_dir: Path): +def monkeypatch_lobster( + monkeypatch: pytest.MonkeyPatch, lobster_test_dir: Path +) -> Generator[ + Callable[[dict[str, str | Path], dict[str, dict[str, Sequence]]], None], + None, + None, +]: """Monkeypatch LOBSTER run calls for testing purposes. This generator can be used as a context manager or pytest fixture ("mock_lobster"). From db23b0cf5c3a5142d6c2001b7c1c03ae1940c7c9 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Mon, 10 Feb 2025 19:43:29 +0100 Subject: [PATCH 27/61] fix final linting --- src/atomate2/utils/testing/lobster.py | 71 +++++++++++++++++---------- tutorials/force_fields/__init__.py | 2 +- tutorials/lobster_workflow.ipynb | 12 ++--- 3 files changed, 51 insertions(+), 34 deletions(-) diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py index 2109436e0a..92aaa876fe 100644 --- a/src/atomate2/utils/testing/lobster.py +++ b/src/atomate2/utils/testing/lobster.py @@ -4,7 +4,6 @@ import logging import shutil -from collections.abc import Callable, Generator from pathlib import Path from typing import TYPE_CHECKING @@ -15,7 +14,7 @@ import atomate2.lobster.run if TYPE_CHECKING: - from collections.abc import Sequence + from collections.abc import Callable, Generator, Sequence logger = logging.getLogger("atomate2") @@ -26,7 +25,7 @@ @pytest.fixture(scope="session") -def lobster_test_dir(test_dir) -> Path: +def lobster_test_dir(test_dir: str | Path) -> Path: """Fixture to provide the test directory for LOBSTER tests. Args: @@ -36,7 +35,7 @@ def lobster_test_dir(test_dir) -> Path: ------- Path: The test directory for LOBSTER tests. """ - return test_dir / "lobster" + return Path(test_dir) / "lobster" def monkeypatch_lobster( @@ -52,34 +51,54 @@ def monkeypatch_lobster( It replaces calls to run_lobster with a mock function that copies reference files instead of running LOBSTER. - The primary idea is that instead of running LOBSTER to generate the output files, - reference files will be copied into the directory instead. This ensures that the - calculation inputs are generated correctly and that the outputs are parsed properly. + The primary idea is that instead of running LOBSTER to + generate the output files, reference files will be copied + into the directory instead. This ensures that the calculation + inputs are generated correctly and that the outputs are + parsed properly. To use the fixture successfully, follow these steps: - 1. Include "mock_lobster" as an argument to any test that would like to use its functionality. - 2. For each job in your workflow, prepare a reference directory containing two folders: - "inputs" (containing the reference input files expected to be produced by - Lobsterin.standard_calculations_from_vasp_files) and "outputs" (containing the expected - output files to be produced by run_lobster). These files should reside in a subdirectory + 1. Include "mock_lobster" as an argument to any test that + would like to use its functionality. + 2. For each job in your workflow, prepare a reference + directory containing two folders: + "inputs" (containing the reference input files expected + to be produced by Lobsterin.standard_calculations_from_vasp_files) + and "outputs" (containing the expected + output files to be produced by run_lobster). + These files should reside in a subdirectory of "tests/test_data/lobster". - 3. Create a dictionary mapping each job name to its reference directory. Note that you should - supply the reference directory relative to the "tests/test_data/lobster" folder. For example, - if your calculation has one job named "lobster_run_0" and the reference files are present in - "tests/test_data/lobster/Si_lobster_run_0", the dictionary would look like: + 3. Create a dictionary mapping each job name + to its reference directory. Note that you should + supply the reference directory relative + to the "tests/test_data/lobster" folder. For example, + if your calculation has one job named + "lobster_run_0" and the reference files are present in + "tests/test_data/lobster/Si_lobster_run_0", + the dictionary would look like: {"lobster_run_0": "Si_lobster_run_0"}. - 4. Optionally, create a dictionary mapping each job name to custom keyword arguments that will be - supplied to fake_run_lobster. This way you can configure which lobsterin settings are expected - for each job. For example, if your calculation has one job named "lobster_run_0" and you wish - to validate that "basisfunctions" is set correctly in the lobsterin, your dictionary would look like: - {"lobster_run_0": {"lobsterin_settings": {"basisfunctions": Ba 5p 5s 6s}}. - 5. Inside the test function, call `mock_lobster(ref_paths, fake_lobster_kwargs)`, where ref_paths is the - dictionary created in step 3 and fake_lobster_kwargs is the dictionary created in step 4. + 4. Optionally, create a dictionary mapping each + job name to custom keyword arguments that will be + supplied to fake_run_lobster. This way you can + configure which lobsterin settings are expected + for each job. For example, if your calculation + has one job named "lobster_run_0" and you wish + to validate that "basisfunctions" is set correctly + in the lobsterin, your dictionary would look like: + {"lobster_run_0": {"lobsterin_settings": + {"basisfunctions": Ba 5p 5s 6s}}. + 5. Inside the test function, call + `mock_lobster(ref_paths, fake_lobster_kwargs)`, + where ref_paths is the + dictionary created in step 3 and + fake_lobster_kwargs is the dictionary created in step 4. 6. Run your LOBSTER job after calling `mock_lobster`. Args: - monkeypatch (pytest.MonkeyPatch): The pytest monkeypatch fixture. - lobster_test_dir (Path): The directory containing reference files for LOBSTER tests. + monkeypatch (pytest.MonkeyPatch): + The pytest monkeypatch fixture. + lobster_test_dir (Path): + The directory containing reference files for LOBSTER tests. """ def mock_run_lobster(*_args, **_kwargs) -> None: @@ -110,7 +129,7 @@ def fake_run_lobster( check_lobster_inputs: Sequence[str] = _LFILES, check_dft_inputs: Sequence[str] = _DFT_FILES, lobsterin_settings: Sequence[str] = (), -): +) -> None: """ Emulate running LOBSTER and validate LOBSTER input files. diff --git a/tutorials/force_fields/__init__.py b/tutorials/force_fields/__init__.py index 90db5c4b7a..978848c13a 100644 --- a/tutorials/force_fields/__init__.py +++ b/tutorials/force_fields/__init__.py @@ -1 +1 @@ -"""Forcefield-based tutorials""" +"""Forcefield-based tutorials.""" diff --git a/tutorials/lobster_workflow.ipynb b/tutorials/lobster_workflow.ipynb index 509f0a5daa..ac9ebc1230 100644 --- a/tutorials/lobster_workflow.ipynb +++ b/tutorials/lobster_workflow.ipynb @@ -143,17 +143,15 @@ " load=True,\n", ")\n", "\n", - "for number, (key, cohp) in enumerate(\n", - " result[\"output\"][\"lobsterpy_data\"][\"cohp_plot_data\"][\"data\"].items()\n", - "):\n", + "for key, cohp in result[\"output\"][\"lobsterpy_data\"][\"cohp_plot_data\"][\"data\"].items():\n", " plotter = CohpPlotter()\n", " cohp_obj = Cohp.from_dict(cohp)\n", " plotter.add_cohp(key, cohp_obj)\n", - " plotter.save_plot(f\"plots_all_bonds{number}.pdf\")\n", + " plotter.show()\n", "\n", - "for number, (key, cohp) in enumerate(\n", - " result[\"output\"][\"lobsterpy_data_cation_anion\"][\"cohp_plot_data\"][\"data\"].items()\n", - "):\n", + "for key, cohp in result[\"output\"][\"lobsterpy_data_cation_anion\"][\"cohp_plot_data\"][\n", + " \"data\"\n", + "].items():\n", " plotter = CohpPlotter()\n", " cohp_obj = Cohp.from_dict(cohp)\n", " plotter.add_cohp(key, cohp_obj)\n", From 62bef208bb58aaaac5bdbf0186e3cf93b8efa14c Mon Sep 17 00:00:00 2001 From: "J. George" Date: Mon, 10 Feb 2025 21:39:34 +0100 Subject: [PATCH 28/61] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 0dfadb588c..d695794c8e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -123,7 +123,7 @@ strict = [ strict-forcefields = [ "calorine==3.0", "chgnet==0.4.0", - "mace-torch>=0.3.6", + "mace-torch==0.3.9", "matgl==1.1.3", "quippy-ase==0.9.14; python_version < '3.12'", "sevenn==0.10.3", From 9b815c1bebd320281d5ef9a0883902cfc4f1d631 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 11 Feb 2025 07:13:10 +0100 Subject: [PATCH 29/61] adapt tutorial --- .../phonon_band_structure.pdf | Bin 0 -> 40548 bytes .../phonon_band_structure.yaml | 9723 +++++++++++++++++ .../phonon_dos.pdf | Bin 0 -> 22903 bytes .../phonon_dos.yaml | 202 + .../phonopy.yaml | 128 + .../phonon_band_structure.pdf | Bin 0 -> 37518 bytes .../phonon_band_structure.yaml | 9723 +++++++++++++++++ .../phonon_dos.pdf | Bin 0 -> 22926 bytes .../phonon_dos.yaml | 202 + .../phonopy.yaml | 128 + .../phonon_band_structure.pdf | Bin 0 -> 37518 bytes .../phonon_band_structure.yaml | 9723 +++++++++++++++++ .../phonon_dos.pdf | Bin 0 -> 22926 bytes .../phonon_dos.yaml | 202 + .../phonopy.yaml | 128 + .../phonon_band_structure.pdf | Bin 0 -> 40548 bytes .../phonon_band_structure.yaml | 9723 +++++++++++++++++ .../phonon_dos.pdf | Bin 0 -> 22903 bytes .../phonon_dos.yaml | 202 + .../phonopy.yaml | 128 + .../phonon_band_structure.pdf | Bin 0 -> 37518 bytes .../phonon_band_structure.yaml | 9723 +++++++++++++++++ .../phonon_dos.pdf | Bin 0 -> 22926 bytes .../phonon_dos.yaml | 202 + .../phonopy.yaml | 128 + .../phonon_band_structure.pdf | Bin 0 -> 37518 bytes .../phonon_band_structure.yaml | 9723 +++++++++++++++++ .../phonon_dos.pdf | Bin 0 -> 22926 bytes .../phonon_dos.yaml | 202 + .../phonopy.yaml | 128 + tutorials/force_fields/phonon_workflow.ipynb | 3199 +++++- 31 files changed, 63498 insertions(+), 19 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create mode 100644 tutorials/force_fields/job_2025-02-11-06-11-49-050842-97424/phonon_band_structure.pdf create mode 100644 tutorials/force_fields/job_2025-02-11-06-11-49-050842-97424/phonon_band_structure.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-11-49-050842-97424/phonon_dos.pdf create mode 100644 tutorials/force_fields/job_2025-02-11-06-11-49-050842-97424/phonon_dos.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-11-49-050842-97424/phonopy.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639/phonon_band_structure.pdf create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639/phonon_band_structure.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639/phonon_dos.pdf create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639/phonon_dos.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639/phonopy.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770/phonon_band_structure.pdf create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770/phonon_band_structure.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770/phonon_dos.pdf create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770/phonon_dos.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770/phonopy.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-59-022401-47274/phonon_band_structure.pdf create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-59-022401-47274/phonon_band_structure.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-59-022401-47274/phonon_dos.pdf create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-59-022401-47274/phonon_dos.yaml create mode 100644 tutorials/force_fields/job_2025-02-11-06-12-59-022401-47274/phonopy.yaml diff --git 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1.8703383562 0.0755638936 + 1.9583870193 0.0837786225 + 2.0464356824 0.0920694923 + 2.1344843455 0.1005830259 + 2.2225330086 0.1097107622 + 2.3105816717 0.1187068172 + 2.3986303348 0.1282816810 + 2.4866789979 0.1399178533 + 2.5747276610 0.1525012078 + 2.6627763241 0.1648087961 + 2.7508249872 0.1772524928 + 2.8388736503 0.1897727025 + 2.9269223134 0.2033379756 + 3.0149709765 0.2191801724 + 3.1030196396 0.2360681415 + 3.1910683027 0.2532334896 + 3.2791169658 0.2708483924 + 3.3671656289 0.2890582373 + 3.4552142920 0.3128682241 + 3.5432629551 0.3386116521 + 3.6313116182 0.3658759980 + 3.7193602813 0.3943474517 + 3.8074089444 0.4247978126 + 3.8954576075 0.4681277783 + 3.9835062706 0.5255586639 + 4.0715549337 0.5998544774 + 4.1596035968 0.6841389948 + 4.2476522599 0.7095737890 + 4.3357009230 0.6946289548 + 4.4237495861 0.7053178723 + 4.5117982492 0.7126996180 + 4.5998469123 0.7099994386 + 4.6878955754 0.7127998186 + 4.7759442385 0.7165348534 + 4.8639929016 0.7159430224 + 4.9520415647 0.7156521414 + 5.0400902278 0.7180041107 + 5.1281388909 0.7209256027 + 5.2161875540 0.7211979434 + 5.3042362171 0.7202485030 + 5.3922848802 0.7218434212 + 5.4803335433 0.7231651244 + 5.5683822064 0.7161060570 + 5.6564308695 0.7259836922 + 5.7444795326 0.7062262601 + 5.8325281957 0.6174126478 + 5.9205768588 0.5707104559 + 6.0086255219 0.4930360250 + 6.0966741850 0.4985245002 + 6.1847228481 0.5508136034 + 6.2727715112 0.5859663299 + 6.3608201743 0.6373001617 + 6.4488688374 0.3929750748 + 6.5369175005 0.2324073201 + 6.6249661636 0.1112890511 + 6.7130148267 0.1153179026 + 6.8010634898 0.1194327630 + 6.8891121529 0.1234226341 + 6.9771608160 0.1271773142 + 7.0652094791 0.1308698606 + 7.1532581422 0.1353673152 + 7.2413068053 0.1416425232 + 7.3293554684 0.1475606013 + 7.4174041315 0.1538730804 + 7.5054527946 0.1595443762 + 7.5935014577 0.1650222683 + 7.6815501208 0.1704228896 + 7.7695987839 0.1758118820 + 7.8576474470 0.1813997417 + 7.9456961101 0.1889200002 + 8.0337447732 0.1989590722 + 8.1217934363 0.2073059213 + 8.2098420994 0.2155881293 + 8.2978907625 0.2241446945 + 8.3859394256 0.2328828545 + 8.4739880887 0.2415570389 + 8.5620367518 0.2515576429 + 8.6500854149 0.2667434611 + 8.7381340780 0.2818632388 + 8.8261827411 0.3001663074 + 8.9142314042 0.3200304768 + 9.0022800673 0.3436257151 + 9.0903287304 0.3735784615 + 9.1783773935 0.4396739502 + 9.2664260566 0.6125818409 + 9.3544747197 0.7122088960 + 9.4425233828 0.4284790531 + 9.5305720459 0.3069897682 + 9.6186207090 0.2351264657 + 9.7066693721 0.1556011843 + 9.7947180352 0.1168097857 + 9.8827666983 0.0750628850 + 9.9708153614 0.0502191813 + 10.0588640245 0.0610283084 + 10.1469126876 0.2512966753 + 10.2349613507 0.6797915858 + 10.3230100138 0.8942840259 + 10.4110586769 0.5447416583 + 10.4991073400 0.4695887106 + 10.5871560031 0.4214532245 + 10.6752046662 0.3839177946 + 10.7632533293 0.3547763862 + 10.8513019924 0.3286136339 + 10.9393506555 0.3122751716 + 11.0273993186 0.3030066921 + 11.1154479817 0.2932757914 + 11.2034966448 0.2839277820 + 11.2915453079 0.2731531897 + 11.3795939710 0.2623605905 + 11.4676426341 0.2506434432 + 11.5556912972 0.2444591746 + 11.6437399603 0.2388773310 + 11.7317886234 0.2328246372 + 11.8198372865 0.2257815159 + 11.9078859496 0.2190545078 + 11.9959346127 0.5199071149 + 12.0839832758 0.9459934521 + 12.1720319389 1.2104781662 + 12.2600806020 1.1787792455 + 12.3481292651 1.2270821987 + 12.4361779282 1.1959707414 + 12.5242265914 1.2080230774 + 12.6122752545 1.2202094029 + 12.7003239176 1.2276868745 + 12.7883725807 1.2369242295 + 12.8764212438 1.2422307397 + 12.9644699069 1.2611566096 + 13.0525185700 1.2683255232 + 13.1405672331 1.2752187063 + 13.2286158962 1.3005352108 + 13.3166645593 1.3050479412 + 13.4047132224 1.0701817534 + 13.4927618855 0.9115507998 + 13.5808105486 0.8069990478 + 13.6688592117 0.7265699841 + 13.7569078748 0.6567255171 + 13.8449565379 0.5945566254 + 13.9330052010 0.5374762943 + 14.0210538641 0.4879876273 + 14.1091025272 0.4331390810 + 14.1971511903 0.3814971897 + 14.2851998534 0.3401450950 + 14.3732485165 0.2817167689 + 14.4612971796 0.2255019100 + 14.5493458427 0.1575094495 + 14.6373945058 0.0335731577 + 14.7254431689 0.0000000000 + 14.8134918320 0.0000000000 + 14.9015404951 0.0000000000 + 14.9895891582 0.0000000000 + 15.0776378213 0.0000000000 + 15.1656864844 0.0000000000 + 15.2537351475 0.0000000000 + 15.3417838106 0.0000000000 + 15.4298324737 0.0000000000 + 15.5178811368 0.0000000000 + 15.6059297999 0.0000000000 + 15.6939784630 0.0000000000 + 15.7820271261 0.0000000000 + 15.8700757892 0.0000000000 + 15.9581244523 0.0000000000 + 16.0461731154 0.0000000000 + 16.1342217785 0.0000000000 diff --git a/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonopy.yaml b/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonopy.yaml new file mode 100644 index 0000000000..116aaef49e --- /dev/null +++ b/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonopy.yaml @@ -0,0 +1,128 @@ +phonopy: + version: "2.30.1" + frequency_unit_conversion_factor: 15.633302 + symmetry_tolerance: 1.00000e-03 + +space_group: + type: "Fd-3m" + number: 227 + Hall_symbol: "F 4d 2 3 -1d" + +primitive_matrix: +- [ 0.000000000000000, 0.500000000000000, 0.500000000000000 ] +- [ 0.500000000000000, 0.000000000000000, 0.500000000000000 ] +- [ 0.500000000000000, 0.500000000000000, 0.000000000000000 ] + +supercell_matrix: +- [ 1, 0, 0 ] +- [ 0, 1, 0 ] +- [ 0, 0, 1 ] + +primitive_cell: + lattice: + - [ 0.000000000000000, 2.734363997691476, 2.734363997691476 ] # a + - [ 2.734363997691476, 0.000000000000000, 2.734363997691476 ] # b + - [ 2.734363997691477, 2.734363997691476, 0.000000000000000 ] # c + points: + - symbol: Si # 1 + coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] + mass: 28.085500 + - symbol: Si # 2 + coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] + mass: 28.085500 + reciprocal_lattice: # without 2pi + - [ -0.182857878622646, 0.182857878622646, 0.182857878622646 ] # a* + - [ 0.182857878622646, -0.182857878622646, 0.182857878622646 ] # b* + - [ 0.182857878622646, 0.182857878622646, -0.182857878622646 ] # c* + +unit_cell: + lattice: + - [ 5.468727995382952, 0.000000000000000, 0.000000000000000 ] # a + - [ 0.000000000000001, 5.468727995382952, 0.000000000000000 ] # b + - [ 0.000000000000000, 0.000000000000000, 5.468727995382952 ] # c + points: + - symbol: Si # 1 + coordinates: [ 0.250000000000000, 0.250000000000000, 0.750000000000000 ] + mass: 28.085500 + reduced_to: 1 + - symbol: Si # 2 + coordinates: [ 0.500000000000000, 0.000000000000000, -0.000000000000000 ] + mass: 28.085500 + reduced_to: 2 + - symbol: Si # 3 + coordinates: [ 0.250000000000000, 0.750000000000000, 0.250000000000000 ] + mass: 28.085500 + reduced_to: 1 + - symbol: Si # 4 + coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] + mass: 28.085500 + reduced_to: 2 + - symbol: Si # 5 + coordinates: [ 0.750000000000000, 0.250000000000000, 0.250000000000000 ] + mass: 28.085500 + reduced_to: 1 + - symbol: Si # 6 + coordinates: [ 0.000000000000000, 0.000000000000000, 0.500000000000000 ] + mass: 28.085500 + reduced_to: 2 + - symbol: Si # 7 + coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] + mass: 28.085500 + reduced_to: 1 + - symbol: Si # 8 + coordinates: [ 0.000000000000000, 0.500000000000000, -0.000000000000000 ] + mass: 28.085500 + reduced_to: 2 + +supercell: + lattice: + - [ 5.468727995382952, 0.000000000000000, 0.000000000000000 ] # a + - [ 0.000000000000001, 5.468727995382952, 0.000000000000000 ] # b + - [ 0.000000000000000, 0.000000000000000, 5.468727995382952 ] # c + points: + - symbol: Si # 1 + coordinates: [ 0.250000000000000, 0.250000000000000, 0.750000000000000 ] + mass: 28.085500 + reduced_to: 1 + - symbol: Si # 2 + coordinates: [ 0.500000000000000, 0.000000000000000, 1.000000000000000 ] + mass: 28.085500 + reduced_to: 2 + - symbol: Si # 3 + coordinates: [ 0.250000000000000, 0.750000000000000, 0.250000000000000 ] + mass: 28.085500 + reduced_to: 1 + - symbol: Si # 4 + coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] + mass: 28.085500 + reduced_to: 2 + - symbol: Si # 5 + coordinates: [ 0.750000000000000, 0.250000000000000, 0.250000000000000 ] + mass: 28.085500 + reduced_to: 1 + - symbol: Si # 6 + coordinates: [ 0.000000000000000, 0.000000000000000, 0.500000000000000 ] + mass: 28.085500 + reduced_to: 2 + - symbol: Si # 7 + coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] + mass: 28.085500 + reduced_to: 1 + - symbol: Si # 8 + coordinates: [ 0.000000000000000, 0.500000000000000, 1.000000000000000 ] + mass: 28.085500 + reduced_to: 2 + +displacements: +- atom: 1 + displacement: + [ 0.0100000000000000, 0.0000000000000000, 0.0000000000000000 ] + forces: + - [ -0.1122161969542503, 0.0000018391019694, 0.0000000163813638 ] + - [ 0.0310748945921659, -0.0157699882984161, 0.0157650932669640 ] + - [ -0.0111228805035353, 0.0000018949785954, 0.0000012983527995 ] + - [ 0.0310732033103704, 0.0157659742981195, -0.0157670788466930 ] + - [ -0.0001304051838815, -0.0000000675927367, 0.0000012644712797 ] + - [ 0.0307263098657131, 0.0153576303273439, 0.0153586976230145 ] + - [ -0.0001303859753534, 0.0000019283108941, 0.0000024035580282 ] + - [ 0.0307255350053310, -0.0153591511771083, -0.0153617449104786 ] diff --git a/tutorials/force_fields/job_2025-02-11-06-11-49-050842-97424/phonon_band_structure.pdf b/tutorials/force_fields/job_2025-02-11-06-11-49-050842-97424/phonon_band_structure.pdf new file mode 100644 index 0000000000000000000000000000000000000000..45ec59beb6e4059e19efad74b1120502c9fc1a6c GIT binary patch literal 37518 zcmZU(1yGwo*EU=Oiff^S;>B7ZNP@crcS>=J6Wm=|pm=Z%5?qQCTuUkL1OgP-;>8OT z`FXzg`DfmlcQUhQcK6yn*R|Q)n{)4BQkQ{naC3rim?{?O%3E=`>A2{e%xrN)MCgFp zKF*eOKxtEVQ+p?CI-t6#wWS*!&m%ySPD~8P($V5SK!N`?0OI86PRINI06<+eGh0h@ zcRK$6smgl0%W1irx?9qL{-;6R)ZN|E)sYVTpDGSe%f{3K>S#^J_upPuCvz=JcRGVd 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+1,9723 @@ +nqpoint: 606 +npath: 6 +segment_nqpoint: +- 101 +- 101 +- 101 +- 101 +- 101 +- 101 +reciprocal_lattice: +- [ -0.18285788, 0.18285788, 0.18285788 ] # a* +- [ 0.18285788, -0.18285788, 0.18285788 ] # b* +- [ 0.18285788, 0.18285788, -0.18285788 ] # c* +natom: 2 +lattice: +- [ 0.000000000000000, 2.734363997691476, 2.734363997691476 ] # a +- [ 2.734363997691476, 0.000000000000000, 2.734363997691476 ] # b +- [ 2.734363997691477, 2.734363997691476, 0.000000000000000 ] # c +points: +- symbol: Si # 1 + coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] + mass: 28.085500 +- symbol: Si # 2 + coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] + mass: 28.085500 + +phonon: +- q-position: [ 0.0000000, 0.0000000, 0.0000000 ] + distance: 0.0000000 + band: + - # 1 + frequency: -0.0080331233 + - # 2 + frequency: -0.0080331233 + - # 3 + frequency: -0.0080331233 + - # 4 + frequency: 14.6667440601 + - # 5 + frequency: 14.6667440601 + - # 6 + frequency: 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angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, -1.348e-07, -1.636e-07) [0.5, -3.746e-08, -3.261e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.459e-08, -1.401e-07, 2.731) [7.333e-09, -1.056e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], input=InputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, relax_cell=True, fix_symmetry=False, symprec=None, steps=500, relax_kwargs={'fmax': 1e-05}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, -1.348e-07, -1.636e-07) [0.5, -3.746e-08, -3.261e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.459e-08, -1.401e-07, 2.731) [7.333e-09, -1.056e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, -1.348e-07, -1.636e-07) [0.5, -3.746e-08, -3.261e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.459e-08, -1.401e-07, 2.731) [7.333e-09, -1.056e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.5106201171875, energy_per_atom=-5.3138275146484375, forces=[(-6.017507985234261e-07, -6.638234481215477e-06, -7.87666067481041e-07), (-5.4389238357543945e-06, 4.51505184173584e-06, -2.2202730178833008e-06), (-1.4603137969970703e-06, -1.9073486328125e-06, -2.8312206268310547e-07), (-2.682209014892578e-07, 1.234118826687336e-06, 1.371721737086773e-06), (8.553266525268555e-06, 5.887122824788094e-07, -1.4404067769646645e-06), (1.1026859283447266e-06, 5.02169132232666e-06, 2.5331974029541016e-06), (3.203749656677246e-06, -1.5348196029663086e-06, -2.339482307434082e-06), (-5.114474333822727e-06, -1.3050157576799393e-06, 3.1035160645842552e-06)], stress=((0.0006924792408186339, -4.4729655696953566e-05, -3.6938730776494e-05), (-4.4729655696953566e-05, 0.0006850838240233392, 2.0533694501513544e-05), (-3.6938730776494e-05, 2.0533694501513544e-05, 0.0006995213391471558)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, energy=-42.51001739501953, forces=[[-1.0136282071471214e-06, -8.860370144248009e-07, -1.444830559194088e-06], [3.5762786865234375e-06, 2.041459083557129e-06, -9.08970832824707e-07], [-2.7120113372802734e-06, -2.637505531311035e-06, 6.258487701416016e-07], [4.500150680541992e-06, 9.605428203940392e-07, 1.2292293831706047e-06], [-3.039836883544922e-06, 7.472699508070946e-07, 3.60771082341671e-07], [8.195638656616211e-07, 5.066394805908203e-07, 1.6689300537109375e-06], [-4.082918167114258e-06, -8.195638656616211e-07, -3.6656856536865234e-06], [1.9170111045241356e-06, 1.3259705156087875e-07, 2.2315653041005135e-06]], stress=((-2.588936067726548, 2.0132524184944748e-05, 2.5232310138558127e-05), (2.0132524184944748e-05, -2.588927114867015, -6.306294065303895e-05), (2.5232310138558127e-05, -6.306294065303895e-05, -2.58892562272376)), magmoms=[0.003035895526409149, 0.003035925328731537, 0.0030358880758285522, 0.003035910427570343, 0.003036022186279297, 0.003035925328731537, 0.0030359849333763123, 0.0030360743403434753]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468502172843737 5.468502173624643 5.46850217375479\n", + " angles : 90.00000011526447 89.99999995388117 89.9999999632024\n", + " volume : 163.53291085316573\n", + " A : 5.468502172843737 1.756043309593872e-09 2.2008683933226213e-09\n", + " B : 1.7560441889960934e-09 5.468502173624643 -5.50061419672616e-09\n", + " C : 2.2008680584734375e-09 -5.500614531575348e-09 5.46850217375479\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 1.579e-08, 5.469) [0.5, 3.733e-09, 1.0]\n", + " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (9.296e-09, 2.316e-09, 2.734) [1.499e-09, 9.264e-10, 0.5]\n", + " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (2.005e-08, 2.734, 1.956e-08) [3.505e-09, 0.5, 4.081e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468502172843737 5.468502173624643 5.46850217375479\n", + " angles : 90.00000011526447 89.99999995388117 89.9999999632024\n", + " volume : 163.53291085316573\n", + " A : 5.468502172843737 1.756043309593872e-09 2.2008683933226213e-09\n", + " B : 1.7560441889960934e-09 5.468502173624643 -5.50061419672616e-09\n", + " C : 2.2008680584734375e-09 -5.500614531575348e-09 5.46850217375479\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 1.579e-08, 5.469) [0.5, 3.733e-09, 1.0]\n", + " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (9.296e-09, 2.316e-09, 2.734) [1.499e-09, 9.264e-10, 0.5]\n", + " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (2.005e-08, 2.734, 1.956e-08) [3.505e-09, 0.5, 4.081e-09], molecule=None, energy=-42.51005172729492, forces=[[-1.841457560658455e-06, -1.7085112631320953e-06, 1.78581103682518e-06], [-2.041459083557129e-06, 1.4901161193847656e-06, 1.9222497940063477e-06], [-1.1920928955078125e-07, -2.682209014892578e-07, -5.066394805908203e-07], [1.6689300537109375e-06, 1.4249235391616821e-06, -2.010725438594818e-06], [3.2782554626464844e-07, 4.85684722661972e-07, -4.377216100692749e-08], [-1.043081283569336e-07, 1.7881393432617188e-07, -1.6391277313232422e-07], [1.8924474716186523e-06, -1.564621925354004e-06, -1.6242265701293945e-06], [1.5005934983491898e-07, -8.451752364635468e-08, 6.848713383078575e-07]], stress=((-2.536166608014681, -3.887377935245278e-06, -3.954693621355416e-05), (-3.887377935245278e-06, -2.536161572031194, -3.8263649412555135e-06), (-3.954693621355416e-05, -3.8263649412555135e-06, -2.5361690327474715)), magmoms=[0.0030512064695358276, 0.0030511990189552307, 0.0030512064695358276, 0.0030512064695358276, 0.0030511394143104553, 0.003051191568374634, 0.00305117666721344, 0.003051169216632843]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468055185584739 5.468055187552339 5.4680551870825616\n", + " angles : 90.0000002267093 89.99999999202647 89.99999993791413\n", + " volume : 163.49281337094024\n", + " A : 5.468055185584739 2.9626001823444003e-09 3.804804927840314e-10\n", + " B : 2.962600698456001e-09 5.468055187552339 -1.0818063436125202e-08\n", + " C : 3.8047932377840965e-10 -1.0818066211872068e-08 5.4680551870825616\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 5.668e-08, 4.103e-09) [0.5, 1.01e-08, 7.156e-10]\n", + " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.461e-08, 6.186e-09, 2.734) [2.637e-09, 2.121e-09, 0.5]\n", + " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (3.955e-08, 2.734, 4.453e-08) [6.963e-09, 0.5, 9.133e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468055185584739 5.468055187552339 5.4680551870825616\n", + " angles : 90.0000002267093 89.99999999202647 89.99999993791413\n", + " volume : 163.49281337094024\n", + " A : 5.468055185584739 2.9626001823444003e-09 3.804804927840314e-10\n", + " B : 2.962600698456001e-09 5.468055187552339 -1.0818063436125202e-08\n", + " C : 3.8047932377840965e-10 -1.0818066211872068e-08 5.4680551870825616\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 5.668e-08, 4.103e-09) [0.5, 1.01e-08, 7.156e-10]\n", + " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.461e-08, 6.186e-09, 2.734) [2.637e-09, 2.121e-09, 0.5]\n", + " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (3.955e-08, 2.734, 4.453e-08) [6.963e-09, 0.5, 9.133e-09], molecule=None, energy=-42.51011657714844, forces=[[-5.430774763226509e-07, -3.7995632737874985e-06, 7.48620368540287e-06], [-3.3229589462280273e-06, -3.2633543014526367e-06, -8.031725883483887e-06], [-2.9653310775756836e-06, 4.544854164123535e-06, 4.738569259643555e-06], [2.980232238769531e-07, 2.542976289987564e-06, -3.646593540906906e-06], [8.463859558105469e-06, -1.8246937543153763e-06, 6.309011951088905e-06], [-2.7865171432495117e-06, -2.682209014892578e-06, -7.212162017822266e-06], [3.069639205932617e-06, 2.8312206268310547e-06, 4.082918167114258e-06], [-2.157175913453102e-06, 1.5852274373173714e-06, -3.6992132663726807e-06]], stress=((-2.4264813955198044, 0.0002661702971243203, -2.3941788710492665e-05), (0.0002661702971243203, -2.4264998607925907, 3.354682909729243e-05), (-2.3941788710492665e-05, 3.354682909729243e-05, -2.4264651684619007)), magmoms=[0.0030816122889518738, 0.003081493079662323, 0.0030813664197921753, 0.0030814260244369507, 0.0030815377831459045, 0.0030814185738563538, 0.0030814260244369507, 0.0030815228819847107]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.467396703824154 5.467396704909678 5.467396707563365\n", + " angles : 90.0000002527664 90.00000007934335 89.9999993260871\n", + " volume : 163.43375544598408\n", + " A : 5.467396703824154 3.215375688852146e-08 -3.7856321839316396e-09\n", + " B : 3.215375698975334e-08 5.467396704909678 -1.2059998486999147e-08\n", + " C : -3.7856327770069295e-09 -1.2059999644732788e-08 5.467396707563365\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 5.101e-08, 5.467) [0.5, 8.595e-09, 1.0]\n", + " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.467, 5.467, 2.734) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.504e-08, 2.734, 2.398e-08) [5.297e-09, 0.5, 5.488e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.467396703824154 5.467396704909678 5.467396707563365\n", + " angles : 90.0000002527664 90.00000007934335 89.9999993260871\n", + " volume : 163.43375544598408\n", + " A : 5.467396703824154 3.215375688852146e-08 -3.7856321839316396e-09\n", + " B : 3.215375698975334e-08 5.467396704909678 -1.2059998486999147e-08\n", + " C : -3.7856327770069295e-09 -1.2059999644732788e-08 5.467396707563365\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 5.101e-08, 5.467) [0.5, 8.595e-09, 1.0]\n", + " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.467, 5.467, 2.734) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.504e-08, 2.734, 2.398e-08) [5.297e-09, 0.5, 5.488e-09], molecule=None, energy=-42.510196685791016, forces=[[2.3085158318281174e-06, -2.089887857437134e-06, -3.2442621886730194e-06], [2.995133399963379e-06, 7.450580596923828e-07, -2.0265579223632812e-06], [-2.0712614059448242e-06, -4.291534423828125e-06, 1.1026859283447266e-06], [1.1324882507324219e-06, 4.942878149449825e-06, 4.804343916475773e-06], [6.556510925292969e-07, 9.528594091534615e-07, -2.739951014518738e-06], [1.2218952178955078e-06, 1.9371509552001953e-07, 3.725290298461914e-07], [-4.231929779052734e-06, -3.606081008911133e-06, -2.130866050720215e-06], [-1.992913894355297e-06, 3.1029339879751205e-06, 3.8262223824858665e-06]], stress=((-2.2549921689369903, -8.352920541349362e-06, -5.9531600217545794e-05), (-8.352920541349362e-06, -2.2549636316972292, -1.385672142591112e-05), (-5.9531600217545794e-05, -1.385672142591112e-05, -2.254964564286764)), magmoms=[0.00312592089176178, 0.003125905990600586, 0.0031259581446647644, 0.003125719726085663, 0.0031259283423423767, 0.0031260624527931213, 0.0031260475516319275, 0.00312592089176178]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.466541821510313 5.466541825121733 5.466541830477119\n", + " angles : 90.00000031000636 90.00000030309221 89.99999879581102\n", + " volume : 163.3571041433684\n", + " A : 5.466541821510313 5.7445325739951046e-08 -1.445888544634015e-08\n", + " B : 5.744532616203014e-08 5.466541825121733 -1.4788721381940212e-08\n", + " C : -1.445888487548972e-08 -1.4788723326637997e-08 5.466541830477119\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.1) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.733, 6.347e-08, 5.467) [0.5, 9.062e-09, 1.0]\n", + " PeriodicSite: Si (1.367, 4.1, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.733, 2.733, 2.733) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.1, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.467, 5.467, 2.733) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.1, 4.1, 4.1) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.467, 2.733, 4.127e-08) [1.0, 0.5, 1.155e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.466541821510313 5.466541825121733 5.466541830477119\n", + " angles : 90.00000031000636 90.00000030309221 89.99999879581102\n", + " volume : 163.3571041433684\n", + " A : 5.466541821510313 5.7445325739951046e-08 -1.445888544634015e-08\n", + " B : 5.744532616203014e-08 5.466541825121733 -1.4788721381940212e-08\n", + " C : -1.445888487548972e-08 -1.4788723326637997e-08 5.466541830477119\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.1) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.733, 6.347e-08, 5.467) [0.5, 9.062e-09, 1.0]\n", + " PeriodicSite: Si (1.367, 4.1, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.733, 2.733, 2.733) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.1, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.467, 5.467, 2.733) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.1, 4.1, 4.1) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.467, 2.733, 4.127e-08) [1.0, 0.5, 1.155e-08], molecule=None, energy=-42.510311126708984, forces=[[2.6674242690205574e-06, 3.357767127454281e-06, -2.8705690056085587e-06], [-1.6689300537109375e-06, -1.996755599975586e-06, 7.703900337219238e-06], [1.9669532775878906e-06, 2.0116567611694336e-06, -4.32133674621582e-06], [-2.086162567138672e-06, -2.982676960527897e-06, 7.980270311236382e-07], [-1.1920928955078125e-07, -1.0611256584525108e-06, -2.795131877064705e-07], [-4.172325134277344e-07, 1.0132789611816406e-06, -1.6689300537109375e-06], [-1.1175870895385742e-06, -1.1622905731201172e-06, -3.069639205932617e-06], [6.82310201227665e-07, 8.709030225872993e-07, 3.634369932115078e-06]], stress=((-2.0118884593973236, -1.9662156738092746e-05, 1.5526901396870438e-05), (-1.9662156738092746e-05, -2.0119024482403436, 7.023391324703004e-06), (1.5526901396870438e-05, 7.023391324703004e-06, -2.011872045821513)), magmoms=[0.0031842663884162903, 0.003184087574481964, 0.003184095025062561, 0.0031841620802879333, 0.0031841248273849487, 0.003184005618095398, 0.003184087574481964, 0.0031840279698371887]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.465511891139474 5.465511895210926 5.465511906935457\n", + " angles : 90.00000034244523 90.00000046229164 89.99999837196914\n", + " volume : 163.26478928247508\n", + " A : 5.4655118911394736 7.764989014140953e-08 -2.2049271714675683e-08\n", + " B : 7.764989135801762e-08 5.465511895210925 -1.633312601003877e-08\n", + " C : -2.2049271243651576e-08 -1.633312765442467e-08 5.465511906935457\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.366, 1.366, 4.099) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.733, 4.464e-08, 5.466) [0.5, 4.053e-09, 1.0]\n", + " PeriodicSite: Si (1.366, 4.099, 1.366) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.733, 2.733, 2.733) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.099, 1.366, 1.366) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.466, 5.466, 2.733) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.099, 4.099, 4.099) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.466, 2.733, 1.167e-07) [1.0, 0.5, 2.688e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.465511891139474 5.465511895210926 5.465511906935457\n", + " angles : 90.00000034244523 90.00000046229164 89.99999837196914\n", + " volume : 163.26478928247508\n", + " A : 5.4655118911394736 7.764989014140953e-08 -2.2049271714675683e-08\n", + " B : 7.764989135801762e-08 5.465511895210925 -1.633312601003877e-08\n", + " C : -2.2049271243651576e-08 -1.633312765442467e-08 5.465511906935457\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.366, 1.366, 4.099) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.733, 4.464e-08, 5.466) [0.5, 4.053e-09, 1.0]\n", + " PeriodicSite: Si (1.366, 4.099, 1.366) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.733, 2.733, 2.733) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.099, 1.366, 1.366) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.466, 5.466, 2.733) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.099, 4.099, 4.099) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.466, 2.733, 1.167e-07) [1.0, 0.5, 2.688e-08], molecule=None, energy=-42.51041793823242, forces=[[2.4889595806598663e-07, 4.639732651412487e-06, 4.6496279537677765e-07], [-1.2516975402832031e-06, -3.769993782043457e-06, 6.854534149169922e-07], [3.3676624298095703e-06, 5.036592483520508e-06, 1.0281801223754883e-06], [-2.592802047729492e-06, -2.534245140850544e-06, 1.3282988220453262e-07], [-8.940696716308594e-07, 3.92901711165905e-06, -1.8774298951029778e-06], [3.7550926208496094e-06, -4.425644874572754e-06, -8.642673492431641e-07], [-1.043081283569336e-06, -1.1026859283447266e-06, 4.813075065612793e-06], [-1.6648555174469948e-06, -1.8245773389935493e-06, -4.329485818743706e-06]], stress=((-1.6906831740976387, 9.426433894625491e-05, 9.228162422444029e-05), (9.426433894625491e-05, -1.690699028119728, -4.727289748436047e-05), (9.228162422444029e-05, -4.727289748436047e-05, -1.6906773920425235)), magmoms=[0.0032541975378990173, 0.0032541826367378235, 0.0032541826367378235, 0.003254227340221405, 0.003254212439060211, 0.003254137933254242, 0.0032541602849960327, 0.003254234790802002]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.464335056998369 5.464335059138653 5.464335079792611\n", + " angles : 90.00000051826392 90.00000031934624 89.99999769813712\n", + " volume : 163.15934960636423\n", + " A : 5.464335056998368 1.0976506471239753e-07 -1.5228127222395356e-08\n", + " B : 1.0976506658527718e-07 5.464335059138652 -2.4713579642085147e-08\n", + " C : -1.5228126797444088e-08 -2.4713580723973096e-08 5.464335079792611\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.366, 1.366, 4.098) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.732, 5.464, 5.464) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.366, 4.098, 1.366) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.732, 2.732, 2.732) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.098, 1.366, 1.366) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.16e-07, 5.464, 2.732) [2.53e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.098, 4.098, 4.098) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.464, 2.732, 1.212e-07) [1.0, 0.5, 2.724e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.464335056998369 5.464335059138653 5.464335079792611\n", + " angles : 90.00000051826392 90.00000031934624 89.99999769813712\n", + " volume : 163.15934960636423\n", + " A : 5.464335056998368 1.0976506471239753e-07 -1.5228127222395356e-08\n", + " B : 1.0976506658527718e-07 5.464335059138652 -2.4713579642085147e-08\n", + " C : -1.5228126797444088e-08 -2.4713580723973096e-08 5.464335079792611\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.366, 1.366, 4.098) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.732, 5.464, 5.464) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.366, 4.098, 1.366) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.732, 2.732, 2.732) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.098, 1.366, 1.366) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.16e-07, 5.464, 2.732) [2.53e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.098, 4.098, 4.098) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.464, 2.732, 1.212e-07) [1.0, 0.5, 2.724e-08], molecule=None, energy=-42.51050567626953, forces=[[-1.9349390640854836e-06, -3.8067810237407684e-07, -1.5243422240018845e-06], [-2.60770320892334e-06, -3.1888484954833984e-06, 3.039836883544922e-06], [2.0116567611694336e-06, 4.9173831939697266e-06, -1.043081283569336e-06], [-1.0728836059570312e-06, -1.8165446817874908e-06, -2.6496127247810364e-07], [7.748603820800781e-07, -8.688075467944145e-07, 1.9837170839309692e-07], [-2.0265579223632812e-06, -2.6226043701171875e-06, 3.606081008911133e-06], [6.735324859619141e-06, 7.778406143188477e-06, -2.86102294921875e-06], [-1.8618302419781685e-06, -3.775232471525669e-06, -1.2100208550691605e-06]], stress=((-1.286992489412312, 2.6355894396989766e-05, 5.599031003530822e-05), (2.6355894396989766e-05, -1.2870006029412635, 2.467087518703579e-08), (5.599031003530822e-05, 2.467087518703579e-08, -1.2869639521725509)), magmoms=[0.0033344700932502747, 0.003334447741508484, 0.003334522247314453, 0.00333423912525177, 0.003334254026412964, 0.0033344626426696777, 0.0033344030380249023, 0.0033344998955726624]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.463046599806876 5.46304659876286 5.463046633984983\n", + " angles : 90.00000067640845 89.99999998124949 89.99999699309747\n", + " volume : 163.0439610969674\n", + " A : 5.463046599806874 1.4335129627483703e-07 8.939138219735487e-10\n", + " B : 1.4335129843829687e-07 5.463046598762858 -3.224714673338457e-08\n", + " C : 8.93914324421651e-10 -3.2247147191376687e-08 5.463046633984983\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.366, 1.366, 4.097) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.732, 5.463, 5.463) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.366, 4.097, 1.366) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.732, 2.732, 2.732) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.097, 1.366, 1.366) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.619e-07, 5.463, 2.732) [3.312e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.097, 4.097, 4.097) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.463, 2.732, 1.105e-07) [1.0, 0.5, 2.302e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.463046599806876 5.46304659876286 5.463046633984983\n", + " angles : 90.00000067640845 89.99999998124949 89.99999699309747\n", + " volume : 163.0439610969674\n", + " A : 5.463046599806874 1.4335129627483703e-07 8.939138219735487e-10\n", + " B : 1.4335129843829687e-07 5.463046598762858 -3.224714673338457e-08\n", + " C : 8.93914324421651e-10 -3.2247147191376687e-08 5.463046633984983\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.366, 1.366, 4.097) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.732, 5.463, 5.463) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.366, 4.097, 1.366) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.732, 2.732, 2.732) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.097, 1.366, 1.366) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.619e-07, 5.463, 2.732) [3.312e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.097, 4.097, 4.097) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.463, 2.732, 1.105e-07) [1.0, 0.5, 2.302e-08], molecule=None, energy=-42.510581970214844, forces=[[2.1886080503463745e-07, 2.294662408530712e-06, 9.520445019006729e-07], [9.313225746154785e-06, 7.808208465576172e-06, -1.8030405044555664e-06], [-4.500150680541992e-06, -1.0609626770019531e-05, -1.4454126358032227e-06], [-4.26173210144043e-06, -6.91611785441637e-06, -2.5193439796566963e-06], [-7.361173629760742e-06, -3.637745976448059e-06, -3.5150442272424698e-06], [-2.7567148208618164e-06, 3.069639205932617e-06, 4.5746564865112305e-06], [7.987022399902344e-06, 3.635883331298828e-06, 5.856156349182129e-06], [1.534121111035347e-06, 4.286179319024086e-06, -2.1241139620542526e-06]], stress=((-0.8041806288287122, 6.897447908546605e-05, -2.080450207505388e-05), (6.897447908546605e-05, -0.804172608558714, -2.8068207817102645e-06), (-2.080450207505388e-05, -2.8068207817102645e-06, -0.8042224088398654)), magmoms=[0.0034221485257148743, 0.0034220293164253235, 0.0034221112728118896, 0.0034221112728118896, 0.003421984612941742, 0.0034221261739730835, 0.0034219548106193542, 0.003422059118747711]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461545147198787 5.461545145259648 5.4615451808891855\n", + " angles : 90.00000084953466 89.99999976930151 89.99999588808815\n", + " volume : 162.9095659883945\n", + " A : 5.461545147198783 1.9597771771340579e-07 1.0995315215276332e-08\n", + " B : 1.9597771812928898e-07 5.461545145259644 -4.048964846185668e-08\n", + " C : 1.0995315545401588e-08 -4.048964886029536e-08 5.4615451808891855\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.462, 5.462) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.462, 5.462, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.462, 2.731, 2.502e-08) [1.0, 0.5, 6.274e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461545147198787 5.461545145259648 5.4615451808891855\n", + " angles : 90.00000084953466 89.99999976930151 89.99999588808815\n", + " volume : 162.9095659883945\n", + " A : 5.461545147198783 1.9597771771340579e-07 1.0995315215276332e-08\n", + " B : 1.9597771812928898e-07 5.461545145259644 -4.048964846185668e-08\n", + " C : 1.0995315545401588e-08 -4.048964886029536e-08 5.4615451808891855\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.462, 5.462) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.462, 5.462, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.462, 2.731, 2.502e-08) [1.0, 0.5, 6.274e-09], molecule=None, energy=-42.51062774658203, forces=[[-6.625778041779995e-06, 3.759050741791725e-07, -1.2055388651788235e-05], [9.238719940185547e-07, 1.7434358596801758e-06, 9.164214134216309e-06], [9.5367431640625e-07, 1.9222497940063477e-06, -8.195638656616211e-07], [-3.2782554626464844e-07, -1.632492057979107e-06, 3.782683052122593e-06], [-4.380941390991211e-06, 1.0987278074026108e-06, -4.718080163002014e-06], [1.0073184967041016e-05, 5.662441253662109e-06, 7.137656211853027e-06], [5.513429641723633e-07, -7.18235969543457e-06, -2.950429916381836e-06], [-1.1813826858997345e-06, -2.082553692162037e-06, 3.441236913204193e-07]], stress=((-0.19357953317739093, -0.00019997761468098542, 3.803595574805208e-08), (-0.00019997761468098542, -0.1935834850255441, -8.99692586828571e-05), (3.803595574805208e-08, -8.99692586828571e-05, -0.19359283423562915)), magmoms=[0.0035246387124061584, 0.0035246461629867554, 0.0035245344042778015, 0.00352458655834198, 0.0035246610641479492, 0.003524668514728546, 0.0035246089100837708, 0.003524407744407654]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.459869448510431 5.459869441846533 5.459869468798026\n", + " angles : 90.0000028550925 89.99999955846455 89.9999988693439\n", + " volume : 162.75966083893866\n", + " A : 5.459869448510431 5.387163515042253e-08 2.103755570557215e-08\n", + " B : 5.3871636068471196e-08 5.459869441846531 -1.3603473569344624e-07\n", + " C : 2.1037551458747527e-08 -1.3603473465465964e-07 5.459869468798024\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 2.266e-07, 1.05e-06) [0.5, 3.656e-08, 1.903e-07]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (9.843e-07, 2.721e-07, 2.73) [1.783e-07, 6.229e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.459869448510431 5.459869441846533 5.459869468798026\n", + " angles : 90.0000028550925 89.99999955846455 89.9999988693439\n", + " volume : 162.75966083893866\n", + " A : 5.459869448510431 5.387163515042253e-08 2.103755570557215e-08\n", + " B : 5.3871636068471196e-08 5.459869441846531 -1.3603473569344624e-07\n", + " C : 2.1037551458747527e-08 -1.3603473465465964e-07 5.459869468798024\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 2.266e-07, 1.05e-06) [0.5, 3.656e-08, 1.903e-07]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (9.843e-07, 2.721e-07, 2.73) [1.783e-07, 6.229e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.51061248779297, forces=[[1.1385418474674225e-05, -6.488990038633347e-07, 2.8136419132351875e-05], [-4.082918167114258e-06, -7.62939453125e-06, -2.41696834564209e-05], [5.97536563873291e-06, -1.5348196029663086e-06, 5.5283308029174805e-06], [1.8775463104248047e-06, 6.395857781171799e-06, -1.3802200555801392e-05], [6.616115570068359e-06, -5.3551048040390015e-08, 1.4450284652411938e-05], [-1.983344554901123e-05, -9.194016456604004e-06, -1.65402889251709e-05], [-3.0249357223510742e-06, 1.0594725608825684e-05, 1.3902783393859863e-05], [1.0242220014333725e-06, 2.0815059542655945e-06, -7.504015229642391e-06]], stress=((0.5372072435196871, 0.00019478386964870507, 5.625113797871147e-05), (0.00019478386964870507, 0.5372567173945013, 7.982633999608061e-05), (5.625113797871147e-05, 7.982633999608061e-05, 0.5372240767607879)), magmoms=[0.003638714551925659, 0.0036384910345077515, 0.0036384984850883484, 0.003638423979282379, 0.0036384984850883484, 0.003638520836830139, 0.003638520836830139, 0.0036385878920555115]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.4598864893880465 5.4598864842935075 5.4598865102096745\n", + " angles : 90.00000280194699 89.99999952101474 89.99999873966406\n", + " volume : 162.76118488100127\n", + " A : 5.4598864893880465 6.005059291439117e-08 2.282197336257326e-08\n", + " B : 6.005059383585618e-08 5.459886484293506 -1.335029632432997e-07\n", + " C : 2.2821968112777438e-08 -1.3350296108807914e-07 5.459886510209673\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 2.018e-07, 9.625e-07) [0.5, 3.147e-08, 1.742e-07]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (9.128e-07, 2.398e-07, 2.73) [1.651e-07, 5.615e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.4598864893880465 5.4598864842935075 5.4598865102096745\n", + " angles : 90.00000280194699 89.99999952101474 89.99999873966406\n", + " volume : 162.76118488100127\n", + " A : 5.4598864893880465 6.005059291439117e-08 2.282197336257326e-08\n", + " B : 6.005059383585618e-08 5.459886484293506 -1.335029632432997e-07\n", + " C : 2.2821968112777438e-08 -1.3350296108807914e-07 5.459886510209673\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 2.018e-07, 9.625e-07) [0.5, 3.147e-08, 1.742e-07]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (9.128e-07, 2.398e-07, 2.73) [1.651e-07, 5.615e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106086730957, forces=[[1.0832329280674458e-05, -8.00122506916523e-06, 1.9390834495425224e-05], [-7.152557373046875e-06, 2.4586915969848633e-06, -1.9177794456481934e-05], [5.677342414855957e-06, 4.917383193969727e-07, 6.973743438720703e-06], [2.086162567138672e-07, 5.9516169130802155e-06, -9.818468242883682e-06], [6.765127182006836e-06, -4.9174996092915535e-06, 1.1430587619543076e-05], [-1.2785196304321289e-05, -1.862645149230957e-06, -1.4156103134155273e-05], [-3.069639205932617e-06, 5.319714546203613e-06, 1.1667609214782715e-05], [-4.847534000873566e-07, 4.775356501340866e-07, -6.305519491434097e-06]], stress=((0.5295171102167974, 9.403638842687665e-05, -0.00015849813369287364), (9.403638842687665e-05, 0.5294849358778512, 5.241133831791735e-05), (-0.00015849813369287364, 5.241133831791735e-05, 0.5295290473628411)), magmoms=[0.0036373361945152283, 0.0036373957991600037, 0.003637343645095825, 0.003637343645095825, 0.00363728404045105, 0.0036373957991600037, 0.003637269139289856, 0.0036375224590301514]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.459920327616994 5.4599203228107065 5.459920349336427\n", + " angles : 90.00000271568209 89.99999960353914 89.9999985539936\n", + " volume : 162.76421112452798\n", + " A : 5.459920327616994 6.889756784663905e-08 1.889009284202428e-08\n", + " B : 6.889756736702278e-08 5.459920322810705 -1.2939354373866184e-07\n", + " C : 1.889008707571444e-08 -1.2939354233568795e-07 5.459920349336425\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 1.911e-07, 8.041e-07) [0.5, 2.869e-08, 1.455e-07]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (7.943e-07, 2.042e-07, 2.73) [1.438e-07, 4.925e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.459920327616994 5.4599203228107065 5.459920349336427\n", + " angles : 90.00000271568209 89.99999960353914 89.9999985539936\n", + " volume : 162.76421112452798\n", + " A : 5.459920327616994 6.889756784663905e-08 1.889009284202428e-08\n", + " B : 6.889756736702278e-08 5.459920322810705 -1.2939354373866184e-07\n", + " C : 1.889008707571444e-08 -1.2939354233568795e-07 5.459920349336425\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 1.911e-07, 8.041e-07) [0.5, 2.869e-08, 1.455e-07]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (7.943e-07, 2.042e-07, 2.73) [1.438e-07, 4.925e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.510616302490234, forces=[[1.2828735634684563e-05, -8.770730346441269e-07, 2.4345004931092262e-05], [-7.003545761108398e-07, -2.7418136596679688e-06, -1.576542854309082e-05], [8.031725883483887e-06, 1.3262033462524414e-06, 7.808208465576172e-06], [6.109476089477539e-06, 6.717396900057793e-06, -1.2885313481092453e-05], [-7.659196853637695e-06, 3.691529855132103e-07, 8.98831058293581e-06], [-1.1101365089416504e-05, -1.0356307029724121e-05, -1.17570161819458e-05], [-1.3932585716247559e-05, 6.988644599914551e-06, 5.0067901611328125e-06], [6.509246304631233e-06, -1.3762619346380234e-06, -5.741836503148079e-06]], stress=((0.5143124502203962, 0.00013634259774259193, 5.943439096416996e-05), (0.00013634259774259193, 0.514330169421555, -8.191316845799929e-05), (5.943439096416996e-05, -8.191316845799929e-05, 0.5142573808083739)), magmoms=[0.0036349892616271973, 0.003635019063949585, 0.003635197877883911, 0.0036350414156913757, 0.0036351680755615234, 0.0036350861191749573, 0.003635115921497345, 0.0036351457238197327]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.4599704815121095 5.4599704776439255 5.459970501930372\n", + " angles : 90.00000270389135 89.99999963003361 89.99999827728837\n", + " volume : 162.7686965275905\n", + " A : 5.459970481512109 8.208243947099918e-08 1.762787755565937e-08\n", + " B : 8.208243894409568e-08 5.459970477643924 -1.2883293686096683e-07\n", + " C : 1.762787291515083e-08 -1.2883293510207843e-07 5.45997050193037\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 1.719e-07, 5.932e-07) [0.5, 2.397e-08, 1.07e-07]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (6.4e-07, 1.249e-07, 2.73) [1.156e-07, 3.468e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.4599704815121095 5.4599704776439255 5.459970501930372\n", + " angles : 90.00000270389135 89.99999963003361 89.99999827728837\n", + " volume : 162.7686965275905\n", + " A : 5.459970481512109 8.208243947099918e-08 1.762787755565937e-08\n", + " B : 8.208243894409568e-08 5.459970477643924 -1.2883293686096683e-07\n", + " C : 1.762787291515083e-08 -1.2883293510207843e-07 5.45997050193037\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 1.719e-07, 5.932e-07) [0.5, 2.397e-08, 1.07e-07]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (6.4e-07, 1.249e-07, 2.73) [1.156e-07, 3.468e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.51061248779297, forces=[[9.906478226184845e-06, -1.18953175842762e-06, 1.0191230103373528e-05], [-5.4389238357543945e-06, 4.470348358154297e-08, -1.1742115020751953e-05], [1.996755599975586e-06, -2.384185791015625e-06, 1.5497207641601562e-06], [6.556510925292969e-07, 6.96815550327301e-06, 1.5585683286190033e-06], [5.781650543212891e-06, -4.7923531383275986e-06, 6.517046131193638e-06], [-1.150369644165039e-05, -4.738569259643555e-06, -2.8461217880249023e-06], [-4.976987838745117e-06, 4.5746564865112305e-06, -3.0547380447387695e-06], [3.6603305488824844e-06, 1.6030389815568924e-06, -2.1852320060133934e-06]], stress=((0.49205825267247016, -2.1876755202970047e-05, -0.00011688593628880498), (-2.1876755202970047e-05, 0.4921022708985066, 1.7712544277257588e-05), (-0.00011688593628880498, 1.7712544277257588e-05, 0.49203596378259157)), magmoms=[0.0036317110061645508, 0.0036317557096481323, 0.0036318525671958923, 0.003631807863712311, 0.003631964325904846, 0.003631889820098877, 0.003631867468357086, 0.00363188236951828]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.460036245799424 5.460036244620607 5.460036264112397\n", + " angles : 90.00000267769764 89.99999975670508 89.99999804749899\n", + " volume : 162.77457816814484\n", + " A : 5.460036245799423 9.30323872984083e-08 1.1592473118221463e-08\n", + " B : 9.303238651345445e-08 5.460036244620604 -1.275864156900285e-07\n", + " C : 1.1592468220515364e-08 -1.275864133149242e-07 5.460036264112396\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 1.545e-07, 3.444e-07) [0.5, 1.977e-08, 6.201e-08]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (4.431e-07, 3.115e-08, 2.73) [8.009e-08, 1.739e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.460036245799424 5.460036244620607 5.460036264112397\n", + " angles : 90.00000267769764 89.99999975670508 89.99999804749899\n", + " volume : 162.77457816814484\n", + " A : 5.460036245799423 9.30323872984083e-08 1.1592473118221463e-08\n", + " B : 9.303238651345445e-08 5.460036244620604 -1.275864156900285e-07\n", + " C : 1.1592468220515364e-08 -1.275864133149242e-07 5.460036264112396\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 1.545e-07, 3.444e-07) [0.5, 1.977e-08, 6.201e-08]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (4.431e-07, 3.115e-08, 2.73) [8.009e-08, 1.739e-08, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.51061248779297, forces=[[7.531489245593548e-06, -1.2904638424515724e-06, 3.321445547044277e-06], [-4.06801700592041e-06, -4.976987838745117e-06, -9.581446647644043e-06], [-1.1771917343139648e-06, 1.0281801223754883e-06, -7.301568984985352e-07], [5.066394805908203e-07, 1.4977995306253433e-06, -1.45728699862957e-06], [5.066394805908203e-06, -1.2775417417287827e-06, 6.663845852017403e-06], [-6.3478946685791016e-06, -2.115964889526367e-06, -5.900859832763672e-06], [-3.427267074584961e-06, 4.336237907409668e-06, 4.202127456665039e-06], [1.921318471431732e-06, 2.7993228286504745e-06, 3.5008415579795837e-06]], stress=((0.4622011189431461, 6.48470987297248e-05, -9.799835281997347e-07), (6.48470987297248e-05, 0.46220587514977285, 6.858724154498056e-05), (-9.799835281997347e-07, 6.858724154498056e-05, 0.4622129628302364)), magmoms=[0.0036270394921302795, 0.003627188503742218, 0.0036271139979362488, 0.003626979887485504, 0.0036272257566452026, 0.0036270171403884888, 0.003627099096775055, 0.003627091646194458]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.4601166744010685 5.460116675861297 5.460116691363573\n", + " angles : 90.00000258797486 89.99999987165455 89.99999777814718\n", + " volume : 162.78177152394014\n", + " A : 5.460116674401068 1.0586796914217971e-07 6.115472939102874e-09\n", + " B : 1.0586796952828918e-07 5.460116675861294 -1.2331313893523934e-07\n", + " C : 6.115467630354005e-09 -1.233131361298998e-07 5.460116691363571\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 1.139e-07, 6.974e-08) [0.5, 1.117e-08, 1.221e-08]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (3.382e-07, 5.46, 2.73) [4.2e-08, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.4601166744010685 5.460116675861297 5.460116691363573\n", + " angles : 90.00000258797486 89.99999987165455 89.99999777814718\n", + " volume : 162.78177152394014\n", + " A : 5.460116674401068 1.0586796914217971e-07 6.115472939102874e-09\n", + " B : 1.0586796952828918e-07 5.460116675861294 -1.2331313893523934e-07\n", + " C : 6.115467630354005e-09 -1.233131361298998e-07 5.460116691363571\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 1.139e-07, 6.974e-08) [0.5, 1.117e-08, 1.221e-08]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (3.382e-07, 5.46, 2.73) [4.2e-08, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106201171875, forces=[[2.5221379473805428e-06, -1.4882534742355347e-06, 2.9033981263637543e-07], [-5.140900611877441e-06, -4.813075065612793e-06, -3.337860107421875e-06], [-1.3560056686401367e-06, 1.8030405044555664e-06, -1.341104507446289e-06], [5.453824996948242e-06, 3.899796865880489e-06, 3.327499143779278e-06], [1.7583370208740234e-06, 2.5707995519042015e-06, 5.4623233154416084e-06], [-7.301568984985352e-07, -1.862645149230957e-06, -2.980232238769531e-07], [-4.693865776062012e-06, -2.175569534301758e-06, -7.0035457611083984e-06], [2.2529857233166695e-06, 2.0457664504647255e-06, 2.92900949716568e-06]], stress=((0.4262083854062738, 4.2087986552164376e-05, 4.0521738174915644e-05), (4.2087986552164376e-05, 0.42616683854250426, -1.5359530206533378e-05), (4.0521738174915644e-05, -1.5359530206533378e-05, 0.42622027592284084)), magmoms=[0.0036214813590049744, 0.0036214739084243774, 0.0036216378211975098, 0.0036218464374542236, 0.003621727228164673, 0.0036216452717781067, 0.0036217719316482544, 0.0036216452717781067]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.460210626941211 5.460210628674449 5.460210643289987\n", + " angles : 90.00000252353483 89.9999999320787 89.99999749103698\n", + " volume : 162.79017464414375\n", + " A : 5.460210626941209 1.195503994765092e-07 3.2364087991896364e-09\n", + " B : 1.195503982175753e-07 5.460210628674447 -1.2024473734147363e-07\n", + " C : 3.236402954634202e-09 -1.202447351442928e-07 5.460210643289986\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.59e-07, 5.46, 2.73) [6.933e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.460210626941211 5.460210628674449 5.460210643289987\n", + " angles : 90.00000252353483 89.9999999320787 89.99999749103698\n", + " volume : 162.79017464414375\n", + " A : 5.460210626941209 1.195503994765092e-07 3.2364087991896364e-09\n", + " B : 1.195503982175753e-07 5.460210628674447 -1.2024473734147363e-07\n", + " C : 3.236402954634202e-09 -1.202447351442928e-07 5.460210643289986\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.59e-07, 5.46, 2.73) [6.933e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106086730957, forces=[[3.0976952984929085e-06, 6.656628102064133e-07, -5.557434633374214e-06], [-5.7220458984375e-06, -2.726912498474121e-06, 8.58306884765625e-06], [3.933906555175781e-06, 1.8775463104248047e-06, -1.1771917343139648e-05], [1.1026859283447266e-06, -2.8407666832208633e-06, 8.43987800180912e-06], [5.0961971282958984e-06, -9.420327842235565e-07, -6.6426582634449e-07], [-9.834766387939453e-07, 8.538365364074707e-06, 2.9802322387695312e-08], [-7.852911949157715e-06, -4.246830940246582e-06, -6.854534149169922e-06], [1.1477386578917503e-06, -3.223540261387825e-07, 7.839989848434925e-06]], stress=((0.38464942128750784, -0.00013145974245155065, -9.107326505002593e-05), (-0.00013145974245155065, 0.3846644592937544, 8.035539558012265e-05), (-9.107326505002593e-05, 8.035539558012265e-05, 0.38464555104093895)), magmoms=[0.0036153867840766907, 0.0036155059933662415, 0.003615342080593109, 0.0036153122782707214, 0.0036154091358184814, 0.0036153197288513184, 0.0036153793334960938, 0.003615260124206543]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.460316785788504 5.46031678861195 5.460316801367637\n", + " angles : 90.0000023708403 90.00000009378996 89.99999738756239\n", + " volume : 162.7996698661292\n", + " A : 5.460316785788502 1.2448331412831257e-07 -4.4691116279055585e-09\n", + " B : 1.2448331311518543e-07 5.460316788611947 -1.1297114182512609e-07\n", + " C : -4.469116689871123e-09 -1.1297113967038858e-07 5.460316801367636\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.46, 5.46, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.460316785788504 5.46031678861195 5.460316801367637\n", + " angles : 90.0000023708403 90.00000009378996 89.99999738756239\n", + " volume : 162.7996698661292\n", + " A : 5.460316785788502 1.2448331412831257e-07 -4.4691116279055585e-09\n", + " B : 1.2448331311518543e-07 5.460316788611947 -1.1297114182512609e-07\n", + " C : -4.469116689871123e-09 -1.1297113967038858e-07 5.460316801367636\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.46, 5.46, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.510616302490234, forces=[[-1.0097865015268326e-06, 3.700493834912777e-06, -7.021008059382439e-06], [6.705522537231445e-07, 1.6689300537109375e-06, 9.655952453613281e-06], [-2.5033950805664062e-06, -2.6971101760864258e-06, -7.703900337219238e-06], [1.8477439880371094e-06, -2.942979335784912e-06, 5.64951915293932e-06], [-1.3113021850585938e-06, 9.669456630945206e-07, -6.827409379184246e-06], [-3.2782554626464844e-07, 8.195638656616211e-07, 6.541609764099121e-06], [2.86102294921875e-06, -5.319714546203613e-06, -4.738569259643555e-06], [-2.832384780049324e-07, 3.8088764995336533e-06, 4.411558620631695e-06]], stress=((0.3376097683619158, -6.75783839001112e-05, -3.734500830717402e-05), (-6.75783839001112e-05, 0.33759463709671583, 8.189984900297531e-05), (-3.734500830717402e-05, 8.189984900297531e-05, 0.3375673821675651)), magmoms=[0.0036081522703170776, 0.003608211874961853, 0.0036081671714782715, 0.003608226776123047, 0.00360812246799469, 0.0036081746220588684, 0.00360821932554245, 0.0036081895232200623]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.460446526443763 5.460446529242143 5.460446538167299\n", + " angles : 90.0000020942262 90.0000003110855 89.99999738863278\n", + " volume : 162.81127470221423\n", + " A : 5.460446526443762 1.2443526624942296e-07 -1.4823651553244156e-08\n", + " B : 1.244352653341237e-07 5.46044652924214 -9.979278008110766e-08\n", + " C : -1.4823656227616615e-08 -9.979277841226609e-08 5.460446538167298\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.46, 5.46, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.460446526443763 5.460446529242143 5.460446538167299\n", + " angles : 90.0000020942262 90.0000003110855 89.99999738863278\n", + " volume : 162.81127470221423\n", + " A : 5.460446526443762 1.2443526624942296e-07 -1.4823651553244156e-08\n", + " B : 1.244352653341237e-07 5.46044652924214 -9.979278008110766e-08\n", + " C : -1.4823656227616615e-08 -9.979277841226609e-08 5.460446538167298\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.46, 5.46, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.510616302490234, forces=[[-9.985058568418026e-06, -1.4045508578419685e-06, -6.743008270859718e-06], [8.195638656616211e-06, -1.996755599975586e-06, 6.198883056640625e-06], [-3.948807716369629e-06, -5.364418029785156e-07, 1.4007091522216797e-06], [6.9141387939453125e-06, 7.701106369495392e-06, 4.110625013709068e-06], [-2.3245811462402344e-06, 4.825415089726448e-06, -1.0868418030440807e-05], [9.968876838684082e-06, -2.562999725341797e-06, 6.586313247680664e-06], [-8.255243301391602e-06, -5.21540641784668e-07, -1.0147690773010254e-05], [-5.690380930900574e-07, -5.470006726682186e-06, 9.368755854666233e-06]], stress=((0.28051899183798645, -5.405423048230191e-05, 5.344174046589173e-05), (-5.405423048230191e-05, 0.28053232786833227, -7.961447528007745e-05), (5.344174046589173e-05, -7.961447528007745e-05, 0.28054333242484136)), magmoms=[0.003599405288696289, 0.0035994797945022583, 0.003599412739276886, 0.0035993531346321106, 0.0035993829369544983, 0.0035994797945022583, 0.0035993754863739014, 0.003599405288696289]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.4606022777865135 5.460602281851383 5.460602288045497\n", + " angles : 90.0000019818485 90.00000041785475 89.99999749953051\n", + " volume : 162.82520696236782\n", + " A : 5.460602277786512 1.1915423468251957e-07 -1.991192173928146e-08\n", + " B : 1.1915423285249888e-07 5.46060228185138 -9.44405211693399e-08\n", + " C : -1.991192665606119e-08 -9.444051894047396e-08 5.4606022880454965\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.803e-08, 2.73, 5.461) [1.532e-09, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.4606022777865135 5.460602281851383 5.460602288045497\n", + " angles : 90.0000019818485 90.00000041785475 89.99999749953051\n", + " volume : 162.82520696236782\n", + " A : 5.460602277786512 1.1915423468251957e-07 -1.991192173928146e-08\n", + " B : 1.1915423285249888e-07 5.46060228185138 -9.44405211693399e-08\n", + " C : -1.991192665606119e-08 -9.444051894047396e-08 5.4606022880454965\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.803e-08, 2.73, 5.461) [1.532e-09, 0.5, 1.0], molecule=None, energy=-42.5106201171875, forces=[[-6.191432476043701e-06, 5.02169132232666e-06, -2.3802276700735092e-06], [-7.599592208862305e-07, 2.8759241104125977e-06, 8.910894393920898e-06], [-4.470348358154297e-07, 1.5050172805786133e-06, -1.6391277313232422e-06], [-1.4603137969970703e-06, -4.014582373201847e-06, 2.9046786949038506e-06], [-5.334615707397461e-06, 4.6298373490571976e-06, -7.736380212008953e-06], [6.243586540222168e-06, -2.652406692504883e-06, 6.780028343200684e-06], [6.899237632751465e-06, -2.2798776626586914e-06, -4.9173831939697266e-06], [9.80566255748272e-07, -5.028676241636276e-06, -1.980341039597988e-06]], stress=((0.21180234434221415, -2.7939016929972935e-05, 0.0001027114747096371), (-2.7939016929972935e-05, 0.21180766010256177, 2.8023296635448354e-05), (0.0001027114747096371, 2.8023296635448354e-05, 0.21176548374085633)), magmoms=[0.0035887807607650757, 0.0035887807607650757, 0.003588743507862091, 0.0035888999700546265, 0.0035888180136680603, 0.003588572144508362, 0.0035887062549591064, 0.003588758409023285]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.460785519264062 5.460785525305992 5.460785523074743\n", + " angles : 90.00000179065846 90.0000002317088 89.99999768901225\n", + " volume : 162.8415991596923\n", + " A : 5.4607855192640615 1.101286050708443e-07 -1.104192756893292e-08\n", + " B : 1.101286031815623e-07 5.46078552530599 -8.533265426612865e-08\n", + " C : -1.1041931892539185e-08 -8.533265134208392e-08 5.460785523074742\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.285e-07, 2.73, 3.825e-08) [1.344e-08, 0.5, 1.482e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.460785519264062 5.460785525305992 5.460785523074743\n", + " angles : 90.00000179065846 90.0000002317088 89.99999768901225\n", + " volume : 162.8415991596923\n", + " A : 5.4607855192640615 1.101286050708443e-07 -1.104192756893292e-08\n", + " B : 1.101286031815623e-07 5.46078552530599 -8.533265426612865e-08\n", + " C : -1.1041931892539185e-08 -8.533265134208392e-08 5.460785523074742\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.285e-07, 2.73, 3.825e-08) [1.344e-08, 0.5, 1.482e-08], molecule=None, energy=-42.510623931884766, forces=[[-6.8999361246824265e-06, 2.364395186305046e-07, -6.280373781919479e-06], [5.647540092468262e-06, 5.289912223815918e-06, 5.409121513366699e-06], [-2.8312206268310547e-06, -9.238719940185547e-07, 2.518296241760254e-06], [-3.3080577850341797e-06, -6.87665306031704e-07, 2.1382002159953117e-06], [-2.2351741790771484e-06, -3.0833762139081955e-06, -9.484472684562206e-06], [8.732080459594727e-06, 3.516674041748047e-06, 4.231929779052734e-06], [-4.3213367462158203e-07, -5.960464477539062e-07, -6.258487701416016e-07], [1.291278749704361e-06, -3.816094249486923e-06, 2.025393769145012e-06]], stress=((0.13175074717993923, -0.00014523759164304312, -2.461230729492583e-06), (-0.00014523759164304312, 0.13170317345630186, -3.5073087803057855e-05), (-2.461230729492583e-06, -3.5073087803057855e-05, 0.13169742637329446)), magmoms=[0.0035761892795562744, 0.0035761594772338867, 0.0035763680934906006, 0.0035761743783950806, 0.003576211631298065, 0.0035760700702667236, 0.003576017916202545, 0.0035760998725891113]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.460996061348722 5.460996059053308 5.460996047208369\n", + " angles : 90.00000176011537 90.00000005799927 89.99999853712589\n", + " volume : 162.8604343095674\n", + " A : 5.460996061348722 6.971499436050956e-08 -2.7640215320131824e-09\n", + " B : 6.971499212777905e-08 5.460996059053307 -8.38803769382965e-08\n", + " C : -2.7640257792770245e-09 -8.388037448297125e-08 5.4609960472083685\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (2.05e-07, 2.73, 2.24e-07) [3.115e-08, 0.5, 4.87e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.460996061348722 5.460996059053308 5.460996047208369\n", + " angles : 90.00000176011537 90.00000005799927 89.99999853712589\n", + " volume : 162.8604343095674\n", + " A : 5.460996061348722 6.971499436050956e-08 -2.7640215320131824e-09\n", + " B : 6.971499212777905e-08 5.460996059053307 -8.38803769382965e-08\n", + " C : -2.7640257792770245e-09 -8.388037448297125e-08 5.4609960472083685\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (2.05e-07, 2.73, 2.24e-07) [3.115e-08, 0.5, 4.87e-08], molecule=None, energy=-42.51062774658203, forces=[[-1.3544922694563866e-06, 6.758957169950008e-06, -7.742433808743954e-06], [2.115964889526367e-06, 1.6838312149047852e-06, 1.4603137969970703e-06], [3.1441450119018555e-06, 3.2782554626464844e-07, 3.7550926208496094e-06], [-1.1622905731201172e-05, -1.139205414801836e-05, 1.7745187506079674e-06], [5.930662155151367e-06, -3.4383265301585197e-06, 5.193403922021389e-06], [-5.27501106262207e-06, 8.612871170043945e-06, 2.086162567138672e-07], [1.2859702110290527e-05, 5.811452865600586e-07, 5.0067901611328125e-06], [-5.87815884500742e-06, -3.111199475824833e-06, -9.713112376630306e-06]], stress=((0.040697312589918974, 1.7470890087806585e-05, 5.109649180820875e-05), (1.7470890087806585e-05, 0.04069438367591165, 0.00020174172527023774), (5.109649180820875e-05, 0.00020174172527023774, 0.04072904977752072)), magmoms=[0.0035618990659713745, 0.003561966121196747, 0.0035619139671325684, 0.0035619735717773438, 0.0035620033740997314, 0.0035618767142295837, 0.003561966121196747, 0.0035620853304862976]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461230999390341 5.461230986963136 5.461230987347953\n", + " angles : 89.99999906898145 89.99999921136417 89.99999915001159\n", + " volume : 162.88145427792256\n", + " A : 5.461230999390341 4.050894539622032e-08 3.75849906981421e-08\n", + " B : 4.050894341289015e-08 5.461230986963136 4.4370695351235936e-08\n", + " C : 3.758498710184151e-08 4.437069809157265e-08 5.461230987347953\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.495e-08, 2.108e-07) [0.5, 2.692e-09, 3.515e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.678e-07, 2.731) [1.0, 5.587e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461230999390341 5.461230986963136 5.461230987347953\n", + " angles : 89.99999906898145 89.99999921136417 89.99999915001159\n", + " volume : 162.88145427792256\n", + " A : 5.461230999390341 4.050894539622032e-08 3.75849906981421e-08\n", + " B : 4.050894341289015e-08 5.461230986963136 4.4370695351235936e-08\n", + " C : 3.758498710184151e-08 4.437069809157265e-08 5.461230987347953\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.495e-08, 2.108e-07) [0.5, 2.692e-09, 3.515e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.678e-07, 2.731) [1.0, 5.587e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106315612793, forces=[[-6.72205351293087e-06, -6.512971594929695e-06, 3.9305305108428e-06], [2.086162567138672e-07, -1.7434358596801758e-06, -1.341104507446289e-07], [7.748603820800781e-07, -8.046627044677734e-07, 1.4603137969970703e-06], [5.155801773071289e-06, 6.146030500531197e-06, -7.579801604151726e-06], [-4.202127456665039e-06, -1.410720869898796e-06, 5.178852006793022e-06], [3.844499588012695e-06, -1.4901161193847656e-08, -8.940696716308594e-07], [-2.2202730178833008e-06, -1.4007091522216797e-06, 3.0547380447387695e-06], [3.155786544084549e-06, 5.6675635278224945e-06, -5.060108378529549e-06]], stress=((-0.06013641576888584, 8.791459044353903e-05, -7.160059748191101e-05), (8.791459044353903e-05, -0.06004577972348509, 1.006679999354065e-05), (-7.160059748191101e-05, 1.006679999354065e-05, -0.06002720953437597)), magmoms=[0.0035460367798805237, 0.003546014428138733, 0.0035459622740745544, 0.003546006977558136, 0.0035460814833641052, 0.00354582816362381, 0.0035459622740745544, 0.003545992076396942]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461229761201413 5.46122975064037 5.46122975140754\n", + " angles : 89.9999990646322 89.99999924229756 89.99999911203\n", + " volume : 162.88134361355043\n", + " A : 5.461229761201413 4.231907120242965e-08 3.611075109519174e-08\n", + " B : 4.231906910748388e-08 5.461229750640369 4.4577962236153174e-08\n", + " C : 3.611074752365085e-08 4.457796548981162e-08 5.46122975140754\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.173e-08, 2.097e-07) [0.5, 1.936e-09, 3.509e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.697e-07, 2.731) [1.0, 5.586e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461229761201413 5.46122975064037 5.46122975140754\n", + " angles : 89.9999990646322 89.99999924229756 89.99999911203\n", + " volume : 162.88134361355043\n", + " A : 5.461229761201413 4.231907120242965e-08 3.611075109519174e-08\n", + " B : 4.231906910748388e-08 5.461229750640369 4.4577962236153174e-08\n", + " C : 3.611074752365085e-08 4.457796548981162e-08 5.46122975140754\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.173e-08, 2.097e-07) [0.5, 1.936e-09, 3.509e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.697e-07, 2.731) [1.0, 5.586e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.51062774658203, forces=[[5.971523933112621e-06, -6.277812644839287e-06, 6.591435521841049e-06], [3.546476364135742e-06, 8.344650268554688e-07, -7.972121238708496e-06], [-8.285045623779297e-06, -5.260109901428223e-06, 2.041459083557129e-06], [4.738569259643555e-06, 5.9871235862374306e-06, -6.758491508662701e-06], [-5.602836608886719e-06, 2.5284243747591972e-06, 2.303975634276867e-06], [4.06801700592041e-06, -8.061528205871582e-06, -8.419156074523926e-06], [-6.16908073425293e-06, -7.152557373046875e-07, 7.793307304382324e-06], [1.8237624317407608e-06, 1.0971911251544952e-05, 4.357658326625824e-06]], stress=((-0.059791206095258516, 0.00011118520953854368, -1.4902008349478536e-05), (0.00011118520953854368, -0.059762464851536905, -7.660076074243482e-05), (-1.4902008349478536e-05, -7.660076074243482e-05, -0.05976434751666002)), magmoms=[0.003545999526977539, 0.0035459548234939575, 0.003545984625816345, 0.003545999526977539, 0.0035462453961372375, 0.0035461634397506714, 0.0035458356142044067, 0.00354602187871933]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461227292048314 5.4612272838180544 5.461227284886689\n", + " angles : 89.99999909713702 89.99999927722268 89.99999902498486\n", + " volume : 162.8811228339738\n", + " A : 5.461227292048313 4.646746573758274e-08 3.444626734325584e-08\n", + " B : 4.646746367704348e-08 5.461227283818054 4.302882107470429e-08\n", + " C : 3.444626366076289e-08 4.302882472244867e-08 5.461227284886689\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.227e-08, 1.878e-07) [0.5, 1.654e-09, 3.124e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.52e-07, 2.731) [1.0, 5.201e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461227292048314 5.4612272838180544 5.461227284886689\n", + " angles : 89.99999909713702 89.99999927722268 89.99999902498486\n", + " volume : 162.8811228339738\n", + " A : 5.461227292048313 4.646746573758274e-08 3.444626734325584e-08\n", + " B : 4.646746367704348e-08 5.461227283818054 4.302882107470429e-08\n", + " C : 3.444626366076289e-08 4.302882472244867e-08 5.461227284886689\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.227e-08, 1.878e-07) [0.5, 1.654e-09, 3.124e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.52e-07, 2.731) [1.0, 5.201e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.510623931884766, forces=[[-1.001986674964428e-06, -6.454065442085266e-06, 7.157446816563606e-06], [2.339482307434082e-06, 1.2516975402832031e-06, -6.735324859619141e-06], [-5.751848220825195e-06, 1.4901161193847656e-08, 4.738569259643555e-06], [8.58306884765625e-06, 6.9623347371816635e-06, -5.1443930715322495e-06], [-4.172325134277344e-07, 1.8621794879436493e-06, 2.6084017008543015e-06], [5.200505256652832e-06, -2.130866050720215e-06, -7.361173629760742e-06], [-8.866190910339355e-06, -3.6656856536865234e-06, 3.948807716369629e-06], [-9.476207196712494e-08, 2.164742909371853e-06, 7.573980838060379e-07]], stress=((-0.05869937689754348, 3.869866907492044e-05, -4.568404587809455e-05), (3.869866907492044e-05, -0.05868535891110047, -5.260252372618456e-05), (-4.568404587809455e-05, -5.260252372618456e-05, -0.05864571219850781)), magmoms=[0.0035462453961372375, 0.0035461783409118652, 0.0035461261868476868, 0.003546088933944702, 0.0035461336374282837, 0.0035462751984596252, 0.003546193242073059, 0.003546282649040222]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461223614377914 5.461223608592931 5.461223610915905\n", + " angles : 89.99999915375841 89.99999933242302 89.99999892651083\n", + " volume : 162.8807939578692\n", + " A : 5.461223614377913 5.1160527152940284e-08 3.1815495588153784e-08\n", + " B : 5.1160524231500766e-08 5.461223608592931 4.0330320779929926e-08\n", + " C : 3.181549362786491e-08 4.033032382492005e-08 5.461223610915905\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.679e-08, 1.484e-07) [0.5, 2.053e-09, 2.425e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.304e-07, 2.731) [1.0, 4.744e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461223614377914 5.461223608592931 5.461223610915905\n", + " angles : 89.99999915375841 89.99999933242302 89.99999892651083\n", + " volume : 162.8807939578692\n", + " A : 5.461223614377913 5.1160527152940284e-08 3.1815495588153784e-08\n", + " B : 5.1160524231500766e-08 5.461223608592931 4.0330320779929926e-08\n", + " C : 3.181549362786491e-08 4.033032382492005e-08 5.461223610915905\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.679e-08, 1.484e-07) [0.5, 2.053e-09, 2.425e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.304e-07, 2.731) [1.0, 4.744e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.510623931884766, forces=[[-5.540205165743828e-07, -6.4087798818945885e-06, 4.978617653250694e-06], [4.082918167114258e-06, 1.9073486328125e-06, -6.586313247680664e-06], [-8.419156074523926e-06, 2.8312206268310547e-07, 1.2665987014770508e-06], [4.32133674621582e-06, -3.2837269827723503e-06, -8.366652764379978e-06], [-3.933906555175781e-06, 3.319699317216873e-06, 2.27196142077446e-06], [7.465481758117676e-06, -7.152557373046875e-07, -6.616115570068359e-06], [-5.4389238357543945e-06, 1.0728836059570312e-06, 1.1712312698364258e-05], [2.2972235456109047e-06, 3.826571628451347e-06, 1.2718373909592628e-06]], stress=((-0.05738146468188604, 8.186289605339598e-05, 3.473598321134316e-05), (8.186289605339598e-05, -0.05738090512816524, -0.00010313454386096701), (3.473598321134316e-05, -0.00010313454386096701, -0.05733778451955593)), magmoms=[0.003546401858329773, 0.003546305000782013, 0.0035463497042655945, 0.003546200692653656, 0.0035462230443954468, 0.003546297550201416, 0.003546416759490967, 0.00354631245136261]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461218755384215 5.461218751815083 5.461218756431072\n", + " angles : 89.99999926313926 89.99999936242656 89.99999879151201\n", + " volume : 162.88035940089338\n", + " A : 5.461218755384215 5.7594272599569195e-08 3.038555505255824e-08\n", + " B : 5.7594269410501795e-08 5.461218751815082 3.511739800295849e-08\n", + " C : 3.038555323779688e-08 3.5117402185686005e-08 5.461218756431072\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 4.789e-08, 9.287e-08) [0.5, 3.497e-09, 1.422e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.095e-07, 2.731) [1.0, 4.291e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461218755384215 5.461218751815083 5.461218756431072\n", + " angles : 89.99999926313926 89.99999936242656 89.99999879151201\n", + " volume : 162.88035940089338\n", + " A : 5.461218755384215 5.7594272599569195e-08 3.038555505255824e-08\n", + " B : 5.7594269410501795e-08 5.461218751815082 3.511739800295849e-08\n", + " C : 3.038555323779688e-08 3.5117402185686005e-08 5.461218756431072\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 4.789e-08, 9.287e-08) [0.5, 3.497e-09, 1.422e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 3.095e-07, 2.731) [1.0, 4.291e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.510623931884766, forces=[[2.6882626116275787e-06, -9.417999535799026e-08, 8.642207831144333e-06], [-4.038214683532715e-06, -5.364418029785156e-07, -1.952052116394043e-06], [-3.6209821701049805e-06, 4.76837158203125e-07, -1.4901161193847656e-08], [-2.592802047729492e-06, 2.8481008484959602e-06, -5.029723979532719e-06], [5.841255187988281e-06, -4.797591827809811e-06, 3.3623073250055313e-06], [-7.316470146179199e-06, -7.197260856628418e-06, -7.897615432739258e-06], [8.38935375213623e-06, 3.606081008911133e-06, 4.604458808898926e-06], [6.776535883545876e-07, 5.678622983396053e-06, -1.7121201381087303e-06]], stress=((-0.055128217621115805, 7.738990999223135e-05, -3.36749975454868e-05), (7.738990999223135e-05, -0.05509466771260605, 0.00012089427259225076), (-3.36749975454868e-05, 0.00012089427259225076, -0.0550907741512988)), magmoms=[0.0035467445850372314, 0.003546707332134247, 0.0035466626286506653, 0.00354669988155365, 0.003546804189682007, 0.00354660302400589, 0.0035467371344566345, 0.003546692430973053]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461212761370182 5.461212760781772 5.46121276757595\n", + " angles : 89.99999928699597 89.99999941020582 89.99999862226728\n", + " volume : 162.879823331453\n", + " A : 5.461212761370182 6.566008616081548e-08 2.8108454703704393e-08\n", + " B : 6.56600834141703e-08 5.461212760781772 3.398039453238679e-08\n", + " C : 2.8108452825723105e-08 3.3980399381085624e-08 5.46121276757595\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.721e-08, 3.584e-08) [0.5, 4.464e-09, 3.99e-09]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 2.704e-07, 2.731) [1.0, 3.438e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461212761370182 5.461212760781772 5.46121276757595\n", + " angles : 89.99999928699597 89.99999941020582 89.99999862226728\n", + " volume : 162.879823331453\n", + " A : 5.461212761370182 6.566008616081548e-08 2.8108454703704393e-08\n", + " B : 6.56600834141703e-08 5.461212760781772 3.398039453238679e-08\n", + " C : 2.8108452825723105e-08 3.3980399381085624e-08 5.46121276757595\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.721e-08, 3.584e-08) [0.5, 4.464e-09, 3.99e-09]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 2.704e-07, 2.731) [1.0, 3.438e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.51062774658203, forces=[[-2.220040187239647e-06, -9.370851330459118e-06, -2.0582228899002075e-06], [1.6391277313232422e-07, -7.897615432739258e-07, 1.2814998626708984e-06], [8.195638656616211e-07, 1.3560056686401367e-06, 1.3113021850585938e-06], [8.046627044677734e-07, 5.418667569756508e-06, -5.310866981744766e-06], [1.1622905731201172e-06, -1.0628718882799149e-06, -1.909327693283558e-06], [2.0265579223632812e-06, -1.1771917343139648e-06, 1.8775463104248047e-06], [2.428889274597168e-06, 1.7881393432617188e-06, 2.682209014892578e-06], [-5.107838660478592e-06, 3.727036528289318e-06, 2.1436717361211777e-06]], stress=((-0.05252202622226507, 6.468060554192511e-05, -3.252010687567403e-05), (6.468060554192511e-05, -0.05251329485274672, 9.507323969559554e-05), (-3.252010687567403e-05, 9.507323969559554e-05, -0.052515888617390025)), magmoms=[0.003546975553035736, 0.0035471469163894653, 0.0035473108291625977, 0.0035471171140670776, 0.003547258675098419, 0.003547191619873047, 0.0035470128059387207, 0.003547191619873047]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461205686174393 5.461205688548012 5.4612056972195475\n", + " angles : 89.99999924463432 89.9999994750412 89.99999842652024\n", + " volume : 162.87919051509851\n", + " A : 5.4612056861743925 7.498891598410131e-08 2.5018492918835918e-08\n", + " B : 7.498891390474313e-08 5.461205688548011 3.5999222108222284e-08\n", + " C : 2.5018491036331963e-08 3.5999227698537656e-08 5.4612056972195475\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 9.972e-08, 5.461) [0.5, 4.802e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 2.345e-07, 2.731) [1.0, 2.591e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461205686174393 5.461205688548012 5.4612056972195475\n", + " angles : 89.99999924463432 89.9999994750412 89.99999842652024\n", + " volume : 162.87919051509851\n", + " A : 5.4612056861743925 7.498891598410131e-08 2.5018492918835918e-08\n", + " B : 7.498891390474313e-08 5.461205688548011 3.5999222108222284e-08\n", + " C : 2.5018491036331963e-08 3.5999227698537656e-08 5.4612056972195475\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 9.972e-08, 5.461) [0.5, 4.802e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 2.345e-07, 2.731) [1.0, 2.591e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106315612793, forces=[[-1.2060627341270447e-06, -8.829054422676563e-06, -1.1455267667770386e-07], [-2.682209014892578e-06, -4.470348358154297e-08, 3.516674041748047e-06], [5.3942203521728516e-06, -1.4603137969970703e-06, -3.2782554626464844e-06], [1.1473894119262695e-05, 1.2190197594463825e-05, 1.6511185094714165e-06], [-4.410743713378906e-06, 1.9080471247434616e-06, -2.0420411601662636e-06], [-2.8014183044433594e-06, -1.862645149230957e-06, 7.227063179016113e-06], [-2.6226043701171875e-06, -6.3478946685791016e-06, -8.940696716308594e-06], [-3.142864443361759e-06, 4.536821506917477e-06, 2.038083039224148e-06]], stress=((-0.04936751297782045, -7.10972188472763e-05, -5.614528246371965e-05), (-7.10972188472763e-05, -0.049317893385891404, 1.8307164024843928e-06), (-5.614528246371965e-05, 1.8307164024843928e-06, -0.04936610243614926)), magmoms=[0.0035476014018058777, 0.003547661006450653, 0.0035476163029670715, 0.00354766845703125, 0.00354766845703125, 0.0035477429628372192, 0.0035478994250297546, 0.0035478025674819946]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461197594712596 5.461197601484644 5.461197610162452\n", + " angles : 89.99999920516764 89.99999957452917 89.99999830243884\n", + " volume : 162.87846680081887\n", + " A : 5.461197594712595 8.090226774703088e-08 2.0277064686914088e-08\n", + " B : 8.090226677445459e-08 5.461197601484643 3.7880069684929044e-08\n", + " C : 2.0277061742692457e-08 3.7880076451011914e-08 5.461197610162452\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.06e-07, 5.461) [0.5, 5.074e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 1.923e-07, 2.731) [1.0, 1.692e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461197594712596 5.461197601484644 5.461197610162452\n", + " angles : 89.99999920516764 89.99999957452917 89.99999830243884\n", + " volume : 162.87846680081887\n", + " A : 5.461197594712595 8.090226774703088e-08 2.0277064686914088e-08\n", + " B : 8.090226677445459e-08 5.461197601484643 3.7880069684929044e-08\n", + " C : 2.0277061742692457e-08 3.7880076451011914e-08 5.461197610162452\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.06e-07, 5.461) [0.5, 5.074e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 1.923e-07, 2.731) [1.0, 1.692e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106315612793, forces=[[-3.1210947781801224e-06, 3.528548404574394e-07, -2.832384780049324e-07], [-2.0116567611694336e-06, -2.816319465637207e-06, -1.4156103134155273e-06], [-2.9802322387695312e-06, 2.1904706954956055e-06, -1.2516975402832031e-06], [-4.76837158203125e-07, -8.513452485203743e-07, 5.279434844851494e-07], [2.384185791015625e-07, -1.0593794286251068e-08, 1.4874385669827461e-06], [6.42240047454834e-06, 2.0563602447509766e-06, -1.8030405044555664e-06], [-3.829598426818848e-06, 1.7136335372924805e-06, 2.0563602447509766e-06], [5.780253559350967e-06, -2.6067718863487244e-06, 8.328352123498917e-07]], stress=((-0.04555815867686884, 8.55035243249833e-05, 1.6405296687402405e-05), (8.55035243249833e-05, -0.04554431846530588, -0.00012110982008064729), (1.6405296687402405e-05, -0.00012110982008064729, -0.04557274787440183)), magmoms=[0.003548353910446167, 0.0035484805703163147, 0.00354824960231781, 0.0035482048988342285, 0.0035483837127685547, 0.0035483837127685547, 0.0035483241081237793, 0.0035484135150909424]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461187559478365 5.461187571219924 5.461187578640475\n", + " angles : 89.99999927903323 89.99999965772383 89.999998099864\n", + " volume : 162.8775691684723\n", + " A : 5.461187559478364 9.055640125072429e-08 1.631214885606237e-08\n", + " B : 9.055639975787291e-08 5.461187571219923 3.435972390916111e-08\n", + " C : 1.6312145257826464e-08 3.435973066505181e-08 5.461187578640475\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 9.443e-08, 5.461) [0.5, 2.708e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 1.62e-07, 2.731) [1.0, 9.944e-09, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461187559478365 5.461187571219924 5.461187578640475\n", + " angles : 89.99999927903323 89.99999965772383 89.999998099864\n", + " volume : 162.8775691684723\n", + " A : 5.461187559478364 9.055640125072429e-08 1.631214885606237e-08\n", + " B : 9.055639975787291e-08 5.461187571219923 3.435972390916111e-08\n", + " C : 1.6312145257826464e-08 3.435973066505181e-08 5.461187578640475\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 9.443e-08, 5.461) [0.5, 2.708e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 1.62e-07, 2.731) [1.0, 9.944e-09, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.510616302490234, forces=[[1.844717189669609e-06, 2.167769707739353e-06, -1.2365635484457016e-06], [-4.783272743225098e-06, -3.606081008911133e-06, 4.500150680541992e-06], [5.066394805908203e-07, -2.2351741790771484e-06, -1.0564923286437988e-05], [-8.940696716308594e-07, -3.285706043243408e-06, -6.207264959812164e-07], [8.940696716308594e-07, 3.832043148577213e-06, -1.23586505651474e-06], [-2.041459083557129e-06, -7.897615432739258e-07, 3.2335519790649414e-06], [2.175569534301758e-06, 1.9371509552001953e-06, -8.791685104370117e-07], [2.2300519049167633e-06, 2.0561274141073227e-06, 6.7872460931539536e-06]], stress=((-0.04161823018369423, -9.600419560479904e-05, 3.846996720166903e-05), (-9.600419560479904e-05, -0.04160733637219237, 3.313755753526941e-05), (3.846996720166903e-05, 3.313755753526941e-05, -0.04165385510391863)), magmoms=[0.003548942506313324, 0.003548957407474518, 0.003548860549926758, 0.003548949956893921, 0.0035489648580551147, 0.003549046814441681, 0.00354902446269989, 0.0035489946603775024]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461175266867033 5.461175284148112 5.461175287699582\n", + " angles : 89.99999931303462 89.99999969466214 89.99999801268014\n", + " volume : 162.8764695204516\n", + " A : 5.461175266867032 9.471118345285396e-08 1.455171579277582e-08\n", + " B : 9.471118210392862e-08 5.461175284148111 3.273921711133284e-08\n", + " C : 1.4551711313654936e-08 3.2739224046993896e-08 5.461175287699582\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.521e-08, 2.779e-08, 2.731) [1.453e-09, 2.091e-09, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461175266867033 5.461175284148112 5.461175287699582\n", + " angles : 89.99999931303462 89.99999969466214 89.99999801268014\n", + " volume : 162.8764695204516\n", + " A : 5.461175266867032 9.471118345285396e-08 1.455171579277582e-08\n", + " B : 9.471118210392862e-08 5.461175284148111 3.273921711133284e-08\n", + " C : 1.4551711313654936e-08 3.2739224046993896e-08 5.461175287699582\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.521e-08, 2.779e-08, 2.731) [1.453e-09, 2.091e-09, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.51063537597656, forces=[[1.9255094230175018e-07, 1.3236422091722488e-07, 4.809116944670677e-07], [-2.2351741790771484e-06, -7.450580596923828e-07, -2.2351741790771484e-06], [-5.125999450683594e-06, 2.0265579223632812e-06, -3.11434268951416e-06], [3.0100345611572266e-06, -1.8546124920248985e-06, 4.496774636209011e-06], [3.933906555175781e-06, 1.7472775653004646e-06, 1.346576027572155e-06], [2.980232238769531e-07, -5.364418029785156e-07, -2.60770320892334e-06], [-3.8743019104003906e-07, -2.682209014892578e-07, -6.854534149169922e-07], [2.2759195417165756e-07, -5.862675607204437e-07, 2.3472821339964867e-06]], stress=((-0.03689695486363464, -2.550381783729844e-06, -3.3039653848768395e-05), (-2.550381783729844e-06, -0.03688308842299101, -8.201567134093593e-05), (-3.3039653848768395e-05, -8.201567134093593e-05, -0.03693913996836698)), magmoms=[0.003549806773662567, 0.0035496950149536133, 0.003549620509147644, 0.0035495832562446594, 0.0035495758056640625, 0.00354950875043869, 0.0035496950149536133, 0.003549635410308838]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461160399918815 5.461160423699676 5.461160419355619\n", + " angles : 89.99999947132143 89.99999978148678 89.99999793005053\n", + " volume : 162.87513948128523\n", + " A : 5.461160399918814 9.864885549661574e-08 1.0413821359219753e-08\n", + " B : 9.864885497372178e-08 5.461160423699675 2.5195556006551843e-08\n", + " C : 1.0413816829610393e-08 2.5195563336728646e-08 5.461160419355619\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.462e-07, 5.461, 2.731) [7.754e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461160399918815 5.461160423699676 5.461160419355619\n", + " angles : 89.99999947132143 89.99999978148678 89.99999793005053\n", + " volume : 162.87513948128523\n", + " A : 5.461160399918814 9.864885549661574e-08 1.0413821359219753e-08\n", + " B : 9.864885497372178e-08 5.461160423699675 2.5195556006551843e-08\n", + " C : 1.0413816829610393e-08 2.5195563336728646e-08 5.461160419355619\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.462e-07, 5.461, 2.731) [7.754e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106201171875, forces=[[5.8140140026807785e-06, -1.7816200852394104e-06, 1.3863900676369667e-06], [4.470348358154297e-07, 1.6987323760986328e-06, -1.7136335372924805e-06], [-1.773238182067871e-06, 2.5331974029541016e-07, -1.773238182067871e-06], [1.0132789611816406e-06, 3.757188096642494e-06, 5.695503205060959e-06], [3.5762786865234375e-07, -1.999898813664913e-06, -2.9382063075900078e-06], [-3.4570693969726562e-06, -5.081295967102051e-06, 1.296401023864746e-06], [2.130866050720215e-06, -1.0281801223754883e-06, -9.5367431640625e-07], [-4.564528353512287e-06, 4.155328497290611e-06, -1.0012881830334663e-06]], stress=((-0.029807079900392993, -4.016861964227014e-06, -0.00011076618176031934), (-4.016861964227014e-06, -0.029893507635518547, -1.169826816380849e-05), (-0.00011076618176031934, -1.169826816380849e-05, -0.029810341049422043)), magmoms=[0.0035507604479789734, 0.003550708293914795, 0.003550790250301361, 0.003550805151462555, 0.003550805151462555, 0.0035508498549461365, 0.0035508200526237488, 0.0035509318113327026]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.4611427329375655 5.461142754765129 5.46114275067071\n", + " angles : 89.9999996524258 90.00000009386827 89.9999978561778\n", + " volume : 162.87355866271415\n", + " A : 5.461142732937565 1.0216912273015565e-07 -4.473521155668069e-09\n", + " B : 1.0216912234775394e-07 5.461142754765128 1.6564500370213464e-08\n", + " C : -4.473526053272148e-09 1.6564507249716458e-08 5.46114275067071\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.378e-07, 5.461, 2.731) [6.932e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 7.533e-09) [1.0, 0.5, 6.82e-10], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.4611427329375655 5.461142754765129 5.46114275067071\n", + " angles : 89.9999996524258 90.00000009386827 89.9999978561778\n", + " volume : 162.87355866271415\n", + " A : 5.461142732937565 1.0216912273015565e-07 -4.473521155668069e-09\n", + " B : 1.0216912234775394e-07 5.461142754765128 1.6564500370213464e-08\n", + " C : -4.473526053272148e-09 1.6564507249716458e-08 5.46114275067071\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.378e-07, 5.461, 2.731) [6.932e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 7.533e-09) [1.0, 0.5, 6.82e-10], molecule=None, energy=-42.510623931884766, forces=[[7.553026080131531e-07, 2.9837246984243393e-06, -3.895256668329239e-06], [-3.471970558166504e-06, -1.817941665649414e-06, 6.258487701416016e-07], [2.8461217880249023e-06, 4.276633262634277e-06, -2.2351741790771484e-06], [-6.5267086029052734e-06, -7.3552364483475685e-06, 2.075568772852421e-06], [3.2782554626464844e-06, -1.448439434170723e-06, 1.657404936850071e-06], [-2.1010637283325195e-06, 1.9669532775878906e-06, 2.8014183044433594e-06], [3.3229589462280273e-06, 6.064772605895996e-06, 1.2367963790893555e-06], [1.8702121451497078e-06, -4.6781497076153755e-06, -2.2101448848843575e-06]], stress=((-0.02260887737684368, 2.9226666706303525e-05, 3.884264726086229e-06), (2.9226666706303525e-05, -0.022606295269569563, -5.85888460603077e-06), (3.884264726086229e-06, -5.85888460603077e-06, -0.022651098910854723)), magmoms=[0.0035520344972610474, 0.003551982343196869, 0.003551870584487915, 0.0035518333315849304, 0.003551870584487915, 0.0035520270466804504, 0.003551796078681946, 0.0035518333315849304]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461122093976055 5.4611221142122695 5.4611221042780675\n", + " angles : 89.99999984920909 90.00000039340502 89.99999769939191\n", + " volume : 162.87171178648435\n", + " A : 5.461122093976054 1.0964072588137889e-07 -1.8748611005076222e-08\n", + " B : 1.0964072548197638e-07 5.461122114212269 7.186281912030892e-09\n", + " C : -1.874861600305571e-08 7.186288712284396e-09 5.4611221042780675\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.008e-07, 5.461, 2.731) [1.037e-10, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 2.408e-08) [1.0, 0.5, 7.184e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461122093976055 5.4611221142122695 5.4611221042780675\n", + " angles : 89.99999984920909 90.00000039340502 89.99999769939191\n", + " volume : 162.87171178648435\n", + " A : 5.461122093976054 1.0964072588137889e-07 -1.8748611005076222e-08\n", + " B : 1.0964072548197638e-07 5.461122114212269 7.186281912030892e-09\n", + " C : -1.874861600305571e-08 7.186288712284396e-09 5.4611221042780675\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.008e-07, 5.461, 2.731) [1.037e-10, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 2.408e-08) [1.0, 0.5, 7.184e-09], molecule=None, energy=-42.510616302490234, forces=[[-4.374305717647076e-06, 4.087341949343681e-06, -3.5373959690332413e-06], [6.556510925292969e-07, 4.395842552185059e-06, 6.794929504394531e-06], [4.5746564865112305e-06, 2.950429916381836e-06, 5.498528480529785e-06], [-6.705522537231445e-06, -4.611210897564888e-06, -2.5401823222637177e-06], [-1.2218952178955078e-06, 1.6944250091910362e-06, -6.6409120336174965e-06], [4.857778549194336e-06, -3.2782554626464844e-07, 5.230307579040527e-06], [3.471970558166504e-06, 2.294778823852539e-06, -4.157423973083496e-06], [-1.3014068827033043e-06, -1.0534538887441158e-05, -7.359776645898819e-07]], stress=((-0.013810065606255457, 2.706261101958237e-05, 3.625324559289261e-05), (2.706261101958237e-05, -0.013857480498237693, -3.057314692169407e-05), (3.625324559289261e-05, -3.057314692169407e-05, -0.013846770290300466)), magmoms=[0.00355326384305954, 0.003553353250026703, 0.00355326384305954, 0.003553256392478943, 0.0035532936453819275, 0.00355326384305954, 0.003553323447704315, 0.0035533085465431213]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461098508629033 5.46109851683837 5.461098503450231\n", + " angles : 90.00000018637719 90.00000052401924 89.99999741826058\n", + " volume : 162.86960075733614\n", + " A : 5.461098508629032 1.2303814989816627e-07 -2.4973221337747587e-08\n", + " B : 1.2303814799020187e-07 5.461098516838369 -8.882194731177955e-09\n", + " C : -2.4973226292142872e-08 -8.882188231152292e-09 5.461098503450231\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.97e-07, 5.461, 2.731) [1.583e-08, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 2.652e-08) [1.0, 0.5, 1.024e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461098508629033 5.46109851683837 5.461098503450231\n", + " angles : 90.00000018637719 90.00000052401924 89.99999741826058\n", + " volume : 162.86960075733614\n", + " A : 5.461098508629032 1.2303814989816627e-07 -2.4973221337747587e-08\n", + " B : 1.2303814799020187e-07 5.461098516838369 -8.882194731177955e-09\n", + " C : -2.4973226292142872e-08 -8.882188231152292e-09 5.461098503450231\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.97e-07, 5.461, 2.731) [1.583e-08, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 2.652e-08) [1.0, 0.5, 1.024e-08], molecule=None, energy=-42.5106315612793, forces=[[8.391216397285461e-07, 1.8442515283823013e-06, -6.439397111535072e-06], [1.7434358596801758e-06, 1.385807991027832e-06, -5.513429641723633e-07], [2.428889274597168e-06, -2.86102294921875e-06, 2.4586915969848633e-06], [1.2516975402832031e-06, -1.7809215933084488e-06, 1.2188684195280075e-06], [-3.2782554626464844e-06, -1.7450656741857529e-06, 7.232301868498325e-06], [4.76837158203125e-07, 7.718801498413086e-06, 3.427267074584961e-07], [-5.260109901428223e-06, -3.5315752029418945e-06, -3.725290298461914e-06], [1.6704434528946877e-06, -1.035747118294239e-06, -5.908077582716942e-07]], stress=((-0.003521054082213591, 3.865828380423877e-05, -7.456690557461041e-06), (3.865828380423877e-05, -0.0035596825964666436, -3.648704756516174e-05), (-7.456690557461041e-06, -3.648704756516174e-05, -0.0035460034023155253)), magmoms=[0.003554694354534149, 0.0035550743341445923, 0.0035549774765968323, 0.0035548135638237, 0.0035547390580177307, 0.0035548657178878784, 0.0035547614097595215, 0.003554992377758026]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461072353836295 5.461072320950066 5.461072314429381\n", + " angles : 90.00000109933951 90.00000077208603 89.99999652680019\n", + " volume : 162.86725843215243\n", + " A : 5.461072353836292 1.655217521572873e-07 -3.6795181681017224e-08\n", + " B : 1.655217495388283e-07 5.461072320950064 -5.239105636022872e-08\n", + " C : -3.679518749549763e-08 -5.2391050313564277e-08 5.461072314429381\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.861e-07, 2.789e-08) [0.5, 1.893e-08, 8.476e-09]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.949e-07, 2.711e-07, 2.731) [3.906e-08, 5.445e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.197e-07, 2.731, -4.795e-09) [6.77e-09, 0.5, 3.919e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461072353836295 5.461072320950066 5.461072314429381\n", + " angles : 90.00000109933951 90.00000077208603 89.99999652680019\n", + " volume : 162.86725843215243\n", + " A : 5.461072353836292 1.655217521572873e-07 -3.6795181681017224e-08\n", + " B : 1.655217495388283e-07 5.461072320950064 -5.239105636022872e-08\n", + " C : -3.679518749549763e-08 -5.2391050313564277e-08 5.461072314429381\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.861e-07, 2.789e-08) [0.5, 1.893e-08, 8.476e-09]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.949e-07, 2.711e-07, 2.731) [3.906e-08, 5.445e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.197e-07, 2.731, -4.795e-09) [6.77e-09, 0.5, 3.919e-09], molecule=None, energy=-42.510623931884766, forces=[[2.216198481619358e-06, 1.0422663763165474e-06, 5.857553333044052e-06], [2.0265579223632812e-06, -6.258487701416016e-06, -4.1425228118896484e-06], [-2.1010637283325195e-06, -4.306435585021973e-06, -5.900859832763672e-06], [2.950429916381836e-06, -1.6880221664905548e-08, 9.423820301890373e-07], [8.046627044677734e-07, 3.021443262696266e-06, 1.5040859580039978e-07], [-4.127621650695801e-06, -2.1010637283325195e-06, -3.4123659133911133e-06], [-4.887580871582031e-06, 5.0514936447143555e-06, 1.9073486328125e-06], [3.0692899599671364e-06, 3.5892007872462273e-06, 4.5620836317539215e-06]], stress=((0.007720371789965869, -5.063595742056782e-05, 4.431740717981195e-05), (-5.063595742056782e-05, 0.00771908073632881, -4.494655789915169e-05), (4.431740717981195e-05, -4.494655789915169e-05, 0.007729216818833756)), magmoms=[0.00355684757232666, 0.0035567954182624817, 0.003556765615940094, 0.0035566389560699463, 0.0035565942525863647, 0.0035566985607147217, 0.003556780517101288, 0.0035566315054893494]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461072504731273 5.461072471819815 5.461072465497233\n", + " angles : 90.000001117773 90.00000075391053 89.99999654756704\n", + " volume : 162.86727193711965\n", + " A : 5.461072504731271 1.64532074551857e-07 -3.5928996373699074e-08\n", + " B : 1.6453207035417356e-07 5.461072471819812 -5.326953977275775e-08\n", + " C : -3.5929001422602334e-08 -5.3269533679068385e-08 5.461072465497233\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.716e-07, 1.902e-08) [0.5, 1.636e-08, 6.772e-09]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.861e-07, 2.66e-07, 2.731) [3.737e-08, 5.358e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.261e-07, 2.731, 5.016e-09) [8.033e-09, 0.5, 5.796e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461072504731273 5.461072471819815 5.461072465497233\n", + " angles : 90.000001117773 90.00000075391053 89.99999654756704\n", + " volume : 162.86727193711965\n", + " A : 5.461072504731271 1.64532074551857e-07 -3.5928996373699074e-08\n", + " B : 1.6453207035417356e-07 5.461072471819812 -5.326953977275775e-08\n", + " C : -3.5929001422602334e-08 -5.3269533679068385e-08 5.461072465497233\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.716e-07, 1.902e-08) [0.5, 1.636e-08, 6.772e-09]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.861e-07, 2.66e-07, 2.731) [3.737e-08, 5.358e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.261e-07, 2.731, 5.016e-09) [8.033e-09, 0.5, 5.796e-09], molecule=None, energy=-42.510623931884766, forces=[[2.1421583369374275e-06, 5.495967343449593e-07, 8.731149137020111e-06], [1.6391277313232422e-06, 1.475214958190918e-06, -1.6391277313232422e-06], [-1.087784767150879e-06, -6.8247318267822266e-06, -9.5367431640625e-07], [6.556510925292969e-07, 1.3064127415418625e-06, -3.967666998505592e-06], [-3.635883331298828e-06, 9.188661351799965e-07, -1.3564713299274445e-06], [-3.4123659133911133e-06, -4.708766937255859e-06, -3.5762786865234375e-06], [1.519918441772461e-06, 1.8477439880371094e-06, 2.1904706954956055e-06], [2.191518433392048e-06, 5.441135726869106e-06, 5.653128027915955e-07]], stress=((0.00786066385657436, 3.5472668607739705e-05, -6.0340375741240905e-05), (3.5472668607739705e-05, 0.007835740401260302, -7.51322424434058e-06), (-6.0340375741240905e-05, -7.51322424434058e-06, 0.007858461342384276)), magmoms=[0.003556877374649048, 0.003556773066520691, 0.003556683659553528, 0.003556564450263977, 0.0035566166043281555, 0.0035567134618759155, 0.0035567134618759155, 0.003556676208972931]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461072809299337 5.461072775830079 5.461072770173597\n", + " angles : 90.00000113774783 90.0000007647358 89.99999655027692\n", + " volume : 162.8672991734013\n", + " A : 5.461072809299334 1.644029391116393e-07 -3.644489577808783e-08\n", + " B : 1.64402935100181e-07 5.461072775830076 -5.422148039732002e-08\n", + " C : -3.644490170004028e-08 -5.422147381659062e-08 5.461072770173597\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.625e-07, 6.336e-09) [0.5, 1.471e-08, 4.497e-09]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.691e-07, 2.496e-07, 2.731) [3.43e-08, 5.068e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.377e-07, 2.731, 1.516e-08) [1.016e-08, 0.5, 7.741e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461072809299337 5.461072775830079 5.461072770173597\n", + " angles : 90.00000113774783 90.0000007647358 89.99999655027692\n", + " volume : 162.8672991734013\n", + " A : 5.461072809299334 1.644029391116393e-07 -3.644489577808783e-08\n", + " B : 1.64402935100181e-07 5.461072775830076 -5.422148039732002e-08\n", + " C : -3.644490170004028e-08 -5.422147381659062e-08 5.461072770173597\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.625e-07, 6.336e-09) [0.5, 1.471e-08, 4.497e-09]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.691e-07, 2.496e-07, 2.731) [3.43e-08, 5.068e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.377e-07, 2.731, 1.516e-08) [1.016e-08, 0.5, 7.741e-09], molecule=None, energy=-42.510623931884766, forces=[[-9.866198524832726e-07, 2.8437934815883636e-06, 7.41099938750267e-06], [5.856156349182129e-06, -3.904104232788086e-06, -3.6209821701049805e-06], [-3.769993782043457e-06, -2.8312206268310547e-07, -1.8775463104248047e-06], [-5.364418029785156e-07, 4.246830940246582e-07, -1.8046703189611435e-06], [-1.9669532775878906e-06, 8.856877684593201e-07, -5.648122169077396e-06], [-9.685754776000977e-07, -5.692243576049805e-06, -4.678964614868164e-06], [1.0281801223754883e-06, 4.857778549194336e-06, 9.655952453613281e-06], [1.2909295037388802e-06, 8.650822564959526e-07, 5.087349563837051e-07]], stress=((0.007610111292130477, 2.314881580945153e-05, 8.882987037875278e-05), (2.314881580945153e-05, 0.007559229789987335, -0.00014633379417566583), (8.882987037875278e-05, -0.00014633379417566583, 0.00758619255632291)), magmoms=[0.003556780517101288, 0.0035566911101341248, 0.0035567507147789, 0.0035567283630371094, 0.0035568922758102417, 0.0035566166043281555, 0.0035567283630371094, 0.0035566240549087524]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461073262606535 5.461073227437163 5.461073223015046\n", + " angles : 90.00000122802824 90.000000730588 89.99999654127818\n", + " volume : 162.8673396661829\n", + " A : 5.461073262606532 1.6483180360778683e-07 -3.481752171260834e-08\n", + " B : 1.648318007689539e-07 5.4610732274371605 -5.852396543724562e-08\n", + " C : -3.4817527655476134e-08 -5.85239585465608e-08 5.461073223015046\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.44e-07, -1.383e-08) [0.5, 1.128e-08, 6.547e-10]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.522e-07, 2.178e-07, 2.731) [3.105e-08, 4.524e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.518e-07, 2.731, 2.395e-08) [1.271e-08, 0.5, 9.743e-09], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461073262606535 5.461073227437163 5.461073223015046\n", + " angles : 90.00000122802824 90.000000730588 89.99999654127818\n", + " volume : 162.8673396661829\n", + " A : 5.461073262606532 1.6483180360778683e-07 -3.481752171260834e-08\n", + " B : 1.648318007689539e-07 5.4610732274371605 -5.852396543724562e-08\n", + " C : -3.4817527655476134e-08 -5.85239585465608e-08 5.461073223015046\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 1.44e-07, -1.383e-08) [0.5, 1.128e-08, 6.547e-10]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.522e-07, 2.178e-07, 2.731) [3.105e-08, 4.524e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.518e-07, 2.731, 2.395e-08) [1.271e-08, 0.5, 9.743e-09], molecule=None, energy=-42.510623931884766, forces=[[2.135639078915119e-06, 2.755783498287201e-06, 5.1120296120643616e-06], [1.9222497940063477e-06, -2.2351741790771484e-07, 2.980232238769531e-07], [8.642673492431641e-07, -8.344650268554688e-07, 3.0100345611572266e-06], [-1.6093254089355469e-06, 3.0782539397478104e-06, -1.8621794879436493e-06], [5.662441253662109e-07, 3.0992086976766586e-06, -7.763621397316456e-06], [-1.8775463104248047e-06, -4.738569259643555e-06, -5.02169132232666e-06], [6.690621376037598e-06, -3.978610038757324e-06, 6.794929504394531e-06], [-8.751754648983479e-06, 9.032664820551872e-07, -6.211921572685242e-07]], stress=((0.007313121597544613, -4.39835442246939e-06, 3.812843417576261e-05), (-4.39835442246939e-06, 0.007298429669445689, 0.00012850438306472248), (3.812843417576261e-05, 0.00012850438306472248, 0.007229176153469546)), magmoms=[0.003556661307811737, 0.003556586802005768, 0.0035566985607147217, 0.0035566315054893494, 0.0035567507147789, 0.0035566017031669617, 0.003556586802005768, 0.0035567209124565125]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461073858829885 5.46107382175221 5.461073816658384\n", + " angles : 90.0000012398713 90.00000067926057 89.99999653555477\n", + " volume : 162.86739287641313\n", + " A : 5.461073858829883 1.651045819600422e-07 -3.2371421276113277e-08\n", + " B : 1.6510457808620514e-07 5.4610738217522075 -5.9088374349405603e-08\n", + " C : -3.2371427235382985e-08 -5.90883682549339e-08 5.461073816658384\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 6.761e-08, 5.461) [0.5, 8.085e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.319e-07, 1.768e-07, 2.731) [2.712e-08, 3.778e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.386e-07, 2.731, 3.167e-08) [1.027e-08, 0.5, 1.121e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461073858829885 5.46107382175221 5.461073816658384\n", + " angles : 90.0000012398713 90.00000067926057 89.99999653555477\n", + " volume : 162.86739287641313\n", + " A : 5.461073858829883 1.651045819600422e-07 -3.2371421276113277e-08\n", + " B : 1.6510457808620514e-07 5.4610738217522075 -5.9088374349405603e-08\n", + " C : -3.2371427235382985e-08 -5.90883682549339e-08 5.461073816658384\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 6.761e-08, 5.461) [0.5, 8.085e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.319e-07, 1.768e-07, 2.731) [2.712e-08, 3.778e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.386e-07, 2.731, 3.167e-08) [1.027e-08, 0.5, 1.121e-08], molecule=None, energy=-42.51062774658203, forces=[[3.2690586522221565e-06, 9.017530828714371e-07, 4.535773769021034e-06], [1.475214958190918e-06, 2.5480985641479492e-06, 2.637505531311035e-06], [-3.6209821701049805e-06, 2.637505531311035e-06, -1.341104507446289e-07], [-3.069639205932617e-06, 2.0512379705905914e-07, -1.6996636986732483e-07], [2.205371856689453e-06, -8.736969903111458e-06, -4.650093615055084e-06], [-5.170702934265137e-06, -3.591179847717285e-06, -5.0514936447143555e-06], [7.644295692443848e-06, 6.258487701416016e-07, 5.736947059631348e-06], [-2.6695197448134422e-06, 5.388516001403332e-06, -2.934597432613373e-06]], stress=((0.007049292018186438, 3.8609460032671186e-05, -0.00011602479367698142), (3.8609460032671186e-05, 0.00704460939870259, 3.587767680574009e-05), (-0.00011602479367698142, 3.587767680574009e-05, 0.007024771470694779)), magmoms=[0.0035566091537475586, 0.00355646014213562, 0.0035566091537475586, 0.003556661307811737, 0.0035565942525863647, 0.0035565942525863647, 0.0035566315054893494, 0.0035564973950386047]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.46107459249961 5.461074553573555 5.46107454732061\n", + " angles : 90.000001229484 90.0000007011267 89.99999650775517\n", + " volume : 162.86745837302067\n", + " A : 5.461074592499608 1.6642944584831288e-07 -3.341349693938585e-08\n", + " B : 1.6642944204897338e-07 5.461074553573552 -5.85933560350574e-08\n", + " C : -3.3413502190717334e-08 -5.8593349989857726e-08 5.46107454732061\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 6.123e-08, 5.461) [0.5, 6.704e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (9.54e-08, 1.288e-07, 2.731) [2.053e-08, 2.895e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.187e-07, 2.731, 2.969e-08) [6.5e-09, 0.5, 1.08e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.46107459249961 5.461074553573555 5.46107454732061\n", + " angles : 90.000001229484 90.0000007011267 89.99999650775517\n", + " volume : 162.86745837302067\n", + " A : 5.461074592499608 1.6642944584831288e-07 -3.341349693938585e-08\n", + " B : 1.6642944204897338e-07 5.461074553573552 -5.85933560350574e-08\n", + " C : -3.3413502190717334e-08 -5.8593349989857726e-08 5.46107454732061\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 6.123e-08, 5.461) [0.5, 6.704e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (9.54e-08, 1.288e-07, 2.731) [2.053e-08, 2.895e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.187e-07, 2.731, 2.969e-08) [6.5e-09, 0.5, 1.08e-08], molecule=None, energy=-42.5106315612793, forces=[[-5.3399708122015e-07, -5.39771281182766e-06, 1.292908564209938e-06], [-7.897615432739258e-07, -4.276633262634277e-06, -4.3213367462158203e-07], [1.6093254089355469e-06, -5.960464477539063e-08, -4.500150680541992e-06], [3.337860107421875e-06, -9.243376553058624e-08, -5.7318247854709625e-06], [-3.2782554626464844e-07, 3.1249364838004112e-06, -2.153683453798294e-08], [-3.904104232788086e-06, -5.960464477539063e-08, 2.518296241760254e-06], [7.450580596923828e-07, 6.377696990966797e-06, 1.862645149230957e-06], [-5.809124559164047e-08, 3.530876711010933e-07, 4.928908310830593e-06]], stress=((0.006490181277413337, -3.398009560062038e-05, 3.4848603757230708e-06), (-3.398009560062038e-05, 0.006599179865034629, 6.528223685839681e-07), (3.4848603757230708e-06, 6.528223685839681e-07, 0.006609414306592059)), magmoms=[0.0035564079880714417, 0.0035564228892326355, 0.0035564005374908447, 0.003556475043296814, 0.0035564079880714417, 0.003556475043296814, 0.003556489944458008, 0.0035564973950386047]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.46107545188849 5.461075414642414 5.4610754076602035\n", + " angles : 90.00000121971524 90.00000071856319 89.99999650460721\n", + " volume : 162.86753534101723\n", + " A : 5.461075451888488 1.6657949400183863e-07 -3.42444719462704e-08\n", + " B : 1.6657948968097433e-07 5.461075414642411 -5.812781757694581e-08\n", + " C : -3.424447645126855e-08 -5.812781076413818e-08 5.4610754076602035\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.991e-08, 5.461) [0.5, 2.7e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (4.885e-08, 8.542e-08, 2.731) [1.208e-08, 2.096e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.001e-07, 2.731, 4.53e-08) [3.069e-09, 0.5, 1.362e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.46107545188849 5.461075414642414 5.4610754076602035\n", + " angles : 90.00000121971524 90.00000071856319 89.99999650460721\n", + " volume : 162.86753534101723\n", + " A : 5.461075451888488 1.6657949400183863e-07 -3.42444719462704e-08\n", + " B : 1.6657948968097433e-07 5.461075414642411 -5.812781757694581e-08\n", + " C : -3.424447645126855e-08 -5.812781076413818e-08 5.4610754076602035\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 3.991e-08, 5.461) [0.5, 2.7e-09, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (4.885e-08, 8.542e-08, 2.731) [1.208e-08, 2.096e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.001e-07, 2.731, 4.53e-08) [3.069e-09, 0.5, 1.362e-08], molecule=None, energy=-42.5106315612793, forces=[[-9.903451427817345e-07, -3.4458935260772705e-07, 1.0466203093528748e-05], [3.069639205932617e-06, -9.08970832824707e-07, -1.8030405044555664e-06], [4.470348358154297e-08, -2.115964889526367e-06, -1.9818544387817383e-06], [6.22868537902832e-06, 2.41247471421957e-06, -5.90132549405098e-06], [-5.27501106262207e-06, 2.3578759282827377e-06, -7.257331162691116e-07], [-3.039836883544922e-06, -3.5762786865234375e-06, -1.9371509552001953e-07], [-1.5348196029663086e-06, 1.5348196029663086e-06, 2.205371856689453e-06], [1.387670636177063e-06, 7.71600753068924e-07, -1.9960571080446243e-06]], stress=((0.00646037265582587, 6.62876034826425e-05, 1.8956245544083858e-05), (6.62876034826425e-05, 0.0064557672664128625, -4.1948163580824015e-05), (1.8956245544083858e-05, -4.1948163580824015e-05, 0.006449379028100372)), magmoms=[0.0035566389560699463, 0.003556661307811737, 0.0035566017031669617, 0.0035565271973609924, 0.0035566911101341248, 0.0035565346479415894, 0.0035566911101341248, 0.003556683659553528]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.46107643731639 5.461076401431189 5.461076393583022\n", + " angles : 90.00000123980483 90.00000072120457 89.99999645613485\n", + " volume : 162.86762356264722\n", + " A : 5.461076437316387 1.6888956635243205e-07 -3.43703583159842e-08\n", + " B : 1.6888956155978297e-07 5.461076401431186 -5.9085235861654385e-08\n", + " C : -3.4370361621936643e-08 -5.908522920875272e-08 5.461076393583022\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (-4.205e-09, 3.221e-08, 2.731) [2.377e-09, 1.131e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (8.958e-08, 2.731, 5.113e-08) [9.401e-10, 0.5, 1.477e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.46107643731639 5.461076401431189 5.461076393583022\n", + " angles : 90.00000123980483 90.00000072120457 89.99999645613485\n", + " volume : 162.86762356264722\n", + " A : 5.461076437316387 1.6888956635243205e-07 -3.43703583159842e-08\n", + " B : 1.6888956155978297e-07 5.461076401431186 -5.9085235861654385e-08\n", + " C : -3.4370361621936643e-08 -5.908522920875272e-08 5.461076393583022\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (-4.205e-09, 3.221e-08, 2.731) [2.377e-09, 1.131e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (8.958e-08, 2.731, 5.113e-08) [9.401e-10, 0.5, 1.477e-08], molecule=None, energy=-42.51062774658203, forces=[[1.3888347893953323e-07, -5.344743840396404e-06, 2.4243490770459175e-06], [-3.4421682357788086e-06, -7.048249244689941e-06, -3.3229589462280273e-06], [1.6987323760986328e-06, 5.200505256652832e-06, -1.6391277313232422e-06], [1.6391277313232422e-06, 2.9212096706032753e-06, -4.3527688831090927e-07], [3.516674041748047e-06, 3.67150641977787e-06, -1.7487909644842148e-06], [1.3560056686401367e-06, -2.339482307434082e-06, 5.960464477539063e-08], [-3.993511199951172e-06, 6.899237632751465e-06, 8.940696716308594e-08], [-8.434290066361427e-07, -3.897584974765778e-06, 4.610163159668446e-06]], stress=((0.006117307209542711, -5.185177321296569e-05, 0.000116928410550937), (-5.185177321296569e-05, 0.006104160611446158, -2.1324142967382397e-05), (0.000116928410550937, -2.1324142967382397e-05, 0.006085383504034035)), magmoms=[0.003556549549102783, 0.00355665385723114, 0.003556475043296814, 0.003556467592716217, 0.003556668758392334, 0.0035565122961997986, 0.003556661307811737, 0.0035565942525863647]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.46107766571608 5.461077630634785 5.461077621153285\n", + " angles : 90.00000127818657 90.00000062241544 89.99999645311453\n", + " volume : 162.867733466913\n", + " A : 5.461077665716077 1.6903354273464786e-07 -2.9662383125644898e-08\n", + " B : 1.6903353774417243e-07 5.461077630634782 -6.09144031466271e-08\n", + " C : -2.9662386885720438e-08 -6.0914396069024e-08 5.461077621153285\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 4.87e-08) [1.0, 0.5, 1.993e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.46107766571608 5.461077630634785 5.461077621153285\n", + " angles : 90.00000127818657 90.00000062241544 89.99999645311453\n", + " volume : 162.867733466913\n", + " A : 5.461077665716077 1.6903354273464786e-07 -2.9662383125644898e-08\n", + " B : 1.6903353774417243e-07 5.461077630634782 -6.09144031466271e-08\n", + " C : -2.9662386885720438e-08 -6.0914396069024e-08 5.461077621153285\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 4.87e-08) [1.0, 0.5, 1.993e-08], molecule=None, energy=-42.51062774658203, forces=[[-3.330758772790432e-06, -1.0757939890027046e-06, -4.004454240202904e-06], [3.2782554626464844e-07, -5.498528480529785e-06, -4.500150680541992e-06], [-3.6954879760742188e-06, -9.98377799987793e-07, -2.175569534301758e-06], [1.7881393432617188e-07, -2.3993197828531265e-06, -1.1688098311424255e-07], [1.8477439880371094e-06, 7.4338167905807495e-06, 4.159519448876381e-07], [2.950429916381836e-06, -9.834766387939453e-07, 4.5746564865112305e-06], [4.470348358154297e-07, 3.1888484954833984e-06, 1.6093254089355469e-06], [1.2828968465328217e-06, 3.685709089040756e-07, 4.215282388031483e-06]], stress=((0.00577001450975943, -9.797479189937175e-05, 0.00012137281670454804), (-9.797479189937175e-05, 0.005738909734435886, -0.0001717809772917571), (0.00012137281670454804, -0.0001717809772917571, 0.005735138575505064)), magmoms=[0.0035564079880714417, 0.00355636328458786, 0.0035564303398132324, 0.0035564452409744263, 0.0035564005374908447, 0.0035564079880714417, 0.0035564303398132324, 0.003556355834007263]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461079181883216 5.461079145988462 5.461079134692925\n", + " angles : 90.0000015028249 90.0000003926635 89.99999655654693\n", + " volume : 162.86786901596554\n", + " A : 5.461079181883213 1.6410432054814516e-07 -1.8713124648325748e-08\n", + " B : 1.6410431736258072e-07 5.461079145988459 -7.16199883657334e-08\n", + " C : -1.8713128298589594e-08 -7.161998087576397e-08 5.461079134692924\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 1.073e-07) [1.0, 0.5, 2.963e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461079181883216 5.461079145988462 5.461079134692925\n", + " angles : 90.0000015028249 90.0000003926635 89.99999655654693\n", + " volume : 162.86786901596554\n", + " A : 5.461079181883213 1.6410432054814516e-07 -1.8713124648325748e-08\n", + " B : 1.6410431736258072e-07 5.461079145988459 -7.16199883657334e-08\n", + " C : -1.8713128298589594e-08 -7.161998087576397e-08 5.461079134692924\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (5.461, 2.731, 1.073e-07) [1.0, 0.5, 2.963e-08], molecule=None, energy=-42.51062774658203, forces=[[9.499490261077881e-08, 1.6575213521718979e-06, -3.736116923391819e-06], [1.6391277313232422e-06, 2.4884939193725586e-06, 5.647540092468262e-06], [2.86102294921875e-06, -6.5267086029052734e-06, -5.811452865600586e-07], [-4.470348358154297e-06, 3.6973506212234497e-06, 1.1962838470935822e-06], [-5.304813385009766e-06, -3.8298312574625015e-06, -2.5865156203508377e-06], [1.8775463104248047e-06, 2.175569534301758e-06, 1.9371509552001953e-06], [-1.2367963790893555e-06, -1.6540288925170898e-06, -2.9355287551879883e-06], [4.561734385788441e-06, 1.950305886566639e-06, 1.0780058801174164e-06]], stress=((0.004825925344682702, -8.256181453351728e-05, -5.919289899885066e-05), (-8.256181453351728e-05, 0.004821707198502254, -4.488505503097474e-05), (-5.919289899885066e-05, -4.488505503097474e-05, 0.004842459501405386)), magmoms=[0.0035563111305236816, 0.0035564452409744263, 0.0035563409328460693, 0.003556184470653534, 0.003556258976459503, 0.003556258976459503, 0.003556288778781891, 0.0035563111305236816]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.4610810150652105 5.4610809780759055 5.461080966394647\n", + " angles : 90.00000178988903 90.00000024934519 89.99999677722519\n", + " volume : 162.86803295431068\n", + " A : 5.461081015065208 1.5358752950077554e-07 -1.1883020975435545e-08\n", + " B : 1.535875261875477e-07 5.461080978075903 -8.530060512375453e-08\n", + " C : -1.1883025350013755e-08 -8.530059729786885e-08 5.461080966394646\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (8.671e-08, 2.731, 1.802e-07) [1.817e-09, 0.5, 4.08e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.4610810150652105 5.4610809780759055 5.461080966394647\n", + " angles : 90.00000178988903 90.00000024934519 89.99999677722519\n", + " volume : 162.86803295431068\n", + " A : 5.461081015065208 1.5358752950077554e-07 -1.1883020975435545e-08\n", + " B : 1.535875261875477e-07 5.461080978075903 -8.530060512375453e-08\n", + " C : -1.1883025350013755e-08 -8.530059729786885e-08 5.461080966394646\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (8.671e-08, 2.731, 1.802e-07) [1.817e-09, 0.5, 4.08e-08], molecule=None, energy=-42.51063537597656, forces=[[-1.416192390024662e-06, 2.291635610163212e-06, -4.6496279537677765e-07], [1.2218952178955078e-06, -2.2351741790771484e-06, -0.0], [1.6093254089355469e-06, 5.230307579040527e-06, 5.170702934265137e-06], [1.2516975402832031e-06, -4.163011908531189e-07, 2.6579946279525757e-06], [5.066394805908203e-07, -1.3989629223942757e-06, 3.282679244875908e-06], [2.250075340270996e-06, -1.1920928955078125e-06, -3.0249357223510742e-06], [-5.379319190979004e-06, 7.748603820800781e-07, -1.862645149230957e-06], [-7.05476850271225e-08, -3.002234734594822e-06, -5.7693105190992355e-06]], stress=((0.00391877874684088, 7.858113602150805e-05, 0.00011425933975807712), (7.858113602150805e-05, 0.003928796434190057, -0.0001336751434989743), (0.00011425933975807712, -0.0001336751434989743, 0.00391451506406206)), magmoms=[0.0035562440752983093, 0.003556162118911743, 0.0035561397671699524, 0.0035562142729759216, 0.003556191921234131, 0.0035562664270401, 0.003556370735168457, 0.0035561248660087585]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461083193252198 5.461083156089196 5.461083142722525\n", + " angles : 90.00000233079872 89.99999988836417 89.99999684605804\n", + " volume : 162.8682277766248\n", + " A : 5.461083193252196 1.5030722776890324e-07 5.320226572966955e-09\n", + " B : 1.5030722524193145e-07 5.4610831560891935 -1.110787411595417e-07\n", + " C : 5.320221107639701e-09 -1.1107873200420445e-07 5.461083142722524\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.122e-07, 2.731, 1.674e-07) [6.783e-09, 0.5, 4.083e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461083193252198 5.461083156089196 5.461083142722525\n", + " angles : 90.00000233079872 89.99999988836417 89.99999684605804\n", + " volume : 162.8682277766248\n", + " A : 5.461083193252196 1.5030722776890324e-07 5.320226572966955e-09\n", + " B : 1.5030722524193145e-07 5.4610831560891935 -1.110787411595417e-07\n", + " C : 5.320221107639701e-09 -1.1107873200420445e-07 5.461083142722524\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.122e-07, 2.731, 1.674e-07) [6.783e-09, 0.5, 4.083e-08], molecule=None, energy=-42.510623931884766, forces=[[4.812609404325485e-07, 3.203749656677246e-07, 9.98377799987793e-07], [-4.470348358154297e-08, 7.212162017822266e-06, -9.98377799987793e-07], [4.783272743225098e-06, -3.948807716369629e-06, 6.154179573059082e-06], [-4.4405460357666016e-06, -1.2035015970468521e-06, -1.5459954738616943e-06], [2.4139881134033203e-06, -2.561137080192566e-09, -1.094886101782322e-06], [1.341104507446289e-06, 5.46872615814209e-06, -7.301568984985352e-07], [-2.995133399963379e-06, -9.387731552124023e-07, -8.493661880493164e-07], [-1.4925608411431313e-06, -6.936141289770603e-06, -1.9042054191231728e-06]], stress=((0.0030066367841575883, 6.619616599311039e-05, -7.958644038868564e-06), (6.619616599311039e-05, 0.003092588935049738, 1.1645501207425582e-05), (-7.958644038868564e-06, 1.1645501207425582e-05, 0.0030898924398405085)), magmoms=[0.0035559311509132385, 0.003556087613105774, 0.0035561323165893555, 0.003555901348590851, 0.0035560503602027893, 0.00355587899684906, 0.0035560578107833862, 0.0035560280084609985]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461085731324427 5.461085704629708 5.461085689253329\n", + " angles : 90.00000283839184 89.99999955010288 89.99999674253932\n", + " volume : 162.86845542324778\n", + " A : 5.461085731324426 1.5524068602473816e-07 2.1440735610673563e-08\n", + " B : 1.5524068254528685e-07 5.4610857046297046 -1.352691400398399e-07\n", + " C : 2.1440728746395132e-08 -1.3526913089706225e-07 5.461085689253327\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.204e-07, 2.731, 1.283e-07) [7.832e-09, 0.5, 3.589e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461085731324427 5.461085704629708 5.461085689253329\n", + " angles : 90.00000283839184 89.99999955010288 89.99999674253932\n", + " volume : 162.86845542324778\n", + " A : 5.461085731324426 1.5524068602473816e-07 2.1440735610673563e-08\n", + " B : 1.5524068254528685e-07 5.4610857046297046 -1.352691400398399e-07\n", + " C : 2.1440728746395132e-08 -1.3526913089706225e-07 5.461085689253327\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.204e-07, 2.731, 1.283e-07) [7.832e-09, 0.5, 3.589e-08], molecule=None, energy=-42.5106201171875, forces=[[2.1813903003931046e-06, -8.932547643780708e-07, -9.216368198394775e-06], [-1.1920928955078125e-06, 4.082918167114258e-06, 1.4007091522216797e-06], [2.5331974029541016e-07, -1.2665987014770508e-06, 2.115964889526367e-06], [-5.125999450683594e-06, 2.6281923055648804e-06, 7.243826985359192e-06], [1.8477439880371094e-06, -4.9782684072852135e-06, -2.93052289634943e-06], [2.1457672119140625e-06, 4.649162292480469e-06, 4.26173210144043e-06], [-8.344650268554688e-07, -5.841255187988281e-06, -4.693865776062012e-06], [7.119961082935333e-07, 1.6695121303200722e-06, 1.7515849322080612e-06]], stress=((0.0020702744512391103, -0.00011417146550736765, -4.634062101380416e-05), (-0.00011417146550736765, 0.0020487230721077366, -3.0297659039652252e-05), (-4.634062101380416e-05, -3.0297659039652252e-05, 0.0020452098324700964)), magmoms=[0.003555692732334137, 0.0035558491945266724, 0.003555789589881897, 0.0035559386014938354, 0.0035557821393013, 0.0035559087991714478, 0.0035557523369789124, 0.003555990755558014]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.5106201171875, forces=[[-6.017507985234261e-07, -6.638234481215477e-06, -7.87666067481041e-07], [-5.4389238357543945e-06, 4.51505184173584e-06, -2.2202730178833008e-06], [-1.4603137969970703e-06, -1.9073486328125e-06, -2.8312206268310547e-07], [-2.682209014892578e-07, 1.234118826687336e-06, 1.371721737086773e-06], [8.553266525268555e-06, 5.887122824788094e-07, -1.4404067769646645e-06], [1.1026859283447266e-06, 5.02169132232666e-06, 2.5331974029541016e-06], [3.203749656677246e-06, -1.5348196029663086e-06, -2.339482307434082e-06], [-5.114474333822727e-06, -1.3050157576799393e-06, 3.1035160645842552e-06]], stress=((0.0006924792408186339, -4.4729655696953566e-05, -3.6938730776494e-05), (-4.4729655696953566e-05, 0.0006850838240233392, 2.0533694501513544e-05), (-3.6938730776494e-05, 2.0533694501513544e-05, 0.0006995213391471558)), magmoms=[0.0035556256771087646, 0.0035556256771087646, 0.0035556554794311523, 0.0035556331276893616, 0.0035555511713027954, 0.003555573523044586, 0.0035556480288505554, 0.003555774688720703]), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.5106201171875, forces=[[-6.017507985234261e-07, -6.638234481215477e-06, -7.87666067481041e-07], [-5.4389238357543945e-06, 4.51505184173584e-06, -2.2202730178833008e-06], [-1.4603137969970703e-06, -1.9073486328125e-06, -2.8312206268310547e-07], [-2.682209014892578e-07, 1.234118826687336e-06, 1.371721737086773e-06], [8.553266525268555e-06, 5.887122824788094e-07, -1.4404067769646645e-06], [1.1026859283447266e-06, 5.02169132232666e-06, 2.5331974029541016e-06], [3.203749656677246e-06, -1.5348196029663086e-06, -2.339482307434082e-06], [-5.114474333822727e-06, -1.3050157576799393e-06, 3.1035160645842552e-06]], stress=((0.0006924792408186339, -4.4729655696953566e-05, -3.6938730776494e-05), (-4.4729655696953566e-05, 0.0006850838240233392, 2.0533694501513544e-05), (-3.6938730776494e-05, 2.0533694501513544e-05, 0.0006995213391471558)), magmoms=[0.0035556256771087646, 0.0035556256771087646, 0.0035556554794311523, 0.0035556331276893616, 0.0035555511713027954, 0.003555573523044586, 0.0035556480288505554, 0.003555774688720703])], elapsed_time=6.860212261788547, n_steps=50), ase_calculator_name='MLFF.CHGNet', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-33-495106-74798', included_objects=[], objects={}, is_force_converged=True, energy_downhill=True, tags=None, forcefield_name='MLFF.CHGNet', forcefield_version='0.4.0'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-33-495106-74798'))},\n", + " '6ad6a228-babe-46ba-b542-88fe297556c2': {1: Response(output=[[1, 0, 0], [0, 1, 0], [0, 0, 1]], detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-067922-60781'))},\n", + " '561033b9-e984-443f-8cd9-2befc39a99bc': {1: Response(output=ForceFieldTaskDocument(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 42, 439200, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=162.86871531495993, density=2.2907810099669352, density_atomic=20.35858941436999, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], input=InputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, relax_cell=False, fix_symmetry=False, symprec=None, steps=1, relax_kwargs={}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.5106201171875, energy_per_atom=-5.3138275146484375, forces=[(-6.017507985234261e-07, -6.638234481215477e-06, -7.87666067481041e-07), (-5.4389238357543945e-06, 4.51505184173584e-06, -2.2202730178833008e-06), (-1.4603137969970703e-06, -1.9073486328125e-06, -2.8312206268310547e-07), (-2.682209014892578e-07, 1.234118826687336e-06, 1.371721737086773e-06), (8.553266525268555e-06, 5.887122824788094e-07, -1.4404067769646645e-06), (1.1026859283447266e-06, 5.02169132232666e-06, 2.5331974029541016e-06), (3.203749656677246e-06, -1.5348196029663086e-06, -2.339482307434082e-06), (-5.114474333822727e-06, -1.3050157576799393e-06, 3.1035160645842552e-06)], stress=((0.0006924792408186339, -4.4729655696953566e-05, -3.6938730776494e-05), (-4.4729655696953566e-05, 0.0006850838240233392, 2.0533694501513544e-05), (-3.6938730776494e-05, 2.0533694501513544e-05, 0.0006995213391471558)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.5106201171875, forces=[[-6.017507985234261e-07, -6.638234481215477e-06, -7.87666067481041e-07], [-5.4389238357543945e-06, 4.51505184173584e-06, -2.2202730178833008e-06], [-1.4603137969970703e-06, -1.9073486328125e-06, -2.8312206268310547e-07], [-2.682209014892578e-07, 1.234118826687336e-06, 1.371721737086773e-06], [8.553266525268555e-06, 5.887122824788094e-07, -1.4404067769646645e-06], [1.1026859283447266e-06, 5.02169132232666e-06, 2.5331974029541016e-06], [3.203749656677246e-06, -1.5348196029663086e-06, -2.339482307434082e-06], [-5.114474333822727e-06, -1.3050157576799393e-06, 3.1035160645842552e-06]], stress=((0.0006924792408186339, -4.4729655696953566e-05, -3.6938730776494e-05), (-4.4729655696953566e-05, 0.0006850838240233392, 2.0533694501513544e-05), (-3.6938730776494e-05, 2.0533694501513544e-05, 0.0006995213391471558)), magmoms=[0.0035556256771087646, 0.0035556256771087646, 0.0035556554794311523, 0.0035556331276893616, 0.0035555511713027954, 0.003555573523044586, 0.0035556480288505554, 0.003555774688720703])], elapsed_time=0.12483632890507579, n_steps=1), ase_calculator_name='MLFF.CHGNet', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-168697-57215', included_objects=[], objects={}, is_force_converged=True, energy_downhill=False, tags=None, forcefield_name='MLFF.CHGNet', forcefield_version='0.4.0'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-168697-57215'))},\n", + " '7c6767ec-4169-4cec-902f-a49c22f66275': {1: Response(output=[Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08]], detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-456705-43647'))},\n", + " 'ac5adeae-7b28-4dc7-9e70-27f4fec64ceb': {1: Response(output=None, detour=None, addition=None, replace=Flow(name='Flow', uuid='e50bd9a3-df9f-4e85-ba27-1421d0a41dd1')\n", + " 1. Job(name='Force field static 1/1', uuid='f720ed71-12ed-407f-a30e-d572ed0a638e')\n", + " 2. Job(name='store_inputs', uuid='ac5adeae-7b28-4dc7-9e70-27f4fec64ceb'), stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-607359-96340')),\n", + " 2: Response(output={'displacement_number': [0], 'forces': [OutputReference(f720ed71-12ed-407f-a30e-d572ed0a638e, .output, .forces)], 'uuids': ['f720ed71-12ed-407f-a30e-d572ed0a638e'], 'dirs': [OutputReference(f720ed71-12ed-407f-a30e-d572ed0a638e, .dir_name)]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-43-053115-70664'))},\n", + " 'f720ed71-12ed-407f-a30e-d572ed0a638e': {1: Response(output=ForceFieldTaskDocument(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 43, 35864, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=162.86871531495993, density=2.2907810099669352, density_atomic=20.35858941436999, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], input=InputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, relax_cell=False, fix_symmetry=False, symprec=None, steps=1, relax_kwargs={}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.51008605957031, energy_per_atom=-5.313760757446289, forces=[(-0.09911756217479706, -8.335337042808533e-07, -3.725290298461914e-06), (0.029023170471191406, -0.009852856397628784, 0.00985950231552124), (-0.015622109174728394, -1.8328428268432617e-06, -2.3245811462402344e-06), (0.029029875993728638, 0.009856561198830605, -0.009851994924247265), (0.007927864789962769, -3.284076228737831e-07, 8.73231329023838e-07), (0.02041563391685486, 0.0061188191175460815, 0.006120339035987854), (0.007928773760795593, -6.452202796936035e-06, -2.905726432800293e-06), (0.020414218306541443, -0.0061131427064538, -0.0061197783797979355)], stress=((0.30832265667092046, -8.336241623780452e-05, -3.569204117039953e-05), (-8.336241623780452e-05, 0.11221636540829433, -0.47120811529846673), (-3.569204117039953e-05, -0.47120811529846673, 0.1122137191854897)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.51008605957031, forces=[[-0.09911756217479706, -8.335337042808533e-07, -3.725290298461914e-06], [0.029023170471191406, -0.009852856397628784, 0.00985950231552124], [-0.015622109174728394, -1.8328428268432617e-06, -2.3245811462402344e-06], [0.029029875993728638, 0.009856561198830605, -0.009851994924247265], [0.007927864789962769, -3.284076228737831e-07, 8.73231329023838e-07], [0.02041563391685486, 0.0061188191175460815, 0.006120339035987854], [0.007928773760795593, -6.452202796936035e-06, -2.905726432800293e-06], [0.020414218306541443, -0.0061131427064538, -0.0061197783797979355]], stress=((0.30832265667092046, -8.336241623780452e-05, -3.569204117039953e-05), (-8.336241623780452e-05, 0.11221636540829433, -0.47120811529846673), (-3.569204117039953e-05, -0.47120811529846673, 0.1122137191854897)), magmoms=[0.0035520046949386597, 0.003614775836467743, 0.003554731607437134, 0.003614552319049835, 0.0035545527935028076, 0.003494061529636383, 0.003554821014404297, 0.0034941285848617554])], elapsed_time=0.23266854835674167, n_steps=1), ase_calculator_name='MLFF.CHGNet', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-749642-11446', included_objects=[], objects={}, is_force_converged=True, energy_downhill=False, tags=None, forcefield_name='MLFF.CHGNet', forcefield_version='0.4.0'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-749642-11446'))},\n", + " 'dcfe1c6c-3c2b-45de-acfe-d514a2a4c5fe': {1: Response(output=PhononBSDOSDoc(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 49, 427055, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=162.86871531495993, density=2.2907810099669352, density_atomic=20.35858941436999, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", + " Lattice\n", + " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", + " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", + " volume : 162.86871531495993\n", + " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", + " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", + " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.731, -1.348e-07, -1.636e-07) [0.5, -3.746e-08, -3.261e-08]\n", + " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (5.459e-08, -1.401e-07, 2.731) [7.333e-09, -1.056e-08, 0.5]\n", + " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], phonon_bandstructure=PhononBandStructureSymmLine(bands=(6, 606), labels=['GAMMA', 'X', 'U', 'K', 'L', 'W']), phonon_dos=PhononDos(frequencies=(201,), densities=(201,), n_positive_freqs=184), free_energies=[5269.8127344094655, 5170.389579463751, 4383.3780421068905, 2752.250812336559, 421.99169492538886], heat_capacities=[0.0, 7.879330124050419, 17.169788286576015, 20.884852674610833, 22.494681848140903], internal_energies=[5269.8127344094655, 5523.860416244734, 6840.4473060421205, 8770.993029236264, 10949.921452152672], entropies=[0.0, 3.534708367809812, 12.285346319676153, 20.062474056332352, 26.319824393068203], temperatures=[0, 100, 200, 300, 400], total_dft_energy=-5.3138275146484375, volume_per_formula_unit=20.35858941436999, formula_units=8, has_imaginary_modes=False, force_constants=None, born=None, epsilon_static=None, supercell_matrix=((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)), primitive_matrix=((2.8984158637352412e-24, 0.5, 0.5), (0.5000000000000001, 4.39214184807516e-25, 0.5000000000000001), (0.5, 0.5, -2.8951321439355976e-24)), code='forcefields', phonopy_settings=PhononComputationalSettings(npoints_band=101, kpath_scheme='seekpath', kpoint_density_dos=7000), thermal_displacement_data=None, jobdirs=PhononJobDirs(displacements_job_dirs=['/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-749642-11446'], static_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-168697-57215', born_run_job_dir=None, optimization_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-33-495106-74798', taskdoc_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639'), uuids=PhononUUIDs(optimization_run_uuid='78215cc7-1754-4951-a8fd-b90f93cc7f87', displacements_uuids=['f720ed71-12ed-407f-a30e-d572ed0a638e'], static_run_uuid='561033b9-e984-443f-8cd9-2befc39a99bc', born_run_uuid=None)), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639'))}}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 3 }, { "cell_type": "markdown", @@ -58,10 +2127,13 @@ }, { "cell_type": "code", - "execution_count": null, "id": "4", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-11T06:12:57.746975Z", + "start_time": "2025-02-11T06:12:49.532315Z" + } + }, "source": [ "from atomate2.forcefields.jobs import ForceFieldRelaxMaker, ForceFieldStaticMaker\n", "\n", @@ -72,13 +2144,1097 @@ " store_force_constants=False,\n", " prefer_90_degrees=False,\n", " generate_frequencies_eigenvectors_kwargs={\"tstep\": 100},\n", - " static_energy_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", - " bulk_relax_maker=ForceFieldRelaxMaker(force_field_name=\"MACE\"),\n", - " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE\"),\n", + " static_energy_maker=ForceFieldStaticMaker(force_field_name=\"MACE_MP_0B3\"),\n", + " bulk_relax_maker=ForceFieldRelaxMaker(force_field_name=\"MACE_MP_0B3\"),\n", + " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE_MP_0B3\"),\n", ").make(si_structure)\n", "\n", "run_locally(flow, create_folders=True, raise_immediately=True)" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:49,615 INFO Started executing jobs locally\n", + "2025-02-11 07:12:49,619 INFO Starting job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n", + "2025-02-11 07:12:49,632 INFO Finished job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n", + "2025-02-11 07:12:49,639 INFO Starting job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/e3nn/o3/_wigner.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " _Jd, _W3j_flat, _W3j_indices = torch.load(os.path.join(os.path.dirname(__file__), 'constants.pt'))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuequivariance or cuequivariance_torch is not available. Cuequivariance acceleration will be disabled.\n", + "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", + "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n", + "Default dtype float32 does not match model dtype float64, converting models to float32.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " torch.load(f=model_path, map_location=device)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:50,673 INFO Finished job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:50,678 INFO Starting job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:50,688 INFO Finished job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:50,692 INFO Starting job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", + "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n", + "Default dtype float32 does not match model dtype float64, converting models to float32.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " torch.load(f=model_path, map_location=device)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,142 INFO Finished job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,147 INFO Starting job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,210 INFO Finished job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n", + "INFO:jobflow.core.job:Finished job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,219 INFO Starting job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,271 INFO Finished job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,277 INFO Starting job - Force field static 1/1 (7513eaaa-8550-4a4c-966b-0a68299a97ed)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - Force field static 1/1 (7513eaaa-8550-4a4c-966b-0a68299a97ed)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", + "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " torch.load(f=model_path, map_location=device)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default dtype float32 does not match model dtype float64, converting models to float32.\n", + "2025-02-11 07:12:51,795 INFO Finished job - Force field static 1/1 (7513eaaa-8550-4a4c-966b-0a68299a97ed)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - Force field static 1/1 (7513eaaa-8550-4a4c-966b-0a68299a97ed)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,799 INFO Starting job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,801 INFO Finished job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:51,804 INFO Starting job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:57,735 INFO Finished job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:57,737 INFO Finished executing jobs locally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.managers.local:Finished executing jobs locally\n" + ] + }, + { + "data": { + "text/plain": [ + "{'9f56521c-fea7-4632-bd6d-e17fa0e1f9bf': {1: Response(output=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : 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[0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, -1.233e-32], molecule=None, energy=-43.068416595458984, energy_per_atom=-5.383552074432373, forces=[(-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06), (-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07), (-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07), (2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07), (1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07), (-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07), (1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06), (-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07)], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, energy=-43.068416595458984, forces=[[-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06], [-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07], [-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07], [2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07], [1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07], [-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07], [1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06], [-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07]], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), magmoms=None), IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, energy=-43.068416595458984, forces=[[-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06], [-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07], [-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07], [2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07], [1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07], [-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07], [1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06], [-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07]], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), magmoms=None)], elapsed_time=0.19847225677222013, n_steps=2), ase_calculator_name='MLFF.MACE_MP_0B3', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-49-632536-97239', included_objects=[], objects={}, is_force_converged=True, energy_downhill=False, tags=None, forcefield_name='MLFF.MACE_MP_0B3', forcefield_version='0.3.10'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-49-632536-97239'))},\n", + " 'ec79a97b-6903-4398-aec9-256aed662ad8': {1: Response(output=[[1, 0, 0], [0, 1, 0], [0, 0, 1]], detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-50-674841-49822'))},\n", + " 'a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f': {1: Response(output=ForceFieldTaskDocument(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 51, 126521, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=163.55317098041343, density=2.2811942925026014, density_atomic=20.44414637255168, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], input=InputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, relax_cell=False, fix_symmetry=False, symprec=None, steps=1, relax_kwargs={}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, energy=-43.068416595458984, energy_per_atom=-5.383552074432373, forces=[(-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06), (-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07), (-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07), (2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07), (1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07), (-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07), (1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06), (-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07)], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], molecule=None, energy=-43.068416595458984, forces=[[-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06], [-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07], [-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07], 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forcefield_version='0.3.10'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-50-690232-69971'))},\n", + " 'b184226f-8eec-4883-a7d1-8d305f3cb6a7': {1: Response(output=[Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [-5.493e-33, 0.5, 1.0]], detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-144560-13048'))},\n", + " '837eb4ef-337b-4bf7-9a4b-95420eb3c6bf': {1: Response(output=None, detour=None, addition=None, replace=Flow(name='Flow', uuid='1efb9ec5-e9ca-4265-be09-5f38119610f7')\n", + " 1. Job(name='Force field static 1/1', uuid='7513eaaa-8550-4a4c-966b-0a68299a97ed')\n", + " 2. Job(name='store_inputs', uuid='837eb4ef-337b-4bf7-9a4b-95420eb3c6bf'), stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-211098-11951')),\n", + " 2: Response(output={'displacement_number': [0], 'forces': [OutputReference(7513eaaa-8550-4a4c-966b-0a68299a97ed, .output, .forces)], 'uuids': ['7513eaaa-8550-4a4c-966b-0a68299a97ed'], 'dirs': [OutputReference(7513eaaa-8550-4a4c-966b-0a68299a97ed, .dir_name)]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-796466-99551'))},\n", + " '7513eaaa-8550-4a4c-966b-0a68299a97ed': {1: Response(output=ForceFieldTaskDocument(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 51, 778086, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=163.55317098041343, density=2.2811942925026014, density_atomic=20.44414637255168, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], input=InputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], molecule=None, relax_cell=False, fix_symmetry=False, symprec=None, steps=1, relax_kwargs={}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], molecule=None, energy=-43.06785202026367, energy_per_atom=-5.383481502532959, forces=[(-0.11221619695425034, 1.839101969380863e-06, 1.6381363820983097e-08), (0.031074894592165947, -0.015769988298416138, 0.01576509326696396), (-0.01112288050353527, 1.8949785953736864e-06, 1.2983527994947508e-06), (0.031073203310370445, 0.015765974298119545, -0.01576707884669304), (-0.00013040518388152122, -6.759273674106225e-08, 1.2644712796827662e-06), (0.03072630986571312, 0.01535763032734394, 0.01535869762301445), (-0.00013038597535341978, 1.928310894072638e-06, 2.4035580281633884e-06), (0.03072553500533104, -0.015359151177108288, -0.015361744910478592)], stress=((1.004973777028884, 2.6366419043288626e-05, 2.2622799793350855e-05), (2.6366419043288626e-05, 1.0171653334977195, -0.7864977054028787), (2.2622799793350855e-05, -0.7864977054028787, 1.0171475210376073)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], molecule=None, energy=-43.06785202026367, forces=[[-0.11221619695425034, 1.839101969380863e-06, 1.6381363820983097e-08], [0.031074894592165947, -0.015769988298416138, 0.01576509326696396], [-0.01112288050353527, 1.8949785953736864e-06, 1.2983527994947508e-06], [0.031073203310370445, 0.015765974298119545, -0.01576707884669304], [-0.00013040518388152122, -6.759273674106225e-08, 1.2644712796827662e-06], [0.03072630986571312, 0.01535763032734394, 0.01535869762301445], [-0.00013038597535341978, 1.928310894072638e-06, 2.4035580281633884e-06], [0.03072553500533104, -0.015359151177108288, -0.015361744910478592]], stress=((1.004973777028884, 2.6366419043288626e-05, 2.2622799793350855e-05), (2.6366419043288626e-05, 1.0171653334977195, -0.7864977054028787), (2.2622799793350855e-05, -0.7864977054028787, 1.0171475210376073)), magmoms=None)], elapsed_time=0.3654345436953008, n_steps=1), ase_calculator_name='MLFF.MACE_MP_0B3', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-274382-24613', included_objects=[], objects={}, is_force_converged=True, energy_downhill=True, tags=None, forcefield_name='MLFF.MACE_MP_0B3', forcefield_version='0.3.10'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-274382-24613'))},\n", + " '4511a343-12d9-4465-9d6c-a6297041c891': {1: Response(output=PhononBSDOSDoc(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 57, 689170, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=163.55317098041343, density=2.2811942925026014, density_atomic=20.44414637255168, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", + " Lattice\n", + " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", + " angles : 90.0 90.0 90.0\n", + " volume : 163.55317098041343\n", + " A : 5.468727995382952 0.0 3.348630117476627e-16\n", + " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", + " C : 0.0 0.0 5.468727995382952\n", + " pbc : True True True\n", + " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", + " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, -6.163e-33]\n", + " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", + " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", + " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", + " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", + " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", + " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, -1.233e-32], phonon_bandstructure=PhononBandStructureSymmLine(bands=(6, 606), labels=['GAMMA', 'X', 'U', 'K', 'L', 'W']), phonon_dos=PhononDos(frequencies=(201,), densities=(201,), n_positive_freqs=184), free_energies=[5448.905893966201, 5310.987704809328, 4440.618556277908, 2746.065655334672, 365.28304032896705], heat_capacities=[0.0, 8.19759957366914, 16.706018543651478, 20.512092766784267, 22.240479219670608], internal_energies=[5448.905893966201, 5750.425742450245, 7044.044005201389, 8931.387036100377, 11079.304972704058], entropies=[0.0, 4.394380376409171, 13.017127244617395, 20.617737935885675, 26.78505483093772], temperatures=[0, 100, 200, 300, 400], total_dft_energy=-5.383552074432373, volume_per_formula_unit=20.44414637255168, formula_units=8, has_imaginary_modes=True, force_constants=None, born=None, epsilon_static=None, supercell_matrix=((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)), primitive_matrix=((0.0, 0.5, 0.5), (0.5, 0.0, 0.5), (0.5, 0.5, 6.162975822039155e-33)), code='forcefields', phonopy_settings=PhononComputationalSettings(npoints_band=101, kpath_scheme='seekpath', kpoint_density_dos=7000), thermal_displacement_data=None, jobdirs=PhononJobDirs(displacements_job_dirs=['/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-274382-24613'], static_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-50-690232-69971', born_run_job_dir=None, optimization_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-49-632536-97239', taskdoc_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770'), uuids=PhononUUIDs(optimization_run_uuid='c1bccb92-12e8-42ec-872e-e27a5a90dd22', displacements_uuids=['7513eaaa-8550-4a4c-966b-0a68299a97ed'], static_run_uuid='a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f', born_run_uuid=None)), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770'))}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Or by using the name only:", + "id": "b3d30da7227c27ee" + }, + { + "metadata": { + "jupyter": { + "is_executing": true + }, + "ExecuteTime": { + "start_time": "2025-02-11T06:12:57.760029Z" + } + }, + "cell_type": "code", + "source": [ + "PhononMaker.from_force_field_name(force_field_name=\"MACE_MP_0B3\")\n", + "run_locally(flow, create_folders=True, raise_immediately=True)" + ], + "id": "c769deefc5d8f6ba", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:57,775 INFO Started executing jobs locally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.managers.local:Started executing jobs locally\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:57,788 INFO Starting job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/flow.py:431: UserWarning: Some jobs are not connected, their ordering may be random\n", + " for node in itergraph(graph):\n", + "INFO:jobflow.core.job:Starting job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:57,800 INFO Finished job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:57,804 INFO Starting job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " torch.load(f=model_path, map_location=device)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", + "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n", + "Default dtype float32 does not match model dtype float64, converting models to float32.\n", + "2025-02-11 07:12:58,115 INFO Finished job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,121 INFO Starting job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,126 INFO Finished job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,131 INFO Starting job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", + "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n", + "Default dtype float32 does not match model dtype float64, converting models to float32.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " torch.load(f=model_path, map_location=device)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,440 INFO Finished job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,444 INFO Starting job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,514 INFO Finished job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n", + "INFO:jobflow.core.job:Finished job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,518 INFO Starting job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,566 INFO Finished job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:58,572 INFO Starting job - Force field static 1/1 (7fff1f8b-48fd-4db4-a29b-efbacca3415a)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - Force field static 1/1 (7fff1f8b-48fd-4db4-a29b-efbacca3415a)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", + "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " torch.load(f=model_path, map_location=device)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default dtype float32 does not match model dtype float64, converting models to float32.\n", + "2025-02-11 07:12:59,013 INFO Finished job - Force field static 1/1 (7fff1f8b-48fd-4db4-a29b-efbacca3415a)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - Force field static 1/1 (7fff1f8b-48fd-4db4-a29b-efbacca3415a)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:59,018 INFO Starting job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:59,020 INFO Finished job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Finished job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-11 07:12:59,024 INFO Starting job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:jobflow.core.job:Starting job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n" + ] + } + ], + "execution_count": null } ], "metadata": { @@ -93,6 +3249,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" + }, + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3 (ipykernel)" } }, "nbformat": 4, From c20cd87c171f2ba8936690002b403dc108b3cb8a Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 11 Feb 2025 07:23:37 +0100 Subject: [PATCH 30/61] fix linting and add a tmp dir to tutorial execution --- pyproject.toml | 2 +- .../phonon_band_structure.pdf | Bin 40548 -> 0 bytes .../phonon_band_structure.yaml | 9723 ----------------- .../phonon_dos.pdf | Bin 22903 -> 0 bytes .../phonon_dos.yaml | 202 - .../phonopy.yaml | 128 - .../phonon_band_structure.pdf | Bin 37518 -> 0 bytes .../phonon_band_structure.yaml | 9723 ----------------- .../phonon_dos.pdf | Bin 22926 -> 0 bytes .../phonon_dos.yaml | 202 - .../phonopy.yaml | 128 - .../phonon_band_structure.pdf | Bin 37518 -> 0 bytes .../phonon_band_structure.yaml | 9723 ----------------- .../phonon_dos.pdf | Bin 22926 -> 0 bytes .../phonon_dos.yaml | 202 - .../phonopy.yaml | 128 - .../phonon_band_structure.pdf | Bin 40548 -> 0 bytes .../phonon_band_structure.yaml | 9723 ----------------- .../phonon_dos.pdf | Bin 22903 -> 0 bytes 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b/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonon_band_structure.yaml deleted file mode 100644 index 99ffb0025a..0000000000 --- a/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonon_band_structure.yaml +++ /dev/null @@ -1,9723 +0,0 @@ -nqpoint: 606 -npath: 6 -segment_nqpoint: -- 101 -- 101 -- 101 -- 101 -- 101 -- 101 -reciprocal_lattice: -- [ -0.18285788, 0.18285788, 0.18285788 ] # a* -- [ 0.18285788, -0.18285788, 0.18285788 ] # b* -- [ 0.18285788, 0.18285788, -0.18285788 ] # c* -natom: 2 -lattice: -- [ 0.000000000000000, 2.734363997691476, 2.734363997691476 ] # a -- [ 2.734363997691476, 0.000000000000000, 2.734363997691476 ] # b -- [ 2.734363997691477, 2.734363997691476, 0.000000000000000 ] # c -points: -- symbol: Si # 1 - coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] - mass: 28.085500 -- symbol: Si # 2 - coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] - mass: 28.085500 - -phonon: -- q-position: [ 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z8kAC#inMF%{p?4o$!Tzy&o;AwLH6S*!XSl@Vv=u!6X?)V9Ev0fhxlR9KI73+eh)%t zLN;eW@CVZDmmfi@J^^J8Y*5oS;)h=|gC+hXRz)AtV?JVsB3xw<52vYAmJAla&H5uW z07f184G~4Rl*)9-5DF9<&JPwBDKVo)7N&(N5+PwF`Jo`tL@p;38{9B!s_=RAqlx^0 zhcp_*`0X+))a+NBL1RTG2YP4VAK3P`=lcEP+zLdbiObC7S&HDXYaaC9IhDtaS5EZ?Yv zvnQ8D&dD3$z(*8AqvHz z5`WJaq`xVFUJy+^B%Lk3J5WzOIG^e71atLwtO|@}gx6{@S1|Lmg@_~Mg(=77m#*^- zHGPvj-Iza9F!)%SkM{G6sGh3j&+YEn0!%~_)Co6nK}`Prw3l?Odam~|PN5)y`{QFP zJ}!7#k%ton0h~MeyqlMwKYd#tjj*-khlu=6gL1s}VUL?UtDob=dItOQA^iX=GuB5j c=mDGS2^1x&OUd7IY7&*ARsmk~Q_GwG1-Q^PvH$=8 diff --git a/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonon_dos.yaml b/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonon_dos.yaml deleted file mode 100644 index 83ecb42cae..0000000000 --- a/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonon_dos.yaml +++ /dev/null @@ -1,202 +0,0 @@ -# Tetrahedron method - -1.4755108417 0.0000000000 - -1.3874621786 0.0000000000 - -1.2994135155 0.0000000000 - -1.2113648524 0.0000000000 - -1.1233161893 0.0000000000 - -1.0352675262 0.0000000000 - -0.9472188631 0.0000000000 - -0.8591702000 0.0000000000 - -0.7711215369 0.0000000000 - -0.6830728738 0.0000000000 - -0.5950242107 0.0000000000 - -0.5069755475 0.0000000000 - -0.4189268844 0.0000000000 - -0.3308782213 0.0000000000 - -0.2428295582 0.0000000000 - -0.1547808951 0.0000000000 - -0.0667322320 0.0000000000 - 0.0213164311 0.0000116906 - 0.1093650942 0.0001870497 - 0.1974137573 0.0005728398 - 0.2854624204 0.0011690607 - 0.3735110835 0.0019757126 - 0.4615597466 0.0029927954 - 0.5496084097 0.0042203092 - 0.6376570728 0.0056582539 - 0.7257057359 0.0073066295 - 0.8137543990 0.0114865036 - 0.9018030621 0.0164783692 - 0.9898517252 0.0200666313 - 1.0779003883 0.0235738233 - 1.1659490514 0.0272650251 - 1.2539977145 0.0312547643 - 1.3420463776 0.0360091326 - 1.4300950407 0.0425925047 - 1.5181437038 0.0502099312 - 1.6061923669 0.0561855154 - 1.6942410300 0.0622826451 - 1.7822896931 0.0685309965 - 1.8703383562 0.0755638936 - 1.9583870193 0.0837786225 - 2.0464356824 0.0920694923 - 2.1344843455 0.1005830259 - 2.2225330086 0.1097107622 - 2.3105816717 0.1187068172 - 2.3986303348 0.1282816810 - 2.4866789979 0.1399178533 - 2.5747276610 0.1525012078 - 2.6627763241 0.1648087961 - 2.7508249872 0.1772524928 - 2.8388736503 0.1897727025 - 2.9269223134 0.2033379756 - 3.0149709765 0.2191801724 - 3.1030196396 0.2360681415 - 3.1910683027 0.2532334896 - 3.2791169658 0.2708483924 - 3.3671656289 0.2890582373 - 3.4552142920 0.3128682241 - 3.5432629551 0.3386116521 - 3.6313116182 0.3658759980 - 3.7193602813 0.3943474517 - 3.8074089444 0.4247978126 - 3.8954576075 0.4681277783 - 3.9835062706 0.5255586639 - 4.0715549337 0.5998544774 - 4.1596035968 0.6841389948 - 4.2476522599 0.7095737890 - 4.3357009230 0.6946289548 - 4.4237495861 0.7053178723 - 4.5117982492 0.7126996180 - 4.5998469123 0.7099994386 - 4.6878955754 0.7127998186 - 4.7759442385 0.7165348534 - 4.8639929016 0.7159430224 - 4.9520415647 0.7156521414 - 5.0400902278 0.7180041107 - 5.1281388909 0.7209256027 - 5.2161875540 0.7211979434 - 5.3042362171 0.7202485030 - 5.3922848802 0.7218434212 - 5.4803335433 0.7231651244 - 5.5683822064 0.7161060570 - 5.6564308695 0.7259836922 - 5.7444795326 0.7062262601 - 5.8325281957 0.6174126478 - 5.9205768588 0.5707104559 - 6.0086255219 0.4930360250 - 6.0966741850 0.4985245002 - 6.1847228481 0.5508136034 - 6.2727715112 0.5859663299 - 6.3608201743 0.6373001617 - 6.4488688374 0.3929750748 - 6.5369175005 0.2324073201 - 6.6249661636 0.1112890511 - 6.7130148267 0.1153179026 - 6.8010634898 0.1194327630 - 6.8891121529 0.1234226341 - 6.9771608160 0.1271773142 - 7.0652094791 0.1308698606 - 7.1532581422 0.1353673152 - 7.2413068053 0.1416425232 - 7.3293554684 0.1475606013 - 7.4174041315 0.1538730804 - 7.5054527946 0.1595443762 - 7.5935014577 0.1650222683 - 7.6815501208 0.1704228896 - 7.7695987839 0.1758118820 - 7.8576474470 0.1813997417 - 7.9456961101 0.1889200002 - 8.0337447732 0.1989590722 - 8.1217934363 0.2073059213 - 8.2098420994 0.2155881293 - 8.2978907625 0.2241446945 - 8.3859394256 0.2328828545 - 8.4739880887 0.2415570389 - 8.5620367518 0.2515576429 - 8.6500854149 0.2667434611 - 8.7381340780 0.2818632388 - 8.8261827411 0.3001663074 - 8.9142314042 0.3200304768 - 9.0022800673 0.3436257151 - 9.0903287304 0.3735784615 - 9.1783773935 0.4396739502 - 9.2664260566 0.6125818409 - 9.3544747197 0.7122088960 - 9.4425233828 0.4284790531 - 9.5305720459 0.3069897682 - 9.6186207090 0.2351264657 - 9.7066693721 0.1556011843 - 9.7947180352 0.1168097857 - 9.8827666983 0.0750628850 - 9.9708153614 0.0502191813 - 10.0588640245 0.0610283084 - 10.1469126876 0.2512966753 - 10.2349613507 0.6797915858 - 10.3230100138 0.8942840259 - 10.4110586769 0.5447416583 - 10.4991073400 0.4695887106 - 10.5871560031 0.4214532245 - 10.6752046662 0.3839177946 - 10.7632533293 0.3547763862 - 10.8513019924 0.3286136339 - 10.9393506555 0.3122751716 - 11.0273993186 0.3030066921 - 11.1154479817 0.2932757914 - 11.2034966448 0.2839277820 - 11.2915453079 0.2731531897 - 11.3795939710 0.2623605905 - 11.4676426341 0.2506434432 - 11.5556912972 0.2444591746 - 11.6437399603 0.2388773310 - 11.7317886234 0.2328246372 - 11.8198372865 0.2257815159 - 11.9078859496 0.2190545078 - 11.9959346127 0.5199071149 - 12.0839832758 0.9459934521 - 12.1720319389 1.2104781662 - 12.2600806020 1.1787792455 - 12.3481292651 1.2270821987 - 12.4361779282 1.1959707414 - 12.5242265914 1.2080230774 - 12.6122752545 1.2202094029 - 12.7003239176 1.2276868745 - 12.7883725807 1.2369242295 - 12.8764212438 1.2422307397 - 12.9644699069 1.2611566096 - 13.0525185700 1.2683255232 - 13.1405672331 1.2752187063 - 13.2286158962 1.3005352108 - 13.3166645593 1.3050479412 - 13.4047132224 1.0701817534 - 13.4927618855 0.9115507998 - 13.5808105486 0.8069990478 - 13.6688592117 0.7265699841 - 13.7569078748 0.6567255171 - 13.8449565379 0.5945566254 - 13.9330052010 0.5374762943 - 14.0210538641 0.4879876273 - 14.1091025272 0.4331390810 - 14.1971511903 0.3814971897 - 14.2851998534 0.3401450950 - 14.3732485165 0.2817167689 - 14.4612971796 0.2255019100 - 14.5493458427 0.1575094495 - 14.6373945058 0.0335731577 - 14.7254431689 0.0000000000 - 14.8134918320 0.0000000000 - 14.9015404951 0.0000000000 - 14.9895891582 0.0000000000 - 15.0776378213 0.0000000000 - 15.1656864844 0.0000000000 - 15.2537351475 0.0000000000 - 15.3417838106 0.0000000000 - 15.4298324737 0.0000000000 - 15.5178811368 0.0000000000 - 15.6059297999 0.0000000000 - 15.6939784630 0.0000000000 - 15.7820271261 0.0000000000 - 15.8700757892 0.0000000000 - 15.9581244523 0.0000000000 - 16.0461731154 0.0000000000 - 16.1342217785 0.0000000000 diff --git a/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonopy.yaml b/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonopy.yaml deleted file mode 100644 index 116aaef49e..0000000000 --- a/tutorials/force_fields/job_2025-02-11-06-10-50-709850-48585/phonopy.yaml +++ /dev/null @@ -1,128 +0,0 @@ -phonopy: - version: "2.30.1" - frequency_unit_conversion_factor: 15.633302 - symmetry_tolerance: 1.00000e-03 - -space_group: - type: "Fd-3m" - number: 227 - Hall_symbol: "F 4d 2 3 -1d" - -primitive_matrix: -- [ 0.000000000000000, 0.500000000000000, 0.500000000000000 ] -- [ 0.500000000000000, 0.000000000000000, 0.500000000000000 ] -- [ 0.500000000000000, 0.500000000000000, 0.000000000000000 ] - -supercell_matrix: -- [ 1, 0, 0 ] -- [ 0, 1, 0 ] -- [ 0, 0, 1 ] - -primitive_cell: - lattice: - - [ 0.000000000000000, 2.734363997691476, 2.734363997691476 ] # a - - [ 2.734363997691476, 0.000000000000000, 2.734363997691476 ] # b - - [ 2.734363997691477, 2.734363997691476, 0.000000000000000 ] # c - points: - - symbol: Si # 1 - coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] - mass: 28.085500 - - symbol: Si # 2 - coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] - mass: 28.085500 - reciprocal_lattice: # without 2pi - - [ -0.182857878622646, 0.182857878622646, 0.182857878622646 ] # a* - - [ 0.182857878622646, -0.182857878622646, 0.182857878622646 ] # b* - - [ 0.182857878622646, 0.182857878622646, -0.182857878622646 ] # c* - -unit_cell: - lattice: - - [ 5.468727995382952, 0.000000000000000, 0.000000000000000 ] # a - - [ 0.000000000000001, 5.468727995382952, 0.000000000000000 ] # b - - [ 0.000000000000000, 0.000000000000000, 5.468727995382952 ] # c - points: - - symbol: Si # 1 - coordinates: [ 0.250000000000000, 0.250000000000000, 0.750000000000000 ] - mass: 28.085500 - reduced_to: 1 - - symbol: Si # 2 - coordinates: [ 0.500000000000000, 0.000000000000000, -0.000000000000000 ] - mass: 28.085500 - reduced_to: 2 - - symbol: Si # 3 - coordinates: [ 0.250000000000000, 0.750000000000000, 0.250000000000000 ] - mass: 28.085500 - reduced_to: 1 - - symbol: Si # 4 - coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] - mass: 28.085500 - reduced_to: 2 - - symbol: Si # 5 - coordinates: [ 0.750000000000000, 0.250000000000000, 0.250000000000000 ] - mass: 28.085500 - reduced_to: 1 - - symbol: Si # 6 - coordinates: [ 0.000000000000000, 0.000000000000000, 0.500000000000000 ] - mass: 28.085500 - reduced_to: 2 - - symbol: Si # 7 - coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] - mass: 28.085500 - reduced_to: 1 - - symbol: Si # 8 - coordinates: [ 0.000000000000000, 0.500000000000000, -0.000000000000000 ] - mass: 28.085500 - reduced_to: 2 - -supercell: - lattice: - - [ 5.468727995382952, 0.000000000000000, 0.000000000000000 ] # a - - [ 0.000000000000001, 5.468727995382952, 0.000000000000000 ] # b - - [ 0.000000000000000, 0.000000000000000, 5.468727995382952 ] # c - points: - - symbol: Si # 1 - coordinates: [ 0.250000000000000, 0.250000000000000, 0.750000000000000 ] - mass: 28.085500 - reduced_to: 1 - - symbol: Si # 2 - coordinates: [ 0.500000000000000, 0.000000000000000, 1.000000000000000 ] - mass: 28.085500 - reduced_to: 2 - - symbol: Si # 3 - coordinates: [ 0.250000000000000, 0.750000000000000, 0.250000000000000 ] - mass: 28.085500 - reduced_to: 1 - - symbol: Si # 4 - coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] - mass: 28.085500 - reduced_to: 2 - - symbol: Si # 5 - coordinates: [ 0.750000000000000, 0.250000000000000, 0.250000000000000 ] - mass: 28.085500 - reduced_to: 1 - - symbol: Si # 6 - coordinates: [ 0.000000000000000, 0.000000000000000, 0.500000000000000 ] - mass: 28.085500 - reduced_to: 2 - - symbol: Si # 7 - coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] - mass: 28.085500 - reduced_to: 1 - - symbol: Si # 8 - coordinates: [ 0.000000000000000, 0.500000000000000, 1.000000000000000 ] - mass: 28.085500 - reduced_to: 2 - -displacements: -- atom: 1 - displacement: - [ 0.0100000000000000, 0.0000000000000000, 0.0000000000000000 ] - forces: - - [ -0.1122161969542503, 0.0000018391019694, 0.0000000163813638 ] - - [ 0.0310748945921659, -0.0157699882984161, 0.0157650932669640 ] - - [ -0.0111228805035353, 0.0000018949785954, 0.0000012983527995 ] - 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99ffb0025a..0000000000 --- a/tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770/phonon_band_structure.yaml +++ /dev/null @@ -1,9723 +0,0 @@ -nqpoint: 606 -npath: 6 -segment_nqpoint: -- 101 -- 101 -- 101 -- 101 -- 101 -- 101 -reciprocal_lattice: -- [ -0.18285788, 0.18285788, 0.18285788 ] # a* -- [ 0.18285788, -0.18285788, 0.18285788 ] # b* -- [ 0.18285788, 0.18285788, -0.18285788 ] # c* -natom: 2 -lattice: -- [ 0.000000000000000, 2.734363997691476, 2.734363997691476 ] # a -- [ 2.734363997691476, 0.000000000000000, 2.734363997691476 ] # b -- [ 2.734363997691477, 2.734363997691476, 0.000000000000000 ] # c -points: -- symbol: Si # 1 - coordinates: [ 0.750000000000000, 0.750000000000000, 0.750000000000000 ] - mass: 28.085500 -- symbol: Si # 2 - coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] - mass: 28.085500 - -phonon: -- q-position: [ 0.0000000, 0.0000000, 0.0000000 ] - distance: 0.0000000 - band: - - # 1 - frequency: -0.0080331233 - - # 2 - frequency: 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angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, -1.348e-07, -1.636e-07) [0.5, -3.746e-08, -3.261e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.459e-08, -1.401e-07, 2.731) [7.333e-09, -1.056e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], input=InputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, relax_cell=True, fix_symmetry=False, symprec=None, steps=500, relax_kwargs={'fmax': 1e-05}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, -1.348e-07, -1.636e-07) [0.5, -3.746e-08, -3.261e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.459e-08, -1.401e-07, 2.731) [7.333e-09, -1.056e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, -1.348e-07, -1.636e-07) [0.5, -3.746e-08, -3.261e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.459e-08, -1.401e-07, 2.731) [7.333e-09, -1.056e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.5106201171875, energy_per_atom=-5.3138275146484375, forces=[(-6.017507985234261e-07, -6.638234481215477e-06, -7.87666067481041e-07), (-5.4389238357543945e-06, 4.51505184173584e-06, -2.2202730178833008e-06), (-1.4603137969970703e-06, -1.9073486328125e-06, -2.8312206268310547e-07), (-2.682209014892578e-07, 1.234118826687336e-06, 1.371721737086773e-06), (8.553266525268555e-06, 5.887122824788094e-07, -1.4404067769646645e-06), (1.1026859283447266e-06, 5.02169132232666e-06, 2.5331974029541016e-06), (3.203749656677246e-06, -1.5348196029663086e-06, -2.339482307434082e-06), (-5.114474333822727e-06, -1.3050157576799393e-06, 3.1035160645842552e-06)], stress=((0.0006924792408186339, -4.4729655696953566e-05, -3.6938730776494e-05), (-4.4729655696953566e-05, 0.0006850838240233392, 2.0533694501513544e-05), (-3.6938730776494e-05, 2.0533694501513544e-05, 0.0006995213391471558)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, energy=-42.51001739501953, forces=[[-1.0136282071471214e-06, -8.860370144248009e-07, -1.444830559194088e-06], [3.5762786865234375e-06, 2.041459083557129e-06, -9.08970832824707e-07], [-2.7120113372802734e-06, -2.637505531311035e-06, 6.258487701416016e-07], [4.500150680541992e-06, 9.605428203940392e-07, 1.2292293831706047e-06], [-3.039836883544922e-06, 7.472699508070946e-07, 3.60771082341671e-07], [8.195638656616211e-07, 5.066394805908203e-07, 1.6689300537109375e-06], [-4.082918167114258e-06, -8.195638656616211e-07, -3.6656856536865234e-06], [1.9170111045241356e-06, 1.3259705156087875e-07, 2.2315653041005135e-06]], stress=((-2.588936067726548, 2.0132524184944748e-05, 2.5232310138558127e-05), (2.0132524184944748e-05, -2.588927114867015, -6.306294065303895e-05), (2.5232310138558127e-05, -6.306294065303895e-05, -2.58892562272376)), magmoms=[0.003035895526409149, 0.003035925328731537, 0.0030358880758285522, 0.003035910427570343, 0.003036022186279297, 0.003035925328731537, 0.0030359849333763123, 0.0030360743403434753]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468502172843737 5.468502173624643 5.46850217375479\n", - " angles : 90.00000011526447 89.99999995388117 89.9999999632024\n", - " volume : 163.53291085316573\n", - " A : 5.468502172843737 1.756043309593872e-09 2.2008683933226213e-09\n", - " B : 1.7560441889960934e-09 5.468502173624643 -5.50061419672616e-09\n", - " C : 2.2008680584734375e-09 -5.500614531575348e-09 5.46850217375479\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 1.579e-08, 5.469) [0.5, 3.733e-09, 1.0]\n", - " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (9.296e-09, 2.316e-09, 2.734) [1.499e-09, 9.264e-10, 0.5]\n", - " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (2.005e-08, 2.734, 1.956e-08) [3.505e-09, 0.5, 4.081e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468502172843737 5.468502173624643 5.46850217375479\n", - " angles : 90.00000011526447 89.99999995388117 89.9999999632024\n", - " volume : 163.53291085316573\n", - " A : 5.468502172843737 1.756043309593872e-09 2.2008683933226213e-09\n", - " B : 1.7560441889960934e-09 5.468502173624643 -5.50061419672616e-09\n", - " C : 2.2008680584734375e-09 -5.500614531575348e-09 5.46850217375479\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 1.579e-08, 5.469) [0.5, 3.733e-09, 1.0]\n", - " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (9.296e-09, 2.316e-09, 2.734) [1.499e-09, 9.264e-10, 0.5]\n", - " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (2.005e-08, 2.734, 1.956e-08) [3.505e-09, 0.5, 4.081e-09], molecule=None, energy=-42.51005172729492, forces=[[-1.841457560658455e-06, -1.7085112631320953e-06, 1.78581103682518e-06], [-2.041459083557129e-06, 1.4901161193847656e-06, 1.9222497940063477e-06], [-1.1920928955078125e-07, -2.682209014892578e-07, -5.066394805908203e-07], [1.6689300537109375e-06, 1.4249235391616821e-06, -2.010725438594818e-06], [3.2782554626464844e-07, 4.85684722661972e-07, -4.377216100692749e-08], [-1.043081283569336e-07, 1.7881393432617188e-07, -1.6391277313232422e-07], [1.8924474716186523e-06, -1.564621925354004e-06, -1.6242265701293945e-06], [1.5005934983491898e-07, -8.451752364635468e-08, 6.848713383078575e-07]], stress=((-2.536166608014681, -3.887377935245278e-06, -3.954693621355416e-05), (-3.887377935245278e-06, -2.536161572031194, -3.8263649412555135e-06), (-3.954693621355416e-05, -3.8263649412555135e-06, -2.5361690327474715)), magmoms=[0.0030512064695358276, 0.0030511990189552307, 0.0030512064695358276, 0.0030512064695358276, 0.0030511394143104553, 0.003051191568374634, 0.00305117666721344, 0.003051169216632843]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468055185584739 5.468055187552339 5.4680551870825616\n", - " angles : 90.0000002267093 89.99999999202647 89.99999993791413\n", - " volume : 163.49281337094024\n", - " A : 5.468055185584739 2.9626001823444003e-09 3.804804927840314e-10\n", - " B : 2.962600698456001e-09 5.468055187552339 -1.0818063436125202e-08\n", - " C : 3.8047932377840965e-10 -1.0818066211872068e-08 5.4680551870825616\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 5.668e-08, 4.103e-09) [0.5, 1.01e-08, 7.156e-10]\n", - " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.461e-08, 6.186e-09, 2.734) [2.637e-09, 2.121e-09, 0.5]\n", - " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (3.955e-08, 2.734, 4.453e-08) [6.963e-09, 0.5, 9.133e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468055185584739 5.468055187552339 5.4680551870825616\n", - " angles : 90.0000002267093 89.99999999202647 89.99999993791413\n", - " volume : 163.49281337094024\n", - " A : 5.468055185584739 2.9626001823444003e-09 3.804804927840314e-10\n", - " B : 2.962600698456001e-09 5.468055187552339 -1.0818063436125202e-08\n", - " C : 3.8047932377840965e-10 -1.0818066211872068e-08 5.4680551870825616\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 5.668e-08, 4.103e-09) [0.5, 1.01e-08, 7.156e-10]\n", - " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.461e-08, 6.186e-09, 2.734) [2.637e-09, 2.121e-09, 0.5]\n", - " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (3.955e-08, 2.734, 4.453e-08) [6.963e-09, 0.5, 9.133e-09], molecule=None, energy=-42.51011657714844, forces=[[-5.430774763226509e-07, -3.7995632737874985e-06, 7.48620368540287e-06], [-3.3229589462280273e-06, -3.2633543014526367e-06, -8.031725883483887e-06], [-2.9653310775756836e-06, 4.544854164123535e-06, 4.738569259643555e-06], [2.980232238769531e-07, 2.542976289987564e-06, -3.646593540906906e-06], [8.463859558105469e-06, -1.8246937543153763e-06, 6.309011951088905e-06], [-2.7865171432495117e-06, -2.682209014892578e-06, -7.212162017822266e-06], [3.069639205932617e-06, 2.8312206268310547e-06, 4.082918167114258e-06], [-2.157175913453102e-06, 1.5852274373173714e-06, -3.6992132663726807e-06]], stress=((-2.4264813955198044, 0.0002661702971243203, -2.3941788710492665e-05), (0.0002661702971243203, -2.4264998607925907, 3.354682909729243e-05), (-2.3941788710492665e-05, 3.354682909729243e-05, -2.4264651684619007)), magmoms=[0.0030816122889518738, 0.003081493079662323, 0.0030813664197921753, 0.0030814260244369507, 0.0030815377831459045, 0.0030814185738563538, 0.0030814260244369507, 0.0030815228819847107]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.467396703824154 5.467396704909678 5.467396707563365\n", - " angles : 90.0000002527664 90.00000007934335 89.9999993260871\n", - " volume : 163.43375544598408\n", - " A : 5.467396703824154 3.215375688852146e-08 -3.7856321839316396e-09\n", - " B : 3.215375698975334e-08 5.467396704909678 -1.2059998486999147e-08\n", - " C : -3.7856327770069295e-09 -1.2059999644732788e-08 5.467396707563365\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 5.101e-08, 5.467) [0.5, 8.595e-09, 1.0]\n", - " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.467, 5.467, 2.734) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.504e-08, 2.734, 2.398e-08) [5.297e-09, 0.5, 5.488e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.467396703824154 5.467396704909678 5.467396707563365\n", - " angles : 90.0000002527664 90.00000007934335 89.9999993260871\n", - " volume : 163.43375544598408\n", - " A : 5.467396703824154 3.215375688852146e-08 -3.7856321839316396e-09\n", - " B : 3.215375698975334e-08 5.467396704909678 -1.2059998486999147e-08\n", - " C : -3.7856327770069295e-09 -1.2059999644732788e-08 5.467396707563365\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.101) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 5.101e-08, 5.467) [0.5, 8.595e-09, 1.0]\n", - " PeriodicSite: Si (1.367, 4.101, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.101, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.467, 5.467, 2.734) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.101, 4.101, 4.101) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.504e-08, 2.734, 2.398e-08) [5.297e-09, 0.5, 5.488e-09], molecule=None, energy=-42.510196685791016, forces=[[2.3085158318281174e-06, -2.089887857437134e-06, -3.2442621886730194e-06], [2.995133399963379e-06, 7.450580596923828e-07, -2.0265579223632812e-06], [-2.0712614059448242e-06, -4.291534423828125e-06, 1.1026859283447266e-06], [1.1324882507324219e-06, 4.942878149449825e-06, 4.804343916475773e-06], [6.556510925292969e-07, 9.528594091534615e-07, -2.739951014518738e-06], [1.2218952178955078e-06, 1.9371509552001953e-07, 3.725290298461914e-07], [-4.231929779052734e-06, -3.606081008911133e-06, -2.130866050720215e-06], [-1.992913894355297e-06, 3.1029339879751205e-06, 3.8262223824858665e-06]], stress=((-2.2549921689369903, -8.352920541349362e-06, -5.9531600217545794e-05), (-8.352920541349362e-06, -2.2549636316972292, -1.385672142591112e-05), (-5.9531600217545794e-05, -1.385672142591112e-05, -2.254964564286764)), magmoms=[0.00312592089176178, 0.003125905990600586, 0.0031259581446647644, 0.003125719726085663, 0.0031259283423423767, 0.0031260624527931213, 0.0031260475516319275, 0.00312592089176178]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.466541821510313 5.466541825121733 5.466541830477119\n", - " angles : 90.00000031000636 90.00000030309221 89.99999879581102\n", - " volume : 163.3571041433684\n", - " A : 5.466541821510313 5.7445325739951046e-08 -1.445888544634015e-08\n", - " B : 5.744532616203014e-08 5.466541825121733 -1.4788721381940212e-08\n", - " C : -1.445888487548972e-08 -1.4788723326637997e-08 5.466541830477119\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.1) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.733, 6.347e-08, 5.467) [0.5, 9.062e-09, 1.0]\n", - " PeriodicSite: Si (1.367, 4.1, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.733, 2.733, 2.733) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.1, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.467, 5.467, 2.733) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.1, 4.1, 4.1) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.467, 2.733, 4.127e-08) [1.0, 0.5, 1.155e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.466541821510313 5.466541825121733 5.466541830477119\n", - " angles : 90.00000031000636 90.00000030309221 89.99999879581102\n", - " volume : 163.3571041433684\n", - " A : 5.466541821510313 5.7445325739951046e-08 -1.445888544634015e-08\n", - " B : 5.744532616203014e-08 5.466541825121733 -1.4788721381940212e-08\n", - " C : -1.445888487548972e-08 -1.4788723326637997e-08 5.466541830477119\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.1) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.733, 6.347e-08, 5.467) [0.5, 9.062e-09, 1.0]\n", - " PeriodicSite: Si (1.367, 4.1, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.733, 2.733, 2.733) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.1, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.467, 5.467, 2.733) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.1, 4.1, 4.1) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.467, 2.733, 4.127e-08) [1.0, 0.5, 1.155e-08], molecule=None, energy=-42.510311126708984, forces=[[2.6674242690205574e-06, 3.357767127454281e-06, -2.8705690056085587e-06], [-1.6689300537109375e-06, -1.996755599975586e-06, 7.703900337219238e-06], [1.9669532775878906e-06, 2.0116567611694336e-06, -4.32133674621582e-06], [-2.086162567138672e-06, -2.982676960527897e-06, 7.980270311236382e-07], [-1.1920928955078125e-07, -1.0611256584525108e-06, -2.795131877064705e-07], [-4.172325134277344e-07, 1.0132789611816406e-06, -1.6689300537109375e-06], [-1.1175870895385742e-06, -1.1622905731201172e-06, -3.069639205932617e-06], [6.82310201227665e-07, 8.709030225872993e-07, 3.634369932115078e-06]], stress=((-2.0118884593973236, -1.9662156738092746e-05, 1.5526901396870438e-05), (-1.9662156738092746e-05, -2.0119024482403436, 7.023391324703004e-06), (1.5526901396870438e-05, 7.023391324703004e-06, -2.011872045821513)), magmoms=[0.0031842663884162903, 0.003184087574481964, 0.003184095025062561, 0.0031841620802879333, 0.0031841248273849487, 0.003184005618095398, 0.003184087574481964, 0.0031840279698371887]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.465511891139474 5.465511895210926 5.465511906935457\n", - " angles : 90.00000034244523 90.00000046229164 89.99999837196914\n", - " volume : 163.26478928247508\n", - " A : 5.4655118911394736 7.764989014140953e-08 -2.2049271714675683e-08\n", - " B : 7.764989135801762e-08 5.465511895210925 -1.633312601003877e-08\n", - " C : -2.2049271243651576e-08 -1.633312765442467e-08 5.465511906935457\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.366, 1.366, 4.099) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.733, 4.464e-08, 5.466) [0.5, 4.053e-09, 1.0]\n", - " PeriodicSite: Si (1.366, 4.099, 1.366) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.733, 2.733, 2.733) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.099, 1.366, 1.366) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.466, 5.466, 2.733) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.099, 4.099, 4.099) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.466, 2.733, 1.167e-07) [1.0, 0.5, 2.688e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.465511891139474 5.465511895210926 5.465511906935457\n", - " angles : 90.00000034244523 90.00000046229164 89.99999837196914\n", - " volume : 163.26478928247508\n", - " A : 5.4655118911394736 7.764989014140953e-08 -2.2049271714675683e-08\n", - " B : 7.764989135801762e-08 5.465511895210925 -1.633312601003877e-08\n", - " C : -2.2049271243651576e-08 -1.633312765442467e-08 5.465511906935457\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.366, 1.366, 4.099) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.733, 4.464e-08, 5.466) [0.5, 4.053e-09, 1.0]\n", - " PeriodicSite: Si (1.366, 4.099, 1.366) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.733, 2.733, 2.733) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.099, 1.366, 1.366) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.466, 5.466, 2.733) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.099, 4.099, 4.099) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.466, 2.733, 1.167e-07) [1.0, 0.5, 2.688e-08], molecule=None, energy=-42.51041793823242, forces=[[2.4889595806598663e-07, 4.639732651412487e-06, 4.6496279537677765e-07], [-1.2516975402832031e-06, -3.769993782043457e-06, 6.854534149169922e-07], [3.3676624298095703e-06, 5.036592483520508e-06, 1.0281801223754883e-06], [-2.592802047729492e-06, -2.534245140850544e-06, 1.3282988220453262e-07], [-8.940696716308594e-07, 3.92901711165905e-06, -1.8774298951029778e-06], [3.7550926208496094e-06, -4.425644874572754e-06, -8.642673492431641e-07], [-1.043081283569336e-06, -1.1026859283447266e-06, 4.813075065612793e-06], [-1.6648555174469948e-06, -1.8245773389935493e-06, -4.329485818743706e-06]], stress=((-1.6906831740976387, 9.426433894625491e-05, 9.228162422444029e-05), (9.426433894625491e-05, -1.690699028119728, -4.727289748436047e-05), (9.228162422444029e-05, -4.727289748436047e-05, -1.6906773920425235)), magmoms=[0.0032541975378990173, 0.0032541826367378235, 0.0032541826367378235, 0.003254227340221405, 0.003254212439060211, 0.003254137933254242, 0.0032541602849960327, 0.003254234790802002]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.464335056998369 5.464335059138653 5.464335079792611\n", - " angles : 90.00000051826392 90.00000031934624 89.99999769813712\n", - " volume : 163.15934960636423\n", - " A : 5.464335056998368 1.0976506471239753e-07 -1.5228127222395356e-08\n", - " B : 1.0976506658527718e-07 5.464335059138652 -2.4713579642085147e-08\n", - " C : -1.5228126797444088e-08 -2.4713580723973096e-08 5.464335079792611\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.366, 1.366, 4.098) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.732, 5.464, 5.464) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.366, 4.098, 1.366) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.732, 2.732, 2.732) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.098, 1.366, 1.366) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.16e-07, 5.464, 2.732) [2.53e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.098, 4.098, 4.098) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.464, 2.732, 1.212e-07) [1.0, 0.5, 2.724e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.464335056998369 5.464335059138653 5.464335079792611\n", - " angles : 90.00000051826392 90.00000031934624 89.99999769813712\n", - " volume : 163.15934960636423\n", - " A : 5.464335056998368 1.0976506471239753e-07 -1.5228127222395356e-08\n", - " B : 1.0976506658527718e-07 5.464335059138652 -2.4713579642085147e-08\n", - " C : -1.5228126797444088e-08 -2.4713580723973096e-08 5.464335079792611\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.366, 1.366, 4.098) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.732, 5.464, 5.464) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.366, 4.098, 1.366) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.732, 2.732, 2.732) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.098, 1.366, 1.366) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.16e-07, 5.464, 2.732) [2.53e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.098, 4.098, 4.098) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.464, 2.732, 1.212e-07) [1.0, 0.5, 2.724e-08], molecule=None, energy=-42.51050567626953, forces=[[-1.9349390640854836e-06, -3.8067810237407684e-07, -1.5243422240018845e-06], [-2.60770320892334e-06, -3.1888484954833984e-06, 3.039836883544922e-06], [2.0116567611694336e-06, 4.9173831939697266e-06, -1.043081283569336e-06], [-1.0728836059570312e-06, -1.8165446817874908e-06, -2.6496127247810364e-07], [7.748603820800781e-07, -8.688075467944145e-07, 1.9837170839309692e-07], [-2.0265579223632812e-06, -2.6226043701171875e-06, 3.606081008911133e-06], [6.735324859619141e-06, 7.778406143188477e-06, -2.86102294921875e-06], [-1.8618302419781685e-06, -3.775232471525669e-06, -1.2100208550691605e-06]], stress=((-1.286992489412312, 2.6355894396989766e-05, 5.599031003530822e-05), (2.6355894396989766e-05, -1.2870006029412635, 2.467087518703579e-08), (5.599031003530822e-05, 2.467087518703579e-08, -1.2869639521725509)), magmoms=[0.0033344700932502747, 0.003334447741508484, 0.003334522247314453, 0.00333423912525177, 0.003334254026412964, 0.0033344626426696777, 0.0033344030380249023, 0.0033344998955726624]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.463046599806876 5.46304659876286 5.463046633984983\n", - " angles : 90.00000067640845 89.99999998124949 89.99999699309747\n", - " volume : 163.0439610969674\n", - " A : 5.463046599806874 1.4335129627483703e-07 8.939138219735487e-10\n", - " B : 1.4335129843829687e-07 5.463046598762858 -3.224714673338457e-08\n", - " C : 8.93914324421651e-10 -3.2247147191376687e-08 5.463046633984983\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.366, 1.366, 4.097) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.732, 5.463, 5.463) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.366, 4.097, 1.366) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.732, 2.732, 2.732) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.097, 1.366, 1.366) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.619e-07, 5.463, 2.732) [3.312e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.097, 4.097, 4.097) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.463, 2.732, 1.105e-07) [1.0, 0.5, 2.302e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.463046599806876 5.46304659876286 5.463046633984983\n", - " angles : 90.00000067640845 89.99999998124949 89.99999699309747\n", - " volume : 163.0439610969674\n", - " A : 5.463046599806874 1.4335129627483703e-07 8.939138219735487e-10\n", - " B : 1.4335129843829687e-07 5.463046598762858 -3.224714673338457e-08\n", - " C : 8.93914324421651e-10 -3.2247147191376687e-08 5.463046633984983\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.366, 1.366, 4.097) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.732, 5.463, 5.463) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.366, 4.097, 1.366) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.732, 2.732, 2.732) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.097, 1.366, 1.366) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.619e-07, 5.463, 2.732) [3.312e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.097, 4.097, 4.097) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.463, 2.732, 1.105e-07) [1.0, 0.5, 2.302e-08], molecule=None, energy=-42.510581970214844, forces=[[2.1886080503463745e-07, 2.294662408530712e-06, 9.520445019006729e-07], [9.313225746154785e-06, 7.808208465576172e-06, -1.8030405044555664e-06], [-4.500150680541992e-06, -1.0609626770019531e-05, -1.4454126358032227e-06], [-4.26173210144043e-06, -6.91611785441637e-06, -2.5193439796566963e-06], [-7.361173629760742e-06, -3.637745976448059e-06, -3.5150442272424698e-06], [-2.7567148208618164e-06, 3.069639205932617e-06, 4.5746564865112305e-06], [7.987022399902344e-06, 3.635883331298828e-06, 5.856156349182129e-06], [1.534121111035347e-06, 4.286179319024086e-06, -2.1241139620542526e-06]], stress=((-0.8041806288287122, 6.897447908546605e-05, -2.080450207505388e-05), (6.897447908546605e-05, -0.804172608558714, -2.8068207817102645e-06), (-2.080450207505388e-05, -2.8068207817102645e-06, -0.8042224088398654)), magmoms=[0.0034221485257148743, 0.0034220293164253235, 0.0034221112728118896, 0.0034221112728118896, 0.003421984612941742, 0.0034221261739730835, 0.0034219548106193542, 0.003422059118747711]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461545147198787 5.461545145259648 5.4615451808891855\n", - " angles : 90.00000084953466 89.99999976930151 89.99999588808815\n", - " volume : 162.9095659883945\n", - " A : 5.461545147198783 1.9597771771340579e-07 1.0995315215276332e-08\n", - " B : 1.9597771812928898e-07 5.461545145259644 -4.048964846185668e-08\n", - " C : 1.0995315545401588e-08 -4.048964886029536e-08 5.4615451808891855\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.462, 5.462) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.462, 5.462, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.462, 2.731, 2.502e-08) [1.0, 0.5, 6.274e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461545147198787 5.461545145259648 5.4615451808891855\n", - " angles : 90.00000084953466 89.99999976930151 89.99999588808815\n", - " volume : 162.9095659883945\n", - " A : 5.461545147198783 1.9597771771340579e-07 1.0995315215276332e-08\n", - " B : 1.9597771812928898e-07 5.461545145259644 -4.048964846185668e-08\n", - " C : 1.0995315545401588e-08 -4.048964886029536e-08 5.4615451808891855\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.462, 5.462) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.462, 5.462, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.462, 2.731, 2.502e-08) [1.0, 0.5, 6.274e-09], molecule=None, energy=-42.51062774658203, forces=[[-6.625778041779995e-06, 3.759050741791725e-07, -1.2055388651788235e-05], [9.238719940185547e-07, 1.7434358596801758e-06, 9.164214134216309e-06], [9.5367431640625e-07, 1.9222497940063477e-06, -8.195638656616211e-07], [-3.2782554626464844e-07, -1.632492057979107e-06, 3.782683052122593e-06], [-4.380941390991211e-06, 1.0987278074026108e-06, -4.718080163002014e-06], [1.0073184967041016e-05, 5.662441253662109e-06, 7.137656211853027e-06], [5.513429641723633e-07, -7.18235969543457e-06, -2.950429916381836e-06], [-1.1813826858997345e-06, -2.082553692162037e-06, 3.441236913204193e-07]], stress=((-0.19357953317739093, -0.00019997761468098542, 3.803595574805208e-08), (-0.00019997761468098542, -0.1935834850255441, -8.99692586828571e-05), (3.803595574805208e-08, -8.99692586828571e-05, -0.19359283423562915)), magmoms=[0.0035246387124061584, 0.0035246461629867554, 0.0035245344042778015, 0.00352458655834198, 0.0035246610641479492, 0.003524668514728546, 0.0035246089100837708, 0.003524407744407654]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.459869448510431 5.459869441846533 5.459869468798026\n", - " angles : 90.0000028550925 89.99999955846455 89.9999988693439\n", - " volume : 162.75966083893866\n", - " A : 5.459869448510431 5.387163515042253e-08 2.103755570557215e-08\n", - " B : 5.3871636068471196e-08 5.459869441846531 -1.3603473569344624e-07\n", - " C : 2.1037551458747527e-08 -1.3603473465465964e-07 5.459869468798024\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 2.266e-07, 1.05e-06) [0.5, 3.656e-08, 1.903e-07]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (9.843e-07, 2.721e-07, 2.73) [1.783e-07, 6.229e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.459869448510431 5.459869441846533 5.459869468798026\n", - " angles : 90.0000028550925 89.99999955846455 89.9999988693439\n", - " volume : 162.75966083893866\n", - " A : 5.459869448510431 5.387163515042253e-08 2.103755570557215e-08\n", - " B : 5.3871636068471196e-08 5.459869441846531 -1.3603473569344624e-07\n", - " C : 2.1037551458747527e-08 -1.3603473465465964e-07 5.459869468798024\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 2.266e-07, 1.05e-06) [0.5, 3.656e-08, 1.903e-07]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (9.843e-07, 2.721e-07, 2.73) [1.783e-07, 6.229e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.51061248779297, forces=[[1.1385418474674225e-05, -6.488990038633347e-07, 2.8136419132351875e-05], [-4.082918167114258e-06, -7.62939453125e-06, -2.41696834564209e-05], [5.97536563873291e-06, -1.5348196029663086e-06, 5.5283308029174805e-06], [1.8775463104248047e-06, 6.395857781171799e-06, -1.3802200555801392e-05], [6.616115570068359e-06, -5.3551048040390015e-08, 1.4450284652411938e-05], [-1.983344554901123e-05, -9.194016456604004e-06, -1.65402889251709e-05], [-3.0249357223510742e-06, 1.0594725608825684e-05, 1.3902783393859863e-05], [1.0242220014333725e-06, 2.0815059542655945e-06, -7.504015229642391e-06]], stress=((0.5372072435196871, 0.00019478386964870507, 5.625113797871147e-05), (0.00019478386964870507, 0.5372567173945013, 7.982633999608061e-05), (5.625113797871147e-05, 7.982633999608061e-05, 0.5372240767607879)), magmoms=[0.003638714551925659, 0.0036384910345077515, 0.0036384984850883484, 0.003638423979282379, 0.0036384984850883484, 0.003638520836830139, 0.003638520836830139, 0.0036385878920555115]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.4598864893880465 5.4598864842935075 5.4598865102096745\n", - " angles : 90.00000280194699 89.99999952101474 89.99999873966406\n", - " volume : 162.76118488100127\n", - " A : 5.4598864893880465 6.005059291439117e-08 2.282197336257326e-08\n", - " B : 6.005059383585618e-08 5.459886484293506 -1.335029632432997e-07\n", - " C : 2.2821968112777438e-08 -1.3350296108807914e-07 5.459886510209673\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 2.018e-07, 9.625e-07) [0.5, 3.147e-08, 1.742e-07]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (9.128e-07, 2.398e-07, 2.73) [1.651e-07, 5.615e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.4598864893880465 5.4598864842935075 5.4598865102096745\n", - " angles : 90.00000280194699 89.99999952101474 89.99999873966406\n", - " volume : 162.76118488100127\n", - " A : 5.4598864893880465 6.005059291439117e-08 2.282197336257326e-08\n", - " B : 6.005059383585618e-08 5.459886484293506 -1.335029632432997e-07\n", - " C : 2.2821968112777438e-08 -1.3350296108807914e-07 5.459886510209673\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 2.018e-07, 9.625e-07) [0.5, 3.147e-08, 1.742e-07]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (9.128e-07, 2.398e-07, 2.73) [1.651e-07, 5.615e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106086730957, forces=[[1.0832329280674458e-05, -8.00122506916523e-06, 1.9390834495425224e-05], [-7.152557373046875e-06, 2.4586915969848633e-06, -1.9177794456481934e-05], [5.677342414855957e-06, 4.917383193969727e-07, 6.973743438720703e-06], [2.086162567138672e-07, 5.9516169130802155e-06, -9.818468242883682e-06], [6.765127182006836e-06, -4.9174996092915535e-06, 1.1430587619543076e-05], [-1.2785196304321289e-05, -1.862645149230957e-06, -1.4156103134155273e-05], [-3.069639205932617e-06, 5.319714546203613e-06, 1.1667609214782715e-05], [-4.847534000873566e-07, 4.775356501340866e-07, -6.305519491434097e-06]], stress=((0.5295171102167974, 9.403638842687665e-05, -0.00015849813369287364), (9.403638842687665e-05, 0.5294849358778512, 5.241133831791735e-05), (-0.00015849813369287364, 5.241133831791735e-05, 0.5295290473628411)), magmoms=[0.0036373361945152283, 0.0036373957991600037, 0.003637343645095825, 0.003637343645095825, 0.00363728404045105, 0.0036373957991600037, 0.003637269139289856, 0.0036375224590301514]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.459920327616994 5.4599203228107065 5.459920349336427\n", - " angles : 90.00000271568209 89.99999960353914 89.9999985539936\n", - " volume : 162.76421112452798\n", - " A : 5.459920327616994 6.889756784663905e-08 1.889009284202428e-08\n", - " B : 6.889756736702278e-08 5.459920322810705 -1.2939354373866184e-07\n", - " C : 1.889008707571444e-08 -1.2939354233568795e-07 5.459920349336425\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 1.911e-07, 8.041e-07) [0.5, 2.869e-08, 1.455e-07]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (7.943e-07, 2.042e-07, 2.73) [1.438e-07, 4.925e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.459920327616994 5.4599203228107065 5.459920349336427\n", - " angles : 90.00000271568209 89.99999960353914 89.9999985539936\n", - " volume : 162.76421112452798\n", - " A : 5.459920327616994 6.889756784663905e-08 1.889009284202428e-08\n", - " B : 6.889756736702278e-08 5.459920322810705 -1.2939354373866184e-07\n", - " C : 1.889008707571444e-08 -1.2939354233568795e-07 5.459920349336425\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 1.911e-07, 8.041e-07) [0.5, 2.869e-08, 1.455e-07]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (7.943e-07, 2.042e-07, 2.73) [1.438e-07, 4.925e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.510616302490234, forces=[[1.2828735634684563e-05, -8.770730346441269e-07, 2.4345004931092262e-05], [-7.003545761108398e-07, -2.7418136596679688e-06, -1.576542854309082e-05], [8.031725883483887e-06, 1.3262033462524414e-06, 7.808208465576172e-06], [6.109476089477539e-06, 6.717396900057793e-06, -1.2885313481092453e-05], [-7.659196853637695e-06, 3.691529855132103e-07, 8.98831058293581e-06], [-1.1101365089416504e-05, -1.0356307029724121e-05, -1.17570161819458e-05], [-1.3932585716247559e-05, 6.988644599914551e-06, 5.0067901611328125e-06], [6.509246304631233e-06, -1.3762619346380234e-06, -5.741836503148079e-06]], stress=((0.5143124502203962, 0.00013634259774259193, 5.943439096416996e-05), (0.00013634259774259193, 0.514330169421555, -8.191316845799929e-05), (5.943439096416996e-05, -8.191316845799929e-05, 0.5142573808083739)), magmoms=[0.0036349892616271973, 0.003635019063949585, 0.003635197877883911, 0.0036350414156913757, 0.0036351680755615234, 0.0036350861191749573, 0.003635115921497345, 0.0036351457238197327]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.4599704815121095 5.4599704776439255 5.459970501930372\n", - " angles : 90.00000270389135 89.99999963003361 89.99999827728837\n", - " volume : 162.7686965275905\n", - " A : 5.459970481512109 8.208243947099918e-08 1.762787755565937e-08\n", - " B : 8.208243894409568e-08 5.459970477643924 -1.2883293686096683e-07\n", - " C : 1.762787291515083e-08 -1.2883293510207843e-07 5.45997050193037\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 1.719e-07, 5.932e-07) [0.5, 2.397e-08, 1.07e-07]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (6.4e-07, 1.249e-07, 2.73) [1.156e-07, 3.468e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.4599704815121095 5.4599704776439255 5.459970501930372\n", - " angles : 90.00000270389135 89.99999963003361 89.99999827728837\n", - " volume : 162.7686965275905\n", - " A : 5.459970481512109 8.208243947099918e-08 1.762787755565937e-08\n", - " B : 8.208243894409568e-08 5.459970477643924 -1.2883293686096683e-07\n", - " C : 1.762787291515083e-08 -1.2883293510207843e-07 5.45997050193037\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 1.719e-07, 5.932e-07) [0.5, 2.397e-08, 1.07e-07]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (6.4e-07, 1.249e-07, 2.73) [1.156e-07, 3.468e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.51061248779297, forces=[[9.906478226184845e-06, -1.18953175842762e-06, 1.0191230103373528e-05], [-5.4389238357543945e-06, 4.470348358154297e-08, -1.1742115020751953e-05], [1.996755599975586e-06, -2.384185791015625e-06, 1.5497207641601562e-06], [6.556510925292969e-07, 6.96815550327301e-06, 1.5585683286190033e-06], [5.781650543212891e-06, -4.7923531383275986e-06, 6.517046131193638e-06], [-1.150369644165039e-05, -4.738569259643555e-06, -2.8461217880249023e-06], [-4.976987838745117e-06, 4.5746564865112305e-06, -3.0547380447387695e-06], [3.6603305488824844e-06, 1.6030389815568924e-06, -2.1852320060133934e-06]], stress=((0.49205825267247016, -2.1876755202970047e-05, -0.00011688593628880498), (-2.1876755202970047e-05, 0.4921022708985066, 1.7712544277257588e-05), (-0.00011688593628880498, 1.7712544277257588e-05, 0.49203596378259157)), magmoms=[0.0036317110061645508, 0.0036317557096481323, 0.0036318525671958923, 0.003631807863712311, 0.003631964325904846, 0.003631889820098877, 0.003631867468357086, 0.00363188236951828]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.460036245799424 5.460036244620607 5.460036264112397\n", - " angles : 90.00000267769764 89.99999975670508 89.99999804749899\n", - " volume : 162.77457816814484\n", - " A : 5.460036245799423 9.30323872984083e-08 1.1592473118221463e-08\n", - " B : 9.303238651345445e-08 5.460036244620604 -1.275864156900285e-07\n", - " C : 1.1592468220515364e-08 -1.275864133149242e-07 5.460036264112396\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 1.545e-07, 3.444e-07) [0.5, 1.977e-08, 6.201e-08]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (4.431e-07, 3.115e-08, 2.73) [8.009e-08, 1.739e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.460036245799424 5.460036244620607 5.460036264112397\n", - " angles : 90.00000267769764 89.99999975670508 89.99999804749899\n", - " volume : 162.77457816814484\n", - " A : 5.460036245799423 9.30323872984083e-08 1.1592473118221463e-08\n", - " B : 9.303238651345445e-08 5.460036244620604 -1.275864156900285e-07\n", - " C : 1.1592468220515364e-08 -1.275864133149242e-07 5.460036264112396\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 1.545e-07, 3.444e-07) [0.5, 1.977e-08, 6.201e-08]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (4.431e-07, 3.115e-08, 2.73) [8.009e-08, 1.739e-08, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.51061248779297, forces=[[7.531489245593548e-06, -1.2904638424515724e-06, 3.321445547044277e-06], [-4.06801700592041e-06, -4.976987838745117e-06, -9.581446647644043e-06], [-1.1771917343139648e-06, 1.0281801223754883e-06, -7.301568984985352e-07], [5.066394805908203e-07, 1.4977995306253433e-06, -1.45728699862957e-06], [5.066394805908203e-06, -1.2775417417287827e-06, 6.663845852017403e-06], [-6.3478946685791016e-06, -2.115964889526367e-06, -5.900859832763672e-06], [-3.427267074584961e-06, 4.336237907409668e-06, 4.202127456665039e-06], [1.921318471431732e-06, 2.7993228286504745e-06, 3.5008415579795837e-06]], stress=((0.4622011189431461, 6.48470987297248e-05, -9.799835281997347e-07), (6.48470987297248e-05, 0.46220587514977285, 6.858724154498056e-05), (-9.799835281997347e-07, 6.858724154498056e-05, 0.4622129628302364)), magmoms=[0.0036270394921302795, 0.003627188503742218, 0.0036271139979362488, 0.003626979887485504, 0.0036272257566452026, 0.0036270171403884888, 0.003627099096775055, 0.003627091646194458]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.4601166744010685 5.460116675861297 5.460116691363573\n", - " angles : 90.00000258797486 89.99999987165455 89.99999777814718\n", - " volume : 162.78177152394014\n", - " A : 5.460116674401068 1.0586796914217971e-07 6.115472939102874e-09\n", - " B : 1.0586796952828918e-07 5.460116675861294 -1.2331313893523934e-07\n", - " C : 6.115467630354005e-09 -1.233131361298998e-07 5.460116691363571\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 1.139e-07, 6.974e-08) [0.5, 1.117e-08, 1.221e-08]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (3.382e-07, 5.46, 2.73) [4.2e-08, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.4601166744010685 5.460116675861297 5.460116691363573\n", - " angles : 90.00000258797486 89.99999987165455 89.99999777814718\n", - " volume : 162.78177152394014\n", - " A : 5.460116674401068 1.0586796914217971e-07 6.115472939102874e-09\n", - " B : 1.0586796952828918e-07 5.460116675861294 -1.2331313893523934e-07\n", - " C : 6.115467630354005e-09 -1.233131361298998e-07 5.460116691363571\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 1.139e-07, 6.974e-08) [0.5, 1.117e-08, 1.221e-08]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (3.382e-07, 5.46, 2.73) [4.2e-08, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106201171875, forces=[[2.5221379473805428e-06, -1.4882534742355347e-06, 2.9033981263637543e-07], [-5.140900611877441e-06, -4.813075065612793e-06, -3.337860107421875e-06], [-1.3560056686401367e-06, 1.8030405044555664e-06, -1.341104507446289e-06], [5.453824996948242e-06, 3.899796865880489e-06, 3.327499143779278e-06], [1.7583370208740234e-06, 2.5707995519042015e-06, 5.4623233154416084e-06], [-7.301568984985352e-07, -1.862645149230957e-06, -2.980232238769531e-07], [-4.693865776062012e-06, -2.175569534301758e-06, -7.0035457611083984e-06], [2.2529857233166695e-06, 2.0457664504647255e-06, 2.92900949716568e-06]], stress=((0.4262083854062738, 4.2087986552164376e-05, 4.0521738174915644e-05), (4.2087986552164376e-05, 0.42616683854250426, -1.5359530206533378e-05), (4.0521738174915644e-05, -1.5359530206533378e-05, 0.42622027592284084)), magmoms=[0.0036214813590049744, 0.0036214739084243774, 0.0036216378211975098, 0.0036218464374542236, 0.003621727228164673, 0.0036216452717781067, 0.0036217719316482544, 0.0036216452717781067]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.460210626941211 5.460210628674449 5.460210643289987\n", - " angles : 90.00000252353483 89.9999999320787 89.99999749103698\n", - " volume : 162.79017464414375\n", - " A : 5.460210626941209 1.195503994765092e-07 3.2364087991896364e-09\n", - " B : 1.195503982175753e-07 5.460210628674447 -1.2024473734147363e-07\n", - " C : 3.236402954634202e-09 -1.202447351442928e-07 5.460210643289986\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.59e-07, 5.46, 2.73) [6.933e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.460210626941211 5.460210628674449 5.460210643289987\n", - " angles : 90.00000252353483 89.9999999320787 89.99999749103698\n", - " volume : 162.79017464414375\n", - " A : 5.460210626941209 1.195503994765092e-07 3.2364087991896364e-09\n", - " B : 1.195503982175753e-07 5.460210628674447 -1.2024473734147363e-07\n", - " C : 3.236402954634202e-09 -1.202447351442928e-07 5.460210643289986\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.59e-07, 5.46, 2.73) [6.933e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106086730957, forces=[[3.0976952984929085e-06, 6.656628102064133e-07, -5.557434633374214e-06], [-5.7220458984375e-06, -2.726912498474121e-06, 8.58306884765625e-06], [3.933906555175781e-06, 1.8775463104248047e-06, -1.1771917343139648e-05], [1.1026859283447266e-06, -2.8407666832208633e-06, 8.43987800180912e-06], [5.0961971282958984e-06, -9.420327842235565e-07, -6.6426582634449e-07], [-9.834766387939453e-07, 8.538365364074707e-06, 2.9802322387695312e-08], [-7.852911949157715e-06, -4.246830940246582e-06, -6.854534149169922e-06], [1.1477386578917503e-06, -3.223540261387825e-07, 7.839989848434925e-06]], stress=((0.38464942128750784, -0.00013145974245155065, -9.107326505002593e-05), (-0.00013145974245155065, 0.3846644592937544, 8.035539558012265e-05), (-9.107326505002593e-05, 8.035539558012265e-05, 0.38464555104093895)), magmoms=[0.0036153867840766907, 0.0036155059933662415, 0.003615342080593109, 0.0036153122782707214, 0.0036154091358184814, 0.0036153197288513184, 0.0036153793334960938, 0.003615260124206543]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.460316785788504 5.46031678861195 5.460316801367637\n", - " angles : 90.0000023708403 90.00000009378996 89.99999738756239\n", - " volume : 162.7996698661292\n", - " A : 5.460316785788502 1.2448331412831257e-07 -4.4691116279055585e-09\n", - " B : 1.2448331311518543e-07 5.460316788611947 -1.1297114182512609e-07\n", - " C : -4.469116689871123e-09 -1.1297113967038858e-07 5.460316801367636\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.46, 5.46, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.460316785788504 5.46031678861195 5.460316801367637\n", - " angles : 90.0000023708403 90.00000009378996 89.99999738756239\n", - " volume : 162.7996698661292\n", - " A : 5.460316785788502 1.2448331412831257e-07 -4.4691116279055585e-09\n", - " B : 1.2448331311518543e-07 5.460316788611947 -1.1297114182512609e-07\n", - " C : -4.469116689871123e-09 -1.1297113967038858e-07 5.460316801367636\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.46, 5.46, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.510616302490234, forces=[[-1.0097865015268326e-06, 3.700493834912777e-06, -7.021008059382439e-06], [6.705522537231445e-07, 1.6689300537109375e-06, 9.655952453613281e-06], [-2.5033950805664062e-06, -2.6971101760864258e-06, -7.703900337219238e-06], [1.8477439880371094e-06, -2.942979335784912e-06, 5.64951915293932e-06], [-1.3113021850585938e-06, 9.669456630945206e-07, -6.827409379184246e-06], [-3.2782554626464844e-07, 8.195638656616211e-07, 6.541609764099121e-06], [2.86102294921875e-06, -5.319714546203613e-06, -4.738569259643555e-06], [-2.832384780049324e-07, 3.8088764995336533e-06, 4.411558620631695e-06]], stress=((0.3376097683619158, -6.75783839001112e-05, -3.734500830717402e-05), (-6.75783839001112e-05, 0.33759463709671583, 8.189984900297531e-05), (-3.734500830717402e-05, 8.189984900297531e-05, 0.3375673821675651)), magmoms=[0.0036081522703170776, 0.003608211874961853, 0.0036081671714782715, 0.003608226776123047, 0.00360812246799469, 0.0036081746220588684, 0.00360821932554245, 0.0036081895232200623]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.460446526443763 5.460446529242143 5.460446538167299\n", - " angles : 90.0000020942262 90.0000003110855 89.99999738863278\n", - " volume : 162.81127470221423\n", - " A : 5.460446526443762 1.2443526624942296e-07 -1.4823651553244156e-08\n", - " B : 1.244352653341237e-07 5.46044652924214 -9.979278008110766e-08\n", - " C : -1.4823656227616615e-08 -9.979277841226609e-08 5.460446538167298\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.46, 5.46, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.460446526443763 5.460446529242143 5.460446538167299\n", - " angles : 90.0000020942262 90.0000003110855 89.99999738863278\n", - " volume : 162.81127470221423\n", - " A : 5.460446526443762 1.2443526624942296e-07 -1.4823651553244156e-08\n", - " B : 1.244352653341237e-07 5.46044652924214 -9.979278008110766e-08\n", - " C : -1.4823656227616615e-08 -9.979277841226609e-08 5.460446538167298\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.46, 5.46) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.46, 5.46, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.46, 2.73, 5.46) [1.0, 0.5, 1.0], molecule=None, energy=-42.510616302490234, forces=[[-9.985058568418026e-06, -1.4045508578419685e-06, -6.743008270859718e-06], [8.195638656616211e-06, -1.996755599975586e-06, 6.198883056640625e-06], [-3.948807716369629e-06, -5.364418029785156e-07, 1.4007091522216797e-06], [6.9141387939453125e-06, 7.701106369495392e-06, 4.110625013709068e-06], [-2.3245811462402344e-06, 4.825415089726448e-06, -1.0868418030440807e-05], [9.968876838684082e-06, -2.562999725341797e-06, 6.586313247680664e-06], [-8.255243301391602e-06, -5.21540641784668e-07, -1.0147690773010254e-05], [-5.690380930900574e-07, -5.470006726682186e-06, 9.368755854666233e-06]], stress=((0.28051899183798645, -5.405423048230191e-05, 5.344174046589173e-05), (-5.405423048230191e-05, 0.28053232786833227, -7.961447528007745e-05), (5.344174046589173e-05, -7.961447528007745e-05, 0.28054333242484136)), magmoms=[0.003599405288696289, 0.0035994797945022583, 0.003599412739276886, 0.0035993531346321106, 0.0035993829369544983, 0.0035994797945022583, 0.0035993754863739014, 0.003599405288696289]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.4606022777865135 5.460602281851383 5.460602288045497\n", - " angles : 90.0000019818485 90.00000041785475 89.99999749953051\n", - " volume : 162.82520696236782\n", - " A : 5.460602277786512 1.1915423468251957e-07 -1.991192173928146e-08\n", - " B : 1.1915423285249888e-07 5.46060228185138 -9.44405211693399e-08\n", - " C : -1.991192665606119e-08 -9.444051894047396e-08 5.4606022880454965\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.803e-08, 2.73, 5.461) [1.532e-09, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.4606022777865135 5.460602281851383 5.460602288045497\n", - " angles : 90.0000019818485 90.00000041785475 89.99999749953051\n", - " volume : 162.82520696236782\n", - " A : 5.460602277786512 1.1915423468251957e-07 -1.991192173928146e-08\n", - " B : 1.1915423285249888e-07 5.46060228185138 -9.44405211693399e-08\n", - " C : -1.991192665606119e-08 -9.444051894047396e-08 5.4606022880454965\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.095) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.095, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.095, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.095, 4.095, 4.095) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.803e-08, 2.73, 5.461) [1.532e-09, 0.5, 1.0], molecule=None, energy=-42.5106201171875, forces=[[-6.191432476043701e-06, 5.02169132232666e-06, -2.3802276700735092e-06], [-7.599592208862305e-07, 2.8759241104125977e-06, 8.910894393920898e-06], [-4.470348358154297e-07, 1.5050172805786133e-06, -1.6391277313232422e-06], [-1.4603137969970703e-06, -4.014582373201847e-06, 2.9046786949038506e-06], [-5.334615707397461e-06, 4.6298373490571976e-06, -7.736380212008953e-06], [6.243586540222168e-06, -2.652406692504883e-06, 6.780028343200684e-06], [6.899237632751465e-06, -2.2798776626586914e-06, -4.9173831939697266e-06], [9.80566255748272e-07, -5.028676241636276e-06, -1.980341039597988e-06]], stress=((0.21180234434221415, -2.7939016929972935e-05, 0.0001027114747096371), (-2.7939016929972935e-05, 0.21180766010256177, 2.8023296635448354e-05), (0.0001027114747096371, 2.8023296635448354e-05, 0.21176548374085633)), magmoms=[0.0035887807607650757, 0.0035887807607650757, 0.003588743507862091, 0.0035888999700546265, 0.0035888180136680603, 0.003588572144508362, 0.0035887062549591064, 0.003588758409023285]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.460785519264062 5.460785525305992 5.460785523074743\n", - " angles : 90.00000179065846 90.0000002317088 89.99999768901225\n", - " volume : 162.8415991596923\n", - " A : 5.4607855192640615 1.101286050708443e-07 -1.104192756893292e-08\n", - " B : 1.101286031815623e-07 5.46078552530599 -8.533265426612865e-08\n", - " C : -1.1041931892539185e-08 -8.533265134208392e-08 5.460785523074742\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.285e-07, 2.73, 3.825e-08) [1.344e-08, 0.5, 1.482e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.460785519264062 5.460785525305992 5.460785523074743\n", - " angles : 90.00000179065846 90.0000002317088 89.99999768901225\n", - " volume : 162.8415991596923\n", - " A : 5.4607855192640615 1.101286050708443e-07 -1.104192756893292e-08\n", - " B : 1.101286031815623e-07 5.46078552530599 -8.533265426612865e-08\n", - " C : -1.1041931892539185e-08 -8.533265134208392e-08 5.460785523074742\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.285e-07, 2.73, 3.825e-08) [1.344e-08, 0.5, 1.482e-08], molecule=None, energy=-42.510623931884766, forces=[[-6.8999361246824265e-06, 2.364395186305046e-07, -6.280373781919479e-06], [5.647540092468262e-06, 5.289912223815918e-06, 5.409121513366699e-06], [-2.8312206268310547e-06, -9.238719940185547e-07, 2.518296241760254e-06], [-3.3080577850341797e-06, -6.87665306031704e-07, 2.1382002159953117e-06], [-2.2351741790771484e-06, -3.0833762139081955e-06, -9.484472684562206e-06], [8.732080459594727e-06, 3.516674041748047e-06, 4.231929779052734e-06], [-4.3213367462158203e-07, -5.960464477539062e-07, -6.258487701416016e-07], [1.291278749704361e-06, -3.816094249486923e-06, 2.025393769145012e-06]], stress=((0.13175074717993923, -0.00014523759164304312, -2.461230729492583e-06), (-0.00014523759164304312, 0.13170317345630186, -3.5073087803057855e-05), (-2.461230729492583e-06, -3.5073087803057855e-05, 0.13169742637329446)), magmoms=[0.0035761892795562744, 0.0035761594772338867, 0.0035763680934906006, 0.0035761743783950806, 0.003576211631298065, 0.0035760700702667236, 0.003576017916202545, 0.0035760998725891113]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.460996061348722 5.460996059053308 5.460996047208369\n", - " angles : 90.00000176011537 90.00000005799927 89.99999853712589\n", - " volume : 162.8604343095674\n", - " A : 5.460996061348722 6.971499436050956e-08 -2.7640215320131824e-09\n", - " B : 6.971499212777905e-08 5.460996059053307 -8.38803769382965e-08\n", - " C : -2.7640257792770245e-09 -8.388037448297125e-08 5.4609960472083685\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (2.05e-07, 2.73, 2.24e-07) [3.115e-08, 0.5, 4.87e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.460996061348722 5.460996059053308 5.460996047208369\n", - " angles : 90.00000176011537 90.00000005799927 89.99999853712589\n", - " volume : 162.8604343095674\n", - " A : 5.460996061348722 6.971499436050956e-08 -2.7640215320131824e-09\n", - " B : 6.971499212777905e-08 5.460996059053307 -8.38803769382965e-08\n", - " C : -2.7640257792770245e-09 -8.388037448297125e-08 5.4609960472083685\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.73, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.73, 2.73, 2.73) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.73) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (2.05e-07, 2.73, 2.24e-07) [3.115e-08, 0.5, 4.87e-08], molecule=None, energy=-42.51062774658203, forces=[[-1.3544922694563866e-06, 6.758957169950008e-06, -7.742433808743954e-06], [2.115964889526367e-06, 1.6838312149047852e-06, 1.4603137969970703e-06], [3.1441450119018555e-06, 3.2782554626464844e-07, 3.7550926208496094e-06], [-1.1622905731201172e-05, -1.139205414801836e-05, 1.7745187506079674e-06], [5.930662155151367e-06, -3.4383265301585197e-06, 5.193403922021389e-06], [-5.27501106262207e-06, 8.612871170043945e-06, 2.086162567138672e-07], [1.2859702110290527e-05, 5.811452865600586e-07, 5.0067901611328125e-06], [-5.87815884500742e-06, -3.111199475824833e-06, -9.713112376630306e-06]], stress=((0.040697312589918974, 1.7470890087806585e-05, 5.109649180820875e-05), (1.7470890087806585e-05, 0.04069438367591165, 0.00020174172527023774), (5.109649180820875e-05, 0.00020174172527023774, 0.04072904977752072)), magmoms=[0.0035618990659713745, 0.003561966121196747, 0.0035619139671325684, 0.0035619735717773438, 0.0035620033740997314, 0.0035618767142295837, 0.003561966121196747, 0.0035620853304862976]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461230999390341 5.461230986963136 5.461230987347953\n", - " angles : 89.99999906898145 89.99999921136417 89.99999915001159\n", - " volume : 162.88145427792256\n", - " A : 5.461230999390341 4.050894539622032e-08 3.75849906981421e-08\n", - " B : 4.050894341289015e-08 5.461230986963136 4.4370695351235936e-08\n", - " C : 3.758498710184151e-08 4.437069809157265e-08 5.461230987347953\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.495e-08, 2.108e-07) [0.5, 2.692e-09, 3.515e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.678e-07, 2.731) [1.0, 5.587e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461230999390341 5.461230986963136 5.461230987347953\n", - " angles : 89.99999906898145 89.99999921136417 89.99999915001159\n", - " volume : 162.88145427792256\n", - " A : 5.461230999390341 4.050894539622032e-08 3.75849906981421e-08\n", - " B : 4.050894341289015e-08 5.461230986963136 4.4370695351235936e-08\n", - " C : 3.758498710184151e-08 4.437069809157265e-08 5.461230987347953\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.495e-08, 2.108e-07) [0.5, 2.692e-09, 3.515e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.678e-07, 2.731) [1.0, 5.587e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106315612793, forces=[[-6.72205351293087e-06, -6.512971594929695e-06, 3.9305305108428e-06], [2.086162567138672e-07, -1.7434358596801758e-06, -1.341104507446289e-07], [7.748603820800781e-07, -8.046627044677734e-07, 1.4603137969970703e-06], [5.155801773071289e-06, 6.146030500531197e-06, -7.579801604151726e-06], [-4.202127456665039e-06, -1.410720869898796e-06, 5.178852006793022e-06], [3.844499588012695e-06, -1.4901161193847656e-08, -8.940696716308594e-07], [-2.2202730178833008e-06, -1.4007091522216797e-06, 3.0547380447387695e-06], [3.155786544084549e-06, 5.6675635278224945e-06, -5.060108378529549e-06]], stress=((-0.06013641576888584, 8.791459044353903e-05, -7.160059748191101e-05), (8.791459044353903e-05, -0.06004577972348509, 1.006679999354065e-05), (-7.160059748191101e-05, 1.006679999354065e-05, -0.06002720953437597)), magmoms=[0.0035460367798805237, 0.003546014428138733, 0.0035459622740745544, 0.003546006977558136, 0.0035460814833641052, 0.00354582816362381, 0.0035459622740745544, 0.003545992076396942]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461229761201413 5.46122975064037 5.46122975140754\n", - " angles : 89.9999990646322 89.99999924229756 89.99999911203\n", - " volume : 162.88134361355043\n", - " A : 5.461229761201413 4.231907120242965e-08 3.611075109519174e-08\n", - " B : 4.231906910748388e-08 5.461229750640369 4.4577962236153174e-08\n", - " C : 3.611074752365085e-08 4.457796548981162e-08 5.46122975140754\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.173e-08, 2.097e-07) [0.5, 1.936e-09, 3.509e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.697e-07, 2.731) [1.0, 5.586e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461229761201413 5.46122975064037 5.46122975140754\n", - " angles : 89.9999990646322 89.99999924229756 89.99999911203\n", - " volume : 162.88134361355043\n", - " A : 5.461229761201413 4.231907120242965e-08 3.611075109519174e-08\n", - " B : 4.231906910748388e-08 5.461229750640369 4.4577962236153174e-08\n", - " C : 3.611074752365085e-08 4.457796548981162e-08 5.46122975140754\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.173e-08, 2.097e-07) [0.5, 1.936e-09, 3.509e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.697e-07, 2.731) [1.0, 5.586e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.51062774658203, forces=[[5.971523933112621e-06, -6.277812644839287e-06, 6.591435521841049e-06], [3.546476364135742e-06, 8.344650268554688e-07, -7.972121238708496e-06], [-8.285045623779297e-06, -5.260109901428223e-06, 2.041459083557129e-06], [4.738569259643555e-06, 5.9871235862374306e-06, -6.758491508662701e-06], [-5.602836608886719e-06, 2.5284243747591972e-06, 2.303975634276867e-06], [4.06801700592041e-06, -8.061528205871582e-06, -8.419156074523926e-06], [-6.16908073425293e-06, -7.152557373046875e-07, 7.793307304382324e-06], [1.8237624317407608e-06, 1.0971911251544952e-05, 4.357658326625824e-06]], stress=((-0.059791206095258516, 0.00011118520953854368, -1.4902008349478536e-05), (0.00011118520953854368, -0.059762464851536905, -7.660076074243482e-05), (-1.4902008349478536e-05, -7.660076074243482e-05, -0.05976434751666002)), magmoms=[0.003545999526977539, 0.0035459548234939575, 0.003545984625816345, 0.003545999526977539, 0.0035462453961372375, 0.0035461634397506714, 0.0035458356142044067, 0.00354602187871933]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461227292048314 5.4612272838180544 5.461227284886689\n", - " angles : 89.99999909713702 89.99999927722268 89.99999902498486\n", - " volume : 162.8811228339738\n", - " A : 5.461227292048313 4.646746573758274e-08 3.444626734325584e-08\n", - " B : 4.646746367704348e-08 5.461227283818054 4.302882107470429e-08\n", - " C : 3.444626366076289e-08 4.302882472244867e-08 5.461227284886689\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.227e-08, 1.878e-07) [0.5, 1.654e-09, 3.124e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.52e-07, 2.731) [1.0, 5.201e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461227292048314 5.4612272838180544 5.461227284886689\n", - " angles : 89.99999909713702 89.99999927722268 89.99999902498486\n", - " volume : 162.8811228339738\n", - " A : 5.461227292048313 4.646746573758274e-08 3.444626734325584e-08\n", - " B : 4.646746367704348e-08 5.461227283818054 4.302882107470429e-08\n", - " C : 3.444626366076289e-08 4.302882472244867e-08 5.461227284886689\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.227e-08, 1.878e-07) [0.5, 1.654e-09, 3.124e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.52e-07, 2.731) [1.0, 5.201e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.510623931884766, forces=[[-1.001986674964428e-06, -6.454065442085266e-06, 7.157446816563606e-06], [2.339482307434082e-06, 1.2516975402832031e-06, -6.735324859619141e-06], [-5.751848220825195e-06, 1.4901161193847656e-08, 4.738569259643555e-06], [8.58306884765625e-06, 6.9623347371816635e-06, -5.1443930715322495e-06], [-4.172325134277344e-07, 1.8621794879436493e-06, 2.6084017008543015e-06], [5.200505256652832e-06, -2.130866050720215e-06, -7.361173629760742e-06], [-8.866190910339355e-06, -3.6656856536865234e-06, 3.948807716369629e-06], [-9.476207196712494e-08, 2.164742909371853e-06, 7.573980838060379e-07]], stress=((-0.05869937689754348, 3.869866907492044e-05, -4.568404587809455e-05), (3.869866907492044e-05, -0.05868535891110047, -5.260252372618456e-05), (-4.568404587809455e-05, -5.260252372618456e-05, -0.05864571219850781)), magmoms=[0.0035462453961372375, 0.0035461783409118652, 0.0035461261868476868, 0.003546088933944702, 0.0035461336374282837, 0.0035462751984596252, 0.003546193242073059, 0.003546282649040222]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461223614377914 5.461223608592931 5.461223610915905\n", - " angles : 89.99999915375841 89.99999933242302 89.99999892651083\n", - " volume : 162.8807939578692\n", - " A : 5.461223614377913 5.1160527152940284e-08 3.1815495588153784e-08\n", - " B : 5.1160524231500766e-08 5.461223608592931 4.0330320779929926e-08\n", - " C : 3.181549362786491e-08 4.033032382492005e-08 5.461223610915905\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.679e-08, 1.484e-07) [0.5, 2.053e-09, 2.425e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.304e-07, 2.731) [1.0, 4.744e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461223614377914 5.461223608592931 5.461223610915905\n", - " angles : 89.99999915375841 89.99999933242302 89.99999892651083\n", - " volume : 162.8807939578692\n", - " A : 5.461223614377913 5.1160527152940284e-08 3.1815495588153784e-08\n", - " B : 5.1160524231500766e-08 5.461223608592931 4.0330320779929926e-08\n", - " C : 3.181549362786491e-08 4.033032382492005e-08 5.461223610915905\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.679e-08, 1.484e-07) [0.5, 2.053e-09, 2.425e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.304e-07, 2.731) [1.0, 4.744e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.510623931884766, forces=[[-5.540205165743828e-07, -6.4087798818945885e-06, 4.978617653250694e-06], [4.082918167114258e-06, 1.9073486328125e-06, -6.586313247680664e-06], [-8.419156074523926e-06, 2.8312206268310547e-07, 1.2665987014770508e-06], [4.32133674621582e-06, -3.2837269827723503e-06, -8.366652764379978e-06], [-3.933906555175781e-06, 3.319699317216873e-06, 2.27196142077446e-06], [7.465481758117676e-06, -7.152557373046875e-07, -6.616115570068359e-06], [-5.4389238357543945e-06, 1.0728836059570312e-06, 1.1712312698364258e-05], [2.2972235456109047e-06, 3.826571628451347e-06, 1.2718373909592628e-06]], stress=((-0.05738146468188604, 8.186289605339598e-05, 3.473598321134316e-05), (8.186289605339598e-05, -0.05738090512816524, -0.00010313454386096701), (3.473598321134316e-05, -0.00010313454386096701, -0.05733778451955593)), magmoms=[0.003546401858329773, 0.003546305000782013, 0.0035463497042655945, 0.003546200692653656, 0.0035462230443954468, 0.003546297550201416, 0.003546416759490967, 0.00354631245136261]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461218755384215 5.461218751815083 5.461218756431072\n", - " angles : 89.99999926313926 89.99999936242656 89.99999879151201\n", - " volume : 162.88035940089338\n", - " A : 5.461218755384215 5.7594272599569195e-08 3.038555505255824e-08\n", - " B : 5.7594269410501795e-08 5.461218751815082 3.511739800295849e-08\n", - " C : 3.038555323779688e-08 3.5117402185686005e-08 5.461218756431072\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 4.789e-08, 9.287e-08) [0.5, 3.497e-09, 1.422e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.095e-07, 2.731) [1.0, 4.291e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461218755384215 5.461218751815083 5.461218756431072\n", - " angles : 89.99999926313926 89.99999936242656 89.99999879151201\n", - " volume : 162.88035940089338\n", - " A : 5.461218755384215 5.7594272599569195e-08 3.038555505255824e-08\n", - " B : 5.7594269410501795e-08 5.461218751815082 3.511739800295849e-08\n", - " C : 3.038555323779688e-08 3.5117402185686005e-08 5.461218756431072\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 4.789e-08, 9.287e-08) [0.5, 3.497e-09, 1.422e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 3.095e-07, 2.731) [1.0, 4.291e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.510623931884766, forces=[[2.6882626116275787e-06, -9.417999535799026e-08, 8.642207831144333e-06], [-4.038214683532715e-06, -5.364418029785156e-07, -1.952052116394043e-06], [-3.6209821701049805e-06, 4.76837158203125e-07, -1.4901161193847656e-08], [-2.592802047729492e-06, 2.8481008484959602e-06, -5.029723979532719e-06], [5.841255187988281e-06, -4.797591827809811e-06, 3.3623073250055313e-06], [-7.316470146179199e-06, -7.197260856628418e-06, -7.897615432739258e-06], [8.38935375213623e-06, 3.606081008911133e-06, 4.604458808898926e-06], [6.776535883545876e-07, 5.678622983396053e-06, -1.7121201381087303e-06]], stress=((-0.055128217621115805, 7.738990999223135e-05, -3.36749975454868e-05), (7.738990999223135e-05, -0.05509466771260605, 0.00012089427259225076), (-3.36749975454868e-05, 0.00012089427259225076, -0.0550907741512988)), magmoms=[0.0035467445850372314, 0.003546707332134247, 0.0035466626286506653, 0.00354669988155365, 0.003546804189682007, 0.00354660302400589, 0.0035467371344566345, 0.003546692430973053]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461212761370182 5.461212760781772 5.46121276757595\n", - " angles : 89.99999928699597 89.99999941020582 89.99999862226728\n", - " volume : 162.879823331453\n", - " A : 5.461212761370182 6.566008616081548e-08 2.8108454703704393e-08\n", - " B : 6.56600834141703e-08 5.461212760781772 3.398039453238679e-08\n", - " C : 2.8108452825723105e-08 3.3980399381085624e-08 5.46121276757595\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.721e-08, 3.584e-08) [0.5, 4.464e-09, 3.99e-09]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 2.704e-07, 2.731) [1.0, 3.438e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461212761370182 5.461212760781772 5.46121276757595\n", - " angles : 89.99999928699597 89.99999941020582 89.99999862226728\n", - " volume : 162.879823331453\n", - " A : 5.461212761370182 6.566008616081548e-08 2.8108454703704393e-08\n", - " B : 6.56600834141703e-08 5.461212760781772 3.398039453238679e-08\n", - " C : 2.8108452825723105e-08 3.3980399381085624e-08 5.46121276757595\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.721e-08, 3.584e-08) [0.5, 4.464e-09, 3.99e-09]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 2.704e-07, 2.731) [1.0, 3.438e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.51062774658203, forces=[[-2.220040187239647e-06, -9.370851330459118e-06, -2.0582228899002075e-06], [1.6391277313232422e-07, -7.897615432739258e-07, 1.2814998626708984e-06], [8.195638656616211e-07, 1.3560056686401367e-06, 1.3113021850585938e-06], [8.046627044677734e-07, 5.418667569756508e-06, -5.310866981744766e-06], [1.1622905731201172e-06, -1.0628718882799149e-06, -1.909327693283558e-06], [2.0265579223632812e-06, -1.1771917343139648e-06, 1.8775463104248047e-06], [2.428889274597168e-06, 1.7881393432617188e-06, 2.682209014892578e-06], [-5.107838660478592e-06, 3.727036528289318e-06, 2.1436717361211777e-06]], stress=((-0.05252202622226507, 6.468060554192511e-05, -3.252010687567403e-05), (6.468060554192511e-05, -0.05251329485274672, 9.507323969559554e-05), (-3.252010687567403e-05, 9.507323969559554e-05, -0.052515888617390025)), magmoms=[0.003546975553035736, 0.0035471469163894653, 0.0035473108291625977, 0.0035471171140670776, 0.003547258675098419, 0.003547191619873047, 0.0035470128059387207, 0.003547191619873047]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461205686174393 5.461205688548012 5.4612056972195475\n", - " angles : 89.99999924463432 89.9999994750412 89.99999842652024\n", - " volume : 162.87919051509851\n", - " A : 5.4612056861743925 7.498891598410131e-08 2.5018492918835918e-08\n", - " B : 7.498891390474313e-08 5.461205688548011 3.5999222108222284e-08\n", - " C : 2.5018491036331963e-08 3.5999227698537656e-08 5.4612056972195475\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 9.972e-08, 5.461) [0.5, 4.802e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 2.345e-07, 2.731) [1.0, 2.591e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461205686174393 5.461205688548012 5.4612056972195475\n", - " angles : 89.99999924463432 89.9999994750412 89.99999842652024\n", - " volume : 162.87919051509851\n", - " A : 5.4612056861743925 7.498891598410131e-08 2.5018492918835918e-08\n", - " B : 7.498891390474313e-08 5.461205688548011 3.5999222108222284e-08\n", - " C : 2.5018491036331963e-08 3.5999227698537656e-08 5.4612056972195475\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 9.972e-08, 5.461) [0.5, 4.802e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 2.345e-07, 2.731) [1.0, 2.591e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106315612793, forces=[[-1.2060627341270447e-06, -8.829054422676563e-06, -1.1455267667770386e-07], [-2.682209014892578e-06, -4.470348358154297e-08, 3.516674041748047e-06], [5.3942203521728516e-06, -1.4603137969970703e-06, -3.2782554626464844e-06], [1.1473894119262695e-05, 1.2190197594463825e-05, 1.6511185094714165e-06], [-4.410743713378906e-06, 1.9080471247434616e-06, -2.0420411601662636e-06], [-2.8014183044433594e-06, -1.862645149230957e-06, 7.227063179016113e-06], [-2.6226043701171875e-06, -6.3478946685791016e-06, -8.940696716308594e-06], [-3.142864443361759e-06, 4.536821506917477e-06, 2.038083039224148e-06]], stress=((-0.04936751297782045, -7.10972188472763e-05, -5.614528246371965e-05), (-7.10972188472763e-05, -0.049317893385891404, 1.8307164024843928e-06), (-5.614528246371965e-05, 1.8307164024843928e-06, -0.04936610243614926)), magmoms=[0.0035476014018058777, 0.003547661006450653, 0.0035476163029670715, 0.00354766845703125, 0.00354766845703125, 0.0035477429628372192, 0.0035478994250297546, 0.0035478025674819946]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461197594712596 5.461197601484644 5.461197610162452\n", - " angles : 89.99999920516764 89.99999957452917 89.99999830243884\n", - " volume : 162.87846680081887\n", - " A : 5.461197594712595 8.090226774703088e-08 2.0277064686914088e-08\n", - " B : 8.090226677445459e-08 5.461197601484643 3.7880069684929044e-08\n", - " C : 2.0277061742692457e-08 3.7880076451011914e-08 5.461197610162452\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.06e-07, 5.461) [0.5, 5.074e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 1.923e-07, 2.731) [1.0, 1.692e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461197594712596 5.461197601484644 5.461197610162452\n", - " angles : 89.99999920516764 89.99999957452917 89.99999830243884\n", - " volume : 162.87846680081887\n", - " A : 5.461197594712595 8.090226774703088e-08 2.0277064686914088e-08\n", - " B : 8.090226677445459e-08 5.461197601484643 3.7880069684929044e-08\n", - " C : 2.0277061742692457e-08 3.7880076451011914e-08 5.461197610162452\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.06e-07, 5.461) [0.5, 5.074e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 1.923e-07, 2.731) [1.0, 1.692e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106315612793, forces=[[-3.1210947781801224e-06, 3.528548404574394e-07, -2.832384780049324e-07], [-2.0116567611694336e-06, -2.816319465637207e-06, -1.4156103134155273e-06], [-2.9802322387695312e-06, 2.1904706954956055e-06, -1.2516975402832031e-06], [-4.76837158203125e-07, -8.513452485203743e-07, 5.279434844851494e-07], [2.384185791015625e-07, -1.0593794286251068e-08, 1.4874385669827461e-06], [6.42240047454834e-06, 2.0563602447509766e-06, -1.8030405044555664e-06], [-3.829598426818848e-06, 1.7136335372924805e-06, 2.0563602447509766e-06], [5.780253559350967e-06, -2.6067718863487244e-06, 8.328352123498917e-07]], stress=((-0.04555815867686884, 8.55035243249833e-05, 1.6405296687402405e-05), (8.55035243249833e-05, -0.04554431846530588, -0.00012110982008064729), (1.6405296687402405e-05, -0.00012110982008064729, -0.04557274787440183)), magmoms=[0.003548353910446167, 0.0035484805703163147, 0.00354824960231781, 0.0035482048988342285, 0.0035483837127685547, 0.0035483837127685547, 0.0035483241081237793, 0.0035484135150909424]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461187559478365 5.461187571219924 5.461187578640475\n", - " angles : 89.99999927903323 89.99999965772383 89.999998099864\n", - " volume : 162.8775691684723\n", - " A : 5.461187559478364 9.055640125072429e-08 1.631214885606237e-08\n", - " B : 9.055639975787291e-08 5.461187571219923 3.435972390916111e-08\n", - " C : 1.6312145257826464e-08 3.435973066505181e-08 5.461187578640475\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 9.443e-08, 5.461) [0.5, 2.708e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 1.62e-07, 2.731) [1.0, 9.944e-09, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461187559478365 5.461187571219924 5.461187578640475\n", - " angles : 89.99999927903323 89.99999965772383 89.999998099864\n", - " volume : 162.8775691684723\n", - " A : 5.461187559478364 9.055640125072429e-08 1.631214885606237e-08\n", - " B : 9.055639975787291e-08 5.461187571219923 3.435972390916111e-08\n", - " C : 1.6312145257826464e-08 3.435973066505181e-08 5.461187578640475\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 9.443e-08, 5.461) [0.5, 2.708e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 1.62e-07, 2.731) [1.0, 9.944e-09, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.510616302490234, forces=[[1.844717189669609e-06, 2.167769707739353e-06, -1.2365635484457016e-06], [-4.783272743225098e-06, -3.606081008911133e-06, 4.500150680541992e-06], [5.066394805908203e-07, -2.2351741790771484e-06, -1.0564923286437988e-05], [-8.940696716308594e-07, -3.285706043243408e-06, -6.207264959812164e-07], [8.940696716308594e-07, 3.832043148577213e-06, -1.23586505651474e-06], [-2.041459083557129e-06, -7.897615432739258e-07, 3.2335519790649414e-06], [2.175569534301758e-06, 1.9371509552001953e-06, -8.791685104370117e-07], [2.2300519049167633e-06, 2.0561274141073227e-06, 6.7872460931539536e-06]], stress=((-0.04161823018369423, -9.600419560479904e-05, 3.846996720166903e-05), (-9.600419560479904e-05, -0.04160733637219237, 3.313755753526941e-05), (3.846996720166903e-05, 3.313755753526941e-05, -0.04165385510391863)), magmoms=[0.003548942506313324, 0.003548957407474518, 0.003548860549926758, 0.003548949956893921, 0.0035489648580551147, 0.003549046814441681, 0.00354902446269989, 0.0035489946603775024]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461175266867033 5.461175284148112 5.461175287699582\n", - " angles : 89.99999931303462 89.99999969466214 89.99999801268014\n", - " volume : 162.8764695204516\n", - " A : 5.461175266867032 9.471118345285396e-08 1.455171579277582e-08\n", - " B : 9.471118210392862e-08 5.461175284148111 3.273921711133284e-08\n", - " C : 1.4551711313654936e-08 3.2739224046993896e-08 5.461175287699582\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.521e-08, 2.779e-08, 2.731) [1.453e-09, 2.091e-09, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461175266867033 5.461175284148112 5.461175287699582\n", - " angles : 89.99999931303462 89.99999969466214 89.99999801268014\n", - " volume : 162.8764695204516\n", - " A : 5.461175266867032 9.471118345285396e-08 1.455171579277582e-08\n", - " B : 9.471118210392862e-08 5.461175284148111 3.273921711133284e-08\n", - " C : 1.4551711313654936e-08 3.2739224046993896e-08 5.461175287699582\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.521e-08, 2.779e-08, 2.731) [1.453e-09, 2.091e-09, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.51063537597656, forces=[[1.9255094230175018e-07, 1.3236422091722488e-07, 4.809116944670677e-07], [-2.2351741790771484e-06, -7.450580596923828e-07, -2.2351741790771484e-06], [-5.125999450683594e-06, 2.0265579223632812e-06, -3.11434268951416e-06], [3.0100345611572266e-06, -1.8546124920248985e-06, 4.496774636209011e-06], [3.933906555175781e-06, 1.7472775653004646e-06, 1.346576027572155e-06], [2.980232238769531e-07, -5.364418029785156e-07, -2.60770320892334e-06], [-3.8743019104003906e-07, -2.682209014892578e-07, -6.854534149169922e-07], [2.2759195417165756e-07, -5.862675607204437e-07, 2.3472821339964867e-06]], stress=((-0.03689695486363464, -2.550381783729844e-06, -3.3039653848768395e-05), (-2.550381783729844e-06, -0.03688308842299101, -8.201567134093593e-05), (-3.3039653848768395e-05, -8.201567134093593e-05, -0.03693913996836698)), magmoms=[0.003549806773662567, 0.0035496950149536133, 0.003549620509147644, 0.0035495832562446594, 0.0035495758056640625, 0.00354950875043869, 0.0035496950149536133, 0.003549635410308838]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461160399918815 5.461160423699676 5.461160419355619\n", - " angles : 89.99999947132143 89.99999978148678 89.99999793005053\n", - " volume : 162.87513948128523\n", - " A : 5.461160399918814 9.864885549661574e-08 1.0413821359219753e-08\n", - " B : 9.864885497372178e-08 5.461160423699675 2.5195556006551843e-08\n", - " C : 1.0413816829610393e-08 2.5195563336728646e-08 5.461160419355619\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.462e-07, 5.461, 2.731) [7.754e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461160399918815 5.461160423699676 5.461160419355619\n", - " angles : 89.99999947132143 89.99999978148678 89.99999793005053\n", - " volume : 162.87513948128523\n", - " A : 5.461160399918814 9.864885549661574e-08 1.0413821359219753e-08\n", - " B : 9.864885497372178e-08 5.461160423699675 2.5195556006551843e-08\n", - " C : 1.0413816829610393e-08 2.5195563336728646e-08 5.461160419355619\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.462e-07, 5.461, 2.731) [7.754e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 5.461) [1.0, 0.5, 1.0], molecule=None, energy=-42.5106201171875, forces=[[5.8140140026807785e-06, -1.7816200852394104e-06, 1.3863900676369667e-06], [4.470348358154297e-07, 1.6987323760986328e-06, -1.7136335372924805e-06], [-1.773238182067871e-06, 2.5331974029541016e-07, -1.773238182067871e-06], [1.0132789611816406e-06, 3.757188096642494e-06, 5.695503205060959e-06], [3.5762786865234375e-07, -1.999898813664913e-06, -2.9382063075900078e-06], [-3.4570693969726562e-06, -5.081295967102051e-06, 1.296401023864746e-06], [2.130866050720215e-06, -1.0281801223754883e-06, -9.5367431640625e-07], [-4.564528353512287e-06, 4.155328497290611e-06, -1.0012881830334663e-06]], stress=((-0.029807079900392993, -4.016861964227014e-06, -0.00011076618176031934), (-4.016861964227014e-06, -0.029893507635518547, -1.169826816380849e-05), (-0.00011076618176031934, -1.169826816380849e-05, -0.029810341049422043)), magmoms=[0.0035507604479789734, 0.003550708293914795, 0.003550790250301361, 0.003550805151462555, 0.003550805151462555, 0.0035508498549461365, 0.0035508200526237488, 0.0035509318113327026]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.4611427329375655 5.461142754765129 5.46114275067071\n", - " angles : 89.9999996524258 90.00000009386827 89.9999978561778\n", - " volume : 162.87355866271415\n", - " A : 5.461142732937565 1.0216912273015565e-07 -4.473521155668069e-09\n", - " B : 1.0216912234775394e-07 5.461142754765128 1.6564500370213464e-08\n", - " C : -4.473526053272148e-09 1.6564507249716458e-08 5.46114275067071\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.378e-07, 5.461, 2.731) [6.932e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 7.533e-09) [1.0, 0.5, 6.82e-10], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.4611427329375655 5.461142754765129 5.46114275067071\n", - " angles : 89.9999996524258 90.00000009386827 89.9999978561778\n", - " volume : 162.87355866271415\n", - " A : 5.461142732937565 1.0216912273015565e-07 -4.473521155668069e-09\n", - " B : 1.0216912234775394e-07 5.461142754765128 1.6564500370213464e-08\n", - " C : -4.473526053272148e-09 1.6564507249716458e-08 5.46114275067071\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.378e-07, 5.461, 2.731) [6.932e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 7.533e-09) [1.0, 0.5, 6.82e-10], molecule=None, energy=-42.510623931884766, forces=[[7.553026080131531e-07, 2.9837246984243393e-06, -3.895256668329239e-06], [-3.471970558166504e-06, -1.817941665649414e-06, 6.258487701416016e-07], [2.8461217880249023e-06, 4.276633262634277e-06, -2.2351741790771484e-06], [-6.5267086029052734e-06, -7.3552364483475685e-06, 2.075568772852421e-06], [3.2782554626464844e-06, -1.448439434170723e-06, 1.657404936850071e-06], [-2.1010637283325195e-06, 1.9669532775878906e-06, 2.8014183044433594e-06], [3.3229589462280273e-06, 6.064772605895996e-06, 1.2367963790893555e-06], [1.8702121451497078e-06, -4.6781497076153755e-06, -2.2101448848843575e-06]], stress=((-0.02260887737684368, 2.9226666706303525e-05, 3.884264726086229e-06), (2.9226666706303525e-05, -0.022606295269569563, -5.85888460603077e-06), (3.884264726086229e-06, -5.85888460603077e-06, -0.022651098910854723)), magmoms=[0.0035520344972610474, 0.003551982343196869, 0.003551870584487915, 0.0035518333315849304, 0.003551870584487915, 0.0035520270466804504, 0.003551796078681946, 0.0035518333315849304]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461122093976055 5.4611221142122695 5.4611221042780675\n", - " angles : 89.99999984920909 90.00000039340502 89.99999769939191\n", - " volume : 162.87171178648435\n", - " A : 5.461122093976054 1.0964072588137889e-07 -1.8748611005076222e-08\n", - " B : 1.0964072548197638e-07 5.461122114212269 7.186281912030892e-09\n", - " C : -1.874861600305571e-08 7.186288712284396e-09 5.4611221042780675\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.008e-07, 5.461, 2.731) [1.037e-10, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 2.408e-08) [1.0, 0.5, 7.184e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461122093976055 5.4611221142122695 5.4611221042780675\n", - " angles : 89.99999984920909 90.00000039340502 89.99999769939191\n", - " volume : 162.87171178648435\n", - " A : 5.461122093976054 1.0964072588137889e-07 -1.8748611005076222e-08\n", - " B : 1.0964072548197638e-07 5.461122114212269 7.186281912030892e-09\n", - " C : -1.874861600305571e-08 7.186288712284396e-09 5.4611221042780675\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.008e-07, 5.461, 2.731) [1.037e-10, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 2.408e-08) [1.0, 0.5, 7.184e-09], molecule=None, energy=-42.510616302490234, forces=[[-4.374305717647076e-06, 4.087341949343681e-06, -3.5373959690332413e-06], [6.556510925292969e-07, 4.395842552185059e-06, 6.794929504394531e-06], [4.5746564865112305e-06, 2.950429916381836e-06, 5.498528480529785e-06], [-6.705522537231445e-06, -4.611210897564888e-06, -2.5401823222637177e-06], [-1.2218952178955078e-06, 1.6944250091910362e-06, -6.6409120336174965e-06], [4.857778549194336e-06, -3.2782554626464844e-07, 5.230307579040527e-06], [3.471970558166504e-06, 2.294778823852539e-06, -4.157423973083496e-06], [-1.3014068827033043e-06, -1.0534538887441158e-05, -7.359776645898819e-07]], stress=((-0.013810065606255457, 2.706261101958237e-05, 3.625324559289261e-05), (2.706261101958237e-05, -0.013857480498237693, -3.057314692169407e-05), (3.625324559289261e-05, -3.057314692169407e-05, -0.013846770290300466)), magmoms=[0.00355326384305954, 0.003553353250026703, 0.00355326384305954, 0.003553256392478943, 0.0035532936453819275, 0.00355326384305954, 0.003553323447704315, 0.0035533085465431213]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461098508629033 5.46109851683837 5.461098503450231\n", - " angles : 90.00000018637719 90.00000052401924 89.99999741826058\n", - " volume : 162.86960075733614\n", - " A : 5.461098508629032 1.2303814989816627e-07 -2.4973221337747587e-08\n", - " B : 1.2303814799020187e-07 5.461098516838369 -8.882194731177955e-09\n", - " C : -2.4973226292142872e-08 -8.882188231152292e-09 5.461098503450231\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.97e-07, 5.461, 2.731) [1.583e-08, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 2.652e-08) [1.0, 0.5, 1.024e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461098508629033 5.46109851683837 5.461098503450231\n", - " angles : 90.00000018637719 90.00000052401924 89.99999741826058\n", - " volume : 162.86960075733614\n", - " A : 5.461098508629032 1.2303814989816627e-07 -2.4973221337747587e-08\n", - " B : 1.2303814799020187e-07 5.461098516838369 -8.882194731177955e-09\n", - " C : -2.4973226292142872e-08 -8.882188231152292e-09 5.461098503450231\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.97e-07, 5.461, 2.731) [1.583e-08, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 2.652e-08) [1.0, 0.5, 1.024e-08], molecule=None, energy=-42.5106315612793, forces=[[8.391216397285461e-07, 1.8442515283823013e-06, -6.439397111535072e-06], [1.7434358596801758e-06, 1.385807991027832e-06, -5.513429641723633e-07], [2.428889274597168e-06, -2.86102294921875e-06, 2.4586915969848633e-06], [1.2516975402832031e-06, -1.7809215933084488e-06, 1.2188684195280075e-06], [-3.2782554626464844e-06, -1.7450656741857529e-06, 7.232301868498325e-06], [4.76837158203125e-07, 7.718801498413086e-06, 3.427267074584961e-07], [-5.260109901428223e-06, -3.5315752029418945e-06, -3.725290298461914e-06], [1.6704434528946877e-06, -1.035747118294239e-06, -5.908077582716942e-07]], stress=((-0.003521054082213591, 3.865828380423877e-05, -7.456690557461041e-06), (3.865828380423877e-05, -0.0035596825964666436, -3.648704756516174e-05), (-7.456690557461041e-06, -3.648704756516174e-05, -0.0035460034023155253)), magmoms=[0.003554694354534149, 0.0035550743341445923, 0.0035549774765968323, 0.0035548135638237, 0.0035547390580177307, 0.0035548657178878784, 0.0035547614097595215, 0.003554992377758026]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461072353836295 5.461072320950066 5.461072314429381\n", - " angles : 90.00000109933951 90.00000077208603 89.99999652680019\n", - " volume : 162.86725843215243\n", - " A : 5.461072353836292 1.655217521572873e-07 -3.6795181681017224e-08\n", - " B : 1.655217495388283e-07 5.461072320950064 -5.239105636022872e-08\n", - " C : -3.679518749549763e-08 -5.2391050313564277e-08 5.461072314429381\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.861e-07, 2.789e-08) [0.5, 1.893e-08, 8.476e-09]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.949e-07, 2.711e-07, 2.731) [3.906e-08, 5.445e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.197e-07, 2.731, -4.795e-09) [6.77e-09, 0.5, 3.919e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461072353836295 5.461072320950066 5.461072314429381\n", - " angles : 90.00000109933951 90.00000077208603 89.99999652680019\n", - " volume : 162.86725843215243\n", - " A : 5.461072353836292 1.655217521572873e-07 -3.6795181681017224e-08\n", - " B : 1.655217495388283e-07 5.461072320950064 -5.239105636022872e-08\n", - " C : -3.679518749549763e-08 -5.2391050313564277e-08 5.461072314429381\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.861e-07, 2.789e-08) [0.5, 1.893e-08, 8.476e-09]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.949e-07, 2.711e-07, 2.731) [3.906e-08, 5.445e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.197e-07, 2.731, -4.795e-09) [6.77e-09, 0.5, 3.919e-09], molecule=None, energy=-42.510623931884766, forces=[[2.216198481619358e-06, 1.0422663763165474e-06, 5.857553333044052e-06], [2.0265579223632812e-06, -6.258487701416016e-06, -4.1425228118896484e-06], [-2.1010637283325195e-06, -4.306435585021973e-06, -5.900859832763672e-06], [2.950429916381836e-06, -1.6880221664905548e-08, 9.423820301890373e-07], [8.046627044677734e-07, 3.021443262696266e-06, 1.5040859580039978e-07], [-4.127621650695801e-06, -2.1010637283325195e-06, -3.4123659133911133e-06], [-4.887580871582031e-06, 5.0514936447143555e-06, 1.9073486328125e-06], [3.0692899599671364e-06, 3.5892007872462273e-06, 4.5620836317539215e-06]], stress=((0.007720371789965869, -5.063595742056782e-05, 4.431740717981195e-05), (-5.063595742056782e-05, 0.00771908073632881, -4.494655789915169e-05), (4.431740717981195e-05, -4.494655789915169e-05, 0.007729216818833756)), magmoms=[0.00355684757232666, 0.0035567954182624817, 0.003556765615940094, 0.0035566389560699463, 0.0035565942525863647, 0.0035566985607147217, 0.003556780517101288, 0.0035566315054893494]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461072504731273 5.461072471819815 5.461072465497233\n", - " angles : 90.000001117773 90.00000075391053 89.99999654756704\n", - " volume : 162.86727193711965\n", - " A : 5.461072504731271 1.64532074551857e-07 -3.5928996373699074e-08\n", - " B : 1.6453207035417356e-07 5.461072471819812 -5.326953977275775e-08\n", - " C : -3.5929001422602334e-08 -5.3269533679068385e-08 5.461072465497233\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.716e-07, 1.902e-08) [0.5, 1.636e-08, 6.772e-09]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.861e-07, 2.66e-07, 2.731) [3.737e-08, 5.358e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.261e-07, 2.731, 5.016e-09) [8.033e-09, 0.5, 5.796e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461072504731273 5.461072471819815 5.461072465497233\n", - " angles : 90.000001117773 90.00000075391053 89.99999654756704\n", - " volume : 162.86727193711965\n", - " A : 5.461072504731271 1.64532074551857e-07 -3.5928996373699074e-08\n", - " B : 1.6453207035417356e-07 5.461072471819812 -5.326953977275775e-08\n", - " C : -3.5929001422602334e-08 -5.3269533679068385e-08 5.461072465497233\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.716e-07, 1.902e-08) [0.5, 1.636e-08, 6.772e-09]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.861e-07, 2.66e-07, 2.731) [3.737e-08, 5.358e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.261e-07, 2.731, 5.016e-09) [8.033e-09, 0.5, 5.796e-09], molecule=None, energy=-42.510623931884766, forces=[[2.1421583369374275e-06, 5.495967343449593e-07, 8.731149137020111e-06], [1.6391277313232422e-06, 1.475214958190918e-06, -1.6391277313232422e-06], [-1.087784767150879e-06, -6.8247318267822266e-06, -9.5367431640625e-07], [6.556510925292969e-07, 1.3064127415418625e-06, -3.967666998505592e-06], [-3.635883331298828e-06, 9.188661351799965e-07, -1.3564713299274445e-06], [-3.4123659133911133e-06, -4.708766937255859e-06, -3.5762786865234375e-06], [1.519918441772461e-06, 1.8477439880371094e-06, 2.1904706954956055e-06], [2.191518433392048e-06, 5.441135726869106e-06, 5.653128027915955e-07]], stress=((0.00786066385657436, 3.5472668607739705e-05, -6.0340375741240905e-05), (3.5472668607739705e-05, 0.007835740401260302, -7.51322424434058e-06), (-6.0340375741240905e-05, -7.51322424434058e-06, 0.007858461342384276)), magmoms=[0.003556877374649048, 0.003556773066520691, 0.003556683659553528, 0.003556564450263977, 0.0035566166043281555, 0.0035567134618759155, 0.0035567134618759155, 0.003556676208972931]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461072809299337 5.461072775830079 5.461072770173597\n", - " angles : 90.00000113774783 90.0000007647358 89.99999655027692\n", - " volume : 162.8672991734013\n", - " A : 5.461072809299334 1.644029391116393e-07 -3.644489577808783e-08\n", - " B : 1.64402935100181e-07 5.461072775830076 -5.422148039732002e-08\n", - " C : -3.644490170004028e-08 -5.422147381659062e-08 5.461072770173597\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.625e-07, 6.336e-09) [0.5, 1.471e-08, 4.497e-09]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.691e-07, 2.496e-07, 2.731) [3.43e-08, 5.068e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.377e-07, 2.731, 1.516e-08) [1.016e-08, 0.5, 7.741e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461072809299337 5.461072775830079 5.461072770173597\n", - " angles : 90.00000113774783 90.0000007647358 89.99999655027692\n", - " volume : 162.8672991734013\n", - " A : 5.461072809299334 1.644029391116393e-07 -3.644489577808783e-08\n", - " B : 1.64402935100181e-07 5.461072775830076 -5.422148039732002e-08\n", - " C : -3.644490170004028e-08 -5.422147381659062e-08 5.461072770173597\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.625e-07, 6.336e-09) [0.5, 1.471e-08, 4.497e-09]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.691e-07, 2.496e-07, 2.731) [3.43e-08, 5.068e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.377e-07, 2.731, 1.516e-08) [1.016e-08, 0.5, 7.741e-09], molecule=None, energy=-42.510623931884766, forces=[[-9.866198524832726e-07, 2.8437934815883636e-06, 7.41099938750267e-06], [5.856156349182129e-06, -3.904104232788086e-06, -3.6209821701049805e-06], [-3.769993782043457e-06, -2.8312206268310547e-07, -1.8775463104248047e-06], [-5.364418029785156e-07, 4.246830940246582e-07, -1.8046703189611435e-06], [-1.9669532775878906e-06, 8.856877684593201e-07, -5.648122169077396e-06], [-9.685754776000977e-07, -5.692243576049805e-06, -4.678964614868164e-06], [1.0281801223754883e-06, 4.857778549194336e-06, 9.655952453613281e-06], [1.2909295037388802e-06, 8.650822564959526e-07, 5.087349563837051e-07]], stress=((0.007610111292130477, 2.314881580945153e-05, 8.882987037875278e-05), (2.314881580945153e-05, 0.007559229789987335, -0.00014633379417566583), (8.882987037875278e-05, -0.00014633379417566583, 0.00758619255632291)), magmoms=[0.003556780517101288, 0.0035566911101341248, 0.0035567507147789, 0.0035567283630371094, 0.0035568922758102417, 0.0035566166043281555, 0.0035567283630371094, 0.0035566240549087524]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461073262606535 5.461073227437163 5.461073223015046\n", - " angles : 90.00000122802824 90.000000730588 89.99999654127818\n", - " volume : 162.8673396661829\n", - " A : 5.461073262606532 1.6483180360778683e-07 -3.481752171260834e-08\n", - " B : 1.648318007689539e-07 5.4610732274371605 -5.852396543724562e-08\n", - " C : -3.4817527655476134e-08 -5.85239585465608e-08 5.461073223015046\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.44e-07, -1.383e-08) [0.5, 1.128e-08, 6.547e-10]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.522e-07, 2.178e-07, 2.731) [3.105e-08, 4.524e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.518e-07, 2.731, 2.395e-08) [1.271e-08, 0.5, 9.743e-09], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461073262606535 5.461073227437163 5.461073223015046\n", - " angles : 90.00000122802824 90.000000730588 89.99999654127818\n", - " volume : 162.8673396661829\n", - " A : 5.461073262606532 1.6483180360778683e-07 -3.481752171260834e-08\n", - " B : 1.648318007689539e-07 5.4610732274371605 -5.852396543724562e-08\n", - " C : -3.4817527655476134e-08 -5.85239585465608e-08 5.461073223015046\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 1.44e-07, -1.383e-08) [0.5, 1.128e-08, 6.547e-10]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.522e-07, 2.178e-07, 2.731) [3.105e-08, 4.524e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.518e-07, 2.731, 2.395e-08) [1.271e-08, 0.5, 9.743e-09], molecule=None, energy=-42.510623931884766, forces=[[2.135639078915119e-06, 2.755783498287201e-06, 5.1120296120643616e-06], [1.9222497940063477e-06, -2.2351741790771484e-07, 2.980232238769531e-07], [8.642673492431641e-07, -8.344650268554688e-07, 3.0100345611572266e-06], [-1.6093254089355469e-06, 3.0782539397478104e-06, -1.8621794879436493e-06], [5.662441253662109e-07, 3.0992086976766586e-06, -7.763621397316456e-06], [-1.8775463104248047e-06, -4.738569259643555e-06, -5.02169132232666e-06], [6.690621376037598e-06, -3.978610038757324e-06, 6.794929504394531e-06], [-8.751754648983479e-06, 9.032664820551872e-07, -6.211921572685242e-07]], stress=((0.007313121597544613, -4.39835442246939e-06, 3.812843417576261e-05), (-4.39835442246939e-06, 0.007298429669445689, 0.00012850438306472248), (3.812843417576261e-05, 0.00012850438306472248, 0.007229176153469546)), magmoms=[0.003556661307811737, 0.003556586802005768, 0.0035566985607147217, 0.0035566315054893494, 0.0035567507147789, 0.0035566017031669617, 0.003556586802005768, 0.0035567209124565125]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461073858829885 5.46107382175221 5.461073816658384\n", - " angles : 90.0000012398713 90.00000067926057 89.99999653555477\n", - " volume : 162.86739287641313\n", - " A : 5.461073858829883 1.651045819600422e-07 -3.2371421276113277e-08\n", - " B : 1.6510457808620514e-07 5.4610738217522075 -5.9088374349405603e-08\n", - " C : -3.2371427235382985e-08 -5.90883682549339e-08 5.461073816658384\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 6.761e-08, 5.461) [0.5, 8.085e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.319e-07, 1.768e-07, 2.731) [2.712e-08, 3.778e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.386e-07, 2.731, 3.167e-08) [1.027e-08, 0.5, 1.121e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461073858829885 5.46107382175221 5.461073816658384\n", - " angles : 90.0000012398713 90.00000067926057 89.99999653555477\n", - " volume : 162.86739287641313\n", - " A : 5.461073858829883 1.651045819600422e-07 -3.2371421276113277e-08\n", - " B : 1.6510457808620514e-07 5.4610738217522075 -5.9088374349405603e-08\n", - " C : -3.2371427235382985e-08 -5.90883682549339e-08 5.461073816658384\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 6.761e-08, 5.461) [0.5, 8.085e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.319e-07, 1.768e-07, 2.731) [2.712e-08, 3.778e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.386e-07, 2.731, 3.167e-08) [1.027e-08, 0.5, 1.121e-08], molecule=None, energy=-42.51062774658203, forces=[[3.2690586522221565e-06, 9.017530828714371e-07, 4.535773769021034e-06], [1.475214958190918e-06, 2.5480985641479492e-06, 2.637505531311035e-06], [-3.6209821701049805e-06, 2.637505531311035e-06, -1.341104507446289e-07], [-3.069639205932617e-06, 2.0512379705905914e-07, -1.6996636986732483e-07], [2.205371856689453e-06, -8.736969903111458e-06, -4.650093615055084e-06], [-5.170702934265137e-06, -3.591179847717285e-06, -5.0514936447143555e-06], [7.644295692443848e-06, 6.258487701416016e-07, 5.736947059631348e-06], [-2.6695197448134422e-06, 5.388516001403332e-06, -2.934597432613373e-06]], stress=((0.007049292018186438, 3.8609460032671186e-05, -0.00011602479367698142), (3.8609460032671186e-05, 0.00704460939870259, 3.587767680574009e-05), (-0.00011602479367698142, 3.587767680574009e-05, 0.007024771470694779)), magmoms=[0.0035566091537475586, 0.00355646014213562, 0.0035566091537475586, 0.003556661307811737, 0.0035565942525863647, 0.0035565942525863647, 0.0035566315054893494, 0.0035564973950386047]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.46107459249961 5.461074553573555 5.46107454732061\n", - " angles : 90.000001229484 90.0000007011267 89.99999650775517\n", - " volume : 162.86745837302067\n", - " A : 5.461074592499608 1.6642944584831288e-07 -3.341349693938585e-08\n", - " B : 1.6642944204897338e-07 5.461074553573552 -5.85933560350574e-08\n", - " C : -3.3413502190717334e-08 -5.8593349989857726e-08 5.46107454732061\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 6.123e-08, 5.461) [0.5, 6.704e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (9.54e-08, 1.288e-07, 2.731) [2.053e-08, 2.895e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.187e-07, 2.731, 2.969e-08) [6.5e-09, 0.5, 1.08e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.46107459249961 5.461074553573555 5.46107454732061\n", - " angles : 90.000001229484 90.0000007011267 89.99999650775517\n", - " volume : 162.86745837302067\n", - " A : 5.461074592499608 1.6642944584831288e-07 -3.341349693938585e-08\n", - " B : 1.6642944204897338e-07 5.461074553573552 -5.85933560350574e-08\n", - " C : -3.3413502190717334e-08 -5.8593349989857726e-08 5.46107454732061\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 6.123e-08, 5.461) [0.5, 6.704e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (9.54e-08, 1.288e-07, 2.731) [2.053e-08, 2.895e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.187e-07, 2.731, 2.969e-08) [6.5e-09, 0.5, 1.08e-08], molecule=None, energy=-42.5106315612793, forces=[[-5.3399708122015e-07, -5.39771281182766e-06, 1.292908564209938e-06], [-7.897615432739258e-07, -4.276633262634277e-06, -4.3213367462158203e-07], [1.6093254089355469e-06, -5.960464477539063e-08, -4.500150680541992e-06], [3.337860107421875e-06, -9.243376553058624e-08, -5.7318247854709625e-06], [-3.2782554626464844e-07, 3.1249364838004112e-06, -2.153683453798294e-08], [-3.904104232788086e-06, -5.960464477539063e-08, 2.518296241760254e-06], [7.450580596923828e-07, 6.377696990966797e-06, 1.862645149230957e-06], [-5.809124559164047e-08, 3.530876711010933e-07, 4.928908310830593e-06]], stress=((0.006490181277413337, -3.398009560062038e-05, 3.4848603757230708e-06), (-3.398009560062038e-05, 0.006599179865034629, 6.528223685839681e-07), (3.4848603757230708e-06, 6.528223685839681e-07, 0.006609414306592059)), magmoms=[0.0035564079880714417, 0.0035564228892326355, 0.0035564005374908447, 0.003556475043296814, 0.0035564079880714417, 0.003556475043296814, 0.003556489944458008, 0.0035564973950386047]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.46107545188849 5.461075414642414 5.4610754076602035\n", - " angles : 90.00000121971524 90.00000071856319 89.99999650460721\n", - " volume : 162.86753534101723\n", - " A : 5.461075451888488 1.6657949400183863e-07 -3.42444719462704e-08\n", - " B : 1.6657948968097433e-07 5.461075414642411 -5.812781757694581e-08\n", - " C : -3.424447645126855e-08 -5.812781076413818e-08 5.4610754076602035\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.991e-08, 5.461) [0.5, 2.7e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (4.885e-08, 8.542e-08, 2.731) [1.208e-08, 2.096e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.001e-07, 2.731, 4.53e-08) [3.069e-09, 0.5, 1.362e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.46107545188849 5.461075414642414 5.4610754076602035\n", - " angles : 90.00000121971524 90.00000071856319 89.99999650460721\n", - " volume : 162.86753534101723\n", - " A : 5.461075451888488 1.6657949400183863e-07 -3.42444719462704e-08\n", - " B : 1.6657948968097433e-07 5.461075414642411 -5.812781757694581e-08\n", - " C : -3.424447645126855e-08 -5.812781076413818e-08 5.4610754076602035\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 3.991e-08, 5.461) [0.5, 2.7e-09, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (4.885e-08, 8.542e-08, 2.731) [1.208e-08, 2.096e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.001e-07, 2.731, 4.53e-08) [3.069e-09, 0.5, 1.362e-08], molecule=None, energy=-42.5106315612793, forces=[[-9.903451427817345e-07, -3.4458935260772705e-07, 1.0466203093528748e-05], [3.069639205932617e-06, -9.08970832824707e-07, -1.8030405044555664e-06], [4.470348358154297e-08, -2.115964889526367e-06, -1.9818544387817383e-06], [6.22868537902832e-06, 2.41247471421957e-06, -5.90132549405098e-06], [-5.27501106262207e-06, 2.3578759282827377e-06, -7.257331162691116e-07], [-3.039836883544922e-06, -3.5762786865234375e-06, -1.9371509552001953e-07], [-1.5348196029663086e-06, 1.5348196029663086e-06, 2.205371856689453e-06], [1.387670636177063e-06, 7.71600753068924e-07, -1.9960571080446243e-06]], stress=((0.00646037265582587, 6.62876034826425e-05, 1.8956245544083858e-05), (6.62876034826425e-05, 0.0064557672664128625, -4.1948163580824015e-05), (1.8956245544083858e-05, -4.1948163580824015e-05, 0.006449379028100372)), magmoms=[0.0035566389560699463, 0.003556661307811737, 0.0035566017031669617, 0.0035565271973609924, 0.0035566911101341248, 0.0035565346479415894, 0.0035566911101341248, 0.003556683659553528]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.46107643731639 5.461076401431189 5.461076393583022\n", - " angles : 90.00000123980483 90.00000072120457 89.99999645613485\n", - " volume : 162.86762356264722\n", - " A : 5.461076437316387 1.6888956635243205e-07 -3.43703583159842e-08\n", - " B : 1.6888956155978297e-07 5.461076401431186 -5.9085235861654385e-08\n", - " C : -3.4370361621936643e-08 -5.908522920875272e-08 5.461076393583022\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (-4.205e-09, 3.221e-08, 2.731) [2.377e-09, 1.131e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (8.958e-08, 2.731, 5.113e-08) [9.401e-10, 0.5, 1.477e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.46107643731639 5.461076401431189 5.461076393583022\n", - " angles : 90.00000123980483 90.00000072120457 89.99999645613485\n", - " volume : 162.86762356264722\n", - " A : 5.461076437316387 1.6888956635243205e-07 -3.43703583159842e-08\n", - " B : 1.6888956155978297e-07 5.461076401431186 -5.9085235861654385e-08\n", - " C : -3.4370361621936643e-08 -5.908522920875272e-08 5.461076393583022\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (-4.205e-09, 3.221e-08, 2.731) [2.377e-09, 1.131e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (8.958e-08, 2.731, 5.113e-08) [9.401e-10, 0.5, 1.477e-08], molecule=None, energy=-42.51062774658203, forces=[[1.3888347893953323e-07, -5.344743840396404e-06, 2.4243490770459175e-06], [-3.4421682357788086e-06, -7.048249244689941e-06, -3.3229589462280273e-06], [1.6987323760986328e-06, 5.200505256652832e-06, -1.6391277313232422e-06], [1.6391277313232422e-06, 2.9212096706032753e-06, -4.3527688831090927e-07], [3.516674041748047e-06, 3.67150641977787e-06, -1.7487909644842148e-06], [1.3560056686401367e-06, -2.339482307434082e-06, 5.960464477539063e-08], [-3.993511199951172e-06, 6.899237632751465e-06, 8.940696716308594e-08], [-8.434290066361427e-07, -3.897584974765778e-06, 4.610163159668446e-06]], stress=((0.006117307209542711, -5.185177321296569e-05, 0.000116928410550937), (-5.185177321296569e-05, 0.006104160611446158, -2.1324142967382397e-05), (0.000116928410550937, -2.1324142967382397e-05, 0.006085383504034035)), magmoms=[0.003556549549102783, 0.00355665385723114, 0.003556475043296814, 0.003556467592716217, 0.003556668758392334, 0.0035565122961997986, 0.003556661307811737, 0.0035565942525863647]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.46107766571608 5.461077630634785 5.461077621153285\n", - " angles : 90.00000127818657 90.00000062241544 89.99999645311453\n", - " volume : 162.867733466913\n", - " A : 5.461077665716077 1.6903354273464786e-07 -2.9662383125644898e-08\n", - " B : 1.6903353774417243e-07 5.461077630634782 -6.09144031466271e-08\n", - " C : -2.9662386885720438e-08 -6.0914396069024e-08 5.461077621153285\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 4.87e-08) [1.0, 0.5, 1.993e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.46107766571608 5.461077630634785 5.461077621153285\n", - " angles : 90.00000127818657 90.00000062241544 89.99999645311453\n", - " volume : 162.867733466913\n", - " A : 5.461077665716077 1.6903354273464786e-07 -2.9662383125644898e-08\n", - " B : 1.6903353774417243e-07 5.461077630634782 -6.09144031466271e-08\n", - " C : -2.9662386885720438e-08 -6.0914396069024e-08 5.461077621153285\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 4.87e-08) [1.0, 0.5, 1.993e-08], molecule=None, energy=-42.51062774658203, forces=[[-3.330758772790432e-06, -1.0757939890027046e-06, -4.004454240202904e-06], [3.2782554626464844e-07, -5.498528480529785e-06, -4.500150680541992e-06], [-3.6954879760742188e-06, -9.98377799987793e-07, -2.175569534301758e-06], [1.7881393432617188e-07, -2.3993197828531265e-06, -1.1688098311424255e-07], [1.8477439880371094e-06, 7.4338167905807495e-06, 4.159519448876381e-07], [2.950429916381836e-06, -9.834766387939453e-07, 4.5746564865112305e-06], [4.470348358154297e-07, 3.1888484954833984e-06, 1.6093254089355469e-06], [1.2828968465328217e-06, 3.685709089040756e-07, 4.215282388031483e-06]], stress=((0.00577001450975943, -9.797479189937175e-05, 0.00012137281670454804), (-9.797479189937175e-05, 0.005738909734435886, -0.0001717809772917571), (0.00012137281670454804, -0.0001717809772917571, 0.005735138575505064)), magmoms=[0.0035564079880714417, 0.00355636328458786, 0.0035564303398132324, 0.0035564452409744263, 0.0035564005374908447, 0.0035564079880714417, 0.0035564303398132324, 0.003556355834007263]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461079181883216 5.461079145988462 5.461079134692925\n", - " angles : 90.0000015028249 90.0000003926635 89.99999655654693\n", - " volume : 162.86786901596554\n", - " A : 5.461079181883213 1.6410432054814516e-07 -1.8713124648325748e-08\n", - " B : 1.6410431736258072e-07 5.461079145988459 -7.16199883657334e-08\n", - " C : -1.8713128298589594e-08 -7.161998087576397e-08 5.461079134692924\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 1.073e-07) [1.0, 0.5, 2.963e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461079181883216 5.461079145988462 5.461079134692925\n", - " angles : 90.0000015028249 90.0000003926635 89.99999655654693\n", - " volume : 162.86786901596554\n", - " A : 5.461079181883213 1.6410432054814516e-07 -1.8713124648325748e-08\n", - " B : 1.6410431736258072e-07 5.461079145988459 -7.16199883657334e-08\n", - " C : -1.8713128298589594e-08 -7.161998087576397e-08 5.461079134692924\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (5.461, 2.731, 1.073e-07) [1.0, 0.5, 2.963e-08], molecule=None, energy=-42.51062774658203, forces=[[9.499490261077881e-08, 1.6575213521718979e-06, -3.736116923391819e-06], [1.6391277313232422e-06, 2.4884939193725586e-06, 5.647540092468262e-06], [2.86102294921875e-06, -6.5267086029052734e-06, -5.811452865600586e-07], [-4.470348358154297e-06, 3.6973506212234497e-06, 1.1962838470935822e-06], [-5.304813385009766e-06, -3.8298312574625015e-06, -2.5865156203508377e-06], [1.8775463104248047e-06, 2.175569534301758e-06, 1.9371509552001953e-06], [-1.2367963790893555e-06, -1.6540288925170898e-06, -2.9355287551879883e-06], [4.561734385788441e-06, 1.950305886566639e-06, 1.0780058801174164e-06]], stress=((0.004825925344682702, -8.256181453351728e-05, -5.919289899885066e-05), (-8.256181453351728e-05, 0.004821707198502254, -4.488505503097474e-05), (-5.919289899885066e-05, -4.488505503097474e-05, 0.004842459501405386)), magmoms=[0.0035563111305236816, 0.0035564452409744263, 0.0035563409328460693, 0.003556184470653534, 0.003556258976459503, 0.003556258976459503, 0.003556288778781891, 0.0035563111305236816]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.4610810150652105 5.4610809780759055 5.461080966394647\n", - " angles : 90.00000178988903 90.00000024934519 89.99999677722519\n", - " volume : 162.86803295431068\n", - " A : 5.461081015065208 1.5358752950077554e-07 -1.1883020975435545e-08\n", - " B : 1.535875261875477e-07 5.461080978075903 -8.530060512375453e-08\n", - " C : -1.1883025350013755e-08 -8.530059729786885e-08 5.461080966394646\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (8.671e-08, 2.731, 1.802e-07) [1.817e-09, 0.5, 4.08e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.4610810150652105 5.4610809780759055 5.461080966394647\n", - " angles : 90.00000178988903 90.00000024934519 89.99999677722519\n", - " volume : 162.86803295431068\n", - " A : 5.461081015065208 1.5358752950077554e-07 -1.1883020975435545e-08\n", - " B : 1.535875261875477e-07 5.461080978075903 -8.530060512375453e-08\n", - " C : -1.1883025350013755e-08 -8.530059729786885e-08 5.461080966394646\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (8.671e-08, 2.731, 1.802e-07) [1.817e-09, 0.5, 4.08e-08], molecule=None, energy=-42.51063537597656, forces=[[-1.416192390024662e-06, 2.291635610163212e-06, -4.6496279537677765e-07], [1.2218952178955078e-06, -2.2351741790771484e-06, -0.0], [1.6093254089355469e-06, 5.230307579040527e-06, 5.170702934265137e-06], [1.2516975402832031e-06, -4.163011908531189e-07, 2.6579946279525757e-06], [5.066394805908203e-07, -1.3989629223942757e-06, 3.282679244875908e-06], [2.250075340270996e-06, -1.1920928955078125e-06, -3.0249357223510742e-06], [-5.379319190979004e-06, 7.748603820800781e-07, -1.862645149230957e-06], [-7.05476850271225e-08, -3.002234734594822e-06, -5.7693105190992355e-06]], stress=((0.00391877874684088, 7.858113602150805e-05, 0.00011425933975807712), (7.858113602150805e-05, 0.003928796434190057, -0.0001336751434989743), (0.00011425933975807712, -0.0001336751434989743, 0.00391451506406206)), magmoms=[0.0035562440752983093, 0.003556162118911743, 0.0035561397671699524, 0.0035562142729759216, 0.003556191921234131, 0.0035562664270401, 0.003556370735168457, 0.0035561248660087585]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461083193252198 5.461083156089196 5.461083142722525\n", - " angles : 90.00000233079872 89.99999988836417 89.99999684605804\n", - " volume : 162.8682277766248\n", - " A : 5.461083193252196 1.5030722776890324e-07 5.320226572966955e-09\n", - " B : 1.5030722524193145e-07 5.4610831560891935 -1.110787411595417e-07\n", - " C : 5.320221107639701e-09 -1.1107873200420445e-07 5.461083142722524\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.122e-07, 2.731, 1.674e-07) [6.783e-09, 0.5, 4.083e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461083193252198 5.461083156089196 5.461083142722525\n", - " angles : 90.00000233079872 89.99999988836417 89.99999684605804\n", - " volume : 162.8682277766248\n", - " A : 5.461083193252196 1.5030722776890324e-07 5.320226572966955e-09\n", - " B : 1.5030722524193145e-07 5.4610831560891935 -1.110787411595417e-07\n", - " C : 5.320221107639701e-09 -1.1107873200420445e-07 5.461083142722524\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.122e-07, 2.731, 1.674e-07) [6.783e-09, 0.5, 4.083e-08], molecule=None, energy=-42.510623931884766, forces=[[4.812609404325485e-07, 3.203749656677246e-07, 9.98377799987793e-07], [-4.470348358154297e-08, 7.212162017822266e-06, -9.98377799987793e-07], [4.783272743225098e-06, -3.948807716369629e-06, 6.154179573059082e-06], [-4.4405460357666016e-06, -1.2035015970468521e-06, -1.5459954738616943e-06], [2.4139881134033203e-06, -2.561137080192566e-09, -1.094886101782322e-06], [1.341104507446289e-06, 5.46872615814209e-06, -7.301568984985352e-07], [-2.995133399963379e-06, -9.387731552124023e-07, -8.493661880493164e-07], [-1.4925608411431313e-06, -6.936141289770603e-06, -1.9042054191231728e-06]], stress=((0.0030066367841575883, 6.619616599311039e-05, -7.958644038868564e-06), (6.619616599311039e-05, 0.003092588935049738, 1.1645501207425582e-05), (-7.958644038868564e-06, 1.1645501207425582e-05, 0.0030898924398405085)), magmoms=[0.0035559311509132385, 0.003556087613105774, 0.0035561323165893555, 0.003555901348590851, 0.0035560503602027893, 0.00355587899684906, 0.0035560578107833862, 0.0035560280084609985]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461085731324427 5.461085704629708 5.461085689253329\n", - " angles : 90.00000283839184 89.99999955010288 89.99999674253932\n", - " volume : 162.86845542324778\n", - " A : 5.461085731324426 1.5524068602473816e-07 2.1440735610673563e-08\n", - " B : 1.5524068254528685e-07 5.4610857046297046 -1.352691400398399e-07\n", - " C : 2.1440728746395132e-08 -1.3526913089706225e-07 5.461085689253327\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.204e-07, 2.731, 1.283e-07) [7.832e-09, 0.5, 3.589e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461085731324427 5.461085704629708 5.461085689253329\n", - " angles : 90.00000283839184 89.99999955010288 89.99999674253932\n", - " volume : 162.86845542324778\n", - " A : 5.461085731324426 1.5524068602473816e-07 2.1440735610673563e-08\n", - " B : 1.5524068254528685e-07 5.4610857046297046 -1.352691400398399e-07\n", - " C : 2.1440728746395132e-08 -1.3526913089706225e-07 5.461085689253327\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.461, 5.461, 2.731) [1.0, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.204e-07, 2.731, 1.283e-07) [7.832e-09, 0.5, 3.589e-08], molecule=None, energy=-42.5106201171875, forces=[[2.1813903003931046e-06, -8.932547643780708e-07, -9.216368198394775e-06], [-1.1920928955078125e-06, 4.082918167114258e-06, 1.4007091522216797e-06], [2.5331974029541016e-07, -1.2665987014770508e-06, 2.115964889526367e-06], [-5.125999450683594e-06, 2.6281923055648804e-06, 7.243826985359192e-06], [1.8477439880371094e-06, -4.9782684072852135e-06, -2.93052289634943e-06], [2.1457672119140625e-06, 4.649162292480469e-06, 4.26173210144043e-06], [-8.344650268554688e-07, -5.841255187988281e-06, -4.693865776062012e-06], [7.119961082935333e-07, 1.6695121303200722e-06, 1.7515849322080612e-06]], stress=((0.0020702744512391103, -0.00011417146550736765, -4.634062101380416e-05), (-0.00011417146550736765, 0.0020487230721077366, -3.0297659039652252e-05), (-4.634062101380416e-05, -3.0297659039652252e-05, 0.0020452098324700964)), magmoms=[0.003555692732334137, 0.0035558491945266724, 0.003555789589881897, 0.0035559386014938354, 0.0035557821393013, 0.0035559087991714478, 0.0035557523369789124, 0.003555990755558014]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.5106201171875, forces=[[-6.017507985234261e-07, -6.638234481215477e-06, -7.87666067481041e-07], [-5.4389238357543945e-06, 4.51505184173584e-06, -2.2202730178833008e-06], [-1.4603137969970703e-06, -1.9073486328125e-06, -2.8312206268310547e-07], [-2.682209014892578e-07, 1.234118826687336e-06, 1.371721737086773e-06], [8.553266525268555e-06, 5.887122824788094e-07, -1.4404067769646645e-06], [1.1026859283447266e-06, 5.02169132232666e-06, 2.5331974029541016e-06], [3.203749656677246e-06, -1.5348196029663086e-06, -2.339482307434082e-06], [-5.114474333822727e-06, -1.3050157576799393e-06, 3.1035160645842552e-06]], stress=((0.0006924792408186339, -4.4729655696953566e-05, -3.6938730776494e-05), (-4.4729655696953566e-05, 0.0006850838240233392, 2.0533694501513544e-05), (-3.6938730776494e-05, 2.0533694501513544e-05, 0.0006995213391471558)), magmoms=[0.0035556256771087646, 0.0035556256771087646, 0.0035556554794311523, 0.0035556331276893616, 0.0035555511713027954, 0.003555573523044586, 0.0035556480288505554, 0.003555774688720703]), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.5106201171875, forces=[[-6.017507985234261e-07, -6.638234481215477e-06, -7.87666067481041e-07], [-5.4389238357543945e-06, 4.51505184173584e-06, -2.2202730178833008e-06], [-1.4603137969970703e-06, -1.9073486328125e-06, -2.8312206268310547e-07], [-2.682209014892578e-07, 1.234118826687336e-06, 1.371721737086773e-06], [8.553266525268555e-06, 5.887122824788094e-07, -1.4404067769646645e-06], [1.1026859283447266e-06, 5.02169132232666e-06, 2.5331974029541016e-06], [3.203749656677246e-06, -1.5348196029663086e-06, -2.339482307434082e-06], [-5.114474333822727e-06, -1.3050157576799393e-06, 3.1035160645842552e-06]], stress=((0.0006924792408186339, -4.4729655696953566e-05, -3.6938730776494e-05), (-4.4729655696953566e-05, 0.0006850838240233392, 2.0533694501513544e-05), (-3.6938730776494e-05, 2.0533694501513544e-05, 0.0006995213391471558)), magmoms=[0.0035556256771087646, 0.0035556256771087646, 0.0035556554794311523, 0.0035556331276893616, 0.0035555511713027954, 0.003555573523044586, 0.0035556480288505554, 0.003555774688720703])], elapsed_time=6.860212261788547, n_steps=50), ase_calculator_name='MLFF.CHGNet', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-33-495106-74798', included_objects=[], objects={}, is_force_converged=True, energy_downhill=True, tags=None, forcefield_name='MLFF.CHGNet', forcefield_version='0.4.0'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-33-495106-74798'))},\n", - " '6ad6a228-babe-46ba-b542-88fe297556c2': {1: Response(output=[[1, 0, 0], [0, 1, 0], [0, 0, 1]], detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-067922-60781'))},\n", - " '561033b9-e984-443f-8cd9-2befc39a99bc': {1: Response(output=ForceFieldTaskDocument(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 42, 439200, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=162.86871531495993, density=2.2907810099669352, density_atomic=20.35858941436999, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], input=InputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, relax_cell=False, fix_symmetry=False, symprec=None, steps=1, relax_kwargs={}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, 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5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, 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1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08]], detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, 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" Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], input=InputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, relax_cell=False, fix_symmetry=False, symprec=None, steps=1, relax_kwargs={}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.51008605957031, energy_per_atom=-5.313760757446289, forces=[(-0.09911756217479706, -8.335337042808533e-07, -3.725290298461914e-06), (0.029023170471191406, -0.009852856397628784, 0.00985950231552124), (-0.015622109174728394, -1.8328428268432617e-06, -2.3245811462402344e-06), (0.029029875993728638, 0.009856561198830605, -0.009851994924247265), (0.007927864789962769, -3.284076228737831e-07, 8.73231329023838e-07), (0.02041563391685486, 0.0061188191175460815, 0.006120339035987854), (0.007928773760795593, -6.452202796936035e-06, -2.905726432800293e-06), (0.020414218306541443, -0.0061131427064538, -0.0061197783797979355)], stress=((0.30832265667092046, -8.336241623780452e-05, -3.569204117039953e-05), (-8.336241623780452e-05, 0.11221636540829433, -0.47120811529846673), (-3.569204117039953e-05, -0.47120811529846673, 0.1122137191854897)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.375, 1.365, 4.096) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, 5.461, 5.461) [0.5, 1.0, 1.0]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (1.941e-07, 5.461, 2.731) [7.333e-09, 1.0, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], molecule=None, energy=-42.51008605957031, forces=[[-0.09911756217479706, -8.335337042808533e-07, -3.725290298461914e-06], [0.029023170471191406, -0.009852856397628784, 0.00985950231552124], [-0.015622109174728394, -1.8328428268432617e-06, -2.3245811462402344e-06], [0.029029875993728638, 0.009856561198830605, -0.009851994924247265], [0.007927864789962769, -3.284076228737831e-07, 8.73231329023838e-07], [0.02041563391685486, 0.0061188191175460815, 0.006120339035987854], [0.007928773760795593, -6.452202796936035e-06, -2.905726432800293e-06], [0.020414218306541443, -0.0061131427064538, -0.0061197783797979355]], stress=((0.30832265667092046, -8.336241623780452e-05, -3.569204117039953e-05), (-8.336241623780452e-05, 0.11221636540829433, -0.47120811529846673), (-3.569204117039953e-05, -0.47120811529846673, 0.1122137191854897)), magmoms=[0.0035520046949386597, 0.003614775836467743, 0.003554731607437134, 0.003614552319049835, 0.0035545527935028076, 0.003494061529636383, 0.003554821014404297, 0.0034941285848617554])], elapsed_time=0.23266854835674167, n_steps=1), ase_calculator_name='MLFF.CHGNet', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-749642-11446', included_objects=[], objects={}, is_force_converged=True, energy_downhill=False, tags=None, forcefield_name='MLFF.CHGNet', forcefield_version='0.4.0'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-749642-11446'))},\n", - " 'dcfe1c6c-3c2b-45de-acfe-d514a2a4c5fe': {1: Response(output=PhononBSDOSDoc(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 49, 427055, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=162.86871531495993, density=2.2907810099669352, density_atomic=20.35858941436999, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", - " Lattice\n", - " abc : 5.461088632636768 5.461088612459011 5.4610885944459415\n", - " angles : 90.00000345878178 89.99999938950288 89.99999707336922\n", - " volume : 162.86871531495993\n", - " A : 5.461088632636766 1.394744129297821e-07 2.9094460478539383e-08\n", - " B : 1.394744099478306e-07 5.461088612459006 -1.6483512804224428e-07\n", - " C : 2.9094453044950987e-08 -1.64835118555008e-07 5.461088594445939\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.365, 1.365, 4.096) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.731, -1.348e-07, -1.636e-07) [0.5, -3.746e-08, -3.261e-08]\n", - " PeriodicSite: Si (1.365, 4.096, 1.365) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.731, 2.731, 2.731) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.096, 1.365, 1.365) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (5.459e-08, -1.401e-07, 2.731) [7.333e-09, -1.056e-08, 0.5]\n", - " PeriodicSite: Si (4.096, 4.096, 4.096) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (1.33e-07, 2.731, 1.232e-07) [1.159e-08, 0.5, 3.765e-08], phonon_bandstructure=PhononBandStructureSymmLine(bands=(6, 606), labels=['GAMMA', 'X', 'U', 'K', 'L', 'W']), phonon_dos=PhononDos(frequencies=(201,), densities=(201,), n_positive_freqs=184), free_energies=[5269.8127344094655, 5170.389579463751, 4383.3780421068905, 2752.250812336559, 421.99169492538886], heat_capacities=[0.0, 7.879330124050419, 17.169788286576015, 20.884852674610833, 22.494681848140903], internal_energies=[5269.8127344094655, 5523.860416244734, 6840.4473060421205, 8770.993029236264, 10949.921452152672], entropies=[0.0, 3.534708367809812, 12.285346319676153, 20.062474056332352, 26.319824393068203], temperatures=[0, 100, 200, 300, 400], total_dft_energy=-5.3138275146484375, volume_per_formula_unit=20.35858941436999, formula_units=8, has_imaginary_modes=False, force_constants=None, born=None, epsilon_static=None, supercell_matrix=((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)), primitive_matrix=((2.8984158637352412e-24, 0.5, 0.5), (0.5000000000000001, 4.39214184807516e-25, 0.5000000000000001), (0.5, 0.5, -2.8951321439355976e-24)), code='forcefields', phonopy_settings=PhononComputationalSettings(npoints_band=101, kpath_scheme='seekpath', kpoint_density_dos=7000), thermal_displacement_data=None, jobdirs=PhononJobDirs(displacements_job_dirs=['/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-749642-11446'], static_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-42-168697-57215', born_run_job_dir=None, optimization_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-33-495106-74798', taskdoc_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639'), uuids=PhononUUIDs(optimization_run_uuid='78215cc7-1754-4951-a8fd-b90f93cc7f87', displacements_uuids=['f720ed71-12ed-407f-a30e-d572ed0a638e'], static_run_uuid='561033b9-e984-443f-8cd9-2befc39a99bc', born_run_uuid=None)), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-43-059155-54639'))}}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 3 + "run_locally(flow, create_folders=True, raise_immediately=True, root_dir=tmp_dir)" + ] }, { "cell_type": "markdown", - "id": "3", + "id": "6", "metadata": {}, "source": [ "One can switch to a different force field as well!" @@ -2127,13 +84,10 @@ }, { "cell_type": "code", - "id": "4", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-11T06:12:57.746975Z", - "start_time": "2025-02-11T06:12:49.532315Z" - } - }, + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], "source": [ "from atomate2.forcefields.jobs import ForceFieldRelaxMaker, ForceFieldStaticMaker\n", "\n", @@ -2149,1092 +103,47 @@ " phonon_displacement_maker=ForceFieldStaticMaker(force_field_name=\"MACE_MP_0B3\"),\n", ").make(si_structure)\n", "\n", - "run_locally(flow, create_folders=True, raise_immediately=True)" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:49,615 INFO Started executing jobs locally\n", - "2025-02-11 07:12:49,619 INFO Starting job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n", - "2025-02-11 07:12:49,632 INFO Finished job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n", - "2025-02-11 07:12:49,639 INFO Starting job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/e3nn/o3/_wigner.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " _Jd, _W3j_flat, _W3j_indices = torch.load(os.path.join(os.path.dirname(__file__), 'constants.pt'))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cuequivariance or cuequivariance_torch is not available. Cuequivariance acceleration will be disabled.\n", - "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", - "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n", - "Default dtype float32 does not match model dtype float64, converting models to float32.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " torch.load(f=model_path, map_location=device)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:50,673 INFO Finished job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:50,678 INFO Starting job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:50,688 INFO Finished job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:50,692 INFO Starting job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", - "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n", - "Default dtype float32 does not match model dtype float64, converting models to float32.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " torch.load(f=model_path, map_location=device)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,142 INFO Finished job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,147 INFO Starting job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,210 INFO Finished job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n", - "INFO:jobflow.core.job:Finished job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,219 INFO Starting job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,271 INFO Finished job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,277 INFO Starting job - Force field static 1/1 (7513eaaa-8550-4a4c-966b-0a68299a97ed)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - Force field static 1/1 (7513eaaa-8550-4a4c-966b-0a68299a97ed)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", - "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " torch.load(f=model_path, map_location=device)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Default dtype float32 does not match model dtype float64, converting models to float32.\n", - "2025-02-11 07:12:51,795 INFO Finished job - Force field static 1/1 (7513eaaa-8550-4a4c-966b-0a68299a97ed)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - Force field static 1/1 (7513eaaa-8550-4a4c-966b-0a68299a97ed)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,799 INFO Starting job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,801 INFO Finished job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:51,804 INFO Starting job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "WARNING:matplotlib.backends.backend_ps:The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:57,735 INFO Finished job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:57,737 INFO Finished executing jobs locally\n" - ] - }, - { - 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" B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, -6.163e-33]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, -1.233e-32], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, -6.163e-33]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, -1.233e-32], molecule=None, energy=-43.068416595458984, energy_per_atom=-5.383552074432373, forces=[(-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06), (-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07), (-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07), (2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07), (1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07), (-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07), (1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06), (-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07)], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, energy=-43.068416595458984, forces=[[-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06], [-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07], [-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07], [2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07], [1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07], [-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07], [1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06], [-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07]], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), magmoms=None), IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, energy=-43.068416595458984, forces=[[-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06], [-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07], [-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07], [2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07], [1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07], [-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07], [1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06], [-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07]], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), magmoms=None)], elapsed_time=0.19847225677222013, n_steps=2), ase_calculator_name='MLFF.MACE_MP_0B3', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-49-632536-97239', included_objects=[], objects={}, is_force_converged=True, energy_downhill=False, tags=None, forcefield_name='MLFF.MACE_MP_0B3', forcefield_version='0.3.10'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-49-632536-97239'))},\n", - " 'ec79a97b-6903-4398-aec9-256aed662ad8': {1: Response(output=[[1, 0, 0], [0, 1, 0], [0, 0, 1]], detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-50-674841-49822'))},\n", - " 'a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f': {1: Response(output=ForceFieldTaskDocument(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 51, 126521, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=163.55317098041343, density=2.2811942925026014, density_atomic=20.44414637255168, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], input=InputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, relax_cell=False, fix_symmetry=False, symprec=None, steps=1, relax_kwargs={}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [0.0, 0.5, 1.0], molecule=None, energy=-43.068416595458984, energy_per_atom=-5.383552074432373, forces=[(-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06), (-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07), (-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07), (2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07), (1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07), (-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07), (1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06), (-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07)], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], molecule=None, energy=-43.068416595458984, forces=[[-5.055798055764171e-07, -7.07663730281638e-07, 2.0456745914998464e-06], [-7.450580596923828e-07, -5.811452865600586e-07, -7.124617695808411e-07], [-6.073151439522917e-07, 2.2622771211899817e-06, -6.360933184623718e-07], [2.4203052362281596e-07, -3.2991829357342795e-07, -1.4184934116201475e-07], [1.7075094547180925e-06, -6.557189067279978e-07, -4.7372486733365804e-07], [-7.795693477419263e-07, -7.551956855422759e-07, -8.326023817062378e-07], [1.5010236893431284e-06, 1.3478899063557037e-06, 1.6715721358195879e-06], [-8.741333772377402e-07, -6.070428071325296e-07, -8.540228009223938e-07]], stress=((1.0080773417413127, -1.9717845151864994e-05, -2.3319795775209615e-05), (-1.9717845151864994e-05, 1.0080551461103875, -1.9429456182296748e-05), (-2.3319795775209615e-05, -1.9429456182296748e-05, 1.0080715596861978)), magmoms=None)], elapsed_time=0.30086394120007753, n_steps=1), ase_calculator_name='MLFF.MACE_MP_0B3', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-50-690232-69971', included_objects=[], objects={}, is_force_converged=True, energy_downhill=False, tags=None, forcefield_name='MLFF.MACE_MP_0B3', forcefield_version='0.3.10'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-50-690232-69971'))},\n", - " 'b184226f-8eec-4883-a7d1-8d305f3cb6a7': {1: Response(output=[Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 5.469) [0.5, 0.0, 1.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 5.469) [-5.493e-33, 0.5, 1.0]], detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-144560-13048'))},\n", - " '837eb4ef-337b-4bf7-9a4b-95420eb3c6bf': {1: Response(output=None, detour=None, addition=None, replace=Flow(name='Flow', uuid='1efb9ec5-e9ca-4265-be09-5f38119610f7')\n", - " 1. Job(name='Force field static 1/1', uuid='7513eaaa-8550-4a4c-966b-0a68299a97ed')\n", - " 2. Job(name='store_inputs', uuid='837eb4ef-337b-4bf7-9a4b-95420eb3c6bf'), stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-211098-11951')),\n", - " 2: Response(output={'displacement_number': [0], 'forces': [OutputReference(7513eaaa-8550-4a4c-966b-0a68299a97ed, .output, .forces)], 'uuids': ['7513eaaa-8550-4a4c-966b-0a68299a97ed'], 'dirs': [OutputReference(7513eaaa-8550-4a4c-966b-0a68299a97ed, .dir_name)]}, detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-796466-99551'))},\n", - " '7513eaaa-8550-4a4c-966b-0a68299a97ed': {1: Response(output=ForceFieldTaskDocument(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 51, 778086, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=163.55317098041343, density=2.2811942925026014, density_atomic=20.44414637255168, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], input=InputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], molecule=None, relax_cell=False, fix_symmetry=False, symprec=None, steps=1, relax_kwargs={}, optimizer_kwargs={}), output=OutputDoc(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], molecule=None, energy=-43.06785202026367, energy_per_atom=-5.383481502532959, forces=[(-0.11221619695425034, 1.839101969380863e-06, 1.6381363820983097e-08), (0.031074894592165947, -0.015769988298416138, 0.01576509326696396), (-0.01112288050353527, 1.8949785953736864e-06, 1.2983527994947508e-06), (0.031073203310370445, 0.015765974298119545, -0.01576707884669304), (-0.00013040518388152122, -6.759273674106225e-08, 1.2644712796827662e-06), (0.03072630986571312, 0.01535763032734394, 0.01535869762301445), (-0.00013038597535341978, 1.928310894072638e-06, 2.4035580281633884e-06), (0.03072553500533104, -0.015359151177108288, -0.015361744910478592)], stress=((1.004973777028884, 2.6366419043288626e-05, 2.2622799793350855e-05), (2.6366419043288626e-05, 1.0171653334977195, -0.7864977054028787), (2.2622799793350855e-05, -0.7864977054028787, 1.0171475210376073)), ionic_steps=[IonicStep(mol_or_struct=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.377, 1.367, 4.102) [0.2518, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, 0.0]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, 0.0], molecule=None, energy=-43.06785202026367, forces=[[-0.11221619695425034, 1.839101969380863e-06, 1.6381363820983097e-08], [0.031074894592165947, -0.015769988298416138, 0.01576509326696396], [-0.01112288050353527, 1.8949785953736864e-06, 1.2983527994947508e-06], [0.031073203310370445, 0.015765974298119545, -0.01576707884669304], [-0.00013040518388152122, -6.759273674106225e-08, 1.2644712796827662e-06], [0.03072630986571312, 0.01535763032734394, 0.01535869762301445], [-0.00013038597535341978, 1.928310894072638e-06, 2.4035580281633884e-06], [0.03072553500533104, -0.015359151177108288, -0.015361744910478592]], stress=((1.004973777028884, 2.6366419043288626e-05, 2.2622799793350855e-05), (2.6366419043288626e-05, 1.0171653334977195, -0.7864977054028787), (2.2622799793350855e-05, -0.7864977054028787, 1.0171475210376073)), magmoms=None)], elapsed_time=0.3654345436953008, n_steps=1), ase_calculator_name='MLFF.MACE_MP_0B3', dir_name='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-274382-24613', included_objects=[], objects={}, is_force_converged=True, energy_downhill=True, tags=None, forcefield_name='MLFF.MACE_MP_0B3', forcefield_version='0.3.10'), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-274382-24613'))},\n", - " '4511a343-12d9-4465-9d6c-a6297041c891': {1: Response(output=PhononBSDOSDoc(builder_meta=EmmetMeta(emmet_version='0.84.5', pymatgen_version='2024.11.13', run_id=None, batch_id=None, database_version=None, build_date=datetime.datetime(2025, 2, 11, 6, 12, 57, 689170, tzinfo=datetime.timezone.utc), license=None), nsites=8, elements=[Element Si], nelements=1, composition=Composition('Si8'), composition_reduced=Composition('Si1'), formula_pretty='Si', formula_anonymous='A', chemsys='Si', volume=163.55317098041343, density=2.2811942925026014, density_atomic=20.44414637255168, symmetry=SymmetryData(crystal_system=, symbol='Fd-3m', number=227, point_group='m-3m', symprec=0.1, angle_tolerance=5.0, version='2.5.0'), structure=Structure Summary\n", - " Lattice\n", - " abc : 5.468727995382952 5.468727995382952 5.468727995382952\n", - " angles : 90.0 90.0 90.0\n", - " volume : 163.55317098041343\n", - " A : 5.468727995382952 0.0 3.348630117476627e-16\n", - " B : 8.794385354296033e-16 5.468727995382952 3.348630117476627e-16\n", - " C : 0.0 0.0 5.468727995382952\n", - " pbc : True True True\n", - " PeriodicSite: Si (1.367, 1.367, 4.102) [0.25, 0.25, 0.75]\n", - " PeriodicSite: Si (2.734, 0.0, 1.674e-16) [0.5, 0.0, -6.163e-33]\n", - " PeriodicSite: Si (1.367, 4.102, 1.367) [0.25, 0.75, 0.25]\n", - " PeriodicSite: Si (2.734, 2.734, 2.734) [0.5, 0.5, 0.5]\n", - " PeriodicSite: Si (4.102, 1.367, 1.367) [0.75, 0.25, 0.25]\n", - " PeriodicSite: Si (0.0, 0.0, 2.734) [0.0, 0.0, 0.5]\n", - " PeriodicSite: Si (4.102, 4.102, 4.102) [0.75, 0.75, 0.75]\n", - " PeriodicSite: Si (4.397e-16, 2.734, 1.674e-16) [0.0, 0.5, -1.233e-32], phonon_bandstructure=PhononBandStructureSymmLine(bands=(6, 606), labels=['GAMMA', 'X', 'U', 'K', 'L', 'W']), phonon_dos=PhononDos(frequencies=(201,), densities=(201,), n_positive_freqs=184), free_energies=[5448.905893966201, 5310.987704809328, 4440.618556277908, 2746.065655334672, 365.28304032896705], heat_capacities=[0.0, 8.19759957366914, 16.706018543651478, 20.512092766784267, 22.240479219670608], internal_energies=[5448.905893966201, 5750.425742450245, 7044.044005201389, 8931.387036100377, 11079.304972704058], entropies=[0.0, 4.394380376409171, 13.017127244617395, 20.617737935885675, 26.78505483093772], temperatures=[0, 100, 200, 300, 400], total_dft_energy=-5.383552074432373, volume_per_formula_unit=20.44414637255168, formula_units=8, has_imaginary_modes=True, force_constants=None, born=None, epsilon_static=None, supercell_matrix=((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)), primitive_matrix=((0.0, 0.5, 0.5), (0.5, 0.0, 0.5), (0.5, 0.5, 6.162975822039155e-33)), code='forcefields', phonopy_settings=PhononComputationalSettings(npoints_band=101, kpath_scheme='seekpath', kpoint_density_dos=7000), thermal_displacement_data=None, jobdirs=PhononJobDirs(displacements_job_dirs=['/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-274382-24613'], static_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-50-690232-69971', born_run_job_dir=None, optimization_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-49-632536-97239', taskdoc_run_job_dir='/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770'), uuids=PhononUUIDs(optimization_run_uuid='c1bccb92-12e8-42ec-872e-e27a5a90dd22', displacements_uuids=['7513eaaa-8550-4a4c-966b-0a68299a97ed'], static_run_uuid='a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f', born_run_uuid=None)), detour=None, addition=None, replace=None, stored_data=None, stop_children=False, stop_jobflow=False, job_dir=PosixPath('/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/tutorials/force_fields/job_2025-02-11-06-12-51-802267-34770'))}}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 4 + "run_locally(flow, create_folders=True, raise_immediately=True, root_dir=tmp_dir)" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Or by using the name only:", - "id": "b3d30da7227c27ee" + "id": "8", + "metadata": {}, + "source": [ + "Or by using the name only:" + ] }, { - "metadata": { - "jupyter": { - "is_executing": true - }, - "ExecuteTime": { - "start_time": "2025-02-11T06:12:57.760029Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], "source": [ "PhononMaker.from_force_field_name(force_field_name=\"MACE_MP_0B3\")\n", - "run_locally(flow, create_folders=True, raise_immediately=True)" - ], - "id": "c769deefc5d8f6ba", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:57,775 INFO Started executing jobs locally\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.managers.local:Started executing jobs locally\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:57,788 INFO Starting job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/flow.py:431: UserWarning: Some jobs are not connected, their ordering may be random\n", - " for node in itergraph(graph):\n", - "INFO:jobflow.core.job:Starting job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:57,800 INFO Finished job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - structure_to_conventional (9f56521c-fea7-4632-bd6d-e17fa0e1f9bf)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:57,804 INFO Starting job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " torch.load(f=model_path, map_location=device)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", - "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n", - "Default dtype float32 does not match model dtype float64, converting models to float32.\n", - "2025-02-11 07:12:58,115 INFO Finished job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - Force field relax (c1bccb92-12e8-42ec-872e-e27a5a90dd22)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,121 INFO Starting job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,126 INFO Finished job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - get_supercell_size (ec79a97b-6903-4398-aec9-256aed662ad8)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,131 INFO Starting job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", - "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n", - "Default dtype float32 does not match model dtype float64, converting models to float32.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " torch.load(f=model_path, map_location=device)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,440 INFO Finished job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - Force field static (a2b577c0-3ab4-49c5-b0dd-354b52cb9c5f)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,444 INFO Starting job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,514 INFO Finished job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n", - "INFO:jobflow.core.job:Finished job - generate_phonon_displacements (b184226f-8eec-4883-a7d1-8d305f3cb6a7)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,518 INFO Starting job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,566 INFO Finished job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - run_phonon_displacements (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:58,572 INFO Starting job - Force field static 1/1 (7fff1f8b-48fd-4db4-a29b-efbacca3415a)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - Force field static 1/1 (7fff1f8b-48fd-4db4-a29b-efbacca3415a)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using Materials Project MACE for MACECalculator with /home/jgeorge/.cache/mace/macemp0b3mediummodel\n", - "Using float32 for MACECalculator, which is faster but less accurate. Recommended for MD. Use float64 for geometry optimization.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/mace/calculators/mace.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", - " torch.load(f=model_path, map_location=device)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Default dtype float32 does not match model dtype float64, converting models to float32.\n", - "2025-02-11 07:12:59,013 INFO Finished job - Force field static 1/1 (7fff1f8b-48fd-4db4-a29b-efbacca3415a)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - Force field static 1/1 (7fff1f8b-48fd-4db4-a29b-efbacca3415a)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:59,018 INFO Starting job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:59,020 INFO Finished job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Finished job - store_inputs (837eb4ef-337b-4bf7-9a4b-95420eb3c6bf, 2)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-11 07:12:59,024 INFO Starting job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:jobflow.core.job:Starting job - generate_frequencies_eigenvectors (4511a343-12d9-4465-9d6c-a6297041c891)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - } - ], - "execution_count": null + "run_locally(flow, create_folders=True, raise_immediately=True, root_dir=tmp_dir)" + ] + }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "Now, we clean up the temporary directory that we made. In reality, you might want to keep this data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "shutil.rmtree(tmp_dir)" + ] } ], "metadata": { @@ -3249,11 +158,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" - }, - "kernelspec": { - "name": "python3", - "language": "python", - "display_name": "Python 3 (ipykernel)" } }, "nbformat": 4, diff --git a/tutorials/mock_lobster.py b/tutorials/mock_lobster.py index 4472ade933..97c1ba01be 100644 --- a/tutorials/mock_lobster.py +++ b/tutorials/mock_lobster.py @@ -2,6 +2,7 @@ import contextlib import os +import shutil import tempfile from collections.abc import Generator from pathlib import Path @@ -36,4 +37,4 @@ def mock_lobster(ref_paths: dict) -> Generator: yield mf(ref_paths, fake_run_lobster_kwargs=fake_run_lobster_kwargs) finally: os.chdir(old_cwd) - # shutil.rmtree(new_path) + 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zQwC@>3I05PqoD{W*bdF3VPQKmkA?$#!TB^IND Date: Tue, 11 Feb 2025 06:51:48 -0500 Subject: [PATCH 32/61] print ref and actual values in testing.lobster.verify_inputs ValueError --- src/atomate2/utils/testing/lobster.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/atomate2/utils/testing/lobster.py b/src/atomate2/utils/testing/lobster.py index 92aaa876fe..9808deb90f 100644 --- a/src/atomate2/utils/testing/lobster.py +++ b/src/atomate2/utils/testing/lobster.py @@ -177,8 +177,12 @@ def verify_inputs(ref_path: str | Path, lobsterin_settings: Sequence[str]) -> No ref = Lobsterin.from_file(Path(ref_path) / "inputs" / "lobsterin") for key in lobsterin_settings: - if user.get(key) != ref.get(key): - raise ValueError(f"lobsterin value of {key} is inconsistent!") + ref_val, user_val = ref.get(key), user.get(key) + if ref_val != user_val: + raise ValueError( + f"lobsterin value of {key} is inconsistent, got {user_val} but " + f"expected {ref_val}!" + ) def copy_lobster_outputs(ref_path: str | Path) -> None: From 35020128180df45bfc1fffb5f5b5176f32c7b1fb Mon Sep 17 00:00:00 2001 From: JaGeo Date: Wed, 12 Feb 2025 18:01:15 +0100 Subject: [PATCH 33/61] add a qha tutorial --- src/atomate2/common/flows/eos.py | 10 + src/atomate2/common/schemas/qha.py | 2 +- src/atomate2/common/utils.py | 1 + .../static_eos_deformation_1/outputs/PCDAT.gz | Bin 136 -> 0 bytes tests/test_data/vasp/Si_qha_2/copy-script.sh | 9 + .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 605 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/CONTCAR.gz | Bin 0 -> 591 bytes .../outputs/INCAR.gz | Bin 0 -> 217 bytes .../outputs/OUTCAR.gz | Bin 0 -> 64201 bytes .../outputs/POSCAR.gz | Bin 0 -> 605 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/vasp.out.gz | Bin 0 -> 1641 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 47222 bytes .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 583 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/CONTCAR.gz | Bin 0 -> 584 bytes .../outputs/INCAR.gz | Bin 0 -> 217 bytes .../outputs/OUTCAR.gz | Bin 0 -> 82716 bytes .../outputs/POSCAR.gz | Bin 0 -> 583 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/vasp.out.gz | Bin 0 -> 1610 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 62914 bytes .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 581 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/CONTCAR.gz | Bin 0 -> 578 bytes .../outputs/INCAR.gz | Bin 0 -> 217 bytes .../outputs/OUTCAR.gz | Bin 0 -> 64385 bytes .../outputs/POSCAR.gz | Bin 0 -> 581 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/vasp.out.gz | Bin 0 -> 1608 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 47456 bytes .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 609 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/CONTCAR.gz | Bin 0 -> 588 bytes 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| Bin 0 -> 575 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/vasp.out.gz | Bin 0 -> 1639 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 46955 bytes .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 601 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/CONTCAR.gz | Bin 0 -> 595 bytes .../outputs/INCAR.gz | Bin 0 -> 217 bytes .../outputs/OUTCAR.gz | Bin 0 -> 64187 bytes .../outputs/POSCAR.gz | Bin 0 -> 601 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 67 bytes .../outputs/vasp.out.gz | Bin 0 -> 1637 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 46978 bytes .../CHG.gz | Bin 0 -> 24 bytes .../CHGCAR.gz | Bin 0 -> 27 bytes .../CONTCAR.gz | Bin 0 -> 321 bytes .../DOSCAR.gz | Bin 0 -> 2962 bytes .../EIGENVAL.gz | Bin 0 -> 27585 bytes .../IBZKPT.gz | Bin 0 -> 3370 bytes .../INCAR.gz | Bin 0 -> 217 bytes .../INCAR.orig.gz | Bin 0 -> 222 bytes .../OSZICAR.gz | Bin 0 -> 525 bytes .../OUTCAR.gz | Bin 0 -> 52223 bytes .../PCDAT.gz | Bin 0 -> 136 bytes .../POSCAR.gz | Bin 0 -> 301 bytes .../POSCAR.orig.gz | Bin 0 -> 306 bytes .../POTCAR.orig.gz | Bin 0 -> 71577 bytes .../POTCAR.spec.gz | Bin 0 -> 35 bytes .../REPORT.gz | Bin 0 -> 27 bytes .../WAVECAR.gz | Bin 0 -> 28 bytes .../XDATCAR.gz | Bin 0 -> 180 bytes .../additional_store_data.json | 1 + .../custodian.json.gz | Bin 0 -> 365 bytes .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 301 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../jfremote_in.json | 1 + .../jfremote_out.json | 1 + .../outputs/CONTCAR.gz | Bin 0 -> 321 bytes .../outputs/INCAR.gz | Bin 0 -> 217 bytes .../outputs/OUTCAR.gz | Bin 0 -> 52223 bytes .../outputs/POSCAR.gz | Bin 0 -> 301 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../outputs/vasp.out.gz | Bin 0 -> 1332 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 30755 bytes .../queue.err | 0 .../queue.out | 0 .../remote_job_data.json | 1 + .../std_err.txt | 0 .../submit.sh | 20 + .../vasp.out.gz | Bin 0 -> 1332 bytes 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create mode 100644 tests/test_data/vasp/Si_qha_2/tight_relax_2_d5/inputs/POTCAR.spec.gz create mode 100644 tests/test_data/vasp/Si_qha_2/tight_relax_2_d5/outputs/CONTCAR.gz create mode 100644 tests/test_data/vasp/Si_qha_2/tight_relax_2_d5/outputs/INCAR.gz create mode 100644 tests/test_data/vasp/Si_qha_2/tight_relax_2_d5/outputs/OUTCAR.gz create mode 100644 tests/test_data/vasp/Si_qha_2/tight_relax_2_d5/outputs/POSCAR.gz create mode 100644 tests/test_data/vasp/Si_qha_2/tight_relax_2_d5/outputs/POTCAR.spec.gz create mode 100644 tests/test_data/vasp/Si_qha_2/tight_relax_2_d5/outputs/vasp.out.gz create mode 100644 tests/test_data/vasp/Si_qha_2/tight_relax_2_d5/outputs/vasprun.xml.gz create mode 100644 tutorials/qha_workflow.ipynb create mode 100644 tutorials/qha_workflow.py diff --git a/src/atomate2/common/flows/eos.py b/src/atomate2/common/flows/eos.py index 03fe6749cf..0427945f59 100644 --- a/src/atomate2/common/flows/eos.py +++ b/src/atomate2/common/flows/eos.py @@ -95,6 +95,11 @@ def make(self, structure: Structure, prev_dir: str | Path = None) -> Flow: ) relax_flow.name = "EOS equilibrium relaxation" + try: + for job in relax_flow.jobs: + job.append_name(" EOS equilibrium relaxation") + except AttributeError: + pass flow_output["initial_relax"] = { "E0": relax_flow.output.output.energy, "V0": relax_flow.output.structure.volume, @@ -153,6 +158,11 @@ def make(self, structure: Structure, prev_dir: str | Path = None) -> Flow: prev_dir=prev_dir, ) relax_job.name += f" deformation {frame_idx}" + try: + for job in relax_job.jobs: + job.append_name(f" deformation {frame_idx}") + except AttributeError: + pass jobs["relax"].append(relax_job) if self.static_maker: diff --git a/src/atomate2/common/schemas/qha.py b/src/atomate2/common/schemas/qha.py index e85b173012..1bef05dfeb 100644 --- a/src/atomate2/common/schemas/qha.py +++ b/src/atomate2/common/schemas/qha.py @@ -38,7 +38,7 @@ class PhononQHADoc(StructureMetadata, extra="allow"): # type: ignore[call-arg] helmholtz_volume: Optional[list[list[float]]] = Field( None, description="Free energies at temperatures and volumes." - "shape (temperatures, volumes)", + "shape (temperatures, volumes)", #TODO: add units here ) volume_temperature: Optional[list[float]] = Field( None, diff --git a/src/atomate2/common/utils.py b/src/atomate2/common/utils.py index ce7e0c4da8..ecefd0a556 100644 --- a/src/atomate2/common/utils.py +++ b/src/atomate2/common/utils.py @@ -77,6 +77,7 @@ def get_supercell_matrix( allow_orthorhombic=allow_orthorhombic, ) transformation.apply_transformation(structure=structure) + print(transformation.transformation_matrix.transpose().tolist()) # matrix from pymatgen has to be transposed return transformation.transformation_matrix.transpose().tolist() diff --git a/tests/test_data/vasp/Si_qha/static_eos_deformation_1/outputs/PCDAT.gz b/tests/test_data/vasp/Si_qha/static_eos_deformation_1/outputs/PCDAT.gz deleted file mode 100644 index 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" 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" 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" 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" 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" 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-12T16:49:57.197344Z", + "start_time": "2025-02-12T16:49:57.190858Z" + } + }, + "cell_type": "code", + "source": [ + "from pymatgen.phonon.bandstructure import PhononBandStructureSymmLine\n", + "from pymatgen.phonon.dos import PhononDos\n", + "from pymatgen.phonon.plotter import PhononBSPlotter, PhononDosPlotter\n", + "\n", + "job_store.connect()\n", + "\n", + "result = job_store.query_one(\n", + " {\"name\": \"analyze_free_energy\"},\n", + " properties=[\n", + " \"output.helmholtz_volume\",\n", + " \"output.temperatures\",\n", + " \"output.volumes\",\n", + " ],\n", + " load=True,\n", + " sort={\"completed_at\": -1}, # to get the latest computation\n", + ")" + ], + "id": "1e88d3d7664d2975", + "outputs": [], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "You can then plot some of the output free energy volume curves", + "id": "2803bb47266fd6ac" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-12T16:54:19.328646Z", + "start_time": "2025-02-12T16:54:19.194322Z" + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "for temp, energy_list in zip(result[\"output\"][\"temperatures\"], result[\"output\"][\"helmholtz_volume\"]):\n", + "\n", + "\n", + " # Create the plot\n", + " plt.plot(result[\"output\"][\"volumes\"], energy_list, marker='o')\n", + " # Add labels and title\n", + "plt.xlabel('Volume')\n", + "plt.ylabel('Free Energy')\n", + "\n", + "# Show the plot\n", + "plt.show()\n" + ], + "id": "759e28b4ad56667f", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "10.0\n", + "20.0\n", + "30.0\n", + "40.0\n", + "50.0\n", + "60.0\n", + "70.0\n", + "80.0\n", + "90.0\n", + "100.0\n", + "110.0\n", + "120.0\n", + "130.0\n", + "140.0\n", + "150.0\n", + "160.0\n", + "170.0\n", + "180.0\n", + "190.0\n", + "200.0\n", + "210.0\n", + "220.0\n", + "230.0\n", + "240.0\n", + "250.0\n", + "260.0\n", + "270.0\n", + "280.0\n", + "290.0\n", + "300.0\n", + "310.0\n", + "320.0\n", + "330.0\n", + "340.0\n", + "350.0\n", + "360.0\n", + "370.0\n", + "380.0\n", + "390.0\n", + "400.0\n", + "410.0\n", + "420.0\n", + "430.0\n", + "440.0\n", + "450.0\n", + "460.0\n", + "470.0\n", + "480.0\n", + "490.0\n", + "500.0\n", + "510.0\n", + "520.0\n", + "530.0\n", + "540.0\n", + "550.0\n", + "560.0\n", + "570.0\n", + "580.0\n", + "590.0\n", + "600.0\n", + "610.0\n", + "620.0\n", + "630.0\n", + "640.0\n", + "650.0\n", + "660.0\n", + "670.0\n", + "680.0\n", + "690.0\n", + "700.0\n", + "710.0\n", + "720.0\n", + "730.0\n", + "740.0\n", + "750.0\n", + "760.0\n", + "770.0\n", + "780.0\n", + "790.0\n", + "800.0\n", + "810.0\n", + "820.0\n", + "830.0\n", + "840.0\n", + "850.0\n", + "860.0\n", + "870.0\n", + "880.0\n", + "890.0\n", + "900.0\n", + "910.0\n", + "920.0\n", + "930.0\n", + "940.0\n", + "950.0\n", + "960.0\n", + "970.0\n", + "980.0\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 23 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "249d87d9c9f04a9" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/qha_workflow.py b/tutorials/qha_workflow.py new file mode 100644 index 0000000000..561199782d --- /dev/null +++ b/tutorials/qha_workflow.py @@ -0,0 +1,215 @@ +#!/usr/bin/env python +# coding: utf-8 + +# This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook: + +# In[1]: + + +from mock_vasp import TEST_DIR, mock_vasp + +ref_paths = { + "phonon static 1/1": "Si_qha_2/phonon_static_1_1", + "static": "Si_qha_2/static", + "tight relax 1 EOS equilibrium relaxation": "Si_qha_2/tight_relax_1", # in fact, we replace all relaxation steps with the same output, also the ISIF=4 ones to save storage + "tight relax 2 EOS equilibrium relaxation": "Si_qha_2/tight_relax_2", + "tight relax 1 deformation 0": "Si_qha_2/tight_relax_1_d0", + "tight relax 1 deformation 1": "Si_qha_2/tight_relax_1_d1", + "tight relax 1 deformation 2": "Si_qha_2/tight_relax_1_d2", + "tight relax 1 deformation 3": "Si_qha_2/tight_relax_1_d3", + "tight relax 1 deformation 4": "Si_qha_2/tight_relax_1_d4", + "tight relax 1 deformation 5": "Si_qha_2/tight_relax_1_d5", + "tight relax 2 deformation 0": "Si_qha_2/tight_relax_2_d0", + "tight relax 2 deformation 1": "Si_qha_2/tight_relax_2_d1", + "tight relax 2 deformation 2": "Si_qha_2/tight_relax_2_d2", + "tight relax 2 deformation 3": "Si_qha_2/tight_relax_2_d3", + "tight relax 2 deformation 4": "Si_qha_2/tight_relax_2_d4", + "tight relax 2 deformation 5": "Si_qha_2/tight_relax_2_d5", + "dft phonon static eos deformation 1":"Si_qha_2/dft_phonon_static_eos_deformation_1", + "dft phonon static eos deformation 2":"Si_qha_2/dft_phonon_static_eos_deformation_2", + "dft phonon static eos deformation 3":"Si_qha_2/dft_phonon_static_eos_deformation_3", + "dft phonon static eos deformation 4":"Si_qha_2/dft_phonon_static_eos_deformation_4", + "dft phonon static eos deformation 5":"Si_qha_2/dft_phonon_static_eos_deformation_5", + "dft phonon static eos deformation 6":"Si_qha_2/dft_phonon_static_eos_deformation_6", + "dft phonon static eos deformation 7":"Si_qha_2/dft_phonon_static_eos_deformation_7", + "dft phonon static 1/1 eos deformation 1": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_1", + "dft phonon static 1/1 eos deformation 2": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_2", + "dft phonon static 1/1 eos deformation 3": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_3", + "dft phonon static 1/1 eos deformation 4": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_4", + "dft phonon static 1/1 eos deformation 5": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_5", + "dft phonon static 1/1 eos deformation 6": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_6", + "dft phonon static 1/1 eos deformation 7": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_7", +} + + +# QHA workflow + +# This tutorial will make use of a quasi-harmonic workflow that allows to include volume-dependent anharmonicity into the calculation of phonon free energies. Please check out the paper by Togo to learn about the exact implementation as we will rely on Phonopy to perform the quasi-harmonic approximation. https://doi.org/10.7566/JPSJ.92.012001. At the moment, we perform harmonic free energy calculation along a volume curve to arrive at free energy-volume curves that are the starting point for the quasi-harmonic approximation. + +# ## Let's run the workflow +# Now, we load a structure and other important functions and classes for running the qha workflow. + +# In[2]: + + +from jobflow import JobStore, run_locally +from maggma.stores import MemoryStore +from pymatgen.core import Structure + +from atomate2.vasp.flows.qha import QhaMaker + +job_store = JobStore(MemoryStore(), additional_stores={"data": MemoryStore()}) +si_structure = Structure.from_file(TEST_DIR / "structures" / "Si_diamond.cif") +si_structure=si_structure.to_conventional() +from mp_api.client import MPRester +mpr = MPRester(api_key='Z4aKTAgeEudmS0bMPkKVS3EtOnej1zah') + + +si_structure = mpr.get_structure_by_material_id("mp-149", conventional_unit_cell=True) + + +# Then one can use the `QhaMaker` to generate a `Flow`. First, the structure will be optimized than the structures will be optimized at constant volume along an energy volume curve. Please make sure the structural optimizations are tight enough. At each of these volumes, a phonon run will then be performed. The quasi-harmonic approximation is only valid if the harmonic phonon curves don't show any imaginary modes. However, for testing, you can also switch off this option. + +# Before we start the quasi-harmonic workflow, we adapt the first relaxation, the relaxation with different volumes and the static runs for the phonon calculation. As we deal with Si, we will not add the non-analytical term correction. + +# In[3]: + + +from atomate2.vasp.flows.core import DoubleRelaxMaker +from atomate2.vasp.jobs.core import TightRelaxMaker +from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator +from atomate2.vasp.flows.phonons import PhononMaker +from atomate2.vasp.jobs.phonons import PhononDisplacementMaker +phonon_bulk_relax_maker_isif3 = DoubleRelaxMaker.from_relax_maker( + TightRelaxMaker( + run_vasp_kwargs={"handlers": ()}, + input_set_generator=TightRelaxSetGenerator( + user_incar_settings={ + "GGA": "PE", + "ISPIN": 1, + "KSPACING": 0.1, + # "EDIFFG": 1e-5, + "ALGO": "Normal", + "LAECHG": False, + "ISMEAR": 0, + "ENCUT": 700, + "IBRION": 1, + "ISYM": 0, + "SIGMA": 0.05, + "LCHARG": False, # Do not write the CHGCAR file + "LWAVE": False, # Do not write the WAVECAR file + "LVTOT": False, # Do not write LOCPOT file + "LORBIT": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR + "LOPTICS": False, # No PCDAT file + "LREAL": False, + "ISIF": 3, + # to be removed + "NPAR": 4, + } + ), + ) +) + +phonon_displacement_maker = PhononDisplacementMaker( + run_vasp_kwargs={"handlers": ()}, input_set_generator=StaticSetGenerator( + user_incar_settings={ + "GGA": "PE", + "IBRION": -1, + "ISPIN": 1, + "ISMEAR": 0, + "ISIF": 3, + "ENCUT": 700, + "EDIFF": 1e-7, + "LAECHG": False, + "LREAL": False, + "ALGO": "Normal", + "NSW": 0, + "LCHARG": False, # Do not write the CHGCAR file + "LWAVE": False, # Do not write the WAVECAR file + "LVTOT": False, # Do not write LOCPOT file + "LORBIT": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR + "LOPTICS": False, # No PCDAT file + "SIGMA": 0.05, + "ISYM": 0, + "KSPACING": 0.1, + "NPAR": 4, + }, + auto_ispin=False, + ) +) + + + +phonon_bulk_relax_maker_isif4 = DoubleRelaxMaker.from_relax_maker( + TightRelaxMaker( + run_vasp_kwargs={"handlers": ()}, + input_set_generator=TightRelaxSetGenerator( + user_incar_settings={ + "GGA": "PE", + "ISPIN": 1, + "KSPACING": 0.1, + "ALGO": "Normal", + "LAECHG": False, + "ISMEAR": 0, + "ENCUT": 700, + "IBRION": 1, + "ISYM": 0, + "SIGMA": 0.05, + "LCHARG": False, # Do not write the CHGCAR file + "LWAVE": False, # Do not write the WAVECAR file + "LVTOT": False, # Do not write LOCPOT file + "LORBIT": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR + "LOPTICS": False, # No PCDAT file + "LREAL": False, + "ISIF": 4, + # to be removed + "NPAR": 4, + } + ), + ) +) + +phonon_displacement_maker.name = "dft phonon static" + + + +# In[4]: + + +flow = QhaMaker( + initial_relax_maker=phonon_bulk_relax_maker_isif3, + eos_relax_maker=phonon_bulk_relax_maker_isif4, + min_length=10, + phonon_maker=PhononMaker(generate_frequencies_eigenvectors_kwargs={"tmin": 0, "tmax": 1000, "tstep": 10}, + bulk_relax_maker=None, + born_maker=None, + static_energy_maker=phonon_displacement_maker, + phonon_displacement_maker=phonon_displacement_maker), + linear_strain=(-0.15, 0.15), + number_of_frames=6, + pressure=None, + t_max=None, + ignore_imaginary_modes=False, + skip_analysis=False, + eos_type="vinet" +).make(structure=si_structure) + + +# In[5]: + + +with mock_vasp(ref_paths=ref_paths) as mf: + run_locally( + flow, + create_folders=True, + ensure_success=True, + raise_immediately=True, + store=job_store, + ) + + +# In[ ]: + + + + From 3dbc9e7b3acc83ee8c489eb9c2355bf1c74b983d Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 06:54:25 +0100 Subject: [PATCH 34/61] add unit in document --- src/atomate2/common/schemas/qha.py | 2 +- tutorials/qha_workflow.ipynb | 508 ++++++++++++----------------- 2 files changed, 205 insertions(+), 305 deletions(-) diff --git a/src/atomate2/common/schemas/qha.py b/src/atomate2/common/schemas/qha.py index 1bef05dfeb..5494bac29e 100644 --- a/src/atomate2/common/schemas/qha.py +++ b/src/atomate2/common/schemas/qha.py @@ -37,7 +37,7 @@ class PhononQHADoc(StructureMetadata, extra="allow"): # type: ignore[call-arg] ) helmholtz_volume: Optional[list[list[float]]] = Field( None, - description="Free energies at temperatures and volumes." + description="Free energies (eV) at temperatures and volumes (Angstrom^3)." "shape (temperatures, volumes)", #TODO: add units here ) volume_temperature: Optional[list[float]] = Field( diff --git a/tutorials/qha_workflow.ipynb b/tutorials/qha_workflow.ipynb index 51b07cc3cc..7ce769f331 100644 --- a/tutorials/qha_workflow.ipynb +++ b/tutorials/qha_workflow.ipynb @@ -9,8 +9,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-12T16:40:53.203237Z", - "start_time": "2025-02-12T16:40:49.221171Z" + "end_time": "2025-02-12T18:47:04.203756Z", + "start_time": "2025-02-12T18:46:50.972235Z" } }, "cell_type": "code", @@ -90,8 +90,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-12T16:40:53.645753Z", - "start_time": "2025-02-12T16:40:53.210026Z" + "end_time": "2025-02-12T18:47:05.535135Z", + "start_time": "2025-02-12T18:47:04.212313Z" } }, "cell_type": "code", @@ -124,8 +124,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-12T16:40:53.796514Z", - "start_time": "2025-02-12T16:40:53.775504Z" + "end_time": "2025-02-12T18:47:05.665837Z", + "start_time": "2025-02-12T18:47:05.644128Z" } }, "cell_type": "code", @@ -234,8 +234,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-12T16:40:54.927734Z", - "start_time": "2025-02-12T16:40:53.833559Z" + "end_time": "2025-02-12T18:47:06.759048Z", + "start_time": "2025-02-12T18:47:05.686555Z" } }, "cell_type": "code", @@ -266,8 +266,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-12T16:42:27.101119Z", - "start_time": "2025-02-12T16:40:54.938319Z" + "end_time": "2025-02-12T18:48:32.593958Z", + "start_time": "2025-02-12T18:47:06.766271Z" } }, "cell_type": "code", @@ -287,8 +287,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:40:54,980 INFO Started executing jobs locally\n", - "2025-02-12 17:40:54,993 INFO Starting job - tight relax 1 EOS equilibrium relaxation (a7de9af1-3ce1-4100-95c9-0a2676a84fa7)\n" + "2025-02-12 19:47:06,802 INFO Started executing jobs locally\n", + "2025-02-12 19:47:06,819 INFO Starting job - tight relax 1 EOS equilibrium relaxation (89034fda-0616-4fa1-98cf-12554839513f)\n" ] }, { @@ -306,16 +306,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:40:56,333 INFO Finished job - tight relax 1 EOS equilibrium relaxation (a7de9af1-3ce1-4100-95c9-0a2676a84fa7)\n", - "2025-02-12 17:40:56,333 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:40:56,334 INFO Starting job - tight relax 2 EOS equilibrium relaxation (274a6a59-a548-418d-84fa-c16e18323e6f)\n" + "2025-02-12 19:47:08,028 INFO Finished job - tight relax 1 EOS equilibrium relaxation (89034fda-0616-4fa1-98cf-12554839513f)\n", + "2025-02-12 19:47:08,029 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:08,030 INFO Starting job - tight relax 2 EOS equilibrium relaxation (aa677af4-d8ef-4a3f-9436-e5ad7b615712)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-40-56-334318-34010/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-08-030242-19998/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -325,18 +325,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:40:57,857 INFO Finished job - tight relax 2 EOS equilibrium relaxation (274a6a59-a548-418d-84fa-c16e18323e6f)\n", - "2025-02-12 17:40:57,857 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:40:57,858 INFO Starting job - apply_strain_to_structure (ebd7fa9a-0ff4-4595-b54e-76d08b6ddefc)\n", - "2025-02-12 17:40:57,892 INFO Finished job - apply_strain_to_structure (ebd7fa9a-0ff4-4595-b54e-76d08b6ddefc)\n", - "2025-02-12 17:40:57,893 INFO Starting job - tight relax 1 deformation 0 (5815867d-309c-4aa8-9650-b19de6d92673)\n" + "2025-02-12 19:47:09,400 INFO Finished job - tight relax 2 EOS equilibrium relaxation (aa677af4-d8ef-4a3f-9436-e5ad7b615712)\n", + "2025-02-12 19:47:09,400 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:09,401 INFO Starting job - apply_strain_to_structure (42396e00-786d-4a80-844f-dff18a2dbc22)\n", + "2025-02-12 19:47:09,435 INFO Finished job - apply_strain_to_structure (42396e00-786d-4a80-844f-dff18a2dbc22)\n", + "2025-02-12 19:47:09,437 INFO Starting job - tight relax 1 deformation 0 (e5c8dad2-8aac-49ca-a241-230316bfe4dc)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-40-57-893262-89260/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-09-436859-95935/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -346,16 +346,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:40:59,077 INFO Finished job - tight relax 1 deformation 0 (5815867d-309c-4aa8-9650-b19de6d92673)\n", - "2025-02-12 17:40:59,077 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:40:59,078 INFO Starting job - tight relax 1 deformation 1 (4b9e5cd0-f94c-435e-86dd-c09c29c875a8)\n" + "2025-02-12 19:47:10,450 INFO Finished job - tight relax 1 deformation 0 (e5c8dad2-8aac-49ca-a241-230316bfe4dc)\n", + "2025-02-12 19:47:10,451 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:10,452 INFO Starting job - tight relax 1 deformation 1 (b91ec7ce-8a80-4ed5-9e9a-4ef495d10a79)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-40-59-078303-44636/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-10-452456-64344/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -365,16 +365,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:00,180 INFO Finished job - tight relax 1 deformation 1 (4b9e5cd0-f94c-435e-86dd-c09c29c875a8)\n", - "2025-02-12 17:41:00,180 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:00,181 INFO Starting job - tight relax 1 deformation 2 (36e96e99-0325-4d02-8850-ba4224f734cc)\n" + "2025-02-12 19:47:11,387 INFO Finished job - tight relax 1 deformation 1 (b91ec7ce-8a80-4ed5-9e9a-4ef495d10a79)\n", + "2025-02-12 19:47:11,388 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:11,388 INFO Starting job - tight relax 1 deformation 2 (6ee35b18-58a4-4a33-aa67-54fe22f6d340)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: 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(6ee35b18-58a4-4a33-aa67-54fe22f6d340)\n", + "2025-02-12 19:47:12,320 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:12,321 INFO Starting job - tight relax 1 deformation 3 (a1d0f042-8e5f-4666-a222-30a29a9c60bd)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-01-388062-55269/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-12-321191-59448/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -403,16 +403,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:02,522 INFO Finished job - tight relax 1 deformation 3 (ca673941-d169-4059-94d8-a2eaa89c7b22)\n", - "2025-02-12 17:41:02,522 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:02,524 INFO Starting job - tight relax 1 deformation 4 (93c3662f-de48-4df3-812f-eb210efda351)\n" + "2025-02-12 19:47:13,255 INFO Finished job - tight relax 1 deformation 3 (a1d0f042-8e5f-4666-a222-30a29a9c60bd)\n", + "2025-02-12 19:47:13,256 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:13,256 INFO Starting job - tight relax 1 deformation 4 (527dddb8-4637-4084-a196-bb3fe42fc6fe)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-02-523735-36340/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-13-256569-43370/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -422,16 +422,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:03,660 INFO Finished job - tight relax 1 deformation 4 (93c3662f-de48-4df3-812f-eb210efda351)\n", - "2025-02-12 17:41:03,661 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:03,662 INFO Starting job - tight relax 1 deformation 5 (00195680-2d1e-4bd4-a421-6dc667e6ac53)\n" + "2025-02-12 19:47:14,195 INFO Finished job - tight relax 1 deformation 4 (527dddb8-4637-4084-a196-bb3fe42fc6fe)\n", + "2025-02-12 19:47:14,195 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:14,196 INFO Starting job - tight relax 1 deformation 5 (ae817857-2e89-4f5c-a85a-5b57563d0c0d)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-03-662070-34501/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-14-196160-86116/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -441,16 +441,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:04,709 INFO Finished job - tight relax 1 deformation 5 (00195680-2d1e-4bd4-a421-6dc667e6ac53)\n", - "2025-02-12 17:41:04,709 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:04,710 INFO Starting job - tight relax 2 deformation 0 (80716899-0d2a-4ddd-b4ee-662df153adf8)\n" + "2025-02-12 19:47:15,064 INFO Finished job - tight relax 1 deformation 5 (ae817857-2e89-4f5c-a85a-5b57563d0c0d)\n", + "2025-02-12 19:47:15,064 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:15,065 INFO Starting job - tight relax 2 deformation 0 (61c6ba30-8669-4969-ae7d-af39abec6f6c)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-04-710422-36802/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-15-065148-97023/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -460,16 +460,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:05,981 INFO Finished job - tight relax 2 deformation 0 (80716899-0d2a-4ddd-b4ee-662df153adf8)\n", - "2025-02-12 17:41:05,982 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:05,982 INFO Starting job - tight relax 2 deformation 1 (0da1eb26-cd3c-4aa2-8421-146ef7294879)\n" + "2025-02-12 19:47:16,129 INFO Finished job - tight relax 2 deformation 0 (61c6ba30-8669-4969-ae7d-af39abec6f6c)\n", + "2025-02-12 19:47:16,130 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:16,131 INFO Starting job - tight relax 2 deformation 1 (06bd1689-fe1a-46c1-be5c-e8012161e458)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-05-982558-74358/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-16-131368-59705/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -479,16 +479,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:07,082 INFO Finished job - tight relax 2 deformation 1 (0da1eb26-cd3c-4aa2-8421-146ef7294879)\n", - "2025-02-12 17:41:07,083 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:07,085 INFO Starting job - tight relax 2 deformation 2 (67e67db2-9bd0-4320-b83e-36daa7b645f6)\n" + "2025-02-12 19:47:17,046 INFO Finished job - tight relax 2 deformation 1 (06bd1689-fe1a-46c1-be5c-e8012161e458)\n", + "2025-02-12 19:47:17,047 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:17,048 INFO Starting job - tight relax 2 deformation 2 (0c74e1df-e05e-4e07-a5dc-2728bccd818a)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-07-084520-65149/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-17-047607-48584/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -498,16 +498,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:08,183 INFO Finished job - tight relax 2 deformation 2 (67e67db2-9bd0-4320-b83e-36daa7b645f6)\n", - "2025-02-12 17:41:08,184 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:08,185 INFO Starting job - tight relax 2 deformation 3 (7d3c806e-484b-4147-befe-8f14c3a38c0b)\n" + "2025-02-12 19:47:18,097 INFO Finished job - tight relax 2 deformation 2 (0c74e1df-e05e-4e07-a5dc-2728bccd818a)\n", + "2025-02-12 19:47:18,101 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:18,102 INFO Starting job - tight relax 2 deformation 3 (d88b02d1-62e3-4293-b2cd-8a5df0e5a107)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-08-185280-85864/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-18-102145-56398/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -517,16 +517,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:09,203 INFO Finished job - tight relax 2 deformation 3 (7d3c806e-484b-4147-befe-8f14c3a38c0b)\n", - "2025-02-12 17:41:09,203 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:09,204 INFO Starting job - tight relax 2 deformation 4 (b861cd5a-7787-42b1-9ab3-e2cda7d3ed2a)\n" + "2025-02-12 19:47:19,036 INFO Finished job - tight relax 2 deformation 3 (d88b02d1-62e3-4293-b2cd-8a5df0e5a107)\n", + "2025-02-12 19:47:19,036 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:19,037 INFO Starting job - tight relax 2 deformation 4 (611c25ff-1ec2-446f-a075-0d4cb80e9b96)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-09-204496-87030/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-19-037094-28354/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -536,16 +536,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:10,402 INFO Finished job - tight relax 2 deformation 4 (b861cd5a-7787-42b1-9ab3-e2cda7d3ed2a)\n", - "2025-02-12 17:41:10,402 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:10,403 INFO Starting job - tight relax 2 deformation 5 (ff04b5fb-df10-40b8-9f96-efb22fc99d1e)\n" + "2025-02-12 19:47:19,971 INFO Finished job - tight relax 2 deformation 4 (611c25ff-1ec2-446f-a075-0d4cb80e9b96)\n", + "2025-02-12 19:47:19,972 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:19,972 INFO Starting job - tight relax 2 deformation 5 (efaba483-4ef1-4193-8523-e3ef769f517a)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-10-403300-87181/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-19-972585-45813/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n", "Error in parsing bandstructure\n", "VASP doesn't properly output efermi for IBRION == 1\n" @@ -555,21 +555,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:11,071 INFO Finished job - tight relax 2 deformation 5 (ff04b5fb-df10-40b8-9f96-efb22fc99d1e)\n", - "2025-02-12 17:41:11,071 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:11,072 INFO Starting job - get_supercell_size (c16b2a05-211d-48e2-bfd7-490b0e5c7776)\n", + "2025-02-12 19:47:20,609 INFO Finished job - tight relax 2 deformation 5 (efaba483-4ef1-4193-8523-e3ef769f517a)\n", + "2025-02-12 19:47:20,609 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:20,610 INFO Starting job - get_supercell_size (bcb0eb7d-d427-42bd-99e5-89a4813b9dab)\n", "[[2, 0, 0], [0, 2, 0], [0, 0, 2]]\n", - "2025-02-12 17:41:11,178 INFO Finished job - get_supercell_size (c16b2a05-211d-48e2-bfd7-490b0e5c7776)\n", - "2025-02-12 17:41:11,179 INFO Starting job - get_phonon_jobs (b29cb68f-bc23-4781-a287-e1a5a0b39e62)\n", - "2025-02-12 17:41:12,464 INFO Finished job - get_phonon_jobs (b29cb68f-bc23-4781-a287-e1a5a0b39e62)\n", - "2025-02-12 17:41:12,503 INFO Starting job - dft phonon static eos deformation 1 (1bdb553b-336e-43b2-be5d-1fd4ebad4222)\n" + "2025-02-12 19:47:20,702 INFO Finished job - get_supercell_size (bcb0eb7d-d427-42bd-99e5-89a4813b9dab)\n", + "2025-02-12 19:47:20,703 INFO Starting job - get_phonon_jobs (6668b4f9-14e6-4972-a7fd-2b8848913b18)\n", + "2025-02-12 19:47:21,710 INFO Finished job - get_phonon_jobs (6668b4f9-14e6-4972-a7fd-2b8848913b18)\n", + "2025-02-12 19:47:21,745 INFO Starting job - dft phonon static eos deformation 1 (52ac2dc2-ca88-4ef9-a7e8-5a38fa2a8097)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-12-503306-51458/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-21-745663-31113/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -577,11 +577,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:13,637 INFO Finished job - dft phonon static eos deformation 1 (1bdb553b-336e-43b2-be5d-1fd4ebad4222)\n", - "2025-02-12 17:41:13,639 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:13,640 INFO Starting job - generate_phonon_displacements eos deformation 1 (b237af3f-d344-4919-b10f-7b88dbad145e)\n", - "2025-02-12 17:41:13,828 INFO Finished job - generate_phonon_displacements eos deformation 1 (b237af3f-d344-4919-b10f-7b88dbad145e)\n", - "2025-02-12 17:41:13,829 INFO Starting job - dft phonon static eos deformation 2 (aebdee1c-b2c5-4f05-9dc3-218129b95871)\n" + "2025-02-12 19:47:22,685 INFO Finished job - dft phonon static eos deformation 1 (52ac2dc2-ca88-4ef9-a7e8-5a38fa2a8097)\n", + "2025-02-12 19:47:22,686 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:22,686 INFO Starting job - generate_phonon_displacements eos deformation 1 (efae740b-6346-48d0-99f4-9b36fd8cdec9)\n", + "2025-02-12 19:47:22,878 INFO Finished job - generate_phonon_displacements eos deformation 1 (efae740b-6346-48d0-99f4-9b36fd8cdec9)\n", + "2025-02-12 19:47:22,879 INFO Starting job - dft phonon static eos deformation 2 (a3c01f73-7744-44f9-ba1a-681446b7eba1)\n" ] }, { @@ -590,7 +590,7 @@ "text": [ "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", " response = function(*self.function_args, **self.function_kwargs)\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-13-828754-22322/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-22-879338-64264/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -598,18 +598,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:15,100 INFO Finished job - dft phonon static eos deformation 2 (aebdee1c-b2c5-4f05-9dc3-218129b95871)\n", - "2025-02-12 17:41:15,100 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:15,101 INFO Starting job - generate_phonon_displacements eos deformation 2 (8e779067-f676-40a7-9587-fb9f4b5aa9c8)\n", - "2025-02-12 17:41:15,289 INFO Finished job - generate_phonon_displacements eos deformation 2 (8e779067-f676-40a7-9587-fb9f4b5aa9c8)\n", - "2025-02-12 17:41:15,291 INFO Starting job - dft phonon static eos deformation 3 (a53816cf-0ed4-4be7-a538-d4a16e72c3f2)\n" + "2025-02-12 19:47:24,134 INFO Finished job - dft phonon static eos deformation 2 (a3c01f73-7744-44f9-ba1a-681446b7eba1)\n", + "2025-02-12 19:47:24,134 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:24,135 INFO Starting job - generate_phonon_displacements eos deformation 2 (d8e9e72f-455e-48ee-a1f1-b951b4fff9f6)\n", + "2025-02-12 19:47:24,320 INFO Finished job - generate_phonon_displacements eos deformation 2 (d8e9e72f-455e-48ee-a1f1-b951b4fff9f6)\n", + "2025-02-12 19:47:24,321 INFO Starting job - dft phonon static eos deformation 3 (8fd434b5-531f-4066-a221-1b0def507d7a)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-15-290655-95877/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-24-321184-21957/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -617,18 +617,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:16,395 INFO Finished job - dft phonon static eos deformation 3 (a53816cf-0ed4-4be7-a538-d4a16e72c3f2)\n", - "2025-02-12 17:41:16,396 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:16,397 INFO Starting job - generate_phonon_displacements eos deformation 3 (b0374273-2bc7-42f6-8d5b-ec7cd92b3ae2)\n", - "2025-02-12 17:41:16,581 INFO Finished job - generate_phonon_displacements eos deformation 3 (b0374273-2bc7-42f6-8d5b-ec7cd92b3ae2)\n", - "2025-02-12 17:41:16,583 INFO Starting job - dft phonon static eos deformation 4 (33375d00-c98a-4b57-bd51-4f31d7f6d8c4)\n" + "2025-02-12 19:47:25,278 INFO Finished job - dft phonon static eos deformation 3 (8fd434b5-531f-4066-a221-1b0def507d7a)\n", + "2025-02-12 19:47:25,279 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:25,279 INFO Starting job - generate_phonon_displacements eos deformation 3 (5c043a94-d142-497b-8c5d-7e4e7f371a59)\n", + "2025-02-12 19:47:25,458 INFO Finished job - generate_phonon_displacements eos deformation 3 (5c043a94-d142-497b-8c5d-7e4e7f371a59)\n", + "2025-02-12 19:47:25,459 INFO Starting job - dft phonon static eos deformation 4 (5234f46a-a21e-4d72-9a2a-06ebec5296c5)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-16-582803-63738/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-25-459143-12350/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -636,18 +636,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:17,670 INFO Finished job - dft phonon static eos deformation 4 (33375d00-c98a-4b57-bd51-4f31d7f6d8c4)\n", - "2025-02-12 17:41:17,671 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:17,673 INFO Starting job - generate_phonon_displacements eos deformation 4 (94c6f4c8-9fe2-4b0a-bff4-9b954e74c364)\n", - "2025-02-12 17:41:17,861 INFO Finished job - generate_phonon_displacements eos deformation 4 (94c6f4c8-9fe2-4b0a-bff4-9b954e74c364)\n", - "2025-02-12 17:41:17,863 INFO Starting job - dft phonon static eos deformation 5 (4d5004bc-30c1-4fb1-9524-97654aae42e1)\n" + "2025-02-12 19:47:26,392 INFO Finished job - dft phonon static eos deformation 4 (5234f46a-a21e-4d72-9a2a-06ebec5296c5)\n", + "2025-02-12 19:47:26,392 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:26,393 INFO Starting job - generate_phonon_displacements eos deformation 4 (1dc4eb5c-8a40-455a-90ab-8bcf4e12ff72)\n", + "2025-02-12 19:47:26,574 INFO Finished job - generate_phonon_displacements eos deformation 4 (1dc4eb5c-8a40-455a-90ab-8bcf4e12ff72)\n", + "2025-02-12 19:47:26,575 INFO Starting job - dft phonon static eos deformation 5 (ffaeb8fd-9843-49e1-b8ef-bba13d0e6b1e)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-17-862805-66119/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-26-575261-72229/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -655,18 +655,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:18,924 INFO Finished job - dft phonon static eos deformation 5 (4d5004bc-30c1-4fb1-9524-97654aae42e1)\n", - "2025-02-12 17:41:18,925 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:18,925 INFO Starting job - generate_phonon_displacements eos deformation 5 (2e4ad379-85d4-41b3-a5b9-7d31bfc9000e)\n", - "2025-02-12 17:41:19,108 INFO Finished job - generate_phonon_displacements eos deformation 5 (2e4ad379-85d4-41b3-a5b9-7d31bfc9000e)\n", - "2025-02-12 17:41:19,109 INFO Starting job - dft phonon static eos deformation 6 (f4e6d90b-478d-4322-aab6-06662627aa38)\n" + "2025-02-12 19:47:27,521 INFO Finished job - dft phonon static eos deformation 5 (ffaeb8fd-9843-49e1-b8ef-bba13d0e6b1e)\n", + "2025-02-12 19:47:27,521 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:27,522 INFO Starting job - generate_phonon_displacements eos deformation 5 (db1692d6-3c7a-4202-9101-6fa9dd74fad0)\n", + "2025-02-12 19:47:27,705 INFO Finished job - generate_phonon_displacements eos deformation 5 (db1692d6-3c7a-4202-9101-6fa9dd74fad0)\n", + "2025-02-12 19:47:27,706 INFO Starting job - dft phonon static eos deformation 6 (aba8bd5f-a531-48a9-bdb9-868c1a291710)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-19-108991-30317/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-27-705670-64869/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -674,18 +674,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:20,218 INFO Finished job - dft phonon static eos deformation 6 (f4e6d90b-478d-4322-aab6-06662627aa38)\n", - "2025-02-12 17:41:20,219 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:20,220 INFO Starting job - generate_phonon_displacements eos deformation 6 (f043b69d-471a-41b9-8b7e-37b7b38eda3c)\n", - "2025-02-12 17:41:20,401 INFO Finished job - generate_phonon_displacements eos deformation 6 (f043b69d-471a-41b9-8b7e-37b7b38eda3c)\n", - "2025-02-12 17:41:20,402 INFO Starting job - dft phonon static eos deformation 7 (493c96ca-8f23-4bce-a731-39d6884de22e)\n" + "2025-02-12 19:47:28,643 INFO Finished job - dft phonon static eos deformation 6 (aba8bd5f-a531-48a9-bdb9-868c1a291710)\n", + "2025-02-12 19:47:28,644 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:28,645 INFO Starting job - generate_phonon_displacements eos deformation 6 (79ca4b63-7b11-4b12-bd1e-8cc5e57d2fd1)\n", + "2025-02-12 19:47:28,822 INFO Finished job - generate_phonon_displacements eos deformation 6 (79ca4b63-7b11-4b12-bd1e-8cc5e57d2fd1)\n", + "2025-02-12 19:47:28,823 INFO Starting job - dft phonon static eos deformation 7 (dafac392-02e2-450c-a438-9f039f16bd38)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-20-402334-74970/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-28-823145-41919/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -693,20 +693,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:21,345 INFO Finished job - dft phonon static eos deformation 7 (493c96ca-8f23-4bce-a731-39d6884de22e)\n", - "2025-02-12 17:41:21,346 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:21,346 INFO Starting job - generate_phonon_displacements eos deformation 7 (8c8ca709-6a5c-48fc-92a2-3a21cebe4b5b)\n", - "2025-02-12 17:41:21,530 INFO Finished job - generate_phonon_displacements eos deformation 7 (8c8ca709-6a5c-48fc-92a2-3a21cebe4b5b)\n", - "2025-02-12 17:41:21,531 INFO Starting job - run_phonon_displacements eos deformation 1 (0dd9edfd-9f45-4cbc-a8aa-b8f68aaa12df)\n", - "2025-02-12 17:41:21,659 INFO Finished job - run_phonon_displacements eos deformation 1 (0dd9edfd-9f45-4cbc-a8aa-b8f68aaa12df)\n", - "2025-02-12 17:41:21,669 INFO Starting job - dft phonon static 1/1 eos deformation 1 (5cf2bd56-fee5-4978-9dd8-33c5fd13c607)\n" + "2025-02-12 19:47:29,642 INFO Finished job - dft phonon static eos deformation 7 (dafac392-02e2-450c-a438-9f039f16bd38)\n", + "2025-02-12 19:47:29,642 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:29,643 INFO Starting job - generate_phonon_displacements eos deformation 7 (2bdf2208-b312-41e1-9b18-3b8fe01893b8)\n", + "2025-02-12 19:47:29,821 INFO Finished job - generate_phonon_displacements eos deformation 7 (2bdf2208-b312-41e1-9b18-3b8fe01893b8)\n", + "2025-02-12 19:47:29,822 INFO Starting job - run_phonon_displacements eos deformation 1 (327e1548-5c3e-4922-8036-1f05c22d61ba)\n", + "2025-02-12 19:47:29,946 INFO Finished job - run_phonon_displacements eos deformation 1 (327e1548-5c3e-4922-8036-1f05c22d61ba)\n", + "2025-02-12 19:47:29,955 INFO Starting job - dft phonon static 1/1 eos deformation 1 (4d0fe4ba-4c73-4158-90e5-cd21e65e3552)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-21-668705-65204/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-29-955382-78882/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -714,20 +714,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:22,802 INFO Finished job - dft phonon static 1/1 eos deformation 1 (5cf2bd56-fee5-4978-9dd8-33c5fd13c607)\n", - "2025-02-12 17:41:22,803 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:22,804 INFO Starting job - store_inputs eos deformation 1 (0dd9edfd-9f45-4cbc-a8aa-b8f68aaa12df, 2)\n", - "2025-02-12 17:41:22,806 INFO Finished job - store_inputs eos deformation 1 (0dd9edfd-9f45-4cbc-a8aa-b8f68aaa12df, 2)\n", - "2025-02-12 17:41:22,807 INFO Starting job - run_phonon_displacements eos deformation 2 (33b099f5-628f-4668-adcf-eedd81ca576c)\n", - "2025-02-12 17:41:22,944 INFO Finished job - run_phonon_displacements eos deformation 2 (33b099f5-628f-4668-adcf-eedd81ca576c)\n", - "2025-02-12 17:41:22,956 INFO Starting job - dft phonon static 1/1 eos deformation 2 (89c1cd10-367e-49d0-9aa1-909dd8e7f00b)\n" + "2025-02-12 19:47:30,947 INFO Finished job - dft phonon static 1/1 eos deformation 1 (4d0fe4ba-4c73-4158-90e5-cd21e65e3552)\n", + "2025-02-12 19:47:30,948 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:30,948 INFO Starting job - store_inputs eos deformation 1 (327e1548-5c3e-4922-8036-1f05c22d61ba, 2)\n", + "2025-02-12 19:47:30,949 INFO Finished job - store_inputs eos deformation 1 (327e1548-5c3e-4922-8036-1f05c22d61ba, 2)\n", + "2025-02-12 19:47:30,950 INFO Starting job - run_phonon_displacements eos deformation 2 (773a6d3b-9d5f-4c06-8ea6-e44d1fc863b4)\n", + "2025-02-12 19:47:31,064 INFO Finished job - run_phonon_displacements eos deformation 2 (773a6d3b-9d5f-4c06-8ea6-e44d1fc863b4)\n", + "2025-02-12 19:47:31,071 INFO Starting job - dft phonon static 1/1 eos deformation 2 (2af444fd-6b11-46f1-a56a-41957a703944)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-22-955850-32022/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-31-071743-51667/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -735,20 +735,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:24,300 INFO Finished job - dft phonon static 1/1 eos deformation 2 (89c1cd10-367e-49d0-9aa1-909dd8e7f00b)\n", - "2025-02-12 17:41:24,301 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:24,303 INFO Starting job - store_inputs eos deformation 2 (33b099f5-628f-4668-adcf-eedd81ca576c, 2)\n", - "2025-02-12 17:41:24,304 INFO Finished job - store_inputs eos deformation 2 (33b099f5-628f-4668-adcf-eedd81ca576c, 2)\n", - "2025-02-12 17:41:24,305 INFO Starting job - run_phonon_displacements eos deformation 3 (31ae2ba8-d79c-4d39-b121-2d83dc09057b)\n", - "2025-02-12 17:41:24,425 INFO Finished job - run_phonon_displacements eos deformation 3 (31ae2ba8-d79c-4d39-b121-2d83dc09057b)\n", - "2025-02-12 17:41:24,434 INFO Starting job - dft phonon static 1/1 eos deformation 3 (b757d2f3-e0af-4a36-8b30-590d66efde42)\n" + "2025-02-12 19:47:32,241 INFO Finished job - dft phonon static 1/1 eos deformation 2 (2af444fd-6b11-46f1-a56a-41957a703944)\n", + "2025-02-12 19:47:32,243 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:32,244 INFO Starting job - store_inputs eos deformation 2 (773a6d3b-9d5f-4c06-8ea6-e44d1fc863b4, 2)\n", + "2025-02-12 19:47:32,245 INFO Finished job - store_inputs eos deformation 2 (773a6d3b-9d5f-4c06-8ea6-e44d1fc863b4, 2)\n", + "2025-02-12 19:47:32,246 INFO Starting job - run_phonon_displacements eos deformation 3 (559ad298-9342-4207-918a-ec2bd0ad885b)\n", + "2025-02-12 19:47:32,362 INFO Finished job - run_phonon_displacements eos deformation 3 (559ad298-9342-4207-918a-ec2bd0ad885b)\n", + "2025-02-12 19:47:32,370 INFO Starting job - dft phonon static 1/1 eos deformation 3 (5a0883c5-dd6a-4b87-8b98-6d5c7a15155e)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-24-433841-48795/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-32-369988-93340/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -756,20 +756,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:25,578 INFO Finished job - dft phonon static 1/1 eos deformation 3 (b757d2f3-e0af-4a36-8b30-590d66efde42)\n", - "2025-02-12 17:41:25,579 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:25,579 INFO Starting job - store_inputs eos deformation 3 (31ae2ba8-d79c-4d39-b121-2d83dc09057b, 2)\n", - "2025-02-12 17:41:25,581 INFO Finished job - store_inputs eos deformation 3 (31ae2ba8-d79c-4d39-b121-2d83dc09057b, 2)\n", - "2025-02-12 17:41:25,582 INFO Starting job - run_phonon_displacements eos deformation 4 (75ecb73d-716d-4522-a642-6a745ed9eb63)\n", - "2025-02-12 17:41:25,701 INFO Finished job - run_phonon_displacements eos deformation 4 (75ecb73d-716d-4522-a642-6a745ed9eb63)\n", - "2025-02-12 17:41:25,709 INFO Starting job - dft phonon static 1/1 eos deformation 4 (710a0275-4bf4-481f-a339-e630142e4072)\n" + "2025-02-12 19:47:33,340 INFO Finished job - dft phonon static 1/1 eos deformation 3 (5a0883c5-dd6a-4b87-8b98-6d5c7a15155e)\n", + "2025-02-12 19:47:33,341 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:33,341 INFO Starting job - store_inputs eos deformation 3 (559ad298-9342-4207-918a-ec2bd0ad885b, 2)\n", + "2025-02-12 19:47:33,343 INFO Finished job - store_inputs eos deformation 3 (559ad298-9342-4207-918a-ec2bd0ad885b, 2)\n", + "2025-02-12 19:47:33,343 INFO Starting job - run_phonon_displacements eos deformation 4 (a8b9b539-030e-481e-ad0f-a0fedeb35d0a)\n", + "2025-02-12 19:47:33,461 INFO Finished job - run_phonon_displacements eos deformation 4 (a8b9b539-030e-481e-ad0f-a0fedeb35d0a)\n", + "2025-02-12 19:47:33,470 INFO Starting job - dft phonon static 1/1 eos deformation 4 (ce2929a8-1592-4395-b169-92e86d131bdb)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-25-709582-81603/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-33-470249-59724/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -777,20 +777,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:26,886 INFO Finished job - dft phonon static 1/1 eos deformation 4 (710a0275-4bf4-481f-a339-e630142e4072)\n", - "2025-02-12 17:41:26,888 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:26,889 INFO Starting job - store_inputs eos deformation 4 (75ecb73d-716d-4522-a642-6a745ed9eb63, 2)\n", - "2025-02-12 17:41:26,891 INFO Finished job - store_inputs eos deformation 4 (75ecb73d-716d-4522-a642-6a745ed9eb63, 2)\n", - "2025-02-12 17:41:26,892 INFO Starting job - run_phonon_displacements eos deformation 5 (573b53b4-5ec0-475f-897c-27f0ed2733f7)\n", - "2025-02-12 17:41:27,011 INFO Finished job - run_phonon_displacements eos deformation 5 (573b53b4-5ec0-475f-897c-27f0ed2733f7)\n", - "2025-02-12 17:41:27,019 INFO Starting job - dft phonon static 1/1 eos deformation 5 (836f013b-caf4-4237-aed8-de0cd0f2b1e5)\n" + "2025-02-12 19:47:34,434 INFO Finished job - dft phonon static 1/1 eos deformation 4 (ce2929a8-1592-4395-b169-92e86d131bdb)\n", + "2025-02-12 19:47:34,435 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:34,436 INFO Starting job - store_inputs eos deformation 4 (a8b9b539-030e-481e-ad0f-a0fedeb35d0a, 2)\n", + "2025-02-12 19:47:34,437 INFO Finished job - store_inputs eos deformation 4 (a8b9b539-030e-481e-ad0f-a0fedeb35d0a, 2)\n", + "2025-02-12 19:47:34,437 INFO Starting job - run_phonon_displacements eos deformation 5 (47cc4fcf-0a53-4489-ba22-94e820dfa5ba)\n", + "2025-02-12 19:47:34,552 INFO Finished job - run_phonon_displacements eos deformation 5 (47cc4fcf-0a53-4489-ba22-94e820dfa5ba)\n", + "2025-02-12 19:47:34,560 INFO Starting job - dft phonon static 1/1 eos deformation 5 (7f139a49-9a2e-4a54-ad92-150a66975e96)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-27-019070-56466/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-34-560260-29290/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -798,20 +798,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:28,127 INFO Finished job - dft phonon static 1/1 eos deformation 5 (836f013b-caf4-4237-aed8-de0cd0f2b1e5)\n", - "2025-02-12 17:41:28,127 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:28,128 INFO Starting job - store_inputs eos deformation 5 (573b53b4-5ec0-475f-897c-27f0ed2733f7, 2)\n", - "2025-02-12 17:41:28,129 INFO Finished job - store_inputs eos deformation 5 (573b53b4-5ec0-475f-897c-27f0ed2733f7, 2)\n", - "2025-02-12 17:41:28,130 INFO Starting job - run_phonon_displacements eos deformation 6 (c0a49cb8-b86d-42ce-9edd-e3ee0e2ba90c)\n", - "2025-02-12 17:41:28,532 INFO Finished job - run_phonon_displacements eos deformation 6 (c0a49cb8-b86d-42ce-9edd-e3ee0e2ba90c)\n", - "2025-02-12 17:41:28,541 INFO Starting job - dft phonon static 1/1 eos deformation 6 (9e6cac01-d0e2-43ce-bef9-b6a518c82c9e)\n" + "2025-02-12 19:47:35,536 INFO Finished job - dft phonon static 1/1 eos deformation 5 (7f139a49-9a2e-4a54-ad92-150a66975e96)\n", + "2025-02-12 19:47:35,537 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:35,538 INFO Starting job - store_inputs eos deformation 5 (47cc4fcf-0a53-4489-ba22-94e820dfa5ba, 2)\n", + "2025-02-12 19:47:35,539 INFO Finished job - store_inputs eos deformation 5 (47cc4fcf-0a53-4489-ba22-94e820dfa5ba, 2)\n", + "2025-02-12 19:47:35,540 INFO Starting job - run_phonon_displacements eos deformation 6 (7723c329-f11b-4511-96b3-080fa574154b)\n", + "2025-02-12 19:47:35,654 INFO Finished job - run_phonon_displacements eos deformation 6 (7723c329-f11b-4511-96b3-080fa574154b)\n", + "2025-02-12 19:47:35,661 INFO Starting job - dft phonon static 1/1 eos deformation 6 (3d705e18-8a04-4b36-9c0b-9bd1f5bba262)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-28-541254-78325/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-35-661730-61610/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -819,20 +819,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:29,677 INFO Finished job - dft phonon static 1/1 eos deformation 6 (9e6cac01-d0e2-43ce-bef9-b6a518c82c9e)\n", - "2025-02-12 17:41:29,678 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:29,679 INFO Starting job - store_inputs eos deformation 6 (c0a49cb8-b86d-42ce-9edd-e3ee0e2ba90c, 2)\n", - "2025-02-12 17:41:29,681 INFO Finished job - store_inputs eos deformation 6 (c0a49cb8-b86d-42ce-9edd-e3ee0e2ba90c, 2)\n", - "2025-02-12 17:41:29,682 INFO Starting job - run_phonon_displacements eos deformation 7 (5f7f52cd-8b60-43ca-89d2-4aede426a9ff)\n", - "2025-02-12 17:41:29,798 INFO Finished job - run_phonon_displacements eos deformation 7 (5f7f52cd-8b60-43ca-89d2-4aede426a9ff)\n", - "2025-02-12 17:41:29,806 INFO Starting job - dft phonon static 1/1 eos deformation 7 (ee164898-de00-42ae-b6c3-c7cee8ac1fe7)\n" + "2025-02-12 19:47:36,626 INFO Finished job - dft phonon static 1/1 eos deformation 6 (3d705e18-8a04-4b36-9c0b-9bd1f5bba262)\n", + "2025-02-12 19:47:36,627 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:36,628 INFO Starting job - store_inputs eos deformation 6 (7723c329-f11b-4511-96b3-080fa574154b, 2)\n", + "2025-02-12 19:47:36,630 INFO Finished job - store_inputs eos deformation 6 (7723c329-f11b-4511-96b3-080fa574154b, 2)\n", + "2025-02-12 19:47:36,630 INFO Starting job - run_phonon_displacements eos deformation 7 (ff8f86f0-451a-4ece-858c-0edb0d5073c1)\n", + "2025-02-12 19:47:36,744 INFO Finished job - run_phonon_displacements eos deformation 7 (ff8f86f0-451a-4ece-858c-0edb0d5073c1)\n", + "2025-02-12 19:47:36,753 INFO Starting job - dft phonon static 1/1 eos deformation 7 (0ed38b7b-f3b2-42b5-bf5a-1caba2e0be52)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-29-806699-13582/POTCAR.spec is not gzipped, skipping...\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-36-753075-52849/POTCAR.spec is not gzipped, skipping...\n", " file_client.gunzip(directory / file, host=host, force=force)\n" ] }, @@ -840,11 +840,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:30,877 INFO Finished job - dft phonon static 1/1 eos deformation 7 (ee164898-de00-42ae-b6c3-c7cee8ac1fe7)\n", - "2025-02-12 17:41:30,878 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 17:41:30,880 INFO Starting job - store_inputs eos deformation 7 (5f7f52cd-8b60-43ca-89d2-4aede426a9ff, 2)\n", - "2025-02-12 17:41:30,881 INFO Finished job - store_inputs eos deformation 7 (5f7f52cd-8b60-43ca-89d2-4aede426a9ff, 2)\n", - "2025-02-12 17:41:30,882 INFO Starting job - generate_frequencies_eigenvectors eos deformation 1 (09ec220c-9f96-491f-ad1f-a2d52fc938d5)\n" + "2025-02-12 19:47:37,647 INFO Finished job - dft phonon static 1/1 eos deformation 7 (0ed38b7b-f3b2-42b5-bf5a-1caba2e0be52)\n", + "2025-02-12 19:47:37,648 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 19:47:37,649 INFO Starting job - store_inputs eos deformation 7 (ff8f86f0-451a-4ece-858c-0edb0d5073c1, 2)\n", + "2025-02-12 19:47:37,650 INFO Finished job - store_inputs eos deformation 7 (ff8f86f0-451a-4ece-858c-0edb0d5073c1, 2)\n", + "2025-02-12 19:47:37,651 INFO Starting job - generate_frequencies_eigenvectors eos deformation 1 (98f01b9e-e077-4c95-9ec2-c3c0804ce17c)\n" ] }, { @@ -872,8 +872,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:38,795 INFO Finished job - generate_frequencies_eigenvectors eos deformation 1 (09ec220c-9f96-491f-ad1f-a2d52fc938d5)\n", - "2025-02-12 17:41:38,797 INFO Starting job - generate_frequencies_eigenvectors eos deformation 2 (0d524c18-51ce-4cdd-8655-cba845e6956b)\n" + "2025-02-12 19:47:45,715 INFO Finished job - generate_frequencies_eigenvectors eos deformation 1 (98f01b9e-e077-4c95-9ec2-c3c0804ce17c)\n", + "2025-02-12 19:47:45,716 INFO Starting job - generate_frequencies_eigenvectors eos deformation 2 (baf15518-066e-43d9-a2fa-4aa991e1abc6)\n" ] }, { @@ -901,8 +901,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:46,445 INFO Finished job - generate_frequencies_eigenvectors eos deformation 2 (0d524c18-51ce-4cdd-8655-cba845e6956b)\n", - "2025-02-12 17:41:46,447 INFO Starting job - generate_frequencies_eigenvectors eos deformation 3 (e5e0512c-98a1-46ef-b6ca-338b11a46333)\n" + "2025-02-12 19:47:53,593 INFO Finished job - generate_frequencies_eigenvectors eos deformation 2 (baf15518-066e-43d9-a2fa-4aa991e1abc6)\n", + "2025-02-12 19:47:53,594 INFO Starting job - generate_frequencies_eigenvectors eos deformation 3 (9f3bca5e-8bd4-4e46-841a-2371595ebdef)\n" ] }, { @@ -930,8 +930,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:41:54,162 INFO Finished job - generate_frequencies_eigenvectors eos deformation 3 (e5e0512c-98a1-46ef-b6ca-338b11a46333)\n", - "2025-02-12 17:41:54,164 INFO Starting job - generate_frequencies_eigenvectors eos deformation 4 (f1cc0b11-e936-4c77-a49c-40c55e638760)\n" + "2025-02-12 19:48:01,055 INFO Finished job - generate_frequencies_eigenvectors eos deformation 3 (9f3bca5e-8bd4-4e46-841a-2371595ebdef)\n", + "2025-02-12 19:48:01,056 INFO Starting job - generate_frequencies_eigenvectors eos deformation 4 (d8487050-4877-46ad-8f54-ed64365328a4)\n" ] }, { @@ -959,8 +959,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:42:01,928 INFO Finished job - generate_frequencies_eigenvectors eos deformation 4 (f1cc0b11-e936-4c77-a49c-40c55e638760)\n", - "2025-02-12 17:42:01,930 INFO Starting job - generate_frequencies_eigenvectors eos deformation 5 (bcaf9e52-5d6b-4f05-913a-45b7159f4208)\n" + "2025-02-12 19:48:08,790 INFO Finished job - generate_frequencies_eigenvectors eos deformation 4 (d8487050-4877-46ad-8f54-ed64365328a4)\n", + "2025-02-12 19:48:08,791 INFO Starting job - generate_frequencies_eigenvectors eos deformation 5 (06ec0aa1-a33b-4732-84e8-cedd8d49adf3)\n" ] }, { @@ -988,8 +988,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:42:09,525 INFO Finished job - generate_frequencies_eigenvectors eos deformation 5 (bcaf9e52-5d6b-4f05-913a-45b7159f4208)\n", - "2025-02-12 17:42:09,527 INFO Starting job - generate_frequencies_eigenvectors eos deformation 6 (2027b3e8-86d0-425b-955b-09909cb3b5eb)\n" + "2025-02-12 19:48:16,375 INFO Finished job - generate_frequencies_eigenvectors eos deformation 5 (06ec0aa1-a33b-4732-84e8-cedd8d49adf3)\n", + "2025-02-12 19:48:16,376 INFO Starting job - generate_frequencies_eigenvectors eos deformation 6 (c2170f53-cd99-40de-a7da-c6b362df5427)\n" ] }, { @@ -1017,8 +1017,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:42:17,464 INFO Finished job - generate_frequencies_eigenvectors eos deformation 6 (2027b3e8-86d0-425b-955b-09909cb3b5eb)\n", - "2025-02-12 17:42:17,466 INFO Starting job - generate_frequencies_eigenvectors eos deformation 7 (8912ece5-0eac-4e72-a17a-4ab07e3fa0bd)\n" + "2025-02-12 19:48:23,709 INFO Finished job - generate_frequencies_eigenvectors eos deformation 6 (c2170f53-cd99-40de-a7da-c6b362df5427)\n", + "2025-02-12 19:48:23,710 INFO Starting job - generate_frequencies_eigenvectors eos deformation 7 (ddd60892-e6dc-48ee-b4c8-b262a67077fd)\n" ] }, { @@ -1046,12 +1046,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-02-12 17:42:25,673 INFO Finished job - generate_frequencies_eigenvectors eos deformation 7 (8912ece5-0eac-4e72-a17a-4ab07e3fa0bd)\n", - "2025-02-12 17:42:25,674 INFO Starting job - store_inputs (b29cb68f-bc23-4781-a287-e1a5a0b39e62, 2)\n", - "2025-02-12 17:42:25,677 INFO Finished job - store_inputs (b29cb68f-bc23-4781-a287-e1a5a0b39e62, 2)\n", - "2025-02-12 17:42:25,678 INFO Starting job - analyze_free_energy (8fa8159d-95dd-4710-bf76-441706d37521)\n", - "2025-02-12 17:42:26,634 INFO Finished job - analyze_free_energy (8fa8159d-95dd-4710-bf76-441706d37521)\n", - "2025-02-12 17:42:26,635 INFO Finished executing jobs locally\n" + "2025-02-12 19:48:31,238 INFO Finished job - generate_frequencies_eigenvectors eos deformation 7 (ddd60892-e6dc-48ee-b4c8-b262a67077fd)\n", + "2025-02-12 19:48:31,239 INFO Starting job - store_inputs (6668b4f9-14e6-4972-a7fd-2b8848913b18, 2)\n", + "2025-02-12 19:48:31,241 INFO Finished job - store_inputs (6668b4f9-14e6-4972-a7fd-2b8848913b18, 2)\n", + "2025-02-12 19:48:31,242 INFO Starting job - analyze_free_energy (8fa7ebdd-cb3c-4eb9-b7ab-3e92b0707ab4)\n", + "2025-02-12 19:48:32,175 INFO Finished job - analyze_free_energy (8fa7ebdd-cb3c-4eb9-b7ab-3e92b0707ab4)\n", + "2025-02-12 19:48:32,175 INFO Finished executing jobs locally\n" ] }, { @@ -1130,8 +1130,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-12T16:49:57.197344Z", - "start_time": "2025-02-12T16:49:57.190858Z" + "end_time": "2025-02-12T18:48:32.619391Z", + "start_time": "2025-02-12T18:48:32.615542Z" } }, "cell_type": "code", @@ -1155,7 +1155,7 @@ ], "id": "1e88d3d7664d2975", "outputs": [], - "execution_count": 14 + "execution_count": 6 }, { "metadata": {}, @@ -1166,8 +1166,8 @@ { "metadata": { "ExecuteTime": { - "end_time": "2025-02-12T16:54:19.328646Z", - "start_time": "2025-02-12T16:54:19.194322Z" + "end_time": "2025-02-12T18:48:32.786445Z", + "start_time": "2025-02-12T18:48:32.660640Z" } }, "cell_type": "code", @@ -1188,111 +1188,6 @@ ], "id": "759e28b4ad56667f", "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0\n", - "10.0\n", - "20.0\n", - "30.0\n", - "40.0\n", - "50.0\n", - "60.0\n", - "70.0\n", - "80.0\n", - "90.0\n", - "100.0\n", - "110.0\n", - "120.0\n", - "130.0\n", - "140.0\n", - 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b/.github/workflows/docs.yml @@ -35,6 +35,10 @@ jobs: python -m pip install --upgrade pip pip install .[strict,docs] + -name: Copy tutorials + run: | + cp -r tutorials docs/ + - name: Build run: sphinx-build docs docs_build diff --git a/.gitignore b/.gitignore index d1f252c4f3..2500a708d8 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,8 @@ docs/_build/* docs/_build/*/* docs/_build/*/*/* docs_build/* +docs/tutorials/* +docs/tutorials/*/* # C extensions *.so From 59e8b94e75691cea44f8c6d85c4be70b282ae7b5 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 07:42:52 +0100 Subject: [PATCH 36/61] fix linting --- src/atomate2/common/schemas/qha.py | 2 +- .../additional_store_data.json | 2 +- .../jfremote_in.json | 2 +- .../jfremote_out.json | 2 +- .../remote_job_data.json | 2 +- .../submit.sh | 6 +- tutorials/phonon_workflow.ipynb | 8 +- tutorials/qha_workflow.ipynb | 1111 ++--------------- tutorials/qha_workflow.py | 215 ---- 9 files changed, 117 insertions(+), 1233 deletions(-) delete mode 100644 tutorials/qha_workflow.py diff --git a/src/atomate2/common/schemas/qha.py b/src/atomate2/common/schemas/qha.py index 5494bac29e..0f5acad6e2 100644 --- a/src/atomate2/common/schemas/qha.py +++ b/src/atomate2/common/schemas/qha.py @@ -38,7 +38,7 @@ class PhononQHADoc(StructureMetadata, extra="allow"): # type: ignore[call-arg] helmholtz_volume: Optional[list[list[float]]] = Field( None, description="Free energies (eV) at temperatures and volumes (Angstrom^3)." - "shape (temperatures, volumes)", #TODO: add units here + "shape (temperatures, volumes)", # TODO: add units here ) volume_temperature: Optional[list[float]] = Field( None, diff --git a/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/additional_store_data.json b/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/additional_store_data.json index 0637a088a0..fe51488c70 100644 --- a/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/additional_store_data.json +++ b/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/additional_store_data.json @@ -1 +1 @@ -[] \ No newline at end of file +[] diff --git a/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/jfremote_in.json b/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/jfremote_in.json index 91203fee18..43282483a0 100644 --- a/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/jfremote_in.json +++ b/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/jfremote_in.json @@ -1 +1 @@ -{"job":{"@module":"jobflow.core.job","@class":"Job","@version":"0.1.18","function":{"@module":"atomate2.vasp.jobs.base","@callable":"BaseVaspMaker.make","@bound":{"@module":"atomate2.vasp.jobs.phonons","@class":"PhononDisplacementMaker","@version":"0.0.18","name":"dft phonon static eos deformation 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phonon static eos deformation 1","@module":"jobflow.core.schemas","@class":"JobStoreDocument","@version":"0.1.18"}] diff --git a/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/submit.sh b/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/submit.sh index a986c9f85c..b1b6caa0f9 100644 --- a/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/submit.sh +++ b/tests/test_data/vasp/Si_qha_2/dft_phonon_static_eos_deformation_1/submit.sh @@ -12,9 +12,9 @@ #SBATCH --error=/hppfs/scratch/00/di82tut/autoplex_test/run/8a/70/ab/8a70ab31-57c5-4b5d-a2f3-1d23e48fc4a1_1/queue.err #SBATCH --get-user-env cd /hppfs/scratch/00/di82tut/autoplex_test/run/8a/70/ab/8a70ab31-57c5-4b5d-a2f3-1d23e48fc4a1_1 -export ATOMATE2_CONFIG_FILE="/dss/dsshome1/00/di82tut/.atomate2/config/atomate2.yaml" -source activate autoplex_test +export ATOMATE2_CONFIG_FILE="/dss/dsshome1/00/di82tut/.atomate2/config/atomate2.yaml" +source activate autoplex_test module load slurm_setup module load vasp/6.1.2 -jf -fe execution run /hppfs/scratch/00/di82tut/autoplex_test/run/8a/70/ab/8a70ab31-57c5-4b5d-a2f3-1d23e48fc4a1_1 \ No newline at end of file +jf -fe execution run /hppfs/scratch/00/di82tut/autoplex_test/run/8a/70/ab/8a70ab31-57c5-4b5d-a2f3-1d23e48fc4a1_1 diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index 2fe093ceda..8115fee2b8 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -35,10 +35,12 @@ ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "This tutorial has been written based on a previous version from Aakash Naik.", - "id": "234e646b3ff60317" + "id": "3", + "metadata": {}, + "source": [ + "This tutorial has been written based on a previous version from Aakash Naik." + ] }, { "cell_type": "markdown", diff --git a/tutorials/qha_workflow.ipynb b/tutorials/qha_workflow.ipynb index 7ce769f331..dbeff2411c 100644 --- a/tutorials/qha_workflow.ipynb +++ b/tutorials/qha_workflow.ipynb @@ -1,23 +1,20 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:", - "id": "8bcad68412b115bf" + "id": "0", + "metadata": {}, + "source": [ + "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-12T18:47:04.203756Z", - "start_time": "2025-02-12T18:46:50.972235Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], "source": [ - "from mock_vasp import TEST_DIR, mock_vasp\n", - "\n", - "\n", "from mock_vasp import TEST_DIR, mock_vasp\n", "\n", "ref_paths = {\n", @@ -37,13 +34,13 @@ " \"tight relax 2 deformation 3\": \"Si_qha_2/tight_relax_2_d3\",\n", " \"tight relax 2 deformation 4\": \"Si_qha_2/tight_relax_2_d4\",\n", " \"tight relax 2 deformation 5\": \"Si_qha_2/tight_relax_2_d5\",\n", - " \"dft phonon static eos deformation 1\":\"Si_qha_2/dft_phonon_static_eos_deformation_1\",\n", - " \"dft phonon static eos deformation 2\":\"Si_qha_2/dft_phonon_static_eos_deformation_2\",\n", - " \"dft phonon static eos deformation 3\":\"Si_qha_2/dft_phonon_static_eos_deformation_3\",\n", - " \"dft phonon static eos deformation 4\":\"Si_qha_2/dft_phonon_static_eos_deformation_4\",\n", - " \"dft phonon static eos deformation 5\":\"Si_qha_2/dft_phonon_static_eos_deformation_5\",\n", - " \"dft phonon static eos deformation 6\":\"Si_qha_2/dft_phonon_static_eos_deformation_6\",\n", - " \"dft phonon static eos deformation 7\":\"Si_qha_2/dft_phonon_static_eos_deformation_7\",\n", + " \"dft phonon static eos deformation 1\": \"Si_qha_2/dft_phonon_static_eos_deformation_1\",\n", + " \"dft phonon static eos deformation 2\": \"Si_qha_2/dft_phonon_static_eos_deformation_2\",\n", + " \"dft phonon static eos deformation 3\": \"Si_qha_2/dft_phonon_static_eos_deformation_3\",\n", + " \"dft phonon static eos deformation 4\": \"Si_qha_2/dft_phonon_static_eos_deformation_4\",\n", + " \"dft phonon static eos deformation 5\": \"Si_qha_2/dft_phonon_static_eos_deformation_5\",\n", + " \"dft phonon static eos deformation 6\": \"Si_qha_2/dft_phonon_static_eos_deformation_6\",\n", + " \"dft phonon static eos deformation 7\": \"Si_qha_2/dft_phonon_static_eos_deformation_7\",\n", " \"dft phonon static 1/1 eos deformation 1\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_1\",\n", " \"dft phonon static 1/1 eos deformation 2\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_2\",\n", " \"dft phonon static 1/1 eos deformation 3\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_3\",\n", @@ -51,50 +48,40 @@ " \"dft phonon static 1/1 eos deformation 5\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_5\",\n", " \"dft phonon static 1/1 eos deformation 6\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_6\",\n", " \"dft phonon static 1/1 eos deformation 7\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_7\",\n", - "}\n" - ], - "id": "eff4e9a6f10ec243", - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "execution_count": 1 + "}" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "QHA workflow", - "id": "e05b21faa1e01338" + "id": "2", + "metadata": {}, + "source": [ + "QHA workflow" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "This tutorial will make use of a quasi-harmonic workflow that allows to include volume-dependent anharmonicity into the calculation of phonon free energies. Please check out the paper by Togo to learn about the exact implementation as we will rely on Phonopy to perform the quasi-harmonic approximation. https://doi.org/10.7566/JPSJ.92.012001. At the moment, we perform harmonic free energy calculation along a volume curve to arrive at free energy-volume curves that are the starting point for the quasi-harmonic approximation.", - "id": "fda4c3b5eb711b" + "id": "3", + "metadata": {}, + "source": [ + "This tutorial will make use of a quasi-harmonic workflow that allows to include volume-dependent anharmonicity into the calculation of phonon free energies. Please check out the paper by Togo to learn about the exact implementation as we will rely on Phonopy to perform the quasi-harmonic approximation. https://doi.org/10.7566/JPSJ.92.012001. At the moment, we perform harmonic free energy calculation along a volume curve to arrive at free energy-volume curves that are the starting point for the quasi-harmonic approximation." + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "4", + "metadata": {}, "source": [ "## Let's run the workflow\n", "Now, we load a structure and other important functions and classes for running the qha workflow." - ], - "id": "d3d984303ba97dbd" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-12T18:47:05.535135Z", - "start_time": "2025-02-12T18:47:04.212313Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], "source": [ "from jobflow import JobStore, run_locally\n", "from maggma.stores import MemoryStore\n", @@ -104,37 +91,37 @@ "\n", "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si_diamond.cif\")" - ], - "id": "823d2c191ab1942a", - "outputs": [], - "execution_count": 2 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Then one can use the `QhaMaker` to generate a `Flow`. First, the structure will be optimized than the structures will be optimized at constant volume along an energy volume curve. Please make sure the structural optimizations are tight enough. At each of these volumes, a phonon run will then be performed. The quasi-harmonic approximation is only valid if the harmonic phonon curves don't show any imaginary modes. However, for testing, you can also switch off this option.", - "id": "8672d51c541f69d6" + "id": "6", + "metadata": {}, + "source": [ + "Then one can use the `QhaMaker` to generate a `Flow`. First, the structure will be optimized than the structures will be optimized at constant volume along an energy volume curve. Please make sure the structural optimizations are tight enough. At each of these volumes, a phonon run will then be performed. The quasi-harmonic approximation is only valid if the harmonic phonon curves don't show any imaginary modes. However, for testing, you can also switch off this option." + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "Before we start the quasi-harmonic workflow, we adapt the first relaxation, the relaxation with different volumes and the static runs for the phonon calculation. As we deal with Si, we will not add the non-analytical term correction.", - "id": "22a3ca35be0297ff" + "id": "7", + "metadata": {}, + "source": [ + "Before we start the quasi-harmonic workflow, we adapt the first relaxation, the relaxation with different volumes and the static runs for the phonon calculation. As we deal with Si, we will not add the non-analytical term correction." + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-12T18:47:05.665837Z", - "start_time": "2025-02-12T18:47:05.644128Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], "source": [ "from atomate2.vasp.flows.core import DoubleRelaxMaker\n", - "from atomate2.vasp.jobs.core import TightRelaxMaker\n", - "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator\n", "from atomate2.vasp.flows.phonons import PhononMaker\n", + "from atomate2.vasp.jobs.core import TightRelaxMaker\n", "from atomate2.vasp.jobs.phonons import PhononDisplacementMaker\n", + "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator\n", + "\n", "phonon_bulk_relax_maker_isif3 = DoubleRelaxMaker.from_relax_maker(\n", " TightRelaxMaker(\n", " run_vasp_kwargs={\"handlers\": ()},\n", @@ -166,7 +153,8 @@ ")\n", "\n", "phonon_displacement_maker = PhononDisplacementMaker(\n", - " run_vasp_kwargs={\"handlers\": ()}, input_set_generator=StaticSetGenerator(\n", + " run_vasp_kwargs={\"handlers\": ()},\n", + " input_set_generator=StaticSetGenerator(\n", " user_incar_settings={\n", " \"GGA\": \"PE\",\n", " \"IBRION\": -1,\n", @@ -190,11 +178,10 @@ " \"NPAR\": 4,\n", " },\n", " auto_ispin=False,\n", - " )\n", + " ),\n", ")\n", "\n", "\n", - "\n", "phonon_bulk_relax_maker_isif4 = DoubleRelaxMaker.from_relax_maker(\n", " TightRelaxMaker(\n", " run_vasp_kwargs={\"handlers\": ()},\n", @@ -224,53 +211,47 @@ " )\n", ")\n", "\n", - "phonon_displacement_maker.name = \"dft phonon static\"\n", - "\n" - ], - "id": "35d5ae5b4763d7e0", - "outputs": [], - "execution_count": 3 + "phonon_displacement_maker.name = \"dft phonon static\"" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-12T18:47:06.759048Z", - "start_time": "2025-02-12T18:47:05.686555Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], "source": [ "flow = QhaMaker(\n", " initial_relax_maker=phonon_bulk_relax_maker_isif3,\n", " eos_relax_maker=phonon_bulk_relax_maker_isif4,\n", " min_length=10,\n", - " phonon_maker=PhononMaker(generate_frequencies_eigenvectors_kwargs={\"tmin\": 0, \"tmax\": 1000, \"tstep\": 10},\n", - "\n", - " bulk_relax_maker=None,\n", - " born_maker=None,\n", - " static_energy_maker=phonon_displacement_maker,\n", - " phonon_displacement_maker=phonon_displacement_maker),\n", + " phonon_maker=PhononMaker(\n", + " generate_frequencies_eigenvectors_kwargs={\n", + " \"tmin\": 0,\n", + " \"tmax\": 1000,\n", + " \"tstep\": 10,\n", + " },\n", + " bulk_relax_maker=None,\n", + " born_maker=None,\n", + " static_energy_maker=phonon_displacement_maker,\n", + " phonon_displacement_maker=phonon_displacement_maker,\n", + " ),\n", " linear_strain=(-0.15, 0.15),\n", " number_of_frames=6,\n", " pressure=None,\n", " t_max=None,\n", " ignore_imaginary_modes=False,\n", " skip_analysis=False,\n", - " eos_type=\"vinet\"\n", + " eos_type=\"vinet\",\n", ").make(structure=si_structure)" - ], - "id": "26d06efc110dc0d0", - "outputs": [], - "execution_count": 4 + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-12T18:48:32.593958Z", - "start_time": "2025-02-12T18:47:06.766271Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", " run_locally(\n", @@ -280,866 +261,15 @@ " raise_immediately=True,\n", " store=job_store,\n", " )" - ], - "id": "2ae17ee0ac92f5e1", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:06,802 INFO Started executing jobs locally\n", - "2025-02-12 19:47:06,819 INFO Starting job - tight relax 1 EOS equilibrium relaxation (89034fda-0616-4fa1-98cf-12554839513f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:08,028 INFO Finished job - tight relax 1 EOS equilibrium relaxation (89034fda-0616-4fa1-98cf-12554839513f)\n", - "2025-02-12 19:47:08,029 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:08,030 INFO Starting job - tight relax 2 EOS equilibrium relaxation (aa677af4-d8ef-4a3f-9436-e5ad7b615712)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-08-030242-19998/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:09,400 INFO Finished job - tight relax 2 EOS equilibrium relaxation (aa677af4-d8ef-4a3f-9436-e5ad7b615712)\n", - "2025-02-12 19:47:09,400 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:09,401 INFO Starting job - apply_strain_to_structure (42396e00-786d-4a80-844f-dff18a2dbc22)\n", - "2025-02-12 19:47:09,435 INFO Finished job - apply_strain_to_structure (42396e00-786d-4a80-844f-dff18a2dbc22)\n", - "2025-02-12 19:47:09,437 INFO Starting job - tight relax 1 deformation 0 (e5c8dad2-8aac-49ca-a241-230316bfe4dc)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-09-436859-95935/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:10,450 INFO Finished job - tight relax 1 deformation 0 (e5c8dad2-8aac-49ca-a241-230316bfe4dc)\n", - "2025-02-12 19:47:10,451 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:10,452 INFO Starting job - tight relax 1 deformation 1 (b91ec7ce-8a80-4ed5-9e9a-4ef495d10a79)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-10-452456-64344/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:11,387 INFO Finished job - tight relax 1 deformation 1 (b91ec7ce-8a80-4ed5-9e9a-4ef495d10a79)\n", - "2025-02-12 19:47:11,388 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:11,388 INFO Starting job - tight relax 1 deformation 2 (6ee35b18-58a4-4a33-aa67-54fe22f6d340)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-11-388433-96516/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:12,320 INFO Finished job - tight relax 1 deformation 2 (6ee35b18-58a4-4a33-aa67-54fe22f6d340)\n", - "2025-02-12 19:47:12,320 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:12,321 INFO Starting job - tight relax 1 deformation 3 (a1d0f042-8e5f-4666-a222-30a29a9c60bd)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-12-321191-59448/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:13,255 INFO Finished job - tight relax 1 deformation 3 (a1d0f042-8e5f-4666-a222-30a29a9c60bd)\n", - "2025-02-12 19:47:13,256 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:13,256 INFO Starting job - tight relax 1 deformation 4 (527dddb8-4637-4084-a196-bb3fe42fc6fe)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-13-256569-43370/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:14,195 INFO Finished job - tight relax 1 deformation 4 (527dddb8-4637-4084-a196-bb3fe42fc6fe)\n", - "2025-02-12 19:47:14,195 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:14,196 INFO Starting job - tight relax 1 deformation 5 (ae817857-2e89-4f5c-a85a-5b57563d0c0d)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-14-196160-86116/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:15,064 INFO Finished job - tight relax 1 deformation 5 (ae817857-2e89-4f5c-a85a-5b57563d0c0d)\n", - "2025-02-12 19:47:15,064 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:15,065 INFO Starting job - tight relax 2 deformation 0 (61c6ba30-8669-4969-ae7d-af39abec6f6c)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-15-065148-97023/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:16,129 INFO Finished job - tight relax 2 deformation 0 (61c6ba30-8669-4969-ae7d-af39abec6f6c)\n", - "2025-02-12 19:47:16,130 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:16,131 INFO Starting job - tight relax 2 deformation 1 (06bd1689-fe1a-46c1-be5c-e8012161e458)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-16-131368-59705/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:17,046 INFO Finished job - tight relax 2 deformation 1 (06bd1689-fe1a-46c1-be5c-e8012161e458)\n", - "2025-02-12 19:47:17,047 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:17,048 INFO Starting job - tight relax 2 deformation 2 (0c74e1df-e05e-4e07-a5dc-2728bccd818a)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4dwclack/job_2025-02-12-18-47-17-047607-48584/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:18,097 INFO Finished job - tight relax 2 deformation 2 (0c74e1df-e05e-4e07-a5dc-2728bccd818a)\n", - "2025-02-12 19:47:18,101 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:18,102 INFO Starting job - 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" file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:20,609 INFO Finished job - tight relax 2 deformation 5 (efaba483-4ef1-4193-8523-e3ef769f517a)\n", - "2025-02-12 19:47:20,609 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-12 19:47:20,610 INFO Starting job - get_supercell_size (bcb0eb7d-d427-42bd-99e5-89a4813b9dab)\n", - "[[2, 0, 0], [0, 2, 0], [0, 0, 2]]\n", - "2025-02-12 19:47:20,702 INFO Finished job - get_supercell_size (bcb0eb7d-d427-42bd-99e5-89a4813b9dab)\n", - "2025-02-12 19:47:20,703 INFO Starting job - get_phonon_jobs (6668b4f9-14e6-4972-a7fd-2b8848913b18)\n", - "2025-02-12 19:47:21,710 INFO Finished job - get_phonon_jobs (6668b4f9-14e6-4972-a7fd-2b8848913b18)\n", - "2025-02-12 19:47:21,745 INFO Starting job - dft phonon static eos deformation 1 (52ac2dc2-ca88-4ef9-a7e8-5a38fa2a8097)\n" - 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"2025-02-12 19:47:37,650 INFO Finished job - store_inputs eos deformation 7 (ff8f86f0-451a-4ece-858c-0edb0d5073c1, 2)\n", - "2025-02-12 19:47:37,651 INFO Starting job - generate_frequencies_eigenvectors eos deformation 1 (98f01b9e-e077-4c95-9ec2-c3c0804ce17c)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:45,715 INFO Finished job - generate_frequencies_eigenvectors eos deformation 1 (98f01b9e-e077-4c95-9ec2-c3c0804ce17c)\n", - "2025-02-12 19:47:45,716 INFO Starting job - generate_frequencies_eigenvectors eos deformation 2 (baf15518-066e-43d9-a2fa-4aa991e1abc6)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:47:53,593 INFO Finished job - generate_frequencies_eigenvectors eos deformation 2 (baf15518-066e-43d9-a2fa-4aa991e1abc6)\n", - "2025-02-12 19:47:53,594 INFO Starting job - generate_frequencies_eigenvectors eos deformation 3 (9f3bca5e-8bd4-4e46-841a-2371595ebdef)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:48:01,055 INFO Finished job - generate_frequencies_eigenvectors eos deformation 3 (9f3bca5e-8bd4-4e46-841a-2371595ebdef)\n", - "2025-02-12 19:48:01,056 INFO Starting job - generate_frequencies_eigenvectors eos deformation 4 (d8487050-4877-46ad-8f54-ed64365328a4)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:48:08,790 INFO Finished job - generate_frequencies_eigenvectors eos deformation 4 (d8487050-4877-46ad-8f54-ed64365328a4)\n", - "2025-02-12 19:48:08,791 INFO Starting job - generate_frequencies_eigenvectors eos deformation 5 (06ec0aa1-a33b-4732-84e8-cedd8d49adf3)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:48:16,375 INFO Finished job - generate_frequencies_eigenvectors eos deformation 5 (06ec0aa1-a33b-4732-84e8-cedd8d49adf3)\n", - "2025-02-12 19:48:16,376 INFO Starting job - generate_frequencies_eigenvectors eos deformation 6 (c2170f53-cd99-40de-a7da-c6b362df5427)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:48:23,709 INFO Finished job - generate_frequencies_eigenvectors eos deformation 6 (c2170f53-cd99-40de-a7da-c6b362df5427)\n", - "2025-02-12 19:48:23,710 INFO Starting job - generate_frequencies_eigenvectors eos deformation 7 (ddd60892-e6dc-48ee-b4c8-b262a67077fd)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-12 19:48:31,238 INFO Finished job - generate_frequencies_eigenvectors eos deformation 7 (ddd60892-e6dc-48ee-b4c8-b262a67077fd)\n", - "2025-02-12 19:48:31,239 INFO Starting job - store_inputs (6668b4f9-14e6-4972-a7fd-2b8848913b18, 2)\n", - "2025-02-12 19:48:31,241 INFO Finished job - store_inputs (6668b4f9-14e6-4972-a7fd-2b8848913b18, 2)\n", - "2025-02-12 19:48:31,242 INFO Starting job - analyze_free_energy (8fa7ebdd-cb3c-4eb9-b7ab-3e92b0707ab4)\n", - "2025-02-12 19:48:32,175 INFO Finished job - analyze_free_energy (8fa7ebdd-cb3c-4eb9-b7ab-3e92b0707ab4)\n", - "2025-02-12 19:48:32,175 INFO Finished executing jobs locally\n" - ] - }, - { - "data": { - "text/plain": [ - "
      " - ], - "image/png": 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" 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" 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ERETGj4devaBgQdizB8LC7K7oqrI9yJQpU4ZDhw6541LX1KxZMypWrMhHH32UofPVR0ZERAQ4cQJuugni42HiRHj6absruqYs6SPz4IMPXnG/ZVluHXZ9LampqU4tLiIiIpIB/fubEBMRYR4r+YjrCjJLlixh+vTpBAcHO+23LIuVK1e6tTCAl19+mZYtW1KmTBnOnDnDzJkzWb58OYsWLXL7vURERHzWqlXw2WfgcMCHH0KuXHZX5DbXFWSaNGlC/vz5adSo0WXHatSo4bai0sTExPD4449z7NgxQkNDqVGjBosWLeLOO+90+71ERER80oUL8OyzZvupp6BePXvrcTOPH7XkKvWRERGRHO2996BvXyhcGHbvNj+9QEb/frs0ePz48eOufFxERESy0qFDMHCg2R450mtCzPVwKcjcdddd7qpDRERE3MmyzCOlxERo2BCefNLuirKES0HGx59KiYiIeK8vv4QFC8DfHz76yOtn8L0al34rh8PhrjpERETEXU6dMhPfAbzyClSpYm89Wcg345mIiEhO1r+/mQCvShUYMMDuarKUgoyIiIgvWbECpkwx25MmQUCAvfVkMZeCTC4fmlBHRETE650/b+aKAbMEwW232VtPNnApyGzatMlddYiIiIirhg0zi0EWL26GW+cAWfpoKSEhISsvLyIiImk2bboUXsaNgwIFbC0nu2Q6yLz33nvXPH7mzBmaN2+e2cuLiIhIRiUnQ+fOcPEi3H+/eeUQmQ4yr7zyCp999tkVjyUmJtKiRQv+/vvvTBcmIiIiGTR8OGzdambu/eADu6vJVpkOMtOnT+fpp5/m22+/ddqfmJhI8+bN+euvv1i2bJnLBYqIiMg1bN5s+saACTFFi9paTna7rtWv/+n+++/n9OnTdOjQgQULFtCkSZP0lpgTJ06wYsUKihcv7s5aRURE5J8uXLj0SKl9e3jwQbsrynaZDjIAXbt2JTY2ljZt2vDNN98waNAgjh49yooVKyhRooS7ahQREZErGTECtmyBQoXgww8hB86471KQAejfvz+xsbE0bdqUcuXKsXz5ckqVKuWO2kRERORqtmyBoUPN9vvvQ3i4vfXYJNNBpn379k7v8+TJQ1hYGL1793baP2fOnMzeQkRERK7kn6OU2raFhx+2uyLbZDrIhIaGOr3v0KGDy8WIiIhIBgwaZDr5Fi4MEybkyEdKaTIdZKZNm+bOOkRERCQjVq6EUaPM9uTJUKyYvfXYLFPDrw8dOnRd5x85ciQztxEREZF/iouDxx8Hy4InnoB27eyuyHaZCjL16tXj6aefZv369Vc9Jy4ujsmTJ1OtWjW+/vrrTBcoIiIi/69nTzh4EMqXh7Fj7a7GI2Tq0dLOnTsZNmwYd955J4GBgdStW5cSJUoQGBjIqVOn2LlzJzt27KBOnTqMGjWKu+++2911i4iI5CxffgnTp4Ofn/mZP7/dFXkEh2VZVmY/fO7cORYsWMDq1as5ePAg586dIywsjNq1a9O8eXOqVavmzlozJT4+ntDQUOLi4ggJCbG7HBERket35AhUrw6nTsErr1yaydeHZfTvt0tBxhsoyIiIiFdLTYXmzWHJEqhbF37+Gfz97a4qy2X073em11oSERGRbPDWWybEBAXB//6XI0LM9VCQERER8VQ//QQDB5rt99+HypXtrccDKciIiIh4or//hg4dICUFOnY0w63lMgoyIiIiniZtnpjDh+HGG3P87L3XoiAjIiLiacaOhe++g4AA+OILDbW+BpdXvwaIjo4mOjqamJgYUlNTnY59/PHH7riFiIhIzrB+PfTvb7ZHj4ZatWwtx9O5HGQGDx7MkCFDiIiIoHjx4jjU9CUiIpI5p07BQw/BhQtw333QvbvdFXk8l4PMxIkT+eSTT3jsscfcUY+IiEjOlJoKjz4K+/ebJQimTFG/mAxwuY9McnIyDRo0cEctIiIiOdewYfD99xAYCF9/DQUK2F2RV3A5yHTt2pWZM2e6oxYREZGcadEieP11s/3hh1C7tr31eBGXHy2dP3+eSZMmsWTJEmrUqEGePHmcjo8ePdrVW4iIiPiugwfhkUfMkOtu3TRfzHVyOchs3bqVWv/fo3r79u1Ox9TxV0RE5BrOn4f774fYWLOO0rhxdlfkdVwOMsuWLXNHHSIiIjlP797w669QqJDpFxMYaHdFXkcT4omIiNjho49g0iQzMmnmTChb1u6KvJJbJsQ7ffo0U6dOZdeuXQBUrVqVLl26EBoa6o7Li4iI+JZVq+C558z20KHQvLm99Xgxl1tkfv31VypWrMh7771HbGwssbGxvPfee1SsWJGNGze6o0YRERHfcfiw6Rdz8SI88AC88ordFXk1h2VZlisXaNiwITfccAOTJ08md27TwHPx4kW6du3KH3/8wcqVK91SaGbFx8cTGhpKXFwcISEhttYiIiI53Nmz0LAhbNwINWvCTz9Bvnx2V+WRMvr32+VHS7/++qtTiAHInTs3/fv3JyIiwtXLi4iI+Ia04dUbN0JYGMybpxDjBi4/WgoJCeHQoUOX7T98+DD5tVqniIiI8c47plNvrlzw5ZdQrpzdFfkEl4PMQw89RJcuXZg9ezaHDx/m8OHDzJo1i65du9KhQwd31CgiIuLdvvsOXnrJbI8dC02a2FqOL3H50dI777yDw+Hg8ccf5+LFiwDkyZOH7t27M3LkSJcLFBER8WqbN0OHDpceLT37rN0V+RSXO/umOXv2LPv27QOgYsWK5M2b1x2XdZk6+4qIiG2OHoX69eHIEWjaFBYuhH8t5SNXlm2dfdPkzZuX6tWru+tyIiIi3i0xEVq3NiGmcmX46iuFmCyQqSDTt29fhg4dSr58+ejbt+81z9WikSIikuOkpsKjj14aobRgARQoYHdVPilTQWbTpk1cuHAhfftqtGikiIjkSAMGmOHV/v7mZ4UKdlfks9zWR8ZTqY+MiIhkq/ffh549zfaMGfDII/bW46Uy+vfb5eHX586d4+zZs+nvDx48yJgxY/jxxx9dvbSIiIh3+fpr6NXLbA8ZohCTDVwOMm3atOGzzz4DzOKR9evX591336VNmzZMmDDB5QJFRES8wsqV0LGjGWb99NPw2mt2V5QjuBxkNm7cSMOGDQH46quvKFasGAcPHuSzzz5j3LhxLhcoIiLi8bZvh3vvhaQkaNsWPvgA1E80W7gcZM6ePZu+FMGPP/5I+/bt8fPz45ZbbuHgwYMuFygiIuLRDh+GFi0gLg5uvfXSMgSSLVwOMjfccAPz5s3j8OHDLFq0iLvuuguAmJgYda4VERHfFhtrQsyRI1ClCnz7LQQF2V1VjuJykBk0aBD9+vWjXLlyREZGEhUVBZjWmdq1a7tcoIiIiEc6cwZatoSdO6FECfjhByhUyO6qchy3DL8+fvw4x44do2bNmvj5mWy0bt06QkJCqFy5sstFukLDr0VExO3OnYO774bly6FwYVixAm6+2e6qfEq2LlFQrFgxihUr5rSvfv367ri0iIiIZ7lwAR580ISY/PlNS4xCjG3cEmSio6OJjo4mJiaG1NRUp2Mff/yxO24hIiJiv5QU6NQJ5s+HwEDzMyLC7qpyNJeDzODBgxkyZAgREREUL15cyxKIiIhvsizo0QM+/xxy5zaT3zVqZHdVOZ7LQWbixIl88sknPPbYY+6oR0RExPNYFvTpAx99BH5+ZumBu++2uyrBDaOWkpOTadCggTtqERER8TyWBS+9BGPHmveTJ5s+MuIRXA4yXbt2ZebMme6oRURExPMMGgRvv222J0yAJ5+0tx5x4vKjpfPnzzNp0iSWLFlCjRo1yJMnj9Px0aNHu3oLERERewwdCm++abbHjoVnnrG3HrmMy0Fm69at1KpVC4Dt27c7HVPHXxER8VojR5rWGIB33rm0qrV4FJeDzLJly9xRh4iIiOd46y14+WWzPXw4vPCCvfXIVbllHhmAnTt3cujQIZKTk9P3ORwOWrdu7a5biIiIZL2hQy+1xLzxxqVAIx7J5SDzxx9/0K5dO7Zt24bD4SBtxYO0x0opKSmu3kJERCTrWZYJMGl9YoYNg1desbcm+U8uj1rq3bs35cuXJyYmhrx587Jjxw5WrlxJREQEy5cvd0OJIiIiWcyyYMCASyHm7bcVYryEyy0ya9asYenSpYSFheHn54efnx+33XYbI0aMoFevXmzatMkddYqIiGQNy4K+fWHMGPN+7Fh17PUiLrfIpKSkkD9/fgDCwsI4evQoAGXLlmX37t2uXp4RI0ZQr1498ufPT9GiRWnbtq1brisiIkJKihlSnRZiPvxQIcbLuBxkqlWrxpYtWwCIjIxk1KhR/PTTTwwZMoQKFSq4XOCKFSvo0aMHv/zyC4sXL+bChQvcddddJCYmunxtERHJwZKT4ZFHYNIks+zAlCnQvbvdVcl1clhpvXMzadGiRSQmJtK+fXv27t3LPffcw549eyhcuDCzZ8/mjjvucFetAPz1118ULVqUFStW0CgDi3XFx8cTGhpKXFwcISEhbq1FRES81NmzcN998MMPkCcPzJwJ999vd1XyDxn9++1yH5nmzZunb99www389ttvxMbGUrBgwSyZEC8uLg6AQoUKXfF4UlISSUlJ6e/j4+PdXoOIiHix06fhnnvgp58gb16YMwf+8bdMvIvLj5b+ybIsLMuiUKFCWRJiUlNTef7557n11lupVq3aFc8ZMWIEoaGh6a/SpUu7vQ4REfFSJ07A7bebEBMaCosXK8R4ObcEmalTp1KtWjUCAwMJDAykWrVqTJkyxR2XdtKjRw+2b9/OrFmzrnrOyy+/TFxcXPrr8OHDbq9DRES80O+/Q1QUbN4MRYvCihXQoIHdVYmLXH60NGjQIEaPHk3Pnj2JiooCzJDsPn36cOjQIYYMGeJykQDPPfcc8+fPZ+XKlZQqVeqq5wUEBBAQEOCWe4qIiI9Yu9Y8Tjp5EipUMH1jbrzR7qrEDVzu7FukSBHGjRtHhw4dnPZ//vnn9OzZk5MnT7pUoGVZ9OzZk7lz57J8+XJuvM7/4amzr4hIDjd/Pjz4IJw7BxER5n14uN1VyX/Its6+Fy5cICIi4rL9devW5eLFi65enh49ejBz5ky++eYb8ufPz/HjxwEIDQ0lKCjI5euLiIgPmzIFnn4aUlOhRQv48ksIDra7KnEjl/vIPPbYY0yYMOGy/ZMmTaJjx46uXp4JEyYQFxdHkyZNKF68ePpr9uzZLl9bRER8VGoqDBwI3bqZ7c6d4dtvFWJ8kFtWv546dSo//vgjt9xyCwBr167l0KFDPP744/Tt2zf9vNGjR1/3tV188iUiIjnNuXMmuHzxhXn/2mswZAhkwWhasZ/LQWb79u3UqVMHgH379gFmqYKwsDC2b9+efl5WDMcWERFxcuIEtGljOvfmyWNm7e3c2e6qJAu5HGSWLVvmjjpERERcs327GZl08CAULGgmumvSxO6qJIu53EfmWkHmo48+cvXyIiIi/23hQjMnzMGDcMMN8MsvCjE5hMtBpkWLFrz44otcuHAhfd/Jkydp3bo1AwYMcPXyIiIiV2dZ8Pbb0KoVnDkDjRqZEHPTTXZXJtnELS0yc+fOpV69euzcuZMFCxZQrVo14uPj2bx5sxtKFBERuYJz5+Dxx6F/fxNounaFH3+EwoXtrkyykctBpkGDBmzevJlq1apRp04d2rVrR58+fVi+fDlly5Z1R40iIiLOjhyBxo3hf/+DXLng/fdNx17N7J7juGWtpT179vDrr79SqlQpcufOze7duzl79qw7Li0iIuJszRqoVw/Wr4dChUwrTI8eGl6dQ7kcZEaOHElUVBR33nkn27dvZ926dWzatIkaNWqwZs0ad9QoIiJiHh99+KFpiTl2DKpVM2Hmjjvsrkxs5HKQGTt2LPPmzWP8+PHpK1+vW7eO9u3b00Q9xkVExB3SJrnr0QMuXID774effzYLQEqO5vI8Mtu2bSMsLMxpX548eXj77be55557XL28iIjkdH/8AffdB5s3g58fvPUWvPCCHiUJ4IYg8+8Q80+NGzd29fIiIpKTff89PPoonDoFRYrA7Nlw++12VyUexC2dfVetWsWjjz5KVFQUR44cAWD69OmsXr3aHZcXEZGc5uJFGDDAzA9z6hTUrw8bNyrEyGVcDjJff/01zZs3JygoiE2bNpGUlARAXFwcw4cPd7lAERHJYY4cMYHlrbfM+x49YOVKKFXK3rrEI7kcZN58800mTpzI5MmTyZMnT/r+W2+9lY0bN7p6eRERyUl++AFq1YLVqyEkBL780swRo/lh5CpcDjK7d++mUaNGl+0PDQ3l9OnTrl5eRERyguRk8yipZUs4eRJq14YNG8zoJJFrcDnIFCtWjL179162f/Xq1VTQsDgREfkvu3bBLbdcepTUvbsZWn3DDfbWJV7B5SDTrVs3evfuzdq1a3E4HBw9epQZM2bQr18/unfv7o4aRUTEF1kWfPAB1KkDmzaZNZK+/tpMehcYaHd14iVcHn49YMAAUlNTadq0KWfPnqVRo0YEBATQr18/evbs6Y4aRUTE1xw7Bk8+afrEADRvDtOmQfHi9tYlXsdhWZbljgslJyezd+9eEhISqFq1KsHBwe64rMvi4+MJDQ0lLi6OkJAQu8sREZEvv4RnnoHYWNPyMmoUPPecJrgTJxn9++1yi0waf39/qlat6q7LiYiIrzl1ygSWmTPN+1q1zOrVN99sa1ni3dwyIZ6IiMg1LVpkFnmcORNy5YLXXoO1axVixGVua5ERERG5TEIC9O8PEyaY9zfdBJ99BpGR9tYlPkMtMiIikjVWrzaPj9JCTM+eZnSSQoy4kYKMiIi41/nzphWmUSPYtw9Kl4YlS2DcOMib1+7qxMdk6tFS3759M3zu6NGjM3MLERHxRhs3wuOPw44d5n3nzjBmDISG2lmV+LBMBZlNmzZl6DyHhtKJiOQMFy7A8OHw5ptm5eqiRWHyZLj3XrsrEx+XqSCzbNkyd9chIiLeascO6NTJrI0EcN99pl9MkSL21iU5gttGLe3cuZNDhw6RnJycvs/hcNC6dWt33UJERDxJSgqMHm2GUicnQ8GCZsmBhx/W5HaSbVwOMn/88Qft2rVj27ZtOBwO0iYKTnuslJKS4uotRETE02zbBk8/DWvWmPetWsGkSVCihL11SY7j8qil3r17U758eWJiYsibNy87duxg5cqVREREsHz5cjeUKCIiHuPsWRgwwCz0uGYN5M8PU6fCd98pxIgtXG6RWbNmDUuXLiUsLAw/Pz/8/Py47bbbGDFiBL169cpwx2AREfFwCxfCs8/CgQPmfbt2Zkh1qVK2liU5m8tBJiUlhfz58wMQFhbG0aNHqVSpEmXLlmX37t0uFygiIjawLDh40Ayn3rQJfvoJ0gZ6lC4N77+vEUniEVwOMtWqVWPLli2UL1+eyMhIRo0ahb+/P5MmTaJChQruqFFERLLLzp0waBAsXWoWefynXLmgd28YPBiCg+2pT+RfXA4yr732GomJiQAMGTKEe+65h4YNG1K4cGFmz57tcoEiIpIN/voL3ngDPvrIjEYCyJPHLPRYp455NW0KlSrZWqbIvzmstGFGbhQbG0vBggU9YkK8+Ph4QkNDiYuLIyQkxO5yREQ8S1ISjB9vJrKLizP72rY1HXpr1YKAADurkxwso3+/s2T160KFCmXFZUVExJ3++sush/Tbb+Z9rVpmXpjbb7e1LJHr4ZYgc/78ebZu3UpMTAypqalOx+5VZzAREc9jWWYdpN9+g/BwGDHCrJGUK5fdlYlcF5eDzA8//MDjjz/OyZMnLzvmcDg0IZ6IiCcaNw6+/948OvrxR6hRw+6KRDLF5QnxevbsyQMPPMCxY8dITU11einEiIh4oM2boX9/s/3uuwox4tVcDjInTpygb9++hIeHu6MeERHJSomJZi2k5GQzD8yzz9pdkYhLXA4y999/v5YiEBHxFr17w+7dULIkfPyxFncUr+fy8OuzZ8/ywAMPUKRIEapXr06ePHmcjvfq1culAl2l4dciIv9v9uxLK1MvXQpNmthdkchVZdvw688//5wff/yRwMBAli9f7jR3jMPhsD3IiIjkeDt2wLRpZrI7gFdfVYgRn+Fyi0yxYsXo1asXAwYMwM/P5SdVbqcWGRHxeTExcOaMmYk3Tx7w94eLF2HuXPP4aP36S+fefrsZpZQ7S6YRE3GbbGuRSU5O5qGHHvLIECMi4tOOH4eXX4ZPPrn2eblzwz33wJNPQsuWCjHiU1xOH506ddKaSiIi2Sk52QybvummSyEmXz7TEvPPzrvVqpmZeo8cMa0zrVsrxIjPcfl/0SkpKYwaNYpFixZRo0aNyzr7jh492tVbiIhImkWLLo08AqhXz6yVFBl56ZyUFPNoSeskSQ7gcpDZtm0btWvXBmD79u1Oxzxh0UgREZ+wbx/07QvffmveFy0KI0dCp07w70f7uXJpqQHJMVwOMsuWLXNHHSIiciUJCTB8uHmUlJxsHg317Amvvw6hoXZXJ2I7l/rIXLhwgaZNm/L777+7qx4REQHzeGjGDKhUySzomJwMd90FW7eafi8KMSKAiy0yefLkYevWre6qRUQkZ7Ms2LDBBJhZs8yoJIDy5eG998ySAnpkL+LE5VFLjz76KFOnTnVHLSIiOdPJkzBkCFSubDrvjhljQkyhQvDmm7BzJ7RpoxAjcgUu95G5ePEiH3/8MUuWLKFu3brky5fP6bhGLYmIXMXFizBxIgwcCKdPm31BQablpWNHaN7cDKkWkatyOchs376dOnXqALBnzx6nYxq1JCJyFStWmE6727aZ9zVqwAsvQLt2kD+/vbWJeBGNWhIRyU5HjpjAkjaRaMGC5vHRU09psjqRTHDLugKrVq3i0UcfpUGDBhw5cgSA6dOns3r1andcXkTE+1kWTJ4MVauaEONwwDPPwO+/w7PPKsSIZJLLQebrr7+mefPmBAUFsXHjRpKSkgCIi4tj+PDhLhcoIuL19u6Fpk1Nq0t8PNSvb0YnTZgAhQvbXZ2IV3M5yLz55ptMnDiRyZMnOy1PcOutt7Jx40ZXLy8i4h0sy4w+OnIEDhww4WXXLnjnHaheHZYtg7x5zRwwP/8M/z8juoi4xuW2zN27d9OoUaPL9oeGhnI6rRe+iIivSUiAdetgzRrz+uUX+Pvvq5/ftClMmgQVKmRfjSI5gMtBplixYuzdu5dy5co57V+9ejUV9H+wIuJrDh6Efv1gzhxITb38eK5ckCfPpVfBgvDKK/DEE5oHRiQLuBxkunXrRu/evfn4449xOBwcPXqUNWvW0K9fPwYOHOiOGkVE7HfuHLz9tlku4Px5s69MGYiKgltuMT9r1dKK0yLZzOUgM2DAAFJTU2natClnz56lUaNGBAQE0K9fP3r27OmOGkVE7GNZ8N138PzzsH+/2de4MYwdCzVr2lqaiIDDsizLHRdKTk5m7969JCQkULVqVYKDg91xWZfFx8cTGhpKXFwcISEhdpcjIt7CskwH3TffND8BSpY0q1A/+KAeE4lksYz+/XbbxAX+/v5UrVrVXZcTEbGHZcGCBTBsmOnAC2aZgBdeMH1dPOT/SRMRwy1BJjo6mujoaGJiYkj9V+e3jz/+2B23EBHJWidPwrffwrhxsGWL2RcYCF27ms69ZcvaW5+IXJHLQWbw4MEMGTKEiIgIihcvrvWVRMR7HDwI8+aZ18qVl0YhBQdD9+7Qty8UK2ZnhSLyH1wOMhMnTuSTTz7hsccec0c9IiJZ6+BB+OIL8/r1V+djtWrBAw+YpQMKFbKlPBG5Pi4HmeTkZBo0aOCOWkREssaff8KXX5o1jtauvbTfzw9uuw3atjWv8uXtqlBEMsnlJQq6du3KzJkz3VHLFa1cuZLWrVtTokQJHA4H8+bNy7J7iYgPOXTILAfQoAGULm0eE61da0YbNWli1jk6dgxWrIA+fRRiRLxUplpk+vbtm76dmprKpEmTWLJkCTVq1HBabwlg9OjRLhWYmJhIzZo1efLJJ2nfvr1L1xIRH/fHH2bG3S+/NMsHpHE44NZb4aGH4P771e9FxIdkKshs2rTJ6X2tWrUA2L59u9N+d3T8bdmyJS1btnT5OiLio3btgq+/Nq/Nmy/tdzigYUPT56V9eyhRwrYSRSTrZCrILFu2jCFDhvDCCy+QL18+d9fkkqSkJJKSktLfx8fH21iNiGSJPXtg1izT52Xnzkv7/fzMY6P77jPhRS0vIj4v0519Bw8ezDPPPONxQWbEiBEMHjzY7jJExN0OHDAjjWbNgn+2CufJA82amfDSpg2EhdlWoohkv0wHGTetbOB2L7/8slMfnvj4eEqXLm1jRSKSKamppp/Ld9+Z17Ztl47lygV33gkPP2zCS4ECtpUpIvZyafi1J05+FxAQQIBWnxXxTufPw5IlMHcuzJ8PMTGXjvn5QaNG0KGDeWyklhcRwcUgc9NNN/1nmImNjXXlFiLi6+LjzdpGc+fCwoWQkHDpWEgItGgBrVtDy5ZQuLB9dYqIR3IpyAwePJjQ0FB31XJFCQkJ7N27N/39/v372bx5M4UKFaJMmTJZem8RySJHjph1jb75BpYuhQsXLh0rVcpMTtemjWmB8fe3rUwR8XwOK5OdXfz8/Dh+/DhFixZ1d01Oli9fzu23337Z/k6dOvHJJ5/85+czugy4iGQhy4KtW01fl2++uXxpgEqVzOOidu0gIsIMnRaRHC2jf78z3SKTXf1jmjRp4rEdi0XkGs6fh2XLTHiZPx8OH750zOGAW24xrS5t2kDlyvbVKSJezedGLYmIjU6cMP1dvvsOFi+GxMRLx4KCzDDpe+81fV7Cw+2rU0R8RqaDTGracvciknOlpprZdBcuNOFl3TrzGClNyZJwzz0muNxxhwkzIiJu5PLq1yKSw5w4AT/+CIsWmZ9//eV8PCLiUnipXVv9XUQkSynIiMi1pabC+vXw/ffmsdGGDc7Hg4NNa8s990CrVlrTSESylYKMiFzu9GnT2rJggXls9O9Wlzp1oHlz84qK0hBpEbGNgoyImH4tO3ea4LJgAfz0E6SkXDoeEgJ33WVaXFq00GKMIuIxFGREcqpz52D5cjM0esECOHjQ+XiVKnD33Sa83HabWZxRRMTDKMiI5CQHDphHRd9/D9HRJsykCQiA2283weXuu6FCBdvKFBHJKAUZEV+WlASrV5vgsnAh7NrlfLxUKRNcWrUyHXbz5bOnThGRTFKQEfEllgW//26GRi9aZGbWPXv20vFcuaBBA7MAY6tWUL26hkeLiFdTkBHxdmlLAcyfb1peDhxwPl6smOmge/fdcOedUKCAHVWKiGQJBRkRb3T8uAku8+ebpQD+2eri728656YNj65RQ60uIuKzFGREvIFlmaUA5s83SwGsX+98vGRJM5Nuq1amw676uohIDqEgI+Kpzp1zXj36zz+dj9erZ8LLPfdArVpqdRGRHElBRsSTHD16afXoJUuch0fnzWv6uKS1vGhSOhERBRkRW1kWbN8O33wD3357+SOjUqUutbrcfrtWjxYR+RcFGZHslpJilgCYM8cEmH+OMnI4oH79S+FFHXVFRK5JQUYkO1y4YPq7zJkDc+dCTMylY4GB5pHRvfea8KJHRiIiGaYgI5JVzp0zK0jPmWP6vJw6delYgQImuLRrZ0KMRhmJiGSKgoyIO505Y0YYzZljJqf75/wuRYtC27Zw333QpImZ70VERFyiICPiqvh40+Ly5Zfwww9mfaM0ZcpA+/am5eXWW80SASIi4jYKMiKZcf68aXn53/8uDy833WRaXe67D+rUUWddEZEspCAjklGWZVaSnj4dvvgC4uIuHatUCR54wLy0EKOISLZRkBH5L3v3mvAyfTrs339pf6lS8Oij0KGDwouIiE0UZESuJDbWtLp89hmsWXNpf3Aw3H8/PP44NG4Mfn721SgiIgoyIulSUsxw6WnTzER1yclmv58f3HWXCS9t2pilAkRExCMoyIjs3g2ffGJaX44evbS/Zk0TXjp0gOLFbStPRESuTkFGcqbkZDPXy4QJsHLlpf2FC0PHjvDEE2ZFaRER8WgKMpKzHDgAkybB1KmXlgnw84OWLU14ueceCAiwtUQREck4BRnxfZYFS5fCuHFm4jrLMvuLF4du3aBrVyhd2t4aRUQkUxRkxHclJpoJ68aPhx07Lu1v2hS6dzdrHeXJY199IiLiMgUZ8T3HjpnWl4kT4fRpsy9fPujcGZ57DipXtrM6ERFxIwUZ8R27d8M775jRR2lDpytUgJ49Tf+X0FB76xMREbdTkBHvt24djBwJ8+Zd6v/SoAG8+CK0bq2FGkVEfJiCjHgny4IVK+DNNyE6+tL+e++F/v3NStMiIuLzFGTEu1gWLFwIw4bBzz+bfblzmzWP+veHKlXsrU9ERLKVgox4B8uC+fNh8GDYsMHsCwiALl1MgClb1t76RETEFgoy4tksy8z9MngwbNxo9uXLB888Ay+8oKUDRERyOAUZ8Uxpj5Beew02bTL78uUzI5BeeAHCwuytT0REPIKCjHiezZuhX79LnXgVYERE5CoUZMRz/PmnaYH57DPTIuPvD716wUsvKcCIiMgVKciI/RIT4a23zGR2586ZfR06wPDhUK6craWJiIhnU5AR+1gWzJxpWlyOHDH7brsN3n0X6te3tzYREfEKCjJij3XroHdv+OUX8758eXj7bWjfHhwOe2sTERGv4Wd3AZLD/PWXWfcoMtKEmHz5zCOknTvhvvsUYkRE5LqoRUayR2oqTJkCAwbAqVNm3+OPw4gRUKKEvbWJiIjXUpCRrLdpE3TvDmvXmvc1a8KECRAVZW9dIiLi9fRoSbJOYiL07QsRESbE5M8PY8bAr78qxIiIiFuoRUayxpIl0K0bHDhg3j/0kBmNVLKkrWWJiIhvUYuMuNfp02YhxzvvNCGmTBmz1MCsWQoxIiLidgoy4j7ffANVq8LHH5v3zz0H27dDixb21iUiIj5Lj5bEdbGxZimBGTPM+0qVzAil226zty4REfF5apER1yxYANWqmRDj52dm6d28WSFGRESyhVpkJHPi4qBPH5g2zbyvVAk+/dRMdCciIpJN1CIj12/FCqhe3YQYh8MMsd60SSFGRESynYKMZNyFC/Dqq3D77XD4MFSsCCtXmmHVQUF2VyciIjmQHi1JxuzdC488AuvXm/dPPgljx0JwsL11iYhIjqYWGbk2yzKPkGrVMiGmQAH44guYOlUhRkREbKcWGbm6uDh45hkzmR1Akybw2WdQurStZYmIiKRRi4xc2S+/mFaYWbMgVy4YPtwsO6AQIyIiHkQtMuIsNRXeegsGDoSUFChXDj7/HG65xe7KRERELqMgI5ccOwaPPQbR0eb9Qw/BRx9BaKi9dYmIiFyFHi2J8f33UKOGCTF585r1kj7/XCFGREQ8moJMTpeUZCa0a9UKTp6EmjVhwwZ44gkz2Z2IiIgH06OlnOz33+Hhh2HjRvO+Z08YNQoCA+2tS0REJIMUZHIiy4Lp06FHD0hIgEKFzFwx995rd2UiIiLXRUEmp4mPh+7dYeZM875RI7NydalS9tYlIiKSCeojk5OsXWvmhpk508wNM3QoLF2qECMiIl5LLTI5QUqK6fsyaBBcvAhly5ow06CB3ZWJiIi4xONbZD744APKlStHYGAgkZGRrFu3zu6SvMv+/Wa16ldeMSHmoYdg82aFGBER8QkeHWRmz55N3759ef3119m4cSM1a9akefPmxMTE2F2a57MsMxdMjRqwapVZ4DFtbpgCBeyuTkRExC0clmVZdhdxNZGRkdSrV4/3338fgNTUVEqXLk3Pnj0ZMGBAhq4RHx9PaGgocbt3ExISYnY6HODvbyZ+8/f3vflSYmLgqafgm2/M+9tug08/hQoV7K1LREQkg9L/fsfFXfr7fQUe20cmOTmZDRs28PLLL6fv8/Pzo1mzZqxZs+aqn0tKSiIpKSn9fXx8vNmoVOnKH/DzM4Emb14oXhzq1IG6dc2rZk0ICnLL75MtLAu++AJ69TJhJk8eePNNeOEF07lXRETEx3hskDl58iQpKSmEh4c77Q8PD+e333676udGjBjB4MGDLz+Q1ury7wao1FQzl0pCgvnjv2WLmVMFzB//evWga1fo0MGEHU/1xx/w7LOwaJF5X726mSumZk176xIREclCHt1HJjNefvll4uLi0l+HDx82B06fNqHFsswrOdnsO3oU9u41AWbuXHjtNWjZEooWNaN9fvnFBJmSJeH55+EaIcoWyckwYgTcfLMJMQEBMGQIrF+vECMiIj7PY1tkwsLCyJUrFydOnHDaf+LECYoVK3bVzwUEBBAQEPDfN8iTxyyI+M9FEWvUgLZtzbZlweHDMGsWTJxoRv+MHWteTZuaUUC3325f/xrLgh9/NI+Nduww++64AyZMgJtusqcmERGRbOaxLTL+/v7UrVuX6Ojo9H2pqalER0cTFRWV9QU4HFCmDPTvb1psvv8eWrc2fWqio02Yue02+OGHyx9XZbVffjGhpUULE2LCwuCzz2DJEoUYERHJUTw2yAD07duXyZMn8+mnn7Jr1y66d+9OYmIiTzzxRPYW4udnHjd9+63pi9Kjh3mE8/PPZn/9+jBvnnkUlZV27DAtRlFRsHy5GXHVp4953PXYY743+kpEROQ/ePTwa4D333+ft99+m+PHj1OrVi3GjRtHZGRkhj+f0eFb1+3oUXjnHfPY6dw5s69MGTPsuUsXuMbjr+ty4QLMnw9TpsDChab1x88POneG11839xQREfExGf377fFBxlVZFmTSxMTA6NEwaRKcOmX25c5tWk66dDGPn4KDr/+6e/bA1Klm/pd/9hO67z6zRlKVKm4pX0RExBMpyPy/LA8yac6dg6++Mi00P/98ab+fnxlRVL8+REaakUSBgSbs5M5thnifPw/btpmRU1u3mp9Hjly6Rni4aYHp0gVuvDHrfgcREREPoSDz/7ItyPzT1q0m0Hz3Hfz5Z+aukdYvp2tXaNXKjLISERHJIRRk/p8tQeafjh6FdevMa+1a2L3bLN6Y9kpJMa0yVaua4d81a5qf1auDHfWKiIh4AAWZ/2d7kBEREZHrltG/3x49/FpERETkWhRkRERExGspyIiIiIjXUpARERERr6UgIyIiIl5LQUZERES8loKMiIiIeC0FGREREfFaCjIiIiLitRRkRERExGspyIiIiIjXUpARERERr6UgIyIiIl5LQUZERES8Vm67C8hqlmUBZjlwERER8Q5pf7fT/o5fjc8Hmb///huA0qVL21yJiIiIXK8zZ84QGhp61eM+H2QKFSoEwKFDh675DyHZJz4+ntKlS3P48GFCQkLsLkfQd+Kp9L14Hn0n2ceyLM6cOUOJEiWueZ7PBxk/P9MNKDQ0VP+j8zAhISH6TjyMvhPPpO/F8+g7yR4ZaYBQZ18RERHxWgoyIiIi4rV8PsgEBATw+uuvExAQYHcp8v/0nXgefSeeSd+L59F34nkc1n+NaxIRERHxUD7fIiMiIiK+S0FGREREvJaCjIiIiHgtBRkRERHxWj4dZD744APKlStHYGAgkZGRrFu3zu6SfNaIESOoV68e+fPnp2jRorRt25bdu3c7nXP+/Hl69OhB4cKFCQ4O5r777uPEiRNO5xw6dIhWrVqRN29eihYtyosvvsjFixez81fxWSNHjsThcPD888+n79N3kv2OHDnCo48+SuHChQkKCqJ69er8+uuv6ccty2LQoEEUL16coKAgmjVrxu+//+50jdjYWDp27EhISAgFChSgS5cuJCQkZPev4jNSUlIYOHAg5cuXJygoiIoVKzJ06FCnNX70vXgwy0fNmjXL8vf3tz7++GNrx44dVrdu3awCBQpYJ06csLs0n9S8eXNr2rRp1vbt263Nmzdbd999t1WmTBkrISEh/ZxnnnnGKl26tBUdHW39+uuv1i233GI1aNAg/fjFixetatWqWc2aNbM2bdpkff/991ZYWJj18ssv2/Er+ZR169ZZ5cqVs2rUqGH17t07fb++k+wVGxtrlS1b1urcubO1du1a648//rAWLVpk7d27N/2ckSNHWqGhoda8efOsLVu2WPfee69Vvnx569y5c+nntGjRwqpZs6b1yy+/WKtWrbJuuOEGq0OHDnb8Sj5h2LBhVuHCha358+db+/fvt7788ksrODjYGjt2bPo5+l48l88Gmfr161s9evRIf5+SkmKVKFHCGjFihI1V5RwxMTEWYK1YscKyLMs6ffq0lSdPHuvLL79MP2fXrl0WYK1Zs8ayLMv6/vvvLT8/P+v48ePp50yYMMEKCQmxkpKSsvcX8CFnzpyxbrzxRmvx4sVW48aN04OMvpPs99JLL1m33XbbVY+npqZaxYoVs95+++30fadPn7YCAgKszz//3LIsy9q5c6cFWOvXr08/Z+HChZbD4bCOHDmSdcX7sFatWllPPvmk07727dtbHTt2tCxL34un88lHS8nJyWzYsIFmzZql7/Pz86NZs2asWbPGxspyjri4OODSop0bNmzgwoULTt9J5cqVKVOmTPp3smbNGqpXr054eHj6Oc2bNyc+Pp4dO3ZkY/W+pUePHrRq1crp3x70ndjh22+/JSIiggceeICiRYtSu3ZtJk+enH58//79HD9+3Ok7CQ0NJTIy0uk7KVCgABEREennNGvWDD8/P9auXZt9v4wPadCgAdHR0ezZsweALVu2sHr1alq2bAnoe/F0Prlo5MmTJ0lJSXH6jy9AeHg4v/32m01V5Rypqak8//zz3HrrrVSrVg2A48eP4+/vT4ECBZzODQ8P5/jx4+nnXOk7Szsm12/WrFls3LiR9evXX3ZM30n2++OPP5gwYQJ9+/bllVdeYf369fTq1Qt/f386deqU/m96pX/zf34nRYsWdTqeO3duChUqpO8kkwYMGEB8fDyVK1cmV65cpKSkMGzYMDp27Aig78XD+WSQEXv16NGD7du3s3r1artLydEOHz5M7969Wbx4MYGBgXaXI5iQHxERwfDhwwGoXbs227dvZ+LEiXTq1Mnm6nKuL774ghkzZjBz5kxuvvlmNm/ezPPPP0+JEiX0vXgBn3y0FBYWRq5cuS4bfXHixAmKFStmU1U5w3PPPcf8+fNZtmwZpUqVSt9frFgxkpOTOX36tNP5//xOihUrdsXvLO2YXJ8NGzYQExNDnTp1yJ07N7lz52bFihWMGzeO3LlzEx4eru8kmxUvXpyqVas67atSpQqHDh0CLv2bXuu/XcWKFSMmJsbp+MWLF4mNjdV3kkkvvvgiAwYM4OGHH6Z69eo89thj9OnThxEjRgD6XjydTwYZf39/6tatS3R0dPq+1NRUoqOjiYqKsrEy32VZFs899xxz585l6dKllC9f3ul43bp1yZMnj9N3snv3bg4dOpT+nURFRbFt2zan/xgsXryYkJCQy/7jL/+tadOmbNu2jc2bN6e/IiIi6NixY/q2vpPsdeutt142LcGePXsoW7YsAOXLl6dYsWJO30l8fDxr1651+k5Onz7Nhg0b0s9ZunQpqampREZGZsNv4XvOnj2Ln5/zn8NcuXKRmpoK6HvxeHb3Ns4qs2bNsgICAqxPPvnE2rlzp/XUU09ZBQoUcBp9Ie7TvXt3KzQ01Fq+fLl17Nix9NfZs2fTz3nmmWesMmXKWEuXLrV+/fVXKyoqyoqKiko/njbU96677rI2b95s/fDDD1aRIkU01NeN/jlqybL0nWS3devWWblz57aGDRtm/f7779aMGTOsvHnzWv/73//Szxk5cqRVoEAB65tvvrG2bt1qtWnT5orDfGvXrm2tXbvWWr16tXXjjTdqmK8LOnXqZJUsWTJ9+PWcOXOssLAwq3///unn6HvxXD4bZCzLssaPH2+VKVPG8vf3t+rXr2/98ssvdpfks4ArvqZNm5Z+zrlz56xnn33WKliwoJU3b16rXbt21rFjx5yuc+DAAatly5ZWUFCQFRYWZr3wwgvWhQsXsvm38V3/DjL6TrLfd999Z1WrVs0KCAiwKleubE2aNMnpeGpqqjVw4EArPDzcCggIsJo2bWrt3r3b6Zy///7b6tChgxUcHGyFhIRYTzzxhHXmzJns/DV8Snx8vNW7d2+rTJkyVmBgoFWhQgXr1VdfdZpiQN+L53JY1j+mLhQRERHxIj7ZR0ZERERyBgUZERER8VoKMiIiIuK1FGRERETEaynIiIiIiNdSkBERERGvpSAjIiIiXktBRkRERLyWgoyISBYZOHAgTz311HV95uGHH+bdd9/NoopEfI+CjIiPczgc13y98cYbdpfoduXKlWPMmDG21nD8+HHGjh3Lq6++mr6vc+fOtG3b1um8r776isDAwPTw8tprrzFs2DDi4uKys1wRr6UgI+Ljjh07lv4aM2YMISEhTvv69etnd4kZYlkWFy9ezNZ7JicnZ/qzU6ZMoUGDBukrW1/tnI4dOzJhwgReeOEFAKpVq0bFihX53//+l+l7i+QkCjIiPq5YsWLpr9DQUBwOh9O+WbNmUaVKFQIDA6lcuTIffvhh+mcPHDiAw+Hgiy++oGHDhgQFBVGvXj327NnD+vXriYiIIDg4mJYtW/LXX3+lfy6t5WHw4MEUKVKEkJAQnnnmGadgkJqayogRIyhfvjxBQUHUrFmTr776Kv348uXLcTgcLFy4kLp16xIQEMDq1avZt28fbdq0ITw8nODgYOrVq8eSJUvSP9ekSRMOHjxInz590ludAN544w1q1arl9G8zZswYypUrd1ndw4YNo0SJElSqVAmAw4cP8+CDD1KgQAEKFSpEmzZtOHDgwDX/3WfNmkXr1q2venzUqFH07NmTWbNm8cQTTzgda926NbNmzbrm9UXEUJARycFmzJjBoEGDGDZsGLt27WL48OEMHDiQTz/91Om8119/nddee42NGzeSO3duHnnkEfr378/YsWNZtWoVe/fuZdCgQU6fiY6OZteuXSxfvpzPP/+cOXPmMHjw4PTjI0aM4LPPPmPixIns2LGDPn368Oijj7JixQqn6wwYMICRI0eya9cuatSoQUJCAnfffTfR0dFs2rSJFi1a0Lp1aw4dOgTAnDlzKFWqFEOGDElvdboe0dHR7N69m8WLFzN//nwuXLhA8+bNyZ8/P6tWreKnn34iODiYFi1aXLXFJjY2lp07dxIREXHF4y+99BJDhw5l/vz5tGvX7rLj9evXZ926dSQlJV1X7SI5ks2rb4tINpo2bZoVGhqa/r5ixYrWzJkznc4ZOnSoFRUVZVmWZe3fv98CrClTpqQf//zzzy3Aio6OTt83YsQIq1KlSunvO3XqZBUqVMhKTExM3zdhwgQrODjYSklJsc6fP2/lzZvX+vnnn53u3aVLF6tDhw6WZVnWsmXLLMCaN2/ef/5eN998szV+/Pj092XLlrXee+89p3Nef/11q2bNmk773nvvPats2bJOdYeHh1tJSUnp+6ZPn25VqlTJSk1NTd+XlJRkBQUFWYsWLbpiPZs2bbIA69ChQ077O3XqZPn7+1/27/dvW7ZssQDrwIEDVz1HRIzcdoYoEbFPYmIi+/bto0uXLnTr1i19/8WLFwkNDXU6t0aNGunb4eHhAFSvXt1pX0xMjNNnatasSd68edPfR0VFkZCQwOHDh0lISODs2bPceeedTp9JTk6mdu3aTvv+3aqRkJDAG2+8wYIFCzh27BgXL17k3Llz6S0yrqpevTr+/v7p77ds2cLevXvJnz+/03nnz59n3759V7zGuXPnAAgMDLzsWI0aNTh58iSvv/469evXJzg4+LJzgoKCADh79mymfw+RnEJBRiSHSkhIAGDy5MlERkY6HcuVK5fT+zx58qRvp/U5+fe+1NTU6773ggULKFmypNOxgIAAp/f58uVzet+vXz8WL17MO++8ww033EBQUBD333//f3bM9fPzw7Isp30XLly47Lx/3y8hIYG6desyY8aMy84tUqTIFe8VFhYGwKlTpy47p2TJknz11VfcfvvttGjRgoULF14WkmJjY695fRG5REFGJIcKDw+nRIkS/PHHH3Ts2NHt19+yZQvnzp1Lb1345ZdfCA4OpnTp0hQqVIiAgAAOHTpE48aNr+u6P/30E507d07vW5KQkHBZx1t/f39SUlKc9hUpUoTjx49jWVZ6GNu8efN/3q9OnTrMnj2bokWLEhISkqEaK1asSEhICDt37uSmm2667HjZsmVZsWJFepj54YcfnMLM9u3bKVWqVHogEpGrU2dfkRxs8ODBjBgxgnHjxrFnzx62bdvGtGnTGD16tMvXTk5OpkuXLuzcuZPvv/+e119/neeeew4/Pz/y589Pv3796NOnD59++in79u1j48aNjB8//rKOxv924403MmfOHDZv3syWLVt45JFHLmsNKleuHCtXruTIkSOcPHkSMKOZ/vrrL0aNGsW+ffv44IMPWLhw4X/+Hh07diQsLIw2bdqwatUq9u/fz/Lly+nVqxd//vnnFT/j5+dHs2bNWL169VWvW7p0aZYvX05MTAzNmzcnPj4+/diqVau46667/rM2EVGQEcnRunbtypQpU5g2bRrVq1encePGfPLJJ5QvX97lazdt2pQbb7yRRo0a8dBDD3Hvvfc6Tb43dOhQBg4cyIgRI6hSpQotWrRgwYIF/3nv0aNHU7BgQRo0aEDr1q1p3rw5derUcTpnyJAhHDhwgIoVK6Y/nqlSpQoffvghH3zwATVr1mTdunUZmkMnb968rFy5kjJlytC+fXuqVKlCly5dOH/+/DVbaLp27cqsWbOu+citVKlSLF++nJMnT6aHmfPnzzNv3jynfksicnUO698PjUVEXNS5c2dOnz7NvHnz7C7FNpZlERkZSZ8+fejQoUOGPzdhwgTmzp3Ljz/+mIXVifgOtciIiGQBh8PBpEmTrns24jx58jB+/PgsqkrE96hFRkTcTi0yIpJdFGRERETEa+nRkoiIiHgtBRkRERHxWgoyIiIi4rUUZERERMRrKciIiIiI11KQEREREa+lICMiIiJeS0FGREREvNb/AXU64zrIZUJNAAAAAElFTkSuQmCC" 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" 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" 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jZcvM48qVTYBYt0767TcTVLLUqWNaKtq1y91rp6SYFo0//jCBpmVLs6J1bmVmSocPm+4qV1k7ybLMlVoTJpjPUqmSWQBz3Lji7RoDroIWGwCe6cABKT7eBJeTJ52PnT5tli7IzDQ/0kOGmNaIrPCRkmJaJNasMa0m3brlbv2mLKVKmSUO8svXV4qKyv/zi0LWFVd//GEWztyxw+xn4DDcFC02AFxbcrL5wV240ASa3buv/pyuXaXRo03rA3InJcXMZ7Nhgwlgp06Zif8AF0GLDQD3deKENHu2GdPy44/mqqEsvr5m/Mett5pupks1bSo1b158tXqKUqXMlWEPPGD+voQauCmCDQD7HDhg1iXat+/Cbdcu082Unn7hvFq1pHvvNWNlbr7ZXH6NwnfttWYmYsCNEWwAFK+kJHPF0sSJZlK7nNSvby477tzZzPHiKoNtAbg0gg2AopeSYi6v/uwzaeZM6dw5s9/PT6pWzYyFqVRJiokx9zffbFppACCPCDYACldqqrnqaO1ac1u3zlxpc/F1CnXrSr16Sd27u95VQgDcGsEGQMFYlpkfZsECc1uyxMz4e6lrrzVLDvTsKTVpQtcSgCJBsAGQP7t3m+UKpk6V/vrL+VhkpHTjjebqmqxbTmsoAUAhItgAyL30dLOQ44QJpnUmS3Cw1KqV1KaNdNddZvFEWmQA2IBgAyBnliXt3SstX25uc+deWPjRx8csRfD44ybQBAXZWioASAQbAJfKyDCT402fbsLMpd1M5ctLjz4qPfaYVLWqPTUCQA4INgCMlBRp0iTpnXfMpHlZ/P3NYN+bbzZT7rdta1aqBgAXRLABvJllSVu3Sl98YQYCnzhh9pcpI/XtK7VvLzVrxvT6ANwGwQbwJpYlbd8uLV5sFpSMj5eOHLlwvEYNaeBAKTZWuuYau6oEgHwj2ADe4Px5ado06T//kbZscT4WHGwWlHz8cbMek5+fPTUCQCEg2ACe7PRp6eOPpTFjzAKTkrl66aabpNtuM4tKNm0qBQTYWiYAFBaCDeCJEhOl8eOlceOk48fNvshIacAA0zJTurSd1QFAkSHYAJ5kwwbp7bdNt9P582Zf9erSv/5lxs0w1wwAD0ewAdxdRoaZDfidd8w6TVlatjQtNA88wLgZAF6DYAO4q2PHzPiZCROkP/4w+/z8pC5dzJVNzZrZWx8A2IBgA7ibjRul994zi0+eO2f2hYebeWf695cqVrS3PgCwEcEGcBerVkkjR5pupyyNGklPPSU99JC5bBsAvBzBBnB1S5dKr79+YTVtX1+pc2fp6afNOBpW0QYAB4IN4KqWL5defvnCgGA/P+mRR6QhQ6TrrrO3NgBwUQQbwNX8+qsJNPPmmccBAWY17eefl6pUsbU0AHB1BBvAVWzfLg0bJn39tXns7y/17m1CDgOCASBXCDaA3ZKTpVdeMVc6ZWSYMTMPP2z21ahhd3UA4FYINoBdLEuaMsXMCpyYaPbde6+58qlBA3trAwA3RbAB7LBxo5lzZvly87hGDWnsWKldO3vrAgA352t3AYBX2blT6t7dzD+zfLlUsqT0xhvSli2EGgAoBC4VbOLi4tS0aVOFhIQoIiJCnTp10o4dO5zOOXfunPr166eyZcuqVKlSevDBB3Xo0CGbKgZyac8eqVcv6W9/MzMGW5ZZ+mDbNnP5dmCg3RUCgEdwqWCzZMkS9evXTytXrtSCBQt0/vx5tWnTRqdPn3acM3DgQH333Xf6+uuvtWTJEv3111964IEHbKwauILkZOnJJ828M5MmmcHB99wjrVsnffWVVKmS3RUCgEfxsSzLsruInBw5ckQRERFasmSJWrVqpaSkJJUvX15Tp05V586dJUnbt29XnTp1tGLFCt14441Xfc3k5GSFhYUpKSlJoaGhRf0R4M3WrjVLHSQkmMd33SW9+qqUi3+nAABnuf39dqkWm0slJSVJksLDwyVJ69at0/nz59W6dWvHObVr11alSpW0YsWKbF8jNTVVycnJTjegSFmW9PbbZrmDhATTKrNwofTjj4QaAChiLhtsMjMzNWDAAN10002qV6+eJCkxMVEBAQEqXbq007mRkZFKzLpc9hJxcXEKCwtz3GJiYoq6dHizo0elDh2kQYOk8+el+++XNmyQ7rjD7soAwCu4bLDp16+ftmzZounTpxfodYYMGaKkpCTHbf/+/YVUIXCJH3+UGjaU5s41g4HHjZP+9z+pTBm7KwMAr+GS89j0799fc+bM0dKlS1Xxoqnko6KilJaWppMnTzq12hw6dEhRUVHZvlZgYKACueIERSklRXruOWnCBPO4Vi3pyy9NyAEAFCuXarGxLEv9+/fXzJkztWjRIlWtWtXpeOPGjVWiRAktXLjQsW/Hjh3at2+fWrRoUdzlAmYumuuvvxBq+vc3VzwRagDAFi7VYtOvXz9NnTpV3377rUJCQhzjZsLCwhQcHKywsDD17t1bgwYNUnh4uEJDQ/XUU0+pRYsWuboiCig0qalmccq33jKDhWNipIkTpTvvtLsyAPBqLnW5t4+PT7b7J06cqJ49e0oyE/Q9++yzmjZtmlJTU9W2bVuNHz8+x66oS3G5Nwps2zapWzezLIIk9ewpvfOOFBZmZ1UA4NFy+/vtUsGmOBBskG+WJX38sfTMM9LZs1LZstInn0gdO9pdGQB4vNz+frtUVxTgsk6ckP75T+mbb8zj1q2lzz6ToqPtrQsA4MSlBg8DLmn1ajMY+JtvJH9/afRo6YcfCDUA4IJosQGuZOpU6dFHzWDhmjXN4yZN7K4KAJADWmyA7GRmmlW3u3c3oaZDB3MZN6EGAFwawQa41KlTUqdO0qhR5vGQIdKsWVJIiJ1VAQByga4o4GJ795rWmS1bzLIIn3xiWm0AAG6BYANk2bRJatdOOnhQqlDBtNI0a2Z3VQCAPCDYAJK0dKl0331SUpJUt640f7500TplAAD3wBgbYNYsqU0bE2puvllatoxQAwBuimAD7/bhh9KDD5ornzp2lH78USpTxu6qAAD5RLCBd7Is6fXXpcceM5d29+ljJuALDra7MgBAARBs4H0yM6UBA6ShQ83jl14yLTf+DDkDAHfHf8nhXc6fl3r1kqZMMY/ffVd6+ml7awIAFBqCDbzHmTNSly7S99+b1plJk5ijBgA8DMEG3uHECTPx3s8/m3E033wj3X233VUBAAoZwQaeLyVFat9eWrVKKl1amjNHuukmu6sCABQBgg08W1qauZx71SpzGffixVLDhnZXBQAoIlwVBc+VmSnFxpq5aUqWNGNrCDUA4NEINvBMlmWudpo+XSpRQpoxQ7rxRrurAgAUMYINPNOrr0rjxkk+PtLkyVLbtnZXBAAoBgQbeJ7335deecVsjx0rPfSQreUAAIoPwQae5dNPpaeeMtvDh0v9+tlbDwCgWBFs4DmmTzdrPknSwIEm2AAAvArBBp7h22+lHj3MoOHHHpPeesuMrwEAeBWCDdzfDz9If/+7lJEhPfKINH48oQYAvBTBBu5t6VKpUyczEV/nzmaMjS//rAHAW/ELAPe1Z490//3SuXPSPfeYFbv9mUwbALwZwQbu6cwZE2qOH5eaNpW+/loKCLC7KgCAzQg2cD+WJfXuLW3cKEVEmFmFg4PtrgoA4AIINnA/Y8aYS7v9/U1LTcWKdlcEAHARBBu4l59+kp57zmy//bbUqpW99QAAXArBBu5jzx6pa1ezanfPnswqDAC4DMEG7iE11VzOffy41KSJNGECc9UAAC5DsIF7eP556ddfpbJlzWDhoCC7KwIAuCCCDVzf7NnSu++a7UmTpJgYW8sBALgugg1c2/79Uq9eZnvgQOnee+2tBwDg0gg2cF3p6VL37mZcTePG0qhRdlcEAHBxBBu4rldflZYtk0JCpC+/ZGZhAMBVEWzgmhYvll5/3Wx/+KFUvbq99QAA3ALBBq7n8GHTBZW1dMJDD9ldEQDATRBs4FoyM6XYWOngQelvf5Pee8/uigAAboRgA9fy1lvS/Plmnpovv5RKlrS7IgCAGyHYwHWsXCm9+KLZfu89qV49e+sBALgdgg1cw8mTUrdu5hLvrl2lPn3srggA4IYINrCfZZkgs3evVK2auQqKdaAAAPlAsIH9PvhA+t//pBIlzLia0FC7KwIAuCmCDey1d6/07LNm+9//Nit3AwCQT/4FefL58+eVmJioM2fOqHz58goPDy+suuANLEvq3186e1a69VZpwAC7KwIAuLk8t9icOnVKEyZM0K233qrQ0FBVqVJFderUUfny5VW5cmX17dtXa9asKYpa4WlmzZLmzjVdUB98wLgaAECB5SnYjBkzRlWqVNHEiRPVunVrzZo1Sxs2bNDOnTu1YsUKDR8+XOnp6WrTpo3atWunXbt2FVXdcHenTklPPWW2n39eql3b3noAAB7Bx7IsK7cnd+vWTS+//LLq1q17xfPOnTunSZMmKSAgQI8++miBiyxMycnJCgsLU1JSkkIZpGqfQYOkt982V0Ft2SIFB9tdEQDAheX29ztPwSa3UlJSVKpUqcJ+2UJBsHEB69ebQcKZmdK8eVK7dnZXBABwcbn9/c7zGJu33377isdPnTqltm3b5vVl4S0yMqTHHzeh5u9/J9QAAApVnoPNiy++qMmTJ2d77PTp02rXrp2OHTtW4MLgoT76SFq9WgoJMV1RAAAUojwHm88//1yPPfaYZs+e7bT/9OnTatu2rY4cOaLFixcXWoHwIImJ0gsvmO2RI6XoaHvrAQB4nDwHm86dO2vs2LHq1q2b4uPjJV1oqTl06JDi4+NVoUKFfBWzdOlSdejQQdHR0fLx8dGsWbOcjvfs2VM+Pj5Ot3Z0ZbiPZ5+VkpKkxo2lJ5+0uxoAgAfK1wR9ffr00fHjx9WxY0d9++23GjZsmP766y8tWbJE0QX4v/DTp0+rYcOGevTRR/XAAw9ke067du00ceJEx+PAwMB8vx+K0U8/SVOnSr6+0n//K/n52V0RAMAD5Xvm4eeee07Hjx/XnXfeqSpVqig+Pl4VK1YsUDHt27dX+/btr3hOYGCgoqKiCvQ+KGbnzl1ooenXz7TYAABQBPIcbC5tSSlRooTKlSunZ555xmn/jBkzClZZDuLj4xUREaEyZcrojjvu0Ouvv66yZcvmeH5qaqpSU1Mdj5OTk4ukLlzBqFHSrl1ShQrS66/bXQ0AwIPlOdiEhYU5Pe7WrVuhFXM17dq10wMPPKCqVasqISFBL774otq3b68VK1bIL4eujbi4OI0YMaLYasQldu6U4uLM9rvvsnI3AKBIFckEfYXBx8dHM2fOVKdOnXI85/fff1f16tX1008/6c4778z2nOxabGJiYpigrzhYltS6tbRokZmv5vvvWQ8KAJAvRTZBnyupVq2aypUrp927d+d4TmBgoEJDQ51uKCZTp5pQExQkjRtHqAEAFDm3DjYHDhzQsWPH8n15OYpQcrJZD0qShg41a0IBAFDE8n1VVFFISUlxan3Zs2ePNmzYoPDwcIWHh2vEiBF68MEHFRUVpYSEBD333HOqUaMGSzi4ojfflA4flq67Tho82O5qAABewqXG2MTHx+v222+/bH9sbKwmTJigTp06af369Tp58qSio6PVpk0bvfbaa4qMjMz1e7AIZjE4eFCqUUM6c0b63/+kHOYkAgAgt3L7+13gFpuWLVsqISFBhw4dKuhL6bbbbtOVctYPP/xQ4PdAMXjlFRNqWrSQ7r/f7moAAF6kwMGma9euOnr0aGHUAk+wfbv0ySdme/RoBgwDAIpVgYPNpRPzwcsNGSJlZEgdO0o332x3NQAAL+PWV0XBxfz8szRrllkPKmtSPgAAihHBBoXDsqR//cts9+4t1aljbz0AAK9EsEHhmDVLWrFCCg42g4cBALABwQYFl55uxtZIZlK+6Gh76wEAeK08DR4elDWTbC6MGTMmz8XATU2eLO3YIZUtKz33nN3VAAC8WJ6Czfr163N1ng+X+HqP1FQpa/X0IUNYvRsAYKs8BZvFixcXVR1wVx9/LO3bZ7qfnnzS7moAAF6uQPPYnDx5Up988om2bdsmSapbt64effRRhYWFFUpxcHFnzkivv262X37ZDBwGAMBG+R48vHbtWlWvXl1vv/22jh8/ruPHj2vMmDGqXr26fv3118KsEa5q/HgpMVGqUsVc4g0AgM3yvQjmLbfcoho1auijjz6Sv79p+ElPT1efPn30+++/a+nSpYVaaGFhEcxCkpwsVasmHTsmTZwo9expd0UAAA9W5Itgrl271inUSJK/v7+ee+45NWnSJL8vC3fx7rsm1NSqJfXoYXc1AABIKkBXVGhoqPbt23fZ/v379yskJKRARcHFHT8uvfmm2R4xQvIv8JJjAAAUinwHm65du6p379768ssvtX//fu3fv1/Tp09Xnz591K1bt8KsEa7mP/8xXVENGkhduthdDQAADvn+X+0333xTPj4++sc//qH09HRJUokSJfTEE09o1KhRhVYgXMzhw9J775nt114zC14CAOAi8j14OMuZM2eUkJAgSapevbpKlixZKIUVFQYPF9CQIdKoUVKTJtLq1RKTMQIAikGRDx7OUrJkSdWvX7+gLwN3cOKENG6c2R46lFADAHA5BQo2586d06ZNm3T48GFlZmY6HbvvvvsKVBhc0Nix0qlTUv360r332l0NAACXyXewmT9/vv7xj3/o6NGjlx3z8fFRRkZGgQqDizl1ylziLUkvvcTYGgCAS8r3r9NTTz2lLl266ODBg8rMzHS6EWo80AcfmMu8r7tO6tzZ7moAAMhWvoPNoUOHNGjQIEVGRhZmPXBFZ89Kb71ltocMkfz87K0HAIAc5DvYdO7cWfHx8YVYClzWJ59Ihw5JlStL3bvbXQ0AADnK9xib999/X126dNGyZctUv359lShRwun4008/XeDi4ALS0qTRo832889Ll3zPAAC4knwHm2nTpunHH39UUFCQ4uPj5XPRpb8+Pj4EG0/x+efS/v1ShQpSr152VwMAwBXlO9i89NJLGjFihF544QX5coWMZ0pPN5PxSdLgwVJQkL31AABwFflOJGlpaeratSuhxpPNmCHt3i2VLSs99pjd1QAAcFX5TiWxsbH68ssvC7MWuBLLurCCd//+0jXX2FsPAAC5kO+uqIyMDI0ePVo//PCDGjRocNng4TFjxhS4ONho6VJpzRrT/dSvn93VAACQK/kONps3b1ajRo0kSVu2bHE65sMaQu4vq7WmZ0+pfHlbSwEAILfyHGyGDRumjh07avHixUVRD1zBtm3SnDlmkcuBA+2uBgCAXMvzGJsDBw6offv2qlixop544gnNnz9faWlpRVEb7JI1y3DHjmYJBQAA3ESeg82nn36qxMRETZs2TSEhIXrmmWdUrlw5Pfjgg5o8ebKOHz9eFHWiuCQmmrlrJHOJNwAAbiRfV0X5+vrqlltu0ejRo7Vjxw6tWrVKzZs313//+19FR0erVatWevPNN/Xnn38Wdr0oau+/b2YbbtFCuukmu6sBACBPfCzLsgrzBY8cOaLZs2dr9uzZuuWWWzTYxf6vPzk5WWFhYUpKSlJoaKjd5biW06elmBjpxAnpf/+THnjA7ooAAJCU+9/vQg82ro5gcwVjx0pPPy1Vry7t2MEq3gAAl5Hb32+mDYaRni69/bbZHjSIUAMAcEt5vtz7gVx0T/j7+ysqKkp33XWXOnTokK/CUMy+/Vbas8csn9Czp93VAACQL3lusQkLC7vqLTg4WLt27VLXrl01bNiwoqgbhW3sWHP/2GNSyZL21gIAQD4V6RibOXPm6Mknn9S+ffuK6i3yjDE22di0SWrY0HQ/7d0rVaxod0UAADgpkjE2eQ0o119/vZo0aZKn58AG779v7u+/n1ADAHBreQo2TZs21WOPPaY1a9bkeE5SUpI++ugj1atXTzNmzNCMGTMKXCSK0PHj0hdfmO2nnrK3FgAACihPg4e3bt2qkSNH6q677lJQUJAaN26s6OhoBQUF6cSJE9q6dat+++033XDDDRo9erTuvvvuoqobheXTT6WzZ6UGDaRbbrG7GgAACiRfY2zOnj2ruXPnavny5frjjz909uxZlStXTo0aNVLbtm1Vr169oqi1UDDG5iIZGVKNGmZczUcfSX362F0RAADZYoK+HBBsLjJ7tlnoskwZ6cABroYCALgsJujD1WUNGu7Th1ADAPAIBBtvtX27tGCB5OsrPfmk3dUAAFAoCDbeKqu1pkMHqUoVW0sBAKCwEGy8UXKy9NlnZptLvAEAHoRg440mT5ZSUqQ6daQ77rC7GgAACk2eF8G81Llz5/TBBx9o9+7dql+/vnr37i1//wK/LIqKZUnjx5vtfv0kHx976wEAoBAVuMUmNjZWa9euVf369TVv3jwNHjy4MOpCUVm6VNq2zVwF1aOH3dUAAFCoCty0snXrVm3evFmS1Lt3bzVr1qzARaEITZhg7rt3l8LC7K0FAIBCVuAWmxIlSji26YJycYcOSVlrdz3xhL21AABQBAocbDZt2qSIiAhFRESofPny2rx5s2M7IiIiT6+1dOlSdejQQdHR0fLx8dGsWbOcjluWpWHDhqlChQoKDg5W69attWvXroJ+BO/x6afS+fNS8+ZSo0Z2VwMAQKHLc7DZunWrhg4dqpMnT0qS0tPTdfjwYR0+fFhHjhzR+fPnHduHDx/O02ufPn1aDRs21Lhx47I9Pnr0aL333nv64IMPtGrVKl1zzTVq27atzp07l9eP4X0yMqT//tds01oDAPBQee47iouLU0pKikqXLn3ZsdTUVO3Zs0e1a9fOVzHt27dX+/btsz1mWZbeeecdvfzyy+rYsaMkafLkyYqMjNSsWbP00EMP5es9vcb8+dIff5h1of7+d7urAQCgSOS5xWblypV6+umnsz0WGBiovn37Ki4ursCFXWrPnj1KTExU69atHfvCwsLUvHlzrVixIsfnpaamKjk52enmlbIGDffqJQUH21sLAABFJM/B5sCBA6pRo0aOxx9//HHNnj27QEVlJzExUZIUGRnptD8yMtJxLDtxcXEKCwtz3GJiYgq9Npe3d6/0/fdm+/HHbS0FAICilOdgEx4eroMHD+Z4vFmzZtq9e3eBiipMQ4YMUVJSkuO2f/9+u0sqfh9+aCbma91aqlnT7moAACgyeQ42rVq10qRJk3J+QV/fIhnMGxUVJUk6dOiQ0/5Dhw45jmUnMDBQoaGhTjevkpYmffKJ2WbQMADAw+U52AwePFgfffSRPvzww2yPr1ixQtWqVStwYZeqWrWqoqKitHDhQse+5ORkrVq1Si1atCj09/MYM2dKhw9L0dHSfffZXQ0AAEUqz1dFNW7cWOPHj9cTTzyhr7/+Wv369dMNN9ygUqVKadmyZXr++edzHFx8NSkpKU7dWHv27NGGDRsUHh6uSpUqacCAAXr99ddVs2ZNVa1aVUOHDlV0dLQ6deqUr/fzClkBtHdviQkUAQAezseyLCs/T1y+fLkGDRqktWvXyuf/F1K0LEtt2rTR7NmzFRAQkOfXjI+P1+23337Z/tjYWE2aNEmWZWn48OH68MMPdfLkSd18880aP368rrvuuly/R3JyssLCwpSUlOT53VIJCVKNGmahyz17pMqV7a4IAIB8ye3vd76DTZbt27fr119/1ZkzZ1SvXj3deOONBXm5IudVwebFF6W4OKltWzOPDQAAbiq3v98F7puoXbt2vifkQxE6f16aONFs9+1rby0AABSTAq8VBRc1d66UmChFREgdOthdDQAAxYJg46k+/tjc9+wp5WO8EwAA7ohg44kOHJDmzTPbvXvbWwsAAMWIYOOJPv1UysyUbr1VysMVYwAAuDuCjafJyLgw0zCDhgEAXoZg42l++knat08qU0Z68EG7qwEAoFgRbDzNRx+Z+x49pKAge2sBAKCYEWw8yaFD0rffmm26oQAAXohg40kmT5bS06XmzaX69e2uBgCAYkew8RSWZa6GkrjEGwDgtQg2nmLVKmn7dik4WOra1e5qAACwBcHGU2S11nTpInn64p4AAOSAYOMJzpyRpk83248+am8tAADYiGDjCf73P+nUKalaNalVK7urAQDANgQbT5DVDdWrl+TjY28tAADYiGDj7n7/XYqPN4EmNtbuagAAsBXBxt1NmmTu77pLiomxtRQAAOxGsHFnGRkXgg2DhgEAINi4tUWLpP37zYKXHTvaXQ0AALYj2LizrEHDDz/MgpcAAIhg475OnJBmzjTbdEMBACCJYOO+pk2TUlOlhg2lRo3srgYAAJdAsHFXEyeae+auAQDAgWDjjrZskdaulUqUkLp3t7saAABcBsHGHWVd4n3vvVK5craWAgCAKyHYuJvz56XPPzfbvXrZWwsAAC6GYONu5s+XDh+WIiKkdu3srgYAAJdCsHE3WYOGH3nEjLEBAAAOBBt3cuSI9N13ZrtnT1tLAQDAFRFs3MnUqVJ6utSkiVSvnt3VAADgcgg27iTraihaawAAyBbBxl1s2GBuAQFSt252VwMAgEsi2LiLrNaajh2l8HBbSwEAwFURbNxBWpo0ZYrZZu4aAAByRLBxB3PnSkePShUqSHfdZXc1AAC4LIKNO8jqhnrkEcnf39ZSAABwZQQbV3fokGmxkbgaCgCAqyDYuLqpU6WMDKlZM6lOHburAQDApRFsXB1z1wAAkGsEG1e2YYO0aZOZu+ahh+yuBgAAl0ewcWUXz11TpoytpQAA4A4INq7q4rlr6IYCACBXCDau6vvvzdw1UVFSmzZ2VwMAgFsg2Lgq5q4BACDPCDau6MiRC3PXxMbaWwsAAG6EYOOKpk6V0tOlJk2kunXtrgYAALdBsHFFzF0DAEC+EGxczcaNZv4a5q4BACDPCDau5rPPzH2HDlLZsvbWAgCAmyHYuJL0dDO+RmLQMAAA+UCwcSU//WRW8y5bVmrXzu5qAABwOwQbV/LFF+b+oYekEiXsrQUAADdEsHEVKSnSzJlm+5FH7K0FAAA3RbBxFTNnSmfOSDVrSs2a2V0NAABuya2CzSuvvCIfHx+nW+3ate0uq3B8/rm579FD8vGxtxYAANyU2y1CVLduXf3000+Ox/6esI7SX39JCxea7e7d7a0FAAA35napwN/fX1FRUbk+PzU1VampqY7HycnJRVFWwUybJmVmSi1bStWr210NAABuy626oiRp165dio6OVrVq1dS9e3ft27fviufHxcUpLCzMcYuJiSmmSvMg62qoHj3srQMAADfnY1mWZXcRuTVv3jylpKSoVq1aOnjwoEaMGKE///xTW7ZsUUhISLbPya7FJiYmRklJSQoNDS2u0nO2ZYtUv765vPvgQWYbBgAgG8nJyQoLC7vq77dbdUW1b9/esd2gQQM1b95clStX1ldffaXevXtn+5zAwEAFBgYWV4l5l9Vac/fdhBoAAArI7bqiLla6dGldd9112r17t92l5E9mpjRlitlm7hoAAArMrYNNSkqKEhISVKFCBbtLyZ8lS6QDB6SwMOmee+yuBgAAt+dWwWbw4MFasmSJ9u7dq19++UX333+//Pz81K1bN7tLy5+sbqguXaSgIHtrAQDAA7jVGJsDBw6oW7duOnbsmMqXL6+bb75ZK1euVPny5e0uLe/OnpW++cZs0w0FAEChcKtgM336dLtLKDyzZ0vJyVLlytLNN9tdDQAAHsGtuqI8SlY3VPfuki9fAwAAhYFfVDscOSLNn2+26YYCAKDQEGzsMH26lJ4uNWkiecoingAAuACCjR1YQgEAgCJBsCluO3ZIq1dLfn6Su16mDgCAiyLYFLes1pq2baWICHtrAQDAwxBsipNl0Q0FAEARItgUp59/lvbulUJCpI4d7a4GAACPQ7ApTp9/bu4ffFAqWdLeWgAA8EAEm+KSmip99ZXZphsKAIAiQbApLnPnSidPStdeK912m93VAADgkQg2xeXiJRT8/OytBQAAD0WwKQ4nTpgWG4luKAAAihDBpjh8842UlibVr29uAACgSBBsigNz1wAAUCwINkVt3z5p6VLJx4clFAAAKGIEm6I2bZq5b9VKiomxtxYAADwcwaaoTZli7rt3t7cOAAC8AMGmKG3aJG3eLAUESJ07210NAAAej2BTlLJaa+65RypTxt5aAADwAgSbopKZKU2darbphgIAoFgQbIrK0qXSgQNSWJhpsQEAAEWOYFNUsrqhOneWgoLsrQUAAC9BsCkK585JX39ttumGAgCg2BBsisL330tJSWYl71tvtbsaAAC8BsGmKGR1Qz38sOTLnxgAgOLCr25hO3ZMmjPHbNMNBQBAsSLYFLZp08xK3tdfLzVsaHc1AAB4FYJNYZs0ydz36mVrGQAAeCOCTWHavFlat04qUcKMrwEAAMWKYFOYslpr7r1XKlfO1lIAAPBGBJvCcv689MUXZrtnT1tLAQDAWxFsCsv8+dLhw1JEhNS+vd3VAADglQg2hSWrG6pHDzPGBgAAFDuCTWE4elT67juzHRtrby0AAHgxgk1hmDrVjLG54QapQQO7qwEAwGsRbAoDc9cAAOASCDYFtXGjtH69GVfTrZvd1QAA4NUINgX12Wfm/r77pLJl7a0FAAAvR7ApiNOnpcmTzTZz1wAAYDuCTUF8/LFZzbtaNaldO7urAQDA6xFs8istTXrrLbP93HOSv7+99QAAAIJNvk2dKu3fL0VFMXcNAAAugmCTH5mZ0r//bbYHDpSCguytBwAASCLY5M+330rbt0thYdLjj9tdDQAA+H8Em7yyLGnUKLPdr58UGmpvPQAAwIFgk1fx8dLq1ab76Zln7K4GAABchGCTV3Fx5r5PHykiwt5aAACAE4JNXqxbJy1YIPn5Sc8+a3c1AADgEgSbvHjjDXP/8MNSlSq2lgIAAC5HsMmt996TZsyQfHzMhHwAAMDlEGxyY9YsacAAsx0XJ9WrZ2c1AAAgBwSbq1m92nQ9WZb02GO01gAA4MIINleyZ4/UoYN09qzUvr30/vumKwoAALgktww248aNU5UqVRQUFKTmzZtr9erVhf8mx4+bMHP4sNSokfTllyx0CQCAi3O7YPPll19q0KBBGj58uH799Vc1bNhQbdu21eHDhwvnDRISpDfflG65RdqxQ4qJkebMkUJCCuf1AQBAkfGxLMuyu4i8aN68uZo2bar3339fkpSZmamYmBg99dRTeuGFF676/OTkZIWFhSnpl18U6u8vnTsnnTkj/fKLNHOmtHnzhZNLl5aWLpXq1y+iTwMAAHLD8fudlKTQKyxn5FZ9K2lpaVq3bp2GDBni2Ofr66vWrVtrxYoV2T4nNTVVqampjsfJyclmo2XL7N/Ez0+67TapUyepSxcpMrKQqgcAAEXNrYLN0aNHlZGRochLwkZkZKS2b9+e7XPi4uI0YsSIyw+UKyeVLGnWfAoKkqpXN2Hm3nul8PAiqB4AABQ1two2+TFkyBANGjTI8Tg5OVkxMTFmLA0rcwMA4FHcKtiUK1dOfn5+OnTokNP+Q4cOKSoqKtvnBAYGKjAwsDjKAwAANnOrq6ICAgLUuHFjLVy40LEvMzNTCxcuVIsWLWysDAAAuAK3arGRpEGDBik2NlZNmjRRs2bN9M477+j06dPq1auX3aUBAACbuV2w6dq1q44cOaJhw4YpMTFR119/vebPn3/ZgGIAAOB93G4em4LK7XXwAADAdeT299utxtgAAABcCcEGAAB4DIINAADwGAQbAADgMQg2AADAYxBsAACAxyDYAAAAj0GwAQAAHoNgAwAAPIbbLalQUFkTLScnJ9tcCQAAyK2s3+2rLZjgdcHm2LFjkqSYmBibKwEAAHl16tQphYWF5Xjc64JNeHi4JGnfvn1X/MOg+CQnJysmJkb79+9n/S4XwXfimvheXA/fSfGxLEunTp1SdHT0Fc/zumDj62uGFYWFhfGP0MWEhobynbgYvhPXxPfievhOikduGiQYPAwAADwGwQYAAHgMrws2gYGBGj58uAIDA+0uBf+P78T18J24Jr4X18N34np8rKtdNwUAAOAmvK7FBgAAeC6CDQAA8BgEGwAA4DEINgAAwGN4VbAZN26cqlSpoqCgIDVv3lyrV6+2uySPFRcXp6ZNmyokJEQRERHq1KmTduzY4XTOuXPn1K9fP5UtW1alSpXSgw8+qEOHDjmds2/fPt1zzz0qWbKkIiIi9K9//Uvp6enF+VE81qhRo+Tj46MBAwY49vGdFL8///xTPXr0UNmyZRUcHKz69etr7dq1juOWZWnYsGGqUKGCgoOD1bp1a+3atcvpNY4fP67u3bsrNDRUpUuXVu/evZWSklLcH8VjZGRkaOjQoapataqCg4NVvXp1vfbaa05rFPG9uDDLS0yfPt0KCAiwPv30U+u3336z+vbta5UuXdo6dOiQ3aV5pLZt21oTJ060tmzZYm3YsMG6++67rUqVKlkpKSmOcx5//HErJibGWrhwobV27VrrxhtvtFq2bOk4np6ebtWrV89q3bq1tX79euv777+3ypUrZw0ZMsSOj+RRVq9ebVWpUsVq0KCB9cwzzzj2850Ur+PHj1uVK1e2evbsaa1atcr6/fffrR9++MHavXu345xRo0ZZYWFh1qxZs6yNGzda9913n1W1alXr7NmzjnPatWtnNWzY0Fq5cqW1bNkyq0aNGla3bt3s+EgeYeTIkVbZsmWtOXPmWHv27LG+/vprq1SpUta7777rOIfvxXV5TbBp1qyZ1a9fP8fjjIwMKzo62oqLi7OxKu9x+PBhS5K1ZMkSy7Is6+TJk1aJEiWsr7/+2nHOtm3bLEnWihUrLMuyrO+//97y9fW1EhMTHedMmDDBCg0NtVJTU4v3A3iQU6dOWTVr1rQWLFhg3XrrrY5gw3dS/J5//nnr5ptvzvF4ZmamFRUVZf3nP/9x7Dt58qQVGBhoTZs2zbIsy9q6daslyVqzZo3jnHnz5lk+Pj7Wn3/+WXTFe7B77rnHevTRR532PfDAA1b37t0ty+J7cXVe0RWVlpamdevWqXXr1o59vr6+at26tVasWGFjZd4jKSlJ0oVFSNetW6fz5887fSe1a9dWpUqVHN/JihUrVL9+fUVGRjrOadu2rZKTk/Xbb78VY/WepV+/frrnnnuc/vYS34kdZs+erSZNmqhLly6KiIhQo0aN9NFHHzmO79mzR4mJiU7fSVhYmJo3b+70nZQuXVpNmjRxnNO6dWv5+vpq1apVxfdhPEjLli21cOFC7dy5U5K0ceNGLV++XO3bt5fE9+LqvGIRzKNHjyojI8PpP8aSFBkZqe3bt9tUlffIzMzUgAEDdNNNN6levXqSpMTERAUEBKh06dJO50ZGRioxMdFxTnbfWdYx5N306dP166+/as2aNZcd4zspfr///rsmTJigQYMG6cUXX9SaNWv09NNPKyAgQLGxsY6/aXZ/84u/k4iICKfj/v7+Cg8P5zvJpxdeeEHJycmqXbu2/Pz8lJGRoZEjR6p79+6SxPfi4rwi2MBe/fr105YtW7R8+XK7S/Fq+/fv1zPPPKMFCxYoKCjI7nIgE/qbNGmiN954Q5LUqFEjbdmyRR988IFiY2Ntrs57ffXVV5oyZYqmTp2qunXrasOGDRowYICio6P5XtyAV3RFlStXTn5+fpdd3XHo0CFFRUXZVJV36N+/v+bMmaPFixerYsWKjv1RUVFKS0vTyZMnnc6/+DuJiorK9jvLOoa8WbdunQ4fPqwbbrhB/v7+8vf315IlS/Tee+/J399fkZGRfCfFrEKFCvrb3/7mtK9OnTrat2+fpAt/0yv9tysqKkqHDx92Op6enq7jx4/zneTTv/71L73wwgt66KGHVL9+fT3yyCMaOHCg4uLiJPG9uDqvCDYBAQFq3LixFi5c6NiXmZmphQsXqkWLFjZW5rksy1L//v01c+ZMLVq0SFWrVnU63rhxY5UoUcLpO9mxY4f27dvn+E5atGihzZs3O/3HYcGCBQoNDb3sxwBXd+edd2rz5s3asGGD49akSRN1797dsc13Urxuuummy6ZB2LlzpypXrixJqlq1qqKiopy+k+TkZK1atcrpOzl58qTWrVvnOGfRokXKzMxU8+bNi+FTeJ4zZ87I19f559HPz0+ZmZmS+F5cnt2jl4vL9OnTrcDAQGvSpEnW1q1brX/+859W6dKlna7uQOF54oknrLCwMCs+Pt46ePCg43bmzBnHOY8//rhVqVIla9GiRdbatWutFi1aWC1atHAcz7q0uE2bNtaGDRus+fPnW+XLl+fS4kJ08VVRlsV3UtxWr15t+fv7WyNHjrR27dplTZkyxSpZsqT1xRdfOM4ZNWqUVbp0aevbb7+1Nm3aZHXs2DHby4obNWpkrVq1ylq+fLlVs2ZNLisugNjYWOvaa691XO49Y8YMq1y5ctZzzz3nOIfvxXV5TbCxLMsaO3asValSJSsgIMBq1qyZtXLlSrtL8liSsr1NnDjRcc7Zs2etJ5980ipTpoxVsmRJ6/7777cOHjzo9Dp79+612rdvbwUHB1vlypWznn32Wev8+fPF/Gk816XBhu+k+H333XdWvXr1rMDAQKt27drWhx9+6HQ8MzPTGjp0qBUZGWkFBgZad955p7Vjxw6nc44dO2Z169bNKlWqlBUaGmr16tXLOnXqVHF+DI+SnJxsPfPMM1alSpWsoKAgq1q1atZLL73kNKUB34vr8rGsi6ZSBAAAcGNeMcYGAAB4B4INAADwGAQbAADgMQg2AADAYxBsAACAxyDYAAAAj0GwAQAAHoNgAwAAPAbBBgCKydChQ/XPf/4zT8956KGH9NZbbxVRRYDnIdgAXsbHx+eKt1deecXuEgtdlSpV9M4779haQ2Jiot5991299NJLjn09e/ZUp06dnM775ptvFBQU5AgzL7/8skaOHKmkpKTiLBdwWwQbwMscPHjQcXvnnXcUGhrqtG/w4MF2l5grlmUpPT29WN8zLS0t38/9+OOP1bJlS8fK3Tmd0717d02YMEHPPvusJKlevXqqXr26vvjii3y/N+BNCDaAl4mKinLcwsLC5OPj47Rv+vTpqlOnjoKCglS7dm2NHz/e8dy9e/fKx8dHX331lW655RYFBweradOm2rlzp9asWaMmTZqoVKlSat++vY4cOeJ4XlbLxIgRI1S+fHmFhobq8ccfdwoKmZmZiouLU9WqVRUcHKyGDRvqm2++cRyPj4+Xj4+P5s2bp8aNGyswMFDLly9XQkKCOnbsqMjISJUqVUpNmzbVTz/95Hjebbfdpj/++EMDBw50tEpJ0iuvvKLrr7/e6W/zzjvvqEqVKpfVPXLkSEVHR6tWrVqSpP379+vvf/+7SpcurfDwcHXs2FF79+694t99+vTp6tChQ47HR48eraeeekrTp09Xr169nI516NBB06dPv+LrAzAINgAcpkyZomHDhmnkyJHatm2b3njjDQ0dOlSfffaZ03nDhw/Xyy+/rF9//VX+/v56+OGH9dxzz+ndd9/VsmXLtHv3bg0bNszpOQsXLtS2bdsUHx+vadOmacaMGRoxYoTjeFxcnCZPnqwPPvhAv/32mwYOHKgePXpoyZIlTq/zwgsvaNSoUdq2bZsaNGiglJQU3X333Vq4cKHWr1+vdu3aqUOHDtq3b58kacaMGapYsaJeffVVR6tUXixcuFA7duzQggULNGfOHJ0/f15t27ZVSEiIli1bpp9//lmlSpVSu3btcmzROX78uLZu3aomTZpke/z555/Xa6+9pjlz5uj++++/7HizZs20evVqpaam5ql2wCvZvLo4ABtNnDjRCgsLczyuXr26NXXqVKdzXnvtNatFixaWZVnWnj17LEnWxx9/7Dg+bdo0S5K1cOFCx764uDirVq1ajsexsbFWeHi4dfr0ace+CRMmWKVKlbIyMjKsc+fOWSVLlrR++eUXp/fu3bu31a1bN8uyLGvx4sWWJGvWrFlX/Vx169a1xo4d63hcuXJl6+2333Y6Z/jw4VbDhg2d9r399ttW5cqVneqOjIy0UlNTHfs+//xzq1atWlZmZqZjX2pqqhUcHGz98MMP2dazfv16S5K1b98+p/2xsbFWQEDAZX+/S23cuNGSZO3duzfHcwAY/naGKgCu4/Tp00pISFDv3r3Vt29fx/709HSFhYU5ndugQQPHdmRkpCSpfv36TvsOHz7s9JyGDRuqZMmSjsctWrRQSkqK9u/fr5SUFJ05c0Z33XWX03PS0tLUqFEjp32XtnqkpKTolVde0dy5c3Xw4EGlp6fr7Nmzjhabgqpfv74CAgIcjzdu3Kjdu3crJCTE6bxz584pISEh29c4e/asJCkoKOiyYw0aNNDRo0c1fPhwNWvWTKVKlbrsnODgYEnSmTNn8v05AG9BsAEgyQQESfroo4/UvHlzp2N+fn5Oj0uUKOHYzhqzcum+zMzMPL/33Llzde211zodCwwMdHp8zTXXOD0ePHiwFixYoDfffFM1atRQcHCwOnfufNWBvr6+vrIsy2nf+fPnLzvv0vdLSUlR48aNNWXKlMvOLV++fLbvVa5cOUnSiRMnLjvn2muv1TfffKPbb79d7dq107x58y4LTcePH7/i6wO4gGADQJJpZYmOjtbvv/+u7t27F/rrb9y4UWfPnnW0PqxcuVKlSpVSTEyMwsPDFRgYqH379unWW2/N0+v+/PPP6tmzp2NsSkpKymUDeQMCApSRkeG0r3z58kpMTJRlWY5wtmHDhqu+3w033KAvv/xSERERCg0NzVWN1atXV2hoqLZu3arrrrvusuOVK1fWkiVLHOFm/vz5TuFmy5YtqlixoiMgAcgZg4cBOIwYMUJxcXF67733tHPnTm3evFkTJ07UmDFjCvzaaWlp6t27t7Zu3arvv/9ew4cPV//+/eXr66uQkBANHjxYAwcO1GeffaaEhAT9+uuvGjt27GUDly9Vs2ZNzZgxQxs2bNDGjRv18MMPX9ZaVKVKFS1dulR//vmnjh49KslcLXXkyBGNHj1aCQkJGjdunObNm3fVz9G9e3eVK1dOHTt21LJly7Rnzx7Fx8fr6aef1oEDB7J9jq+vr1q3bq3ly5fn+LoxMTGKj4/X4cOH1bZtWyUnJzuOLVu2TG3atLlqbQAINgAu0qdPH3388ceaOHGi6tevr1tvvVWTJk1S1apVC/zad955p2rWrKlWrVqpa9euuu+++5wmA3zttdc0dOhQxcXFqU6dOmrXrp3mzp171fceM2aMypQpo5YtW6pDhw5q27atbrjhBqdzXn31Ve3du1fVq1d3dOfUqVNH48eP17hx49SwYUOtXr06V3P4lCxZUkuXLlWlSpX0wAMPqE6dOurdu7fOnTt3xRacPn36aPr06VfsoqtYsaLi4+N19OhRR7g5d+6cZs2a5TTuCUDOfKxLO5kBoJD17NlTJ0+e1KxZs+wuxTaWZal58+YaOHCgunXrluvnTZgwQTNnztSPP/5YhNUBnoMWGwAoBj4+Pvrwww/zPFtyiRIlNHbs2CKqCvA8tNgAKHK02AAoLgQbAADgMeiKAgAAHoNgAwAAPAbBBgAAeAyCDQAA8BgEGwAA4DEINgAAwGMQbAAAgMcg2AAAAI/xf/ZVxOK3Wxi3AAAAAElFTkSuQmCC" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 5 + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-12T18:48:32.619391Z", - "start_time": "2025-02-12T18:48:32.615542Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": {}, + "outputs": [], "source": [ - "from pymatgen.phonon.bandstructure import PhononBandStructureSymmLine\n", - "from pymatgen.phonon.dos import PhononDos\n", - "from pymatgen.phonon.plotter import PhononBSPlotter, PhononDosPlotter\n", - "\n", "job_store.connect()\n", "\n", "result = job_store.query_one(\n", @@ -1152,75 +282,42 @@ " load=True,\n", " sort={\"completed_at\": -1}, # to get the latest computation\n", ")" - ], - "id": "1e88d3d7664d2975", - "outputs": [], - "execution_count": 6 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "You can then plot some of the output free energy volume curves", - "id": "2803bb47266fd6ac" + "id": "12", + "metadata": {}, + "source": [ + "You can then plot some of the output free energy volume curves" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-12T18:48:32.786445Z", - "start_time": "2025-02-12T18:48:32.660640Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "13", + "metadata": {}, + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", - "for temp, energy_list in zip(result[\"output\"][\"temperatures\"], result[\"output\"][\"helmholtz_volume\"]):\n", - "\n", - "\n", + "for temp, energy_list in zip(\n", + " result[\"output\"][\"temperatures\"],\n", + " result[\"output\"][\"helmholtz_volume\"],\n", + " strict=False,\n", + "):\n", " # Create the plot\n", - " plt.plot(result[\"output\"][\"volumes\"], energy_list, marker='o')\n", + " plt.plot(result[\"output\"][\"volumes\"], energy_list, marker=\"o\")\n", " # Add labels and title\n", - "plt.xlabel('Volume')\n", - "plt.ylabel('Free Energy')\n", + "plt.xlabel(\"Volume\")\n", + "plt.ylabel(\"Free Energy\")\n", "\n", "# Show the plot\n", - "plt.show()\n" - ], - "id": "759e28b4ad56667f", - "outputs": [ - { - "data": { - "text/plain": [ - "
      " - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 7 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-12T18:48:32.807413Z", - "start_time": "2025-02-12T18:48:32.805756Z" - } - }, - "cell_type": "code", - "source": "", - "id": "249d87d9c9f04a9", - "outputs": [], - "execution_count": null + "plt.show()" + ] } ], "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, "language_info": { "codemirror_mode": { "name": "ipython", diff --git a/tutorials/qha_workflow.py b/tutorials/qha_workflow.py deleted file mode 100644 index 561199782d..0000000000 --- a/tutorials/qha_workflow.py +++ /dev/null @@ -1,215 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -# This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook: - -# In[1]: - - -from mock_vasp import TEST_DIR, mock_vasp - -ref_paths = { - "phonon static 1/1": "Si_qha_2/phonon_static_1_1", - "static": "Si_qha_2/static", - "tight relax 1 EOS equilibrium relaxation": "Si_qha_2/tight_relax_1", # in fact, we replace all relaxation steps with the same output, also the ISIF=4 ones to save storage - "tight relax 2 EOS equilibrium relaxation": "Si_qha_2/tight_relax_2", - "tight relax 1 deformation 0": "Si_qha_2/tight_relax_1_d0", - "tight relax 1 deformation 1": "Si_qha_2/tight_relax_1_d1", - "tight relax 1 deformation 2": "Si_qha_2/tight_relax_1_d2", - "tight relax 1 deformation 3": "Si_qha_2/tight_relax_1_d3", - "tight relax 1 deformation 4": "Si_qha_2/tight_relax_1_d4", - "tight relax 1 deformation 5": "Si_qha_2/tight_relax_1_d5", - "tight relax 2 deformation 0": "Si_qha_2/tight_relax_2_d0", - "tight relax 2 deformation 1": "Si_qha_2/tight_relax_2_d1", - "tight relax 2 deformation 2": "Si_qha_2/tight_relax_2_d2", - "tight relax 2 deformation 3": "Si_qha_2/tight_relax_2_d3", - "tight relax 2 deformation 4": "Si_qha_2/tight_relax_2_d4", - "tight relax 2 deformation 5": "Si_qha_2/tight_relax_2_d5", - "dft phonon static eos deformation 1":"Si_qha_2/dft_phonon_static_eos_deformation_1", - "dft phonon static eos deformation 2":"Si_qha_2/dft_phonon_static_eos_deformation_2", - "dft phonon static eos deformation 3":"Si_qha_2/dft_phonon_static_eos_deformation_3", - "dft phonon static eos deformation 4":"Si_qha_2/dft_phonon_static_eos_deformation_4", - "dft phonon static eos deformation 5":"Si_qha_2/dft_phonon_static_eos_deformation_5", - "dft phonon static eos deformation 6":"Si_qha_2/dft_phonon_static_eos_deformation_6", - "dft phonon static eos deformation 7":"Si_qha_2/dft_phonon_static_eos_deformation_7", - "dft phonon static 1/1 eos deformation 1": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_1", - "dft phonon static 1/1 eos deformation 2": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_2", - "dft phonon static 1/1 eos deformation 3": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_3", - "dft phonon static 1/1 eos deformation 4": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_4", - "dft phonon static 1/1 eos deformation 5": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_5", - "dft phonon static 1/1 eos deformation 6": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_6", - "dft phonon static 1/1 eos deformation 7": "Si_qha_2/dft_phonon_static_1_1_eos_deformation_7", -} - - -# QHA workflow - -# This tutorial will make use of a quasi-harmonic workflow that allows to include volume-dependent anharmonicity into the calculation of phonon free energies. Please check out the paper by Togo to learn about the exact implementation as we will rely on Phonopy to perform the quasi-harmonic approximation. https://doi.org/10.7566/JPSJ.92.012001. At the moment, we perform harmonic free energy calculation along a volume curve to arrive at free energy-volume curves that are the starting point for the quasi-harmonic approximation. - -# ## Let's run the workflow -# Now, we load a structure and other important functions and classes for running the qha workflow. - -# In[2]: - - -from jobflow import JobStore, run_locally -from maggma.stores import MemoryStore -from pymatgen.core import Structure - -from atomate2.vasp.flows.qha import QhaMaker - -job_store = JobStore(MemoryStore(), additional_stores={"data": MemoryStore()}) -si_structure = Structure.from_file(TEST_DIR / "structures" / "Si_diamond.cif") -si_structure=si_structure.to_conventional() -from mp_api.client import MPRester -mpr = MPRester(api_key='Z4aKTAgeEudmS0bMPkKVS3EtOnej1zah') - - -si_structure = mpr.get_structure_by_material_id("mp-149", conventional_unit_cell=True) - - -# Then one can use the `QhaMaker` to generate a `Flow`. First, the structure will be optimized than the structures will be optimized at constant volume along an energy volume curve. Please make sure the structural optimizations are tight enough. At each of these volumes, a phonon run will then be performed. The quasi-harmonic approximation is only valid if the harmonic phonon curves don't show any imaginary modes. However, for testing, you can also switch off this option. - -# Before we start the quasi-harmonic workflow, we adapt the first relaxation, the relaxation with different volumes and the static runs for the phonon calculation. As we deal with Si, we will not add the non-analytical term correction. - -# In[3]: - - -from atomate2.vasp.flows.core import DoubleRelaxMaker -from atomate2.vasp.jobs.core import TightRelaxMaker -from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator -from atomate2.vasp.flows.phonons import PhononMaker -from atomate2.vasp.jobs.phonons import PhononDisplacementMaker -phonon_bulk_relax_maker_isif3 = DoubleRelaxMaker.from_relax_maker( - TightRelaxMaker( - run_vasp_kwargs={"handlers": ()}, - input_set_generator=TightRelaxSetGenerator( - user_incar_settings={ - "GGA": "PE", - "ISPIN": 1, - "KSPACING": 0.1, - # "EDIFFG": 1e-5, - "ALGO": "Normal", - "LAECHG": False, - "ISMEAR": 0, - "ENCUT": 700, - "IBRION": 1, - "ISYM": 0, - "SIGMA": 0.05, - "LCHARG": False, # Do not write the CHGCAR file - "LWAVE": False, # Do not write the WAVECAR file - "LVTOT": False, # Do not write LOCPOT file - "LORBIT": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR - "LOPTICS": False, # No PCDAT file - "LREAL": False, - "ISIF": 3, - # to be removed - "NPAR": 4, - } - ), - ) -) - -phonon_displacement_maker = PhononDisplacementMaker( - run_vasp_kwargs={"handlers": ()}, input_set_generator=StaticSetGenerator( - user_incar_settings={ - "GGA": "PE", - "IBRION": -1, - "ISPIN": 1, - "ISMEAR": 0, - "ISIF": 3, - "ENCUT": 700, - "EDIFF": 1e-7, - "LAECHG": False, - "LREAL": False, - "ALGO": "Normal", - "NSW": 0, - "LCHARG": False, # Do not write the CHGCAR file - "LWAVE": False, # Do not write the WAVECAR file - "LVTOT": False, # Do not write LOCPOT file - "LORBIT": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR - "LOPTICS": False, # No PCDAT file - "SIGMA": 0.05, - "ISYM": 0, - "KSPACING": 0.1, - "NPAR": 4, - }, - auto_ispin=False, - ) -) - - - -phonon_bulk_relax_maker_isif4 = DoubleRelaxMaker.from_relax_maker( - TightRelaxMaker( - run_vasp_kwargs={"handlers": ()}, - input_set_generator=TightRelaxSetGenerator( - user_incar_settings={ - "GGA": "PE", - "ISPIN": 1, - "KSPACING": 0.1, - "ALGO": "Normal", - "LAECHG": False, - "ISMEAR": 0, - "ENCUT": 700, - "IBRION": 1, - "ISYM": 0, - "SIGMA": 0.05, - "LCHARG": False, # Do not write the CHGCAR file - "LWAVE": False, # Do not write the WAVECAR file - "LVTOT": False, # Do not write LOCPOT file - "LORBIT": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR - "LOPTICS": False, # No PCDAT file - "LREAL": False, - "ISIF": 4, - # to be removed - "NPAR": 4, - } - ), - ) -) - -phonon_displacement_maker.name = "dft phonon static" - - - -# In[4]: - - -flow = QhaMaker( - initial_relax_maker=phonon_bulk_relax_maker_isif3, - eos_relax_maker=phonon_bulk_relax_maker_isif4, - min_length=10, - phonon_maker=PhononMaker(generate_frequencies_eigenvectors_kwargs={"tmin": 0, "tmax": 1000, "tstep": 10}, - bulk_relax_maker=None, - born_maker=None, - static_energy_maker=phonon_displacement_maker, - phonon_displacement_maker=phonon_displacement_maker), - linear_strain=(-0.15, 0.15), - number_of_frames=6, - pressure=None, - t_max=None, - ignore_imaginary_modes=False, - skip_analysis=False, - eos_type="vinet" -).make(structure=si_structure) - - -# In[5]: - - -with mock_vasp(ref_paths=ref_paths) as mf: - run_locally( - flow, - create_folders=True, - ensure_success=True, - raise_immediately=True, - store=job_store, - ) - - -# In[ ]: - - - - From 5d30a76fd7c8b21827ec0d22787961ed3abd9201 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 07:44:38 +0100 Subject: [PATCH 37/61] fix syntax --- .github/workflows/docs.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index db3c2f0745..1765327b24 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -35,9 +35,8 @@ jobs: python -m pip install --upgrade pip pip install .[strict,docs] - -name: Copy tutorials - run: | - cp -r tutorials docs/ + - name: Copy tutorials + run: cp -r tutorials docs/ - name: Build run: sphinx-build docs docs_build From 2a447fe1c738ecd0c42037ac762feb1d7bc0a4c4 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 09:12:32 +0100 Subject: [PATCH 38/61] add tutorials to doc attempt2 --- .github/workflows/docs.yml | 5 +++- docs/index.md | 1 + tutorials/force_fields/phonon_workflow.ipynb | 12 ++++++--- tutorials/openmm_tutorial.ipynb | 24 ++++++++++------- tutorials/phonon_workflow.ipynb | 4 +-- tutorials/qha_workflow.ipynb | 28 +++++++++++--------- tutorials/tutorials.md | 16 +++++++++++ 7 files changed, 60 insertions(+), 30 deletions(-) create mode 100644 tutorials/tutorials.md diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 1765327b24..9b20a8dd75 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -36,7 +36,10 @@ jobs: pip install .[strict,docs] - name: Copy tutorials - run: cp -r tutorials docs/ + run: | + cp -r tutorials docs/ + jupyter nbconvert --to markdown docs/tutorials/*.ipynb + jupyter nbconvert --to markdown docs/tutorials/*/*.ipynb - name: Build run: sphinx-build docs docs_build diff --git a/docs/index.md b/docs/index.md index 085bf4de64..a40f5a5d39 100644 --- a/docs/index.md +++ b/docs/index.md @@ -9,6 +9,7 @@ user/docs-schemas-emmet user/fireworks user/atomate-1-vs-2 user/codes/index +tutorials/tutorials ``` ```{toctree} diff --git a/tutorials/force_fields/phonon_workflow.ipynb b/tutorials/force_fields/phonon_workflow.ipynb index e2537bb392..090f8a643a 100644 --- a/tutorials/force_fields/phonon_workflow.ipynb +++ b/tutorials/force_fields/phonon_workflow.ipynb @@ -1,12 +1,16 @@ { "cells": [ { + "metadata": {}, "cell_type": "markdown", - "id": "0", + "source": "# Phonon Workflow Tutorial with Force Fields", + "id": "86e9d7e2e155339d" + }, + { "metadata": {}, - "source": [ - "We start with imports necessary to test the tutorial automatically. In practice, you can load a structure file from any other place and you are also not required to generate the data in a temporary directory." - ] + "cell_type": "markdown", + "source": "We start with imports necessary to test the tutorial automatically. In practice, you can load a structure file from any other place and you are also not required to generate the data in a temporary directory.", + "id": "0" }, { "cell_type": "code", diff --git a/tutorials/openmm_tutorial.ipynb b/tutorials/openmm_tutorial.ipynb index 8cfb3baf9c..a07b3b55df 100644 --- a/tutorials/openmm_tutorial.ipynb +++ b/tutorials/openmm_tutorial.ipynb @@ -1,15 +1,20 @@ { "cells": [ { + "metadata": {}, "cell_type": "markdown", - "id": "0", + "source": "# OpenMM Tutorial", + "id": "ace71c1812743076" + }, + { "metadata": { "jupyter": { "outputs_hidden": false } }, + "cell_type": "markdown", "source": [ - "### Installing Atomate2 From Source with OpenMM\n", + "## Installing Atomate2 From Source with OpenMM\n", "\n", "```bash\n", "# setting up our conda environment\n", @@ -46,14 +51,15 @@ "pip uninstall bson\n", "pip install pymongo\n", "```" - ] + ], + "id": "0" }, { "cell_type": "markdown", "id": "1", "metadata": {}, "source": [ - "### Understanding Atomate2 OpenMM \n", + "## Understanding Atomate2 OpenMM\n", "\n", "Atomate2 is really just a collection of jobflow workflows relevant to materials science. In all the workflows, we pass our system of interest between different jobs to perform the desired simulation. Representing the intermediate state of a classical molecular dynamics simulation, however, is challenging. While the intermediate representation between stages of a periodic DFT simulation can include just the elements, xyz coordinates, and box vectors, classical molecular dynamics systems must also include velocities and forces. The latter is particularly challenging because all MD engines represent forces differently. Rather than implement our own representation, we use the `openff.interchange.Interchange` object, which catalogs the necessary system properties and interfaces with a variety of MD engines. This is the object that we pass between stages of a classical MD simulation and it is the starting point of our workflow." ] @@ -63,7 +69,7 @@ "id": "2", "metadata": {}, "source": [ - "### Pouring a Glass of Wine\n", + "## Pouring a Glass of Wine\n", "\n", "The first job we need to create generates the `Interchange` object. To specify the system of interest, we use give it the SMILES strings, counts, and names (optional) of the molecules we want to include." ] @@ -173,7 +179,7 @@ "id": "8", "metadata": {}, "source": [ - "### The basic simulation\n", + "## The basic simulation\n", "\n", "To run a production simulation, we will create a production flow, link it to our `elyte_interchange_job`, and then run both locally.\n", "\n", @@ -245,7 +251,7 @@ "id": "11", "metadata": {}, "source": [ - "### Configuring the Simulation\n", + "## Configuring the Simulation\n", "\n", "All OpenMM jobs, i.e. anything in `atomate2.openmm.jobs`, inherits from the `BaseOpenMMMaker` class. `BaseOpenMMMaker` is highly configurable, you can change the timestep, temperature, reporting frequencies, output types, and a range of other properties. See the docstring for the full list of options.\n", "\n", @@ -277,7 +283,7 @@ "id": "14", "metadata": {}, "source": [ - "### Running with Databases\n", + "## Running with Databases\n", "\n", "Before trying this, you should have a basic understanding of JobFlow and [Stores](https://materialsproject.github.io/jobflow/stores.html).\n", "\n", @@ -366,7 +372,7 @@ "id": "15", "metadata": {}, "source": [ - "### Running on GPU(s)\n", + "## Running on GPU(s)\n", "\n", "Running on a GPU is nearly as simple as running on a CPU. The only difference is that you need to specify the `platform_properties` argument in the `EnergyMinimizationMaker` with the `DeviceIndex` of the GPU you want to use." ] diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index 8115fee2b8..3a449a2f26 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -30,9 +30,7 @@ "cell_type": "markdown", "id": "2", "metadata": {}, - "source": [ - "# Phonon Workflow" - ] + "source": "# Phonon Workflow Tutorial with VASP" }, { "cell_type": "markdown", diff --git a/tutorials/qha_workflow.ipynb b/tutorials/qha_workflow.ipynb index dbeff2411c..a01c2b0460 100644 --- a/tutorials/qha_workflow.ipynb +++ b/tutorials/qha_workflow.ipynb @@ -1,12 +1,16 @@ { "cells": [ { + "metadata": {}, "cell_type": "markdown", - "id": "0", + "source": "# Quasi-harmonic Workflow Tutorial with VASP", + "id": "b2ab3e6e84f7233b" + }, + { "metadata": {}, - "source": [ - "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:" - ] + "cell_type": "markdown", + "source": "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:", + "id": "0" }, { "cell_type": "code", @@ -51,14 +55,6 @@ "}" ] }, - { - "cell_type": "markdown", - "id": "2", - "metadata": {}, - "source": [ - "QHA workflow" - ] - }, { "cell_type": "markdown", "id": "3", @@ -263,6 +259,12 @@ " )" ] }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Let's retrieve the data and analyze it", + "id": "2dc7b73048bebabf" + }, { "cell_type": "code", "execution_count": null, @@ -307,7 +309,7 @@ " strict=False,\n", "):\n", " # Create the plot\n", - " plt.plot(result[\"output\"][\"volumes\"], energy_list, marker=\"o\")\n", + " plt.plot(result[\"output\"][\"volumes\"], energy_list, marker=\"o\", label=temp)\n", " # Add labels and title\n", "plt.xlabel(\"Volume\")\n", "plt.ylabel(\"Free Energy\")\n", diff --git a/tutorials/tutorials.md b/tutorials/tutorials.md new file mode 100644 index 0000000000..ddb0e74861 --- /dev/null +++ b/tutorials/tutorials.md @@ -0,0 +1,16 @@ +(tutorials)= + +# Tutorials + +The section includes tutorials for workflows in atomate2. +They can also be found in the form of jupyternotebooks in: +[https://github.com/materialsproject/atomate2/tree/main/tutorials](https://github.com/materialsproject/atomate2/tree/main/tutorials) + +```{toctree} +blob_storage +lobster_workflow +openmm_tutorial +phonon_workflow +qha_workflow +force_fields/phonon_workflow +``` From 1563de07e4d1aca90552b04fcc4fa06459875006 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 09:26:27 +0100 Subject: [PATCH 39/61] fix line length --- tutorials/force_fields/phonon_workflow.ipynb | 38 ++++---- tutorials/openmm_tutorial.ipynb | 48 +++++----- tutorials/phonon_workflow.ipynb | 8 +- tutorials/qha_workflow.ipynb | 93 +++++++++++++------- tutorials/tutorials.md | 2 +- 5 files changed, 112 insertions(+), 77 deletions(-) diff --git a/tutorials/force_fields/phonon_workflow.ipynb b/tutorials/force_fields/phonon_workflow.ipynb index 090f8a643a..a231b1faef 100644 --- a/tutorials/force_fields/phonon_workflow.ipynb +++ b/tutorials/force_fields/phonon_workflow.ipynb @@ -1,21 +1,25 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# Phonon Workflow Tutorial with Force Fields", - "id": "86e9d7e2e155339d" + "id": "0", + "metadata": {}, + "source": [ + "# Phonon Workflow Tutorial with Force Fields" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "We start with imports necessary to test the tutorial automatically. In practice, you can load a structure file from any other place and you are also not required to generate the data in a temporary directory.", - "id": "0" + "id": "1", + "metadata": {}, + "source": [ + "We start with imports necessary to test the tutorial automatically. In practice, you can load a structure file from any other place and you are also not required to generate the data in a temporary directory." + ] }, { "cell_type": "code", "execution_count": null, - "id": "1", + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -29,7 +33,7 @@ }, { "cell_type": "markdown", - "id": "2", + "id": "3", "metadata": {}, "source": [ "First, we load a structure from a file." @@ -38,7 +42,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3", + "id": "4", "metadata": {}, "outputs": [], "source": [ @@ -49,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "4", + "id": "5", "metadata": {}, "source": [ "Then, we load the `PhononMaker` and run_locally to perform the calculation directly here in the notebook." @@ -58,7 +62,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5", + "id": "6", "metadata": {}, "outputs": [], "source": [ @@ -80,7 +84,7 @@ }, { "cell_type": "markdown", - "id": "6", + "id": "7", "metadata": {}, "source": [ "One can switch to a different force field as well!" @@ -89,7 +93,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7", + "id": "8", "metadata": {}, "outputs": [], "source": [ @@ -112,7 +116,7 @@ }, { "cell_type": "markdown", - "id": "8", + "id": "9", "metadata": {}, "source": [ "Or by using the name only:" @@ -121,7 +125,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9", + "id": "10", "metadata": {}, "outputs": [], "source": [ @@ -131,7 +135,7 @@ }, { "cell_type": "markdown", - "id": "10", + "id": "11", "metadata": {}, "source": [ "Now, we clean up the temporary directory that we made. In reality, you might want to keep this data." @@ -140,7 +144,7 @@ { "cell_type": "code", "execution_count": null, - "id": "11", + "id": "12", "metadata": {}, "outputs": [], "source": [ diff --git a/tutorials/openmm_tutorial.ipynb b/tutorials/openmm_tutorial.ipynb index a07b3b55df..1d5124ad63 100644 --- a/tutorials/openmm_tutorial.ipynb +++ b/tutorials/openmm_tutorial.ipynb @@ -1,18 +1,21 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# OpenMM Tutorial", - "id": "ace71c1812743076" + "id": "0", + "metadata": {}, + "source": [ + "# OpenMM Tutorial" + ] }, { + "cell_type": "markdown", + "id": "1", "metadata": { "jupyter": { "outputs_hidden": false } }, - "cell_type": "markdown", "source": [ "## Installing Atomate2 From Source with OpenMM\n", "\n", @@ -51,12 +54,11 @@ "pip uninstall bson\n", "pip install pymongo\n", "```" - ], - "id": "0" + ] }, { "cell_type": "markdown", - "id": "1", + "id": "2", "metadata": {}, "source": [ "## Understanding Atomate2 OpenMM\n", @@ -66,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "2", + "id": "3", "metadata": {}, "source": [ "## Pouring a Glass of Wine\n", @@ -77,7 +79,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3", + "id": "4", "metadata": { "jupyter": { "outputs_hidden": false @@ -98,7 +100,7 @@ }, { "cell_type": "markdown", - "id": "4", + "id": "5", "metadata": {}, "source": [ "If you are wondering what arguments are allowed in the dictionaries, check out the `create_mol_spec` function in the `atomate2.openff.utils` module. Under the hood, this is being called on each mol_spec dict. Meaning the code below is functionally identical to the code above." @@ -107,7 +109,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5", + "id": "6", "metadata": {}, "outputs": [], "source": [ @@ -120,7 +122,7 @@ }, { "cell_type": "markdown", - "id": "6", + "id": "7", "metadata": {}, "source": [ "In a more complex simulation we might want to scale the ion charges and include custom partial charges. An example with the Gen2 electrolyte is shown below. This yields the `elyte_interchange_job` object, which we can pass to the next stage of the simulation.\n", @@ -131,7 +133,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7", + "id": "8", "metadata": { "jupyter": { "outputs_hidden": false @@ -176,7 +178,7 @@ }, { "cell_type": "markdown", - "id": "8", + "id": "9", "metadata": {}, "source": [ "## The basic simulation\n", @@ -190,7 +192,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9", + "id": "10", "metadata": {}, "outputs": [], "source": [ @@ -221,7 +223,7 @@ }, { "cell_type": "markdown", - "id": "10", + "id": "11", "metadata": {}, "source": [ "When the above code is executed, you should expect to see something like this:\n", @@ -248,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "11", + "id": "12", "metadata": {}, "source": [ "## Configuring the Simulation\n", @@ -261,7 +263,7 @@ { "cell_type": "code", "execution_count": null, - "id": "12", + "id": "13", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +274,7 @@ }, { "cell_type": "markdown", - "id": "13", + "id": "14", "metadata": {}, "source": [ "Perhaps we want to record a trajectory with velocities but only for the final NVT run. " @@ -280,7 +282,7 @@ }, { "cell_type": "markdown", - "id": "14", + "id": "15", "metadata": {}, "source": [ "## Running with Databases\n", @@ -369,7 +371,7 @@ }, { "cell_type": "markdown", - "id": "15", + "id": "16", "metadata": {}, "source": [ "## Running on GPU(s)\n", @@ -380,7 +382,7 @@ { "cell_type": "code", "execution_count": null, - "id": "16", + "id": "17", "metadata": {}, "outputs": [], "source": [ @@ -400,7 +402,7 @@ }, { "cell_type": "markdown", - "id": "17", + "id": "18", "metadata": {}, "source": [ "To run on a system with multiple GPUs, the 'DeviceIndex' can be changed to a different number for each job." diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index 3a449a2f26..1f092cb056 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -30,7 +30,9 @@ "cell_type": "markdown", "id": "2", "metadata": {}, - "source": "# Phonon Workflow Tutorial with VASP" + "source": [ + "# Phonon Workflow Tutorial with VASP" + ] }, { "cell_type": "markdown", @@ -73,7 +75,9 @@ "\n", "from atomate2.vasp.flows.phonons import PhononMaker\n", "\n", - "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", + "job_store = JobStore(\n", + " MemoryStore(), additional_stores={\"data\": MemoryStore()}\n", + ")\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")" ] }, diff --git a/tutorials/qha_workflow.ipynb b/tutorials/qha_workflow.ipynb index a01c2b0460..559ce51f33 100644 --- a/tutorials/qha_workflow.ipynb +++ b/tutorials/qha_workflow.ipynb @@ -1,21 +1,25 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# Quasi-harmonic Workflow Tutorial with VASP", - "id": "b2ab3e6e84f7233b" + "id": "0", + "metadata": {}, + "source": [ + "# Quasi-harmonic Workflow Tutorial with VASP" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:", - "id": "0" + "id": "1", + "metadata": {}, + "source": [ + "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:" + ] }, { "cell_type": "code", "execution_count": null, - "id": "1", + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +98,15 @@ "id": "6", "metadata": {}, "source": [ - "Then one can use the `QhaMaker` to generate a `Flow`. First, the structure will be optimized than the structures will be optimized at constant volume along an energy volume curve. Please make sure the structural optimizations are tight enough. At each of these volumes, a phonon run will then be performed. The quasi-harmonic approximation is only valid if the harmonic phonon curves don't show any imaginary modes. However, for testing, you can also switch off this option." + "Then one can use the `QhaMaker` to generate a `Flow`.\n", + "First, the structure will be optimized than the\n", + "structures will be optimized at constant volume\n", + "along an energy volume curve. Please make sure the structural optimizations are tight enough. At each of these\n", + "volumes, a phonon run will then be performed.\n", + "The quasi-harmonic approximation is only valid\n", + "if the harmonic phonon curves don't show any\n", + "imaginary modes. However, for testing, you\n", + "can also switch off this option." ] }, { @@ -102,7 +114,11 @@ "id": "7", "metadata": {}, "source": [ - "Before we start the quasi-harmonic workflow, we adapt the first relaxation, the relaxation with different volumes and the static runs for the phonon calculation. As we deal with Si, we will not add the non-analytical term correction." + "Before we start the quasi-harmonic workflow,\n", + "we adapt the first relaxation, the relaxation\n", + "with different volumes and the static runs for\n", + "the phonon calculation. As we deal with Si,\n", + "we will not add the non-analytical term correction." ] }, { @@ -134,14 +150,13 @@ " \"IBRION\": 1,\n", " \"ISYM\": 0,\n", " \"SIGMA\": 0.05,\n", - " \"LCHARG\": False, # Do not write the CHGCAR file\n", - " \"LWAVE\": False, # Do not write the WAVECAR file\n", - " \"LVTOT\": False, # Do not write LOCPOT file\n", - " \"LORBIT\": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR\n", - " \"LOPTICS\": False, # No PCDAT file\n", + " \"LCHARG\": False,\n", + " \"LWAVE\": False,\n", + " \"LVTOT\": False,\n", + " \"LORBIT\": None,\n", + " \"LOPTICS\": False,\n", " \"LREAL\": False,\n", " \"ISIF\": 3,\n", - " # to be removed\n", " \"NPAR\": 4,\n", " }\n", " ),\n", @@ -163,11 +178,11 @@ " \"LREAL\": False,\n", " \"ALGO\": \"Normal\",\n", " \"NSW\": 0,\n", - " \"LCHARG\": False, # Do not write the CHGCAR file\n", - " \"LWAVE\": False, # Do not write the WAVECAR file\n", - " \"LVTOT\": False, # Do not write LOCPOT file\n", - " \"LORBIT\": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR\n", - " \"LOPTICS\": False, # No PCDAT file\n", + " \"LCHARG\": False,\n", + " \"LWAVE\": False,\n", + " \"LVTOT\": False,\n", + " \"LORBIT\": None,\n", + " \"LOPTICS\": False,\n", " \"SIGMA\": 0.05,\n", " \"ISYM\": 0,\n", " \"KSPACING\": 0.1,\n", @@ -193,14 +208,13 @@ " \"IBRION\": 1,\n", " \"ISYM\": 0,\n", " \"SIGMA\": 0.05,\n", - " \"LCHARG\": False, # Do not write the CHGCAR file\n", - " \"LWAVE\": False, # Do not write the WAVECAR file\n", - " \"LVTOT\": False, # Do not write LOCPOT file\n", - " \"LORBIT\": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR\n", - " \"LOPTICS\": False, # No PCDAT file\n", + " \"LCHARG\": False,\n", + " \"LWAVE\": False,\n", + " \"LVTOT\": False,\n", + " \"LORBIT\": None,\n", + " \"LOPTICS\": False,\n", " \"LREAL\": False,\n", " \"ISIF\": 4,\n", - " # to be removed\n", " \"NPAR\": 4,\n", " }\n", " ),\n", @@ -246,7 +260,11 @@ "cell_type": "code", "execution_count": null, "id": "10", - "metadata": {}, + "metadata": { + "jupyter": { + "is_executing": true + } + }, "outputs": [], "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", @@ -260,15 +278,17 @@ ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## Let's retrieve the data and analyze it", - "id": "2dc7b73048bebabf" + "id": "11", + "metadata": {}, + "source": [ + "## Let's retrieve the data and analyze it" + ] }, { "cell_type": "code", "execution_count": null, - "id": "11", + "id": "12", "metadata": {}, "outputs": [], "source": [ @@ -288,7 +308,7 @@ }, { "cell_type": "markdown", - "id": "12", + "id": "13", "metadata": {}, "source": [ "You can then plot some of the output free energy volume curves" @@ -297,7 +317,7 @@ { "cell_type": "code", "execution_count": null, - "id": "13", + "id": "14", "metadata": {}, "outputs": [], "source": [ @@ -309,7 +329,12 @@ " strict=False,\n", "):\n", " # Create the plot\n", - " plt.plot(result[\"output\"][\"volumes\"], energy_list, marker=\"o\", label=temp)\n", + " plt.plot(\n", + " result[\"output\"][\"volumes\"],\n", + " energy_list,\n", + " marker=\"o\",\n", + " label=temp,\n", + " )\n", " # Add labels and title\n", "plt.xlabel(\"Volume\")\n", "plt.ylabel(\"Free Energy\")\n", diff --git a/tutorials/tutorials.md b/tutorials/tutorials.md index ddb0e74861..1f9fe68d4a 100644 --- a/tutorials/tutorials.md +++ b/tutorials/tutorials.md @@ -2,7 +2,7 @@ # Tutorials -The section includes tutorials for workflows in atomate2. +The section includes tutorials for workflows in atomate2. They can also be found in the form of jupyternotebooks in: [https://github.com/materialsproject/atomate2/tree/main/tutorials](https://github.com/materialsproject/atomate2/tree/main/tutorials) From afc820a9eeda3ff80176ff481cccc76f2c22d3a9 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 09:27:51 +0100 Subject: [PATCH 40/61] fix line length --- tutorials/qha_workflow.ipynb | 42 ++++++++++++++++++++++++------------ 1 file changed, 28 insertions(+), 14 deletions(-) diff --git a/tutorials/qha_workflow.ipynb b/tutorials/qha_workflow.ipynb index 559ce51f33..d54214e492 100644 --- a/tutorials/qha_workflow.ipynb +++ b/tutorials/qha_workflow.ipynb @@ -28,20 +28,34 @@ "ref_paths = {\n", " \"phonon static 1/1\": \"Si_qha_2/phonon_static_1_1\",\n", " \"static\": \"Si_qha_2/static\",\n", - " \"tight relax 1 EOS equilibrium relaxation\": \"Si_qha_2/tight_relax_1\",\n", - " \"tight relax 2 EOS equilibrium relaxation\": \"Si_qha_2/tight_relax_2\",\n", - " \"tight relax 1 deformation 0\": \"Si_qha_2/tight_relax_1_d0\",\n", - " \"tight relax 1 deformation 1\": \"Si_qha_2/tight_relax_1_d1\",\n", - " \"tight relax 1 deformation 2\": \"Si_qha_2/tight_relax_1_d2\",\n", - " \"tight relax 1 deformation 3\": \"Si_qha_2/tight_relax_1_d3\",\n", - " \"tight relax 1 deformation 4\": \"Si_qha_2/tight_relax_1_d4\",\n", - " \"tight relax 1 deformation 5\": \"Si_qha_2/tight_relax_1_d5\",\n", - " \"tight relax 2 deformation 0\": \"Si_qha_2/tight_relax_2_d0\",\n", - " \"tight relax 2 deformation 1\": \"Si_qha_2/tight_relax_2_d1\",\n", - " \"tight relax 2 deformation 2\": \"Si_qha_2/tight_relax_2_d2\",\n", - " \"tight relax 2 deformation 3\": \"Si_qha_2/tight_relax_2_d3\",\n", - " \"tight relax 2 deformation 4\": \"Si_qha_2/tight_relax_2_d4\",\n", - " \"tight relax 2 deformation 5\": \"Si_qha_2/tight_relax_2_d5\",\n", + " \"tight relax 1 EOS equilibrium relaxation\":\n", + " \"Si_qha_2/tight_relax_1\",\n", + " \"tight relax 2 EOS equilibrium relaxation\":\n", + " \"Si_qha_2/tight_relax_2\",\n", + " \"tight relax 1 deformation 0\":\n", + " \"Si_qha_2/tight_relax_1_d0\",\n", + " \"tight relax 1 deformation 1\":\n", + " \"Si_qha_2/tight_relax_1_d1\",\n", + " \"tight relax 1 deformation 2\":\n", + " \"Si_qha_2/tight_relax_1_d2\",\n", + " \"tight relax 1 deformation 3\":\n", + " \"Si_qha_2/tight_relax_1_d3\",\n", + " \"tight relax 1 deformation 4\":\n", + " \"Si_qha_2/tight_relax_1_d4\",\n", + " \"tight relax 1 deformation 5\":\n", + " \"Si_qha_2/tight_relax_1_d5\",\n", + " \"tight relax 2 deformation 0\":\n", + " \"Si_qha_2/tight_relax_2_d0\",\n", + " \"tight relax 2 deformation 1\":\n", + " \"Si_qha_2/tight_relax_2_d1\",\n", + " \"tight relax 2 deformation 2\":\n", + " \"Si_qha_2/tight_relax_2_d2\",\n", + " \"tight relax 2 deformation 3\":\n", + " \"Si_qha_2/tight_relax_2_d3\",\n", + " \"tight relax 2 deformation 4\":\n", + " \"Si_qha_2/tight_relax_2_d4\",\n", + " \"tight relax 2 deformation 5\":\n", + " \"Si_qha_2/tight_relax_2_d5\",\n", " \"dft phonon static eos deformation 1\": \"Si_qha_2/dft_phonon_static_eos_deformation_1\",\n", " \"dft phonon static eos deformation 2\": \"Si_qha_2/dft_phonon_static_eos_deformation_2\",\n", " \"dft phonon static eos deformation 3\": \"Si_qha_2/dft_phonon_static_eos_deformation_3\",\n", From 647e5bcf6cb5d0bf021c3abfdd4882620b955e96 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 09:39:07 +0100 Subject: [PATCH 41/61] final fix linting --- src/atomate2/common/utils.py | 1 - tutorials/qha_workflow.ipynb | 68 ++++++++++++++++++++++++++++-------- 2 files changed, 53 insertions(+), 16 deletions(-) diff --git a/src/atomate2/common/utils.py b/src/atomate2/common/utils.py index ecefd0a556..ce7e0c4da8 100644 --- a/src/atomate2/common/utils.py +++ b/src/atomate2/common/utils.py @@ -77,7 +77,6 @@ def get_supercell_matrix( allow_orthorhombic=allow_orthorhombic, ) transformation.apply_transformation(structure=structure) - print(transformation.transformation_matrix.transpose().tolist()) # matrix from pymatgen has to be transposed return transformation.transformation_matrix.transpose().tolist() diff --git a/tutorials/qha_workflow.ipynb b/tutorials/qha_workflow.ipynb index d54214e492..7c67f585f5 100644 --- a/tutorials/qha_workflow.ipynb +++ b/tutorials/qha_workflow.ipynb @@ -56,20 +56,48 @@ " \"Si_qha_2/tight_relax_2_d4\",\n", " \"tight relax 2 deformation 5\":\n", " \"Si_qha_2/tight_relax_2_d5\",\n", - " \"dft phonon static eos deformation 1\": \"Si_qha_2/dft_phonon_static_eos_deformation_1\",\n", - " \"dft phonon static eos deformation 2\": \"Si_qha_2/dft_phonon_static_eos_deformation_2\",\n", - " \"dft phonon static eos deformation 3\": \"Si_qha_2/dft_phonon_static_eos_deformation_3\",\n", - " \"dft phonon static eos deformation 4\": \"Si_qha_2/dft_phonon_static_eos_deformation_4\",\n", - " \"dft phonon static eos deformation 5\": \"Si_qha_2/dft_phonon_static_eos_deformation_5\",\n", - " \"dft phonon static eos deformation 6\": \"Si_qha_2/dft_phonon_static_eos_deformation_6\",\n", - " \"dft phonon static eos deformation 7\": \"Si_qha_2/dft_phonon_static_eos_deformation_7\",\n", - " \"dft phonon static 1/1 eos deformation 1\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_1\",\n", - " \"dft phonon static 1/1 eos deformation 2\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_2\",\n", - " \"dft phonon static 1/1 eos deformation 3\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_3\",\n", - " \"dft phonon static 1/1 eos deformation 4\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_4\",\n", - " \"dft phonon static 1/1 eos deformation 5\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_5\",\n", - " \"dft phonon static 1/1 eos deformation 6\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_6\",\n", - " \"dft phonon static 1/1 eos deformation 7\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_7\",\n", + " \"dft phonon static eos deformation 1\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_1\",\n", + " \"dft phonon static eos deformation 2\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_2\",\n", + " \"dft phonon static eos deformation 3\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_3\",\n", + " \"dft phonon static eos deformation 4\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_4\",\n", + " \"dft phonon static eos deformation 5\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_5\",\n", + " \"dft phonon static eos deformation 6\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_6\",\n", + " \"dft phonon static eos deformation 7\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_7\",\n", + " \"dft phonon static 1/1 eos deformation 1\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_1\",\n", + " \"dft phonon static 1/1 eos deformation 2\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_2\",\n", + " \"dft phonon static 1/1 eos deformation 3\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_3\",\n", + " \"dft phonon static 1/1 eos deformation 4\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_4\",\n", + " \"dft phonon static 1/1 eos deformation 5\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_5\",\n", + " \"dft phonon static 1/1 eos deformation 6\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_6\",\n", + " \"dft phonon static 1/1 eos deformation 7\":\n", + " \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_7\",\n", "}" ] }, @@ -78,7 +106,17 @@ "id": "3", "metadata": {}, "source": [ - "This tutorial will make use of a quasi-harmonic workflow that allows to include volume-dependent anharmonicity into the calculation of phonon free energies. Please check out the paper by Togo to learn about the exact implementation as we will rely on Phonopy to perform the quasi-harmonic approximation. https://doi.org/10.7566/JPSJ.92.012001. At the moment, we perform harmonic free energy calculation along a volume curve to arrive at free energy-volume curves that are the starting point for the quasi-harmonic approximation." + "This tutorial will make use of a quasi-harmonic workflow\n", + " that allows to include volume-dependent anharmonicity \n", + " into the calculation of phonon free energies.\n", + " Please check out the paper by Togo to learn about the\n", + "exact implementation as we will rely on Phonopy to perform the quasi-harmonic approximation.\n", + " https://doi.org/10.7566/JPSJ.92.012001.\n", + " At the moment, we perform harmonic free\n", + " energy calculation along a volume curve\n", + " to arrive at free energy-volume curves\n", + " that are the starting point for the q\n", + " uasi-harmonic approximation." ] }, { From 981c61465bb6ffdf7189c85f52257cada60679d5 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 11:05:01 +0100 Subject: [PATCH 42/61] test fixing the linter again --- tutorials/phonon_workflow.ipynb | 4 +- tutorials/qha_workflow.ipynb | 112 ++++++++++++-------------------- tutorials/tutorials.md | 2 +- 3 files changed, 44 insertions(+), 74 deletions(-) diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index 1f092cb056..16608d1999 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -75,9 +75,7 @@ "\n", "from atomate2.vasp.flows.phonons import PhononMaker\n", "\n", - "job_store = JobStore(\n", - " MemoryStore(), additional_stores={\"data\": MemoryStore()}\n", - ")\n", + "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si.cif\")" ] }, diff --git a/tutorials/qha_workflow.ipynb b/tutorials/qha_workflow.ipynb index 7c67f585f5..7cfd64db2b 100644 --- a/tutorials/qha_workflow.ipynb +++ b/tutorials/qha_workflow.ipynb @@ -28,76 +28,48 @@ "ref_paths = {\n", " \"phonon static 1/1\": \"Si_qha_2/phonon_static_1_1\",\n", " \"static\": \"Si_qha_2/static\",\n", - " \"tight relax 1 EOS equilibrium relaxation\":\n", - " \"Si_qha_2/tight_relax_1\",\n", - " \"tight relax 2 EOS equilibrium relaxation\":\n", - " \"Si_qha_2/tight_relax_2\",\n", - " \"tight relax 1 deformation 0\":\n", - " \"Si_qha_2/tight_relax_1_d0\",\n", - " \"tight relax 1 deformation 1\":\n", - " \"Si_qha_2/tight_relax_1_d1\",\n", - " \"tight relax 1 deformation 2\":\n", - " \"Si_qha_2/tight_relax_1_d2\",\n", - " \"tight relax 1 deformation 3\":\n", - " \"Si_qha_2/tight_relax_1_d3\",\n", - " \"tight relax 1 deformation 4\":\n", - " \"Si_qha_2/tight_relax_1_d4\",\n", - " \"tight relax 1 deformation 5\":\n", - " \"Si_qha_2/tight_relax_1_d5\",\n", - " \"tight relax 2 deformation 0\":\n", - " \"Si_qha_2/tight_relax_2_d0\",\n", - " \"tight relax 2 deformation 1\":\n", - " \"Si_qha_2/tight_relax_2_d1\",\n", - " \"tight relax 2 deformation 2\":\n", - " \"Si_qha_2/tight_relax_2_d2\",\n", - " \"tight relax 2 deformation 3\":\n", - " \"Si_qha_2/tight_relax_2_d3\",\n", - " \"tight relax 2 deformation 4\":\n", - " \"Si_qha_2/tight_relax_2_d4\",\n", - " \"tight relax 2 deformation 5\":\n", - " \"Si_qha_2/tight_relax_2_d5\",\n", - " \"dft phonon static eos deformation 1\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_eos_deformation_1\",\n", - " \"dft phonon static eos deformation 2\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_eos_deformation_2\",\n", - " \"dft phonon static eos deformation 3\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_eos_deformation_3\",\n", - " \"dft phonon static eos deformation 4\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_eos_deformation_4\",\n", - " \"dft phonon static eos deformation 5\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_eos_deformation_5\",\n", - " \"dft phonon static eos deformation 6\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_eos_deformation_6\",\n", - " \"dft phonon static eos deformation 7\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_eos_deformation_7\",\n", - " \"dft phonon static 1/1 eos deformation 1\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_1_1_eos_deformation_1\",\n", - " \"dft phonon static 1/1 eos deformation 2\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_1_1_eos_deformation_2\",\n", - " \"dft phonon static 1/1 eos deformation 3\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_1_1_eos_deformation_3\",\n", - " \"dft phonon static 1/1 eos deformation 4\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_1_1_eos_deformation_4\",\n", - " \"dft phonon static 1/1 eos deformation 5\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_1_1_eos_deformation_5\",\n", - " \"dft phonon static 1/1 eos deformation 6\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_1_1_eos_deformation_6\",\n", - " \"dft phonon static 1/1 eos deformation 7\":\n", - " \"Si_qha_2/\"\n", - " \"dft_phonon_static_1_1_eos_deformation_7\",\n", + " \"tight relax 1 EOS equilibrium relaxation\": \"Si_qha_2/tight_relax_1\",\n", + " \"tight relax 2 EOS equilibrium relaxation\": \"Si_qha_2/tight_relax_2\",\n", + " \"tight relax 1 deformation 0\": \"Si_qha_2/tight_relax_1_d0\",\n", + " \"tight relax 1 deformation 1\": \"Si_qha_2/tight_relax_1_d1\",\n", + " \"tight relax 1 deformation 2\": \"Si_qha_2/tight_relax_1_d2\",\n", + " \"tight relax 1 deformation 3\": \"Si_qha_2/tight_relax_1_d3\",\n", + " \"tight relax 1 deformation 4\": \"Si_qha_2/tight_relax_1_d4\",\n", + " \"tight relax 1 deformation 5\": \"Si_qha_2/tight_relax_1_d5\",\n", + " \"tight relax 2 deformation 0\": \"Si_qha_2/tight_relax_2_d0\",\n", + " \"tight relax 2 deformation 1\": \"Si_qha_2/tight_relax_2_d1\",\n", + " \"tight relax 2 deformation 2\": \"Si_qha_2/tight_relax_2_d2\",\n", + " \"tight relax 2 deformation 3\": \"Si_qha_2/tight_relax_2_d3\",\n", + " \"tight relax 2 deformation 4\": \"Si_qha_2/tight_relax_2_d4\",\n", + " \"tight relax 2 deformation 5\": \"Si_qha_2/tight_relax_2_d5\",\n", + " \"dft phonon static eos deformation 1\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_1\",\n", + " \"dft phonon static eos deformation 2\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_2\",\n", + " \"dft phonon static eos deformation 3\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_3\",\n", + " \"dft phonon static eos deformation 4\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_4\",\n", + " \"dft phonon static eos deformation 5\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_5\",\n", + " \"dft phonon static eos deformation 6\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_6\",\n", + " \"dft phonon static eos deformation 7\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_eos_deformation_7\",\n", + " \"dft phonon static 1/1 eos deformation 1\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_1\",\n", + " \"dft phonon static 1/1 eos deformation 2\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_2\",\n", + " \"dft phonon static 1/1 eos deformation 3\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_3\",\n", + " \"dft phonon static 1/1 eos deformation 4\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_4\",\n", + " \"dft phonon static 1/1 eos deformation 5\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_5\",\n", + " \"dft phonon static 1/1 eos deformation 6\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_6\",\n", + " \"dft phonon static 1/1 eos deformation 7\": \"Si_qha_2/\"\n", + " \"dft_phonon_static_1_1_eos_deformation_7\",\n", "}" ] }, diff --git a/tutorials/tutorials.md b/tutorials/tutorials.md index 1f9fe68d4a..6f3b61d53f 100644 --- a/tutorials/tutorials.md +++ b/tutorials/tutorials.md @@ -1,5 +1,5 @@ (tutorials)= - +[comment]: # This file will be copied to the docs to help including the tutorials # Tutorials The section includes tutorials for workflows in atomate2. From f019a298861d9a417fe8235b3693095077ff32e4 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 13:49:55 +0100 Subject: [PATCH 43/61] update tests --- tests/vasp/flows/test_eos.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/vasp/flows/test_eos.py b/tests/vasp/flows/test_eos.py index 86b4d541d8..b1c6871a6b 100644 --- a/tests/vasp/flows/test_eos.py +++ b/tests/vasp/flows/test_eos.py @@ -80,12 +80,12 @@ def test_mp_eos_maker( base_ref_path = "Si_EOS_MP_GGA/" ref_paths = {} expected_incars = { - "EOS MP GGA relax 1": expected_incar_relax_1, - "EOS MP GGA relax 2": expected_incar_relax, + "EOS MP GGA relax 1 EOS equilibrium relaxation": expected_incar_relax_1, + "EOS MP GGA relax 2 EOS equilibrium relaxation": expected_incar_relax, } for idx in range(2): - ref_paths[f"EOS MP GGA relax {idx + 1}"] = ( + ref_paths[f"EOS MP GGA relax {idx + 1} EOS equilibrium relaxation"] = ( f"mp-149-PBE-EOS_MP_GGA_relax_{idx + 1}" ) @@ -118,7 +118,7 @@ def test_mp_eos_maker( ) structure = Structure.from_file( - f"{vasp_test_dir}/{ref_paths['EOS MP GGA relax 1']}/inputs/POSCAR.gz" + f"{vasp_test_dir}/{ref_paths['EOS MP GGA relax 1 EOS equilibrium relaxation']}/inputs/POSCAR.gz" ) # cannot perform least-squares fit for four parameters with only 3 data points @@ -153,7 +153,7 @@ def test_mp_eos_maker( # deformation jobs not included in this assert len(job_output) == len(ref_paths) - ref_energies = {"EOS MP GGA relax 1": -10.849349, "EOS MP GGA relax 2": -10.849357} + ref_energies = {"EOS MP GGA relax 1 EOS equilibrium relaxation": -10.849349, "EOS MP GGA relax 2 EOS equilibrium relaxation": -10.849357} if do_statics: ref_energies["EOS equilibrium static"] = -10.849357 From 31ab096c29eee01057b028558b71b8f41d63e991 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 13:53:36 +0100 Subject: [PATCH 44/61] fix linting --- tests/vasp/flows/test_eos.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/vasp/flows/test_eos.py b/tests/vasp/flows/test_eos.py index b1c6871a6b..e85c5c5e6b 100644 --- a/tests/vasp/flows/test_eos.py +++ b/tests/vasp/flows/test_eos.py @@ -153,7 +153,10 @@ def test_mp_eos_maker( # deformation jobs not included in this assert len(job_output) == len(ref_paths) - ref_energies = {"EOS MP GGA relax 1 EOS equilibrium relaxation": -10.849349, "EOS MP GGA relax 2 EOS equilibrium relaxation": -10.849357} + ref_energies = { + "EOS MP GGA relax 1 EOS equilibrium relaxation": -10.849349, + "EOS MP GGA relax 2 EOS equilibrium relaxation": -10.849357, + } if do_statics: ref_energies["EOS equilibrium static"] = -10.849357 From 4d0452af6832af278b32f914f4c7e8b97af6139a Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 13:56:39 +0100 Subject: [PATCH 45/61] fix linting --- tests/vasp/flows/test_eos.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/vasp/flows/test_eos.py b/tests/vasp/flows/test_eos.py index e85c5c5e6b..c1ed39a2a9 100644 --- a/tests/vasp/flows/test_eos.py +++ b/tests/vasp/flows/test_eos.py @@ -118,7 +118,9 @@ def test_mp_eos_maker( ) structure = Structure.from_file( - f"{vasp_test_dir}/{ref_paths['EOS MP GGA relax 1 EOS equilibrium relaxation']}/inputs/POSCAR.gz" + f"{vasp_test_dir}/{ + ref_paths['EOS MP GGA relax 1 EOS equilibrium relaxation'] + }/inputs/POSCAR.gz" ) # cannot perform least-squares fit for four parameters with only 3 data points From 2f6118dc5887ccdc4bb86f2c2ffbc2a212dff7b9 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 14:11:59 +0100 Subject: [PATCH 46/61] fix more linting --- src/atomate2/common/flows/eos.py | 10 ++++++---- tests/vasp/flows/test_eos.py | 16 ++++++++++------ 2 files changed, 16 insertions(+), 10 deletions(-) diff --git a/src/atomate2/common/flows/eos.py b/src/atomate2/common/flows/eos.py index 0427945f59..ba29f3215c 100644 --- a/src/atomate2/common/flows/eos.py +++ b/src/atomate2/common/flows/eos.py @@ -96,8 +96,9 @@ def make(self, structure: Structure, prev_dir: str | Path = None) -> Flow: relax_flow.name = "EOS equilibrium relaxation" try: - for job in relax_flow.jobs: - job.append_name(" EOS equilibrium relaxation") + if len(relax_flow.jobs) > 1: + for job in relax_flow.jobs: + job.append_name(" EOS equilibrium relaxation") except AttributeError: pass flow_output["initial_relax"] = { @@ -159,8 +160,9 @@ def make(self, structure: Structure, prev_dir: str | Path = None) -> Flow: ) relax_job.name += f" deformation {frame_idx}" try: - for job in relax_job.jobs: - job.append_name(f" deformation {frame_idx}") + if len(relax_job.jobs) > 1: + for job in relax_job.jobs: + job.append_name(f" deformation {frame_idx}") except AttributeError: pass jobs["relax"].append(relax_job) diff --git a/tests/vasp/flows/test_eos.py b/tests/vasp/flows/test_eos.py index c1ed39a2a9..20614e664d 100644 --- a/tests/vasp/flows/test_eos.py +++ b/tests/vasp/flows/test_eos.py @@ -23,7 +23,11 @@ expected_incar_deform = expected_incar_relax | {"ISIF": 2} -expected_incar_static = expected_incar_relax | {"NSW": 0, "IBRION": -1, "ISMEAR": -5} +expected_incar_static = expected_incar_relax | { + "NSW": 0, + "IBRION": -1, + "ISMEAR": -5, +} expected_incar_static.pop("ISIF") @@ -77,11 +81,13 @@ def test_mp_eos_maker( n_frames: int = 2, linear_strain: tuple = (-0.05, 0.05), ): + relax_job_name_1 = "EOS MP GGA relax 1 EOS equilibrium relaxation" + relax_job_name_2 = "EOS MP GGA relax 2 EOS equilibrium relaxation" base_ref_path = "Si_EOS_MP_GGA/" ref_paths = {} expected_incars = { - "EOS MP GGA relax 1 EOS equilibrium relaxation": expected_incar_relax_1, - "EOS MP GGA relax 2 EOS equilibrium relaxation": expected_incar_relax, + relax_job_name_1: expected_incar_relax_1, + relax_job_name_2: expected_incar_relax, } for idx in range(2): @@ -118,9 +124,7 @@ def test_mp_eos_maker( ) structure = Structure.from_file( - f"{vasp_test_dir}/{ - ref_paths['EOS MP GGA relax 1 EOS equilibrium relaxation'] - }/inputs/POSCAR.gz" + f"{vasp_test_dir}/{ref_paths[relax_job_name_1]}/inputs/POSCAR.gz" ) # cannot perform least-squares fit for four parameters with only 3 data points From f00f486a46071c25188589f0b092a106724fa735 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 15:17:25 +0100 Subject: [PATCH 47/61] fix more linting --- tutorials/grueneisen_workflow.ipynb | 636 ++++++++++++++++++++++++++++ 1 file changed, 636 insertions(+) create mode 100644 tutorials/grueneisen_workflow.ipynb diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb new file mode 100644 index 0000000000..24b66c82b7 --- /dev/null +++ b/tutorials/grueneisen_workflow.ipynb @@ -0,0 +1,636 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:" + ] + }, + { + "cell_type": "code", + "id": "1", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-14T13:54:28.240644Z", + "start_time": "2025-02-14T13:54:24.779096Z" + } + }, + "source": [ + "from mock_vasp import TEST_DIR, mock_vasp\n", + "\n", + "ref_paths = {\n", + " \"tight relax 1\": \"Si_gruneisen_1/tight_relax_1\",\n", + " \"tight relax 2\": \"Si_gruneisen_1/tight_relax_2\",\n", + " \"tight relax 1 plus\": \"Si_gruneisen_1/tight_relax_plus\",\n", + " \"tight relax 2 plus\": \"Si_gruneisen_1/tight_relax_plus_2\",\n", + " \"tight relax 1 minus\": \"Si_gruneisen_1/tight_relax_minus\",\n", + " \"tight relax 2 minus\": \"Si_gruneisen_1/tight_relax_minus_2\",\n", + " \"phonon static 1/1 ground\": \"Si_gruneisen_1/phonon_ground\",\n", + " \"phonon static 1/1 plus\": \"Si_gruneisen_1/phonon_plus\",\n", + " \"phonon static 1/1 minus\": \"Si_gruneisen_1/phonon_minus\",\n", + " }" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": "# Grüneisen Workflow Tutorial with VASP" + }, + { + "cell_type": "markdown", + "id": "4", + "metadata": {}, + "source": [ + "## Background\n", + "The Grüneisen workflow is based on the implementation in Phonopy.\n", + "\n", + "If you want to read more about Phonopy, please read Togo’s paper: https://doi.org/10.7566/JPSJ.92.012001" + ] + }, + { + "cell_type": "markdown", + "id": "5", + "metadata": {}, + "source": [ + "## Let's run the workflow\n", + "Now, we load a structure and other important functions and classes for running the Grüneisen workflow." + ] + }, + { + "cell_type": "code", + "id": "6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-14T13:54:29.174236Z", + "start_time": "2025-02-14T13:54:28.247415Z" + } + }, + "source": [ + "from jobflow import JobStore, run_locally\n", + "from maggma.stores import MemoryStore\n", + "from pymatgen.core import Structure\n", + "\n", + "from atomate2.vasp.flows.gruneisen import GruneisenMaker, PhononMaker\n", + "\n", + "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", + "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si_diamond.cif\")" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "7", + "metadata": {}, + "source": "Then one can use the `GruneisenMaker` to generate a `Flow`." + }, + { + "cell_type": "code", + "id": "8", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-14T13:54:29.791837Z", + "start_time": "2025-02-14T13:54:29.296381Z" + } + }, + "source": [ + "flow = GruneisenMaker(\n", + " symprec=1e-4,\n", + " phonon_maker=PhononMaker(\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " min_length=10,\n", + " born_maker=None,\n", + " bulk_relax_maker=None,\n", + " static_energy_maker=None,\n", + " ),\n", + " ).make(structure=si_structure)\n" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":14: UserWarning: You are using different symmetry precisions in the phonon makers and other parts of the Grüneisen workflow.\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "9", + "metadata": {}, + "source": "The Grüneisen parameter workflow will perform 3 different phonon runs at 3 different volumes at and around the equilibrium to compute the mode Grüneisen values." + }, + { + "cell_type": "code", + "id": "10", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-14T13:54:30.006465Z", + "start_time": "2025-02-14T13:54:29.798275Z" + } + }, + "source": [ + "flow.draw_graph().show()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
      " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "11", + "metadata": {}, + "source": [ + "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`" + ] + }, + { + "cell_type": "code", + "id": "12", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-14T13:55:19.750921Z", + "start_time": "2025-02-14T13:54:30.013578Z" + } + }, + "source": [ + "with mock_vasp(ref_paths=ref_paths) as mf:\n", + " run_locally(\n", + " flow,\n", + " create_folders=True,\n", + " ensure_success=True,\n", + " raise_immediately=True,\n", + " store=job_store,\n", + " )" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:30,030 INFO Started executing jobs locally\n", + "2025-02-14 14:54:30,037 INFO Starting job - tight relax 1 (635f3da5-ae43-4708-89d1-df7a122a4279)\n", + "2025-02-14 14:54:30,343 INFO Finished job - tight relax 1 (635f3da5-ae43-4708-89d1-df7a122a4279)\n", + "2025-02-14 14:54:30,344 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:30,345 INFO Starting job - tight relax 2 (c0ce18a9-cf40-48e0-8fd4-b509df9f81bd)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-30-344976-38705/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:30,977 INFO Finished job - tight relax 2 (c0ce18a9-cf40-48e0-8fd4-b509df9f81bd)\n", + "2025-02-14 14:54:30,978 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:30,978 INFO Starting job - shrink_expand_structure (5b972212-ce75-45e8-9ae1-8ae26567bcc3)\n", + "2025-02-14 14:54:30,997 INFO Finished job - shrink_expand_structure (5b972212-ce75-45e8-9ae1-8ae26567bcc3)\n", + "2025-02-14 14:54:30,997 INFO Starting job - tight relax 1 plus (07a9e9e2-34f4-4db5-8b81-a8682d69ae0c)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-30-997414-38649/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:31,439 INFO Finished job - tight relax 1 plus (07a9e9e2-34f4-4db5-8b81-a8682d69ae0c)\n", + "2025-02-14 14:54:31,439 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:31,440 INFO Starting job - tight relax 1 minus (02947bae-ebc8-4050-af46-1693de1b3d3e)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-31-440259-43132/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:31,878 INFO Finished job - tight relax 1 minus (02947bae-ebc8-4050-af46-1693de1b3d3e)\n", + "2025-02-14 14:54:31,879 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:31,880 INFO Starting job - tight relax 2 plus (ff4b871a-86d9-433c-97d2-33059eb34f66)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-31-880034-84607/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:32,291 INFO Finished job - tight relax 2 plus (ff4b871a-86d9-433c-97d2-33059eb34f66)\n", + "2025-02-14 14:54:32,292 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:32,292 INFO Starting job - tight relax 2 minus (4ca671fb-d350-4433-baf6-6c38a13f1995)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-32-292580-50166/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:32,827 INFO Finished job - tight relax 2 minus (4ca671fb-d350-4433-baf6-6c38a13f1995)\n", + "2025-02-14 14:54:32,827 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:32,828 INFO Starting job - run_phonon_jobs (faf17a4a-76d2-4bbf-9cb4-e0746d5ef3e5)\n", + "2025-02-14 14:54:32,987 INFO Finished job - run_phonon_jobs (faf17a4a-76d2-4bbf-9cb4-e0746d5ef3e5)\n", + "2025-02-14 14:54:33,011 INFO Starting job - get_supercell_size ground (dfe385c6-5a47-4221-b406-050f6e1aa08a)\n", + "2025-02-14 14:54:33,015 INFO Finished job - get_supercell_size ground (dfe385c6-5a47-4221-b406-050f6e1aa08a)\n", + "2025-02-14 14:54:33,016 INFO Starting job - generate_phonon_displacements ground (1eea40c3-a340-4412-9b31-51075764455a)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/flow.py:431: UserWarning: Some jobs are not connected, their ordering may be random\n", + " for node in itergraph(graph):\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:33,218 INFO Finished job - generate_phonon_displacements ground (1eea40c3-a340-4412-9b31-51075764455a)\n", + "2025-02-14 14:54:33,219 INFO Starting job - run_phonon_displacements ground (09a05460-f5a0-43d6-b4c2-e6d32dbbc8ea)\n", + "2025-02-14 14:54:33,350 INFO Finished job - run_phonon_displacements ground (09a05460-f5a0-43d6-b4c2-e6d32dbbc8ea)\n", + "2025-02-14 14:54:33,360 INFO Starting job - phonon static 1/1 ground (053a5674-3e05-4aee-a68e-67c1e6095858)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-33-360435-77497/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:35,024 INFO Finished job - phonon static 1/1 ground (053a5674-3e05-4aee-a68e-67c1e6095858)\n", + "2025-02-14 14:54:35,025 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:35,026 INFO Starting job - store_inputs ground (09a05460-f5a0-43d6-b4c2-e6d32dbbc8ea, 2)\n", + "2025-02-14 14:54:35,027 INFO Finished job - store_inputs ground (09a05460-f5a0-43d6-b4c2-e6d32dbbc8ea, 2)\n", + "2025-02-14 14:54:35,028 INFO Starting job - generate_frequencies_eigenvectors ground (adde9cb7-328f-4b0d-b455-fe3fead5df18)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:41,975 INFO Finished job - generate_frequencies_eigenvectors ground (adde9cb7-328f-4b0d-b455-fe3fead5df18)\n", + "2025-02-14 14:54:41,976 INFO Starting job - get_supercell_size plus (3e352e55-8d05-4968-af9c-57b1b869b85b)\n", + "2025-02-14 14:54:41,979 INFO Finished job - get_supercell_size plus (3e352e55-8d05-4968-af9c-57b1b869b85b)\n", + "2025-02-14 14:54:41,980 INFO Starting job - generate_phonon_displacements plus (6b6e501b-15c6-4d3a-b953-ca06c2dfc8c6)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:42,162 INFO Finished job - generate_phonon_displacements plus (6b6e501b-15c6-4d3a-b953-ca06c2dfc8c6)\n", + "2025-02-14 14:54:42,163 INFO Starting job - run_phonon_displacements plus (082a22ea-ef9f-4e32-9187-3c6cb90b2794)\n", + "2025-02-14 14:54:42,277 INFO Finished job - run_phonon_displacements plus (082a22ea-ef9f-4e32-9187-3c6cb90b2794)\n", + "2025-02-14 14:54:42,285 INFO Starting job - phonon static 1/1 plus (e8b282de-c12d-4373-ab83-f6b9a35e59d7)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-42-284684-83186/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:43,987 INFO Finished job - phonon static 1/1 plus (e8b282de-c12d-4373-ab83-f6b9a35e59d7)\n", + "2025-02-14 14:54:43,988 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:43,989 INFO Starting job - store_inputs plus (082a22ea-ef9f-4e32-9187-3c6cb90b2794, 2)\n", + "2025-02-14 14:54:43,991 INFO Finished job - store_inputs plus (082a22ea-ef9f-4e32-9187-3c6cb90b2794, 2)\n", + "2025-02-14 14:54:43,992 INFO Starting job - generate_frequencies_eigenvectors plus (c51572ce-1304-4014-855d-475ced9e9e54)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:50,351 INFO Finished job - generate_frequencies_eigenvectors plus (c51572ce-1304-4014-855d-475ced9e9e54)\n", + "2025-02-14 14:54:50,352 INFO Starting job - get_supercell_size minus (f1dc850a-c282-429c-9a9e-f8b54e9f17c6)\n", + "2025-02-14 14:54:50,355 INFO Finished job - get_supercell_size minus (f1dc850a-c282-429c-9a9e-f8b54e9f17c6)\n", + "2025-02-14 14:54:50,356 INFO Starting job - generate_phonon_displacements minus (c942e3f1-38cf-4f4e-ab4a-fe71cbc53017)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:50,539 INFO Finished job - generate_phonon_displacements minus (c942e3f1-38cf-4f4e-ab4a-fe71cbc53017)\n", + "2025-02-14 14:54:50,540 INFO Starting job - run_phonon_displacements minus (0c49112c-7ce8-4a5b-ae24-7cdc6059568c)\n", + "2025-02-14 14:54:50,654 INFO Finished job - run_phonon_displacements minus (0c49112c-7ce8-4a5b-ae24-7cdc6059568c)\n", + "2025-02-14 14:54:50,662 INFO Starting job - phonon static 1/1 minus (2c3adcee-9d8f-4ccd-a97f-f9a09fa85130)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-50-662182-29638/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:52,357 INFO Finished job - phonon static 1/1 minus (2c3adcee-9d8f-4ccd-a97f-f9a09fa85130)\n", + "2025-02-14 14:54:52,358 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-14 14:54:52,358 INFO Starting job - store_inputs minus (0c49112c-7ce8-4a5b-ae24-7cdc6059568c, 2)\n", + "2025-02-14 14:54:52,361 INFO Finished job - store_inputs minus (0c49112c-7ce8-4a5b-ae24-7cdc6059568c, 2)\n", + "2025-02-14 14:54:52,363 INFO Starting job - generate_frequencies_eigenvectors minus (077a6d1a-bf33-4f71-b44e-ec3597670123)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:54:58,741 INFO Finished job - generate_frequencies_eigenvectors minus (077a6d1a-bf33-4f71-b44e-ec3597670123)\n", + "2025-02-14 14:54:58,742 INFO Starting job - compute_gruneisen_param (46e720e5-fb55-4256-9763-f7cd6bafbedb)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-14 14:55:19,732 INFO Finished job - compute_gruneisen_param (46e720e5-fb55-4256-9763-f7cd6bafbedb)\n", + "2025-02-14 14:55:19,733 INFO Finished executing jobs locally\n" + ] + } + ], + "execution_count": 5 + }, + { + "cell_type": "code", + "id": "13", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-14T13:55:19.763673Z", + "start_time": "2025-02-14T13:55:19.757545Z" + } + }, + "source": [ + "job_store.connect()\n", + "\n", + "result = job_store.query_one(\n", + " {\"name\": \"compute_gruneisen_param\"},\n", + " properties=[\n", + " \"output.gruneisen_band_structure\",\n", + " \"output.gruneisen_parameter\",\n", + " ],\n", + " load=True,\n", + " sort={\"completed_at\": -1}, # to get the latest computation\n", + ")" + ], + "outputs": [], + "execution_count": 6 + }, + { + "cell_type": "code", + "id": "14", + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-14T14:15:29.981093Z", + "start_time": "2025-02-14T14:15:29.802700Z" + } + }, + "source": [ + "from pymatgen.phonon.plotter import GruneisenPhononBSPlotter\n", + "from pymatgen.phonon.gruneisen import GruneisenPhononBandStructureSymmLine\n", + "bs=GruneisenPhononBandStructureSymmLine.from_dict(result[\"output\"][\"gruneisen_band_structure\"])\n", + "plt=GruneisenPhononBSPlotter(bs=bs)\n", + "plt.get_plot(ylim=[-2,2])" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
      " + ], + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 17 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "c084a91d4846e568" + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + }, + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3 (ipykernel)" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1bcb184229ed3c8866217b16b581c0f0f76cb930 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Fri, 14 Feb 2025 15:23:37 +0100 Subject: [PATCH 48/61] fix more linting --- tutorials/grueneisen_workflow.ipynb | 580 ++++------------------------ 1 file changed, 78 insertions(+), 502 deletions(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 24b66c82b7..dacb2b56f8 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -10,49 +10,41 @@ }, { "cell_type": "code", + "execution_count": null, "id": "1", "metadata": { - "ExecuteTime": { - "end_time": "2025-02-14T13:54:28.240644Z", - "start_time": "2025-02-14T13:54:24.779096Z" + "jupyter": { + "is_executing": true } }, + "outputs": [], "source": [ "from mock_vasp import TEST_DIR, mock_vasp\n", "\n", "ref_paths = {\n", - " \"tight relax 1\": \"Si_gruneisen_1/tight_relax_1\",\n", - " \"tight relax 2\": \"Si_gruneisen_1/tight_relax_2\",\n", - " \"tight relax 1 plus\": \"Si_gruneisen_1/tight_relax_plus\",\n", - " \"tight relax 2 plus\": \"Si_gruneisen_1/tight_relax_plus_2\",\n", - " \"tight relax 1 minus\": \"Si_gruneisen_1/tight_relax_minus\",\n", - " \"tight relax 2 minus\": \"Si_gruneisen_1/tight_relax_minus_2\",\n", - " \"phonon static 1/1 ground\": \"Si_gruneisen_1/phonon_ground\",\n", - " \"phonon static 1/1 plus\": \"Si_gruneisen_1/phonon_plus\",\n", - " \"phonon static 1/1 minus\": \"Si_gruneisen_1/phonon_minus\",\n", - " }" - ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "execution_count": 1 + " \"tight relax 1\": \"Si_gruneisen_1/tight_relax_1\",\n", + " \"tight relax 2\": \"Si_gruneisen_1/tight_relax_2\",\n", + " \"tight relax 1 plus\": \"Si_gruneisen_1/tight_relax_plus\",\n", + " \"tight relax 2 plus\": \"Si_gruneisen_1/tight_relax_plus_2\",\n", + " \"tight relax 1 minus\": \"Si_gruneisen_1/tight_relax_minus\",\n", + " \"tight relax 2 minus\": \"Si_gruneisen_1/tight_relax_minus_2\",\n", + " \"phonon static 1/1 ground\": \"Si_gruneisen_1/phonon_ground\",\n", + " \"phonon static 1/1 plus\": \"Si_gruneisen_1/phonon_plus\",\n", + " \"phonon static 1/1 minus\": \"Si_gruneisen_1/phonon_minus\",\n", + "}" + ] }, { "cell_type": "markdown", "id": "2", "metadata": {}, - "source": "# Grüneisen Workflow Tutorial with VASP" + "source": [ + "# Grüneisen Workflow Tutorial with VASP" + ] }, { "cell_type": "markdown", - "id": "4", + "id": "3", "metadata": {}, "source": [ "## Background\n", @@ -63,7 +55,7 @@ }, { "cell_type": "markdown", - "id": "5", + "id": "4", "metadata": {}, "source": [ "## Let's run the workflow\n", @@ -72,13 +64,10 @@ }, { "cell_type": "code", - "id": "6", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-14T13:54:29.174236Z", - "start_time": "2025-02-14T13:54:28.247415Z" - } - }, + "execution_count": null, + "id": "5", + "metadata": {}, + "outputs": [], "source": [ "from jobflow import JobStore, run_locally\n", "from maggma.stores import MemoryStore\n", @@ -88,85 +77,58 @@ "\n", "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si_diamond.cif\")" - ], - "outputs": [], - "execution_count": 2 + ] }, { "cell_type": "markdown", - "id": "7", + "id": "6", "metadata": {}, - "source": "Then one can use the `GruneisenMaker` to generate a `Flow`." + "source": [ + "Then one can use the `GruneisenMaker` to generate a `Flow`." + ] }, { "cell_type": "code", - "id": "8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-14T13:54:29.791837Z", - "start_time": "2025-02-14T13:54:29.296381Z" - } - }, + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], "source": [ "flow = GruneisenMaker(\n", - " symprec=1e-4,\n", - " phonon_maker=PhononMaker(\n", - " create_thermal_displacements=False,\n", - " store_force_constants=False,\n", - " prefer_90_degrees=False,\n", - " min_length=10,\n", - " born_maker=None,\n", - " bulk_relax_maker=None,\n", - " static_energy_maker=None,\n", - " ),\n", - " ).make(structure=si_structure)\n" - ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":14: UserWarning: You are using different symmetry precisions in the phonon makers and other parts of the Grüneisen workflow.\n" - ] - } - ], - "execution_count": 3 + " symprec=1e-4,\n", + " phonon_maker=PhononMaker(\n", + " create_thermal_displacements=False,\n", + " store_force_constants=False,\n", + " prefer_90_degrees=False,\n", + " min_length=10,\n", + " born_maker=None,\n", + " bulk_relax_maker=None,\n", + " static_energy_maker=None,\n", + " ),\n", + ").make(structure=si_structure)" + ] }, { "cell_type": "markdown", - "id": "9", + "id": "8", "metadata": {}, - "source": "The Grüneisen parameter workflow will perform 3 different phonon runs at 3 different volumes at and around the equilibrium to compute the mode Grüneisen values." + "source": [ + "The Grüneisen parameter workflow will perform 3 different phonon runs at 3 different volumes at and around the equilibrium to compute the mode Grüneisen values." + ] }, { "cell_type": "code", - "id": "10", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-14T13:54:30.006465Z", - "start_time": "2025-02-14T13:54:29.798275Z" - } - }, + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], "source": [ "flow.draw_graph().show()" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
      " - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 4 + ] }, { "cell_type": "markdown", - "id": "11", + "id": "10", "metadata": {}, "source": [ "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`" @@ -174,13 +136,14 @@ }, { "cell_type": "code", - "id": "12", + "execution_count": null, + "id": "11", "metadata": { - "ExecuteTime": { - "end_time": "2025-02-14T13:55:19.750921Z", - "start_time": "2025-02-14T13:54:30.013578Z" + "jupyter": { + "is_executing": true } }, + "outputs": [], "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", " run_locally(\n", @@ -190,363 +153,14 @@ " raise_immediately=True,\n", " store=job_store,\n", " )" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:30,030 INFO Started executing jobs locally\n", - "2025-02-14 14:54:30,037 INFO Starting job - tight relax 1 (635f3da5-ae43-4708-89d1-df7a122a4279)\n", - "2025-02-14 14:54:30,343 INFO Finished job - tight relax 1 (635f3da5-ae43-4708-89d1-df7a122a4279)\n", - "2025-02-14 14:54:30,344 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:30,345 INFO Starting job - tight relax 2 (c0ce18a9-cf40-48e0-8fd4-b509df9f81bd)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-30-344976-38705/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:30,977 INFO Finished job - tight relax 2 (c0ce18a9-cf40-48e0-8fd4-b509df9f81bd)\n", - "2025-02-14 14:54:30,978 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:30,978 INFO Starting job - shrink_expand_structure (5b972212-ce75-45e8-9ae1-8ae26567bcc3)\n", - "2025-02-14 14:54:30,997 INFO Finished job - shrink_expand_structure (5b972212-ce75-45e8-9ae1-8ae26567bcc3)\n", - "2025-02-14 14:54:30,997 INFO Starting job - tight relax 1 plus (07a9e9e2-34f4-4db5-8b81-a8682d69ae0c)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-30-997414-38649/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:31,439 INFO Finished job - tight relax 1 plus (07a9e9e2-34f4-4db5-8b81-a8682d69ae0c)\n", - "2025-02-14 14:54:31,439 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:31,440 INFO Starting job - tight relax 1 minus (02947bae-ebc8-4050-af46-1693de1b3d3e)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-31-440259-43132/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:31,878 INFO Finished job - tight relax 1 minus (02947bae-ebc8-4050-af46-1693de1b3d3e)\n", - "2025-02-14 14:54:31,879 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:31,880 INFO Starting job - tight relax 2 plus (ff4b871a-86d9-433c-97d2-33059eb34f66)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-31-880034-84607/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:32,291 INFO Finished job - tight relax 2 plus (ff4b871a-86d9-433c-97d2-33059eb34f66)\n", - "2025-02-14 14:54:32,292 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:32,292 INFO Starting job - tight relax 2 minus (4ca671fb-d350-4433-baf6-6c38a13f1995)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-32-292580-50166/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:32,827 INFO Finished job - tight relax 2 minus (4ca671fb-d350-4433-baf6-6c38a13f1995)\n", - "2025-02-14 14:54:32,827 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:32,828 INFO Starting job - run_phonon_jobs (faf17a4a-76d2-4bbf-9cb4-e0746d5ef3e5)\n", - "2025-02-14 14:54:32,987 INFO Finished job - run_phonon_jobs (faf17a4a-76d2-4bbf-9cb4-e0746d5ef3e5)\n", - "2025-02-14 14:54:33,011 INFO Starting job - get_supercell_size ground (dfe385c6-5a47-4221-b406-050f6e1aa08a)\n", - "2025-02-14 14:54:33,015 INFO Finished job - get_supercell_size ground (dfe385c6-5a47-4221-b406-050f6e1aa08a)\n", - "2025-02-14 14:54:33,016 INFO Starting job - generate_phonon_displacements ground (1eea40c3-a340-4412-9b31-51075764455a)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/flow.py:431: UserWarning: Some jobs are not connected, their ordering may be random\n", - " for node in itergraph(graph):\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:33,218 INFO Finished job - generate_phonon_displacements ground (1eea40c3-a340-4412-9b31-51075764455a)\n", - "2025-02-14 14:54:33,219 INFO Starting job - run_phonon_displacements ground (09a05460-f5a0-43d6-b4c2-e6d32dbbc8ea)\n", - "2025-02-14 14:54:33,350 INFO Finished job - run_phonon_displacements ground (09a05460-f5a0-43d6-b4c2-e6d32dbbc8ea)\n", - "2025-02-14 14:54:33,360 INFO Starting job - phonon static 1/1 ground (053a5674-3e05-4aee-a68e-67c1e6095858)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-33-360435-77497/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:35,024 INFO Finished job - phonon static 1/1 ground (053a5674-3e05-4aee-a68e-67c1e6095858)\n", - "2025-02-14 14:54:35,025 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:35,026 INFO Starting job - store_inputs ground (09a05460-f5a0-43d6-b4c2-e6d32dbbc8ea, 2)\n", - "2025-02-14 14:54:35,027 INFO Finished job - store_inputs ground (09a05460-f5a0-43d6-b4c2-e6d32dbbc8ea, 2)\n", - "2025-02-14 14:54:35,028 INFO Starting job - generate_frequencies_eigenvectors ground (adde9cb7-328f-4b0d-b455-fe3fead5df18)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:41,975 INFO Finished job - generate_frequencies_eigenvectors ground (adde9cb7-328f-4b0d-b455-fe3fead5df18)\n", - "2025-02-14 14:54:41,976 INFO Starting job - get_supercell_size plus (3e352e55-8d05-4968-af9c-57b1b869b85b)\n", - "2025-02-14 14:54:41,979 INFO Finished job - get_supercell_size plus (3e352e55-8d05-4968-af9c-57b1b869b85b)\n", - "2025-02-14 14:54:41,980 INFO Starting job - generate_phonon_displacements plus (6b6e501b-15c6-4d3a-b953-ca06c2dfc8c6)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:42,162 INFO Finished job - generate_phonon_displacements plus (6b6e501b-15c6-4d3a-b953-ca06c2dfc8c6)\n", - "2025-02-14 14:54:42,163 INFO Starting job - run_phonon_displacements plus (082a22ea-ef9f-4e32-9187-3c6cb90b2794)\n", - "2025-02-14 14:54:42,277 INFO Finished job - run_phonon_displacements plus (082a22ea-ef9f-4e32-9187-3c6cb90b2794)\n", - "2025-02-14 14:54:42,285 INFO Starting job - phonon static 1/1 plus (e8b282de-c12d-4373-ab83-f6b9a35e59d7)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-42-284684-83186/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:43,987 INFO Finished job - phonon static 1/1 plus (e8b282de-c12d-4373-ab83-f6b9a35e59d7)\n", - "2025-02-14 14:54:43,988 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:43,989 INFO Starting job - store_inputs plus (082a22ea-ef9f-4e32-9187-3c6cb90b2794, 2)\n", - "2025-02-14 14:54:43,991 INFO Finished job - store_inputs plus (082a22ea-ef9f-4e32-9187-3c6cb90b2794, 2)\n", - "2025-02-14 14:54:43,992 INFO Starting job - generate_frequencies_eigenvectors plus (c51572ce-1304-4014-855d-475ced9e9e54)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:50,351 INFO Finished job - generate_frequencies_eigenvectors plus (c51572ce-1304-4014-855d-475ced9e9e54)\n", - "2025-02-14 14:54:50,352 INFO Starting job - get_supercell_size minus (f1dc850a-c282-429c-9a9e-f8b54e9f17c6)\n", - "2025-02-14 14:54:50,355 INFO Finished job - get_supercell_size minus (f1dc850a-c282-429c-9a9e-f8b54e9f17c6)\n", - "2025-02-14 14:54:50,356 INFO Starting job - generate_phonon_displacements minus (c942e3f1-38cf-4f4e-ab4a-fe71cbc53017)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:50,539 INFO Finished job - generate_phonon_displacements minus (c942e3f1-38cf-4f4e-ab4a-fe71cbc53017)\n", - "2025-02-14 14:54:50,540 INFO Starting job - run_phonon_displacements minus (0c49112c-7ce8-4a5b-ae24-7cdc6059568c)\n", - "2025-02-14 14:54:50,654 INFO Finished job - run_phonon_displacements minus (0c49112c-7ce8-4a5b-ae24-7cdc6059568c)\n", - "2025-02-14 14:54:50,662 INFO Starting job - phonon static 1/1 minus (2c3adcee-9d8f-4ccd-a97f-f9a09fa85130)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp9vbm1xrm/job_2025-02-14-13-54-50-662182-29638/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:52,357 INFO Finished job - phonon static 1/1 minus (2c3adcee-9d8f-4ccd-a97f-f9a09fa85130)\n", - "2025-02-14 14:54:52,358 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-14 14:54:52,358 INFO Starting job - store_inputs minus (0c49112c-7ce8-4a5b-ae24-7cdc6059568c, 2)\n", - "2025-02-14 14:54:52,361 INFO Finished job - store_inputs minus (0c49112c-7ce8-4a5b-ae24-7cdc6059568c, 2)\n", - "2025-02-14 14:54:52,363 INFO Starting job - generate_frequencies_eigenvectors minus (077a6d1a-bf33-4f71-b44e-ec3597670123)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:54:58,741 INFO Finished job - generate_frequencies_eigenvectors minus (077a6d1a-bf33-4f71-b44e-ec3597670123)\n", - "2025-02-14 14:54:58,742 INFO Starting job - compute_gruneisen_param (46e720e5-fb55-4256-9763-f7cd6bafbedb)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-14 14:55:19,732 INFO Finished job - compute_gruneisen_param (46e720e5-fb55-4256-9763-f7cd6bafbedb)\n", - "2025-02-14 14:55:19,733 INFO Finished executing jobs locally\n" - ] - } - ], - "execution_count": 5 + ] }, { "cell_type": "code", - "id": "13", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-14T13:55:19.763673Z", - "start_time": "2025-02-14T13:55:19.757545Z" - } - }, + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], "source": [ "job_store.connect()\n", "\n", @@ -559,57 +173,24 @@ " load=True,\n", " sort={\"completed_at\": -1}, # to get the latest computation\n", ")" - ], - "outputs": [], - "execution_count": 6 + ] }, { "cell_type": "code", - "id": "14", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-14T14:15:29.981093Z", - "start_time": "2025-02-14T14:15:29.802700Z" - } - }, - "source": [ - "from pymatgen.phonon.plotter import GruneisenPhononBSPlotter\n", - "from pymatgen.phonon.gruneisen import GruneisenPhononBandStructureSymmLine\n", - "bs=GruneisenPhononBandStructureSymmLine.from_dict(result[\"output\"][\"gruneisen_band_structure\"])\n", - "plt=GruneisenPhononBSPlotter(bs=bs)\n", - "plt.get_plot(ylim=[-2,2])" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
      " - ], - "image/png": 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AD547Mjkcoc1lJmbYkQkAEKzWrd0JFxoA+8eMOxzLmN5/333MjkwAgGCRmAE8WNlfxvwAfcYZoc8FAGi+/F3OZPaZCTUxE+sNgMNZMfPhh8bYujVfwgAAgkdiBvBgZWLG/GDcq1focwEAmi8zMbNuXcPXWbUzExUz/vvyS2M85xz7nwUAaLpIzAAe7NiRyfxADQBAMMw+M41VzFi9lImKmcYVFBjjyJH2PwsA0HSRmAE8WJWY8dyR6bLLQpsLANC8DRxojAUF0pEj9V9nJmby843dmYIV60uZwlUxc+CAsZujJF1/vb3PAgA0bSRmAA+ezX9DsWyZ+5gdmQAAocjIcPePaWg5U06O0XC+pkbatSv455nvgbG6XXa4KmbmzDHG+Hipf397nwUAaNpIzAAerKqYWbrUGFNSaAYIAAidPw2AHQ5rljPF+lIms2LG7sTMggXGaMXyZwBA80ZiBvBgfpgL9UOW+Y1mhw6hzQMAgBTePjPme2CsNv81K2bsXsq0aZMx9utn73MAAE0fiRnAg1UVM+YHYvMDMgAAofB3ZyZzyVMoOzN57srkdAY/TyQ4neFLzOzfb4xjx9r7HABA00diBvhedbVUVGQch5qYMRM85jecAACEYsAAY6lSYWHDS4ysXMpUUSGdOBH8PJFw7Jjxfi7Zm5jJzXU/57rr7HsOAKB5IDEDfO/wYeObtrg4KSsr+HkqK6XycuP40kutiQ0A0Ly1aiX16mUcN1Q1Y0VipmVLKS3NOI615UxmvBkZUlKSfc/597+NMTFR6tzZvucAAJoHEjPA98xvINu2NXZYCNbKle7j4cNDiwkAAJNZhdlQYsZcyrR7t/tLgmB4LmeKJeFaxmQ2+e/Y0d7nAACaBxIzwPes2ip7yRJjTE42vkkDAMAK/iRmsrONahFJ2rEj+GfFamImXDsyffONMQ4YYO9zAADNA4kZ4HtW7chk7pjB9pkAACv5k5hp7ltmm+/ldlbM1NZKR44Yx5dfbt9zAADNB4kZ4HtW7chk7oTBjkwAACuZDYD37Gm4ksWKnZlidcvscCxlWrPGvVvVD39o33MAAM0HiRnge1YlZg4cMMaBA0ObBwAAT61auZMudjcAZilT/d57zxhTUqTMTPueAwBoPkjMAN+zIjFTXS2VlRnHo0aFHBIAAF78Wc7UnJcyhaNiZsUKY+zSxb5nAACaFxIzwPesSMx88YX7ePTo0OIBAMBXIDszNcelTOGomDH/dx082L5nAACaFxIzwPes+DD32WfGmJRk7MoEAICVAqmYOXBAOnkyuOfE6lImuytmamulo0eN4wkT7HkGAKD5ITEDfM+KihlzRya7t+kEADRP5vbMhYXuJISvzExj22wp+OVMsZqYsbtiZskSd+PfyZPteQYAoPkhMQPI+AbMiu2y8/KMsXv30GMCAMBXerp/DYDNa0JNzBQXS1VVwc0Rbp7bWNtVMTN3rjGmpkotW9rzDABA80NiBpDxwbOmxjgO5Vu2/fuN0fxGEwAAq5nLmcwqzbqE2gA4K0uK+/5TYn2VOdGmuNhIzkjuiiGrff65MfIFDADASiRmALmXMbVpIyUkBDdHba1UUmIcjxhhTVwAAPgyEzNff13/NaEmZuLi3FUnsbKcyUwgZWZKiYn2PGP7dmO88EJ75gcANE8kZgC5EzOhVMts2OA+HjMmpHAAAKjXwIHGaPfOTLG2Zbbd/WWqq6Xjx43jK6+05xkAgOaJxAwga/rLLFpkjAkJRg8AAADsYC6XLSiQiorqvibUihkp9rbMtntHpk8/dR+zIxMAwEokZgBZsyPTmjXGaNe6dgAAJGOpTo8exnF9y5l69jTGoiKj90owYm1nJrsrZj76yBhbtZKSk+15BgCgeSIxA8iaxMy2bcbYtWvI4QAA0CCzz0x9y5nS0qScHOM41J2ZYmUpk90VM198YYzdutkzPwCg+SIxA8iaxMzevcZ4/vmhxwMAQEPMPjPr19d/TajLmWJtKZPdFTM7dhjjRRfZMz8AoPkiMQPImsTMiRPGOGxY6PEAANCQxipmpNATM7G2lMnOipnKSvf7PI1/AQBWIzEDKPRv2XJzJafTOB471pqYAACoj9kAeMcO6dixuq8JdWemWFvKZL6X25GYofEvAMBOJGYAhV4xY+7IFB9vXwk1AACmrCx3T7MNG+q+xqqKGbMSJdqZcdrxPuzZ+Dcx0fr5AQDNG4kZNHtOZ+iJmc8/N8Y2bayJCQCAxph9ZupbzuSZmDGrOgNhvicePBjc/eFmZ48Zc+fF7t2tnxsAABIzaPZOnJAqKozjYBMzW7caY+fO1sQEAEBjGusz06OH5HAY73PB9IkxlwR59leJVjU1xtbgkj1LmXbuNMYLL7R+bgAASMyg2TOrZVq1klJSgpujsNAYzz3XmpgAAGiMWTHz9dd1/z452f2FQTDLmVq2NLbdlqK/AXBRkbuqJzvb2rmrq2n8CwCwF4kZNHtW7MhkNl685JKQwwEAwC9mA+C8PKmkpO5rrGoAHO2JGbO/TJs2UosW1s7t2fh3/Hhr5wYAQCIxA4S8Jn3PHqm21jgeN86amAAAaEz79tKZZxqVIhs31n1Nc9ky287+Mmbj37Q0owoJAACrkZhBsxdqxczChcYYFyd162ZNTAAA+MNczrR+fd2/by6JGbNixo7+MmvXGiPv8QAAu5CYQbMXamJm9WpjzMiwJh4AAPzVWJ8ZcylTsIkZ870x2hMzdlbMbN9ujDT+BQDYhcQMmr1QEzObNhljx47WxAMAgL/MPjP+VMyYy24D0dwrZqqrpePHjePLL7d2bgAATCRm0OyFmpgpKDDG3r2tiQcAAH+ZFTPffCOdOnX677t2leLjpfJyad++wOc3EzPme2W0sqtiZskS9/FVV1k7NwAAJhIzaPZC/TBXXGyMF19sTTwAAPirY0dje+jqamnLltN/n5Agde9uHAeznKm5V8yYjX9TU2n8CwCwD4kZNHuhVMwcOyZVVRnHl11mWUgAAPjF4fC/AXAwW2bHWmLG6oqZL74wxq5drZ0XAABPJGbQ7IWSmFm0yBgdDuncc62LCQAAfzWWmAmlAXCsJGbM+KyumDEb/15wgbXzAgDgicRMlNu7d6/ee+89TZ8+XZdeeqnS09PlcDhcP13D/BXOqFGjvJ4f6M+bb74Z1ngbU1YmlZQYx8EkZlasMMbUVGO7bAAAws1MzKxbV/fvQ9ky23xvLCoylktFKzsqZmprpaNHjeNx46ybFwAAXy0iHQBOt2rVKv3xj3/UmjVrtC+YTn3wm1ktk5wstWoV+P0bNxpjTo51MQEAEAhzZ6bNm43ltQkJ3r8PZSlTmzbGFw+1tdKRI9IZZ4QWqx2qq43EkWRtxcznn0tOp3E8caJ18wIA4Ivv+KPQl19+qffee4+kTBh4LmNyOAK/f+dOYzQ/9AIAEG7du0vp6VJFhbRt2+m/N5cy7dwp1dQENnd8vNFcWIre5UxmUsbhkLKyrJv3gw+MMSXF+N8XAAC7UDETY9LS0lRirr2JAgsXLgzo+r59+9oUSXBC3ZHJLJ0eNMiaeAAACFRcnNS/v7R8ufT111K/ft6/79RJSkoyEje7d0vdugU2f7t2xvtltG6Zbb6XZ2UZiSSrrF5tjB07WjcnAAB1ITETxVq1aqULLrhAgwcP1oUXXqjBgwdr165dGj16dKRDc7ksxrciCqXxb2WldOqUcTxqlGUhAQAQsAEDjMTM+vXSlCnev4uLk3r2lL75xljOFExiRoreihm7dmTKyzPG/v2tnRcAAF8kZqLQxIkTNW7cOPXu3VtxPh1ld+3aFaGomqZQEjNm419JGjLEmngAAAiG2QD466/r/v1ZZxmJme++k8aPD2zuaE/M2LUjk7lEKsa/gwIAxAASM1GoR48ekQ6h2QglMbN0qTEmJ0uJiZaFBABAwMwGwBs2GI16fXcKDKUBsPkeGa2JGTsqZsz/HSXp6qutmxcAgLrQ/BfNWiiJGXNbUqtLpwEACFTv3kYfmRMn3I3pPZkNgIPZMrs5Vsy8/74xJibyPg8AsB+JGTRroTT/NT/cUuAEAIi0hAR309+6ljOZFTNNMTFjVsxYmZhZudIYO3Swbk4AAOpDYgbNWigVM/v3GyNNAQEA0cDsM7N+/em/MxMz+flG8/pAxEpixsrKltxcYzz3XOvmBACgPvSYQciOHz+ugoICHTt2TGlpacrKylLHjh0Vb+WelTYJNjFTWyuVlhrHw4dbGxMAAMEw+8zUVTHToYOUmmq8d+3aJfXq5f+80Z6YCaX6tT7m54ORI62bEwCA+pCYQUgGDBigTZs2qdbskPe9tLQ0DR06VNdee62mTJmipKSkCEVYv8pK6ehR4zjQxMyGDe7jMWMsCwkAgKCZiZn16yWnU3I43L9zOIyqmQ0bjOVMwSRmDh48fd5oYPVSpt27pepq4/gHP7BmTgAAGsJSJoRkw4YNpyVlJKmkpEQLFizQ3Xffra5du2rOnDkRiK5h5jdsLVpIrVsHdu9nnxljQoKUnm5tXAAABOO884zdmA4flvbtO/33ZgPgQHdmMr+8KC93V4tGE6ub/773njHGx9NHDgAQHiRmYLsDBw7o+uuv1wMPPBDQfRUVFTpx4oTXj5U8P8j5bivamLVrjbFNG0tDAgAgaCkpxu5MkndlpynYBsCpqVLLlsZxtC1nqqpyV79atZRp6VJjtLKZMAAADSExg4AlJydr4sSJeuWVV7R69WodOnRIlZWVOnnypHbs2KG3335bV155pRw+tc5/+MMf9PTTT/v9nKeeekoZGRmun06dOln65wil8W9enjF26WJdPAAAhKqhPjNmYibQihkpevvMHDlijHFx1n1ZsmmTMZpJLgAA7EZiBgG5//77tWfPHn3wwQe65557dMkll6ht27ZKSEhQWlqaunfvrh//+Mf66KOPtHz5cp155ple9//617/Wxo0b/XrWww8/rOPHj7t+CgsLLf2zhJKY2bPHGM2tSQEAiAYNJWbMpUxNactss79Mdnbg1a/1MZeBDRlizXwAADSGxAwCMmnSJGVlZfl17bBhw7R06VJlZ2e7XnM6nXrkkUf8uj8pKUnp6eleP1YyP1wGk5g5ftwYL7nEungAAAiVPxUzhYVGv5hARGtixur+MseOSadOGceTJlkzJwAAjSExA1v17NlTzz33nNdr8+fPV3FxcYQicjMrZgJdk757t7FdtiSNHWttTAAAhKJ/f2PctctIMnjKypIyM43j7dsDmzdaEzNmxYxV/WU++sgYHQ5p8GBr5gQAoDEkZmC7KVOmqK3HV1m1tbVatGhRBCMyBLuUaeFCY4yLkzp3tjYmAABC0aaNu/+ZbwNghyP45UzRmpixumLG/HiSmWnd0igAABrDWw5sFxcXp1GjRnm9lmd2z42gYBMzX3xhjBkZ1sYDAIAV7GgAHK2JGbNixqrEzPr1xsg22QCAcCIxg7Dw3VHpsPlJKoLMD5eBlj9v3myMPn2NAQCICv4kZppaxYxVS5ny842RZUwAgHAiMYOwSEhI8DqvqqqKUCRuwVbMFBQYI9toAgCikT87M1Exc7rqaunkSeP48stDnw8AAH+RmEFYHDhwwOu8rVU1x0GqqXF/mAs0MVNUZIwXXmhtTAAAWMFsAJyb695hyETFTP2WLHEfjxsX+nwAAPiLxAzCYuXKlV7nvkubwq2oyL2zUiA5opISySz2GT3a+rgAAAhVx45SdrbxJcSWLd6/MxMzBw9KJ074P6eZ+Dh82P3+GQ2srJiZP98YU1Ol5OTQ5wMAwF8kZmC7ZcuWaceOHV6vjRkzJkLRGMxv2LKypBYt/L9v8WL38cCB1sYEAIAVHA531YzvzkwZGe4kSyBVM9nZxlhbKxUXhxqhdazclWnNGmOM8HdHAIBmiMQMbFVaWqpf/OIXXq/169dP3bt3j1BEhmD7y6xYYYypqWyjCQCIXlb3mUlIMLbilqJnOVNlpXT8uHFsxVKm7duN0UxqAQAQLvzTshlzOBxeP0uXLm3w+vvuu0/79u3ze/4jR45o0qRJ2rRpk9frM2fODCZcSwWbmDG/eQz0PgAAwqm+ihmp6fSZOXLEGOPjpdatQ5/P7CHHUmUAQLgFsIgD4bRq1SqVl5ef9vrGjRu9zk+dOqVFixbVOUdOTo769OljWUwvvvii/vKXv2jChAn64Q9/qKFDh6pr166nXVdYWKh//vOfev75509r+nv11VfrmmuusSymYAWbmDFXZJkfagEAiEZmYmbjRqPXTHy8+3eh7My0bVv0JGbMOLKzQ69i/eYbd++cSZNCmwsAgECRmIlSP/7xj1Vg7svcgIMHD2rs2LF1/u7WW2/Vm2++aWlcFRUVmjt3rubOnStJSk9PV4cOHZSRkaGqqiodPHiw3qqa4cOHa/bs2ZbGE6xgd3EwEzpmiTgAANGoVy8pJUUqLTW+VDCTMVLwFTPmlxnme2GkWdn498MPjTEhQTrjjNDnAwAgECRmEJITJ07oRCPbOsTFxWnatGl64oknlJCQEKbIGhZMxUxtrVRWZhwPH259TAAAWCU+XurXT1q71ljO5JmY8ayYcTqNZsH+iLalTFY2/jV7yJGUAQBEAj1m4LfXXntNP/rRj/ze6vqMM87Qfffdp7y8PD3zzDNRk5SRgkvMrFvnPh41ytJwAACwXH0NgHv0MMZjx9x9VfwRbYkZs2LGisa/33xjjL17hz4XAACBomImSuXn59v+DKfTGdD1d911l+666y5JUlFRkXJzc1VQUKDDhw+rtLRU8fHxat26tbKzszVgwICI77zUkGASM599ZoyJiVLLltbHBACAleprANyypbEldGGhsZzJ3Aq7MdGWmAl2WXJdzJZ4Q4eGPhcAAIEiMYOgZGVladiwYRo2bFikQwlKMB/mvvrKGLOyrI8HAACrNbRl9llnGYmZb7+VLrnEv/miLTFjVY+Z4mKposI4njgxtLkAAAgGS5nQ7Did7g+VgVTM5OUZYx0bUQEAEHX69TN2Kzp40F0RYjL7zATSALipJmY++sgYHQ53lREAAOFEYgbNzvHj7m/GAknM7NljjOeea31MAABYrWVLdwLGdzmTuTNTIFtmR1tixqqlTOZS5datQ992GwCAYPD2g2bH7C+Tni4lJ/t/n7n5lL8l3wAARFp9y5lCqZg5cUI6dSr02EJlVcWM+b9NFLfGAwA0cSRm0OwE8w3b7t3GdtmSNGaM9TEBAGAHc2nOxo3er3tWzPi7F0BGhmRusGgmRSLJqoqZggJjHDQotHkAAAgWiRk0O8HsyLRwoTHGxUmdO1sfEwAAdqhvZ6Zu3aT4eKmsTNq/37+5HA53EsR8L42Uigp3JWsoFTO1te55Lrss9LgAAAgGiRk0O8EkZr74whgzMqyPBwAAu5x/vjF++61UWup+PTHR3cw+mD4zka6YOXLEGFu0kDIzg59n5Ur38YQJIYUEAEDQSMyg2Qmm9HnLFmPMybE+HgAA7NK+vdShg7FcafNm79/F8s5M5vOzs0Nr2Pvxx8aYkmI0SwYAIBJIzKDZCaZixlx/3ru39fEAAGCn+pYzxfLOTFY1/v38c2PkixcAQCSRmEGzE0xipqjIGGkMCACINeZyJt/ETFOomAm18a+ZlDr33NDmAQAgFCRm0OwEmpgpK5MqK41jdmQCAMQaKmYan2fEiNDmAQAgFCRm0OwE+i3b0qXu4wsusDwcAABsZSZmNm2Samrcr5sVMzt2eL/ekGhLzIRSMbN7t1RdbRxPnhx6TAAABIvEDJqdQCtmVqwwxpYtQ2swCABAJPTsabyHlZd7L1vq1ElKSjKqQnfv9m8us0Il0okZ8/mhVMx89JExxsdLPXqEHhMAAMHin5loVsrKpJIS49jfxMzXXxtjqOvYAQCIhPh46bzzjGPP5UyeCQl/+8w0pYoZsyI2KyvkcAAACAmJGTQrZrVMcrLUqpV/92zfbox8mwYAiFXmcibzywaTuZzJ3z4znokZp9OS0IJiRcWMuX047+8AgEgjMYNmxbO/jMPh3z0HDhijuasFAACxxkzMbNzo/brZANjfihkzEVJZKZ04YUloQbGiYqaw0BgHDw49HgAAQkFiBs1KoP1lamul0lLjeNgwe2ICAMBu5pcLvomZQCtmWraU0tKM40guZwp1V6bKSvf7+/jx1sQEAECwSMygWQk0MbNli/t49Gjr4wEAIBz69TMqRQ8ccL8XSoFXzEiR7zNTUeGu1gk2MbNkifv4sstCjwkAgFCQmEGzEmhiZvFiY2zRQsrMtCUkAABsl5rqTsJ4Vs2YFTO7dhlVJP4wEzNm1Uq4mc8N5b3500+NsWVLKTHRkrAAAAgaiRk0K549Zvyxdq0xtmljTzwAAISL2WfGc2emM84wlibV1ko7d/o3T6QrZjwb//rbL86X+f7esaM1MQEAEAoSM2hWAq2Yyc01xk6d7IkHAIBwqavPjMMR+HKmSCdmrGj8a/5Z+/ULPR4AAEJFYgbNSqCJGXPHhj597IkHAIBwqatiRnIvZ4qVxIwVW2UfOWKMw4eHHg8AAKEiMYNmJdDEzLFjxnjxxbaEAwBA2JgVM3l5Unm5+3WzYsbfnZkinZgJdUemggKppsY4njTJmpgAAAgFiRk0K4H0mDlyxP3BbcwY+2ICACAccnKk7Gzjve2bb9yvB7pldrQkZoJdyvThh8YYHy9162ZNTAAAhILEDJqNqiqpuNg49qdixtyRyeGQevWyLy4AAMLB4ai7z0ygPWbMSpVYXcq0fLkxZmVZEw8AAKEiMYNmw/wgFx/v3y5Lq1YZY1qafTEBABBOdfWZMStm9uyRysoanyPWK2Y2bzbGnj2tiQcAgFCRmEGz4bmMKc6Pv/mbNhnjGWfYFxMAAOFUV2KmTRt39cj27Y3PYSZEPJf8hlOoFTN79hjjoEHWxAMAQKhIzKDZMBv/+vsN286dxmiWeAMAEOvMpUybNklOp/v1QBoAZ2cbo9MpFRVZG58/Qmn+W10tlZQYx2PHWhcTAAChaGH3A3bu3KmNGzcqPz9fhYWFOn78uEpLSyVJqampysjIUOfOndW1a1edd9556t69u90hoZkKdEcm84Of+e0iAACxrndvKTFROnFCys93N789+2zpiy/8S8y0aGFU2BQVGdUrwS4pClYoS5nM/jKSdNll1sQDAECoLE/MFBYWat68efrkk0+0YsUKHTP3G/ZTZmamhg8frvHjx+uKK65Qly5drA4RzVQgiZnqavdWoiNG2BcTAADhlJAg9eljLGXauNE7MSP53wC4XTt3YiacTp2STp40joOpmFmwwBhTUqTkZOviAgAgFJYkZkpLS/WPf/xDb7/9tlavXi3n97WxTs8aWUkOh6PO+z2vO3r0qD788EN9+P1ehpdccoluueUW/fjHP1YaXVgRgkC2yv7yS/fx8OH2xAMAQCScf747MXP11cZrgSxlkoz30tzc8CdmzGqZhAQpIyPw+9euNcYzz7QuJgAAQhVSj5ndu3fr/vvvV8eOHXXPPfdo1apVqq2tdSVafBMxTqezzh9P5j3m7z7//HPde++96tixo+6//34VFBSEEjKasUAqZpYtM8bERKllS/tiAgAg3Mwlup5bZgdTMSO531vDxbPxbz3f9zXITDz17WtdTAAAhCqoipm9e/fq97//vf72t7+pqqpKTqezziRMp06d1K9fP/Xu3Vs5OTnq0KGD0tLS1LJlSzmdTpWXl6ukpET79u3Tvn37tG3bNm3evFl7zHb5Hk6cOKE///nPeuWVV3T77bfrN7/5jc7k6w4EIJDEzFdfGaO5SwUAAE2F2QDYMzFjbh19+LB09KjUunXDc5iJGbOCJVxCafzref+QIdbEAwCAFQJKzJw6dUpPP/20nnvuOZ06dcorIeN0OtWxY0dNmjRJo0eP1ogRI9Q2yHfNQ4cOafny5Vq6dKk++OADr0RNZWWl/vKXv+itt97SAw88oOnTpyuZRcLwQyBLmfLyjJEWRwCApsZMzOzcaTQBTk+X0tKknBxp3z6jaubCCxuew3wvDfdSpkDey30dOCBVVRnHEydaFxMAAKEKaCnT2Wefrd/97ncqLy93LUHKzMzU//zP/2jt2rXavXu3XnrpJV177bVBJ2UkqV27dvrhD3+ol156Sbt379batWv1P//zP2r9/dc3ZrXN7373O/Xu3Tvo56B5CaRiZu9eY6TUGQDQ1LRpI3XsaBxv2uR+3VzO5E+fmUglZkKpmJk3zxjj4qRzzrEuJgAAQhVQYsazcuXcc8/Vm2++qX379unFF1/UoEGDLA/ONGjQIL344ovat2+f3nzzTfXr10+SkaApLCy07bloOmpr3R/m/EnMHD9ujBdfbF9MAABESl3LmcwGwP70mTETI7G0lMncKjsz07JwAACwRMDNf88//3x9+OGH2rRpk6ZMmaKkpCQ74qpTYmKipkyZoo0bN+rDDz9Uf7N7HdCIoiKppsY4buzD3L59RiJHksaMsTcuAAAioa7ETCxUzISylGnDBmM0twgHACBaBNRj5p133tENN9xgVywBufLKK3XllVfqX//6V6RDQQwwP8i1aWNssdmQxYuN0eHgwxsAoGmqa2emQCpmYnEpk7mx58CB1sUDAIAVAqqYiZakjKdojAnRJ5D+MqtXG2N6un3xAAAQSWbFzObN7opSz4qZ71sJ1stMzJw4IZ06ZU+MdfHcLjsQtbVGrJI0erS1MQEAEKqAlzIBschMzPhT+rx5szF26GBfPAAARFKPHlLLllJ5ubR9u/Fa9+5GY9yTJ93vm/XJzJRafF93Hc4+M+azAl3KtH69O9k0YYK1MQEAECoSM2gWzG/Y/KmYyc83RvObQwAAmpr4eOn7vRRcy5mSkqQuXYzjxpYzORyRWc4UbMXM/PnGmJRE818AQPQhMYNmIZClTEeOGCNr0AEATdl55xljrDQALi+XSku9n+2vL74I7j4AAMKBxAyaBX+XMlVWutfKDx9ub0wAAERSQ1tm+5OYMatWwpWYMZcxJSQE3gcuN9cYe/WyNiYAAKwQ0K5M/oqPj3cdp6Wl6Z///KcmBLmg97PPPtO4ceMkSQ6HQ9XV1ZbECOs4nU6tX79eGzZs0KHvP521b99e559/vgYOHCiHwxHhCP2vmDG/UZOkYcPsiwcAgEgzEzObNrlfMytmAtmZKVw9ZjyXMQX60eLAAWO8+GJrYwIAwAq2JGacHq38T548qcmTJ+ull17S3XffHfJ8zc3evXu1du1arVmzRmvXrtVXX32lkydPun7fpUsX5ZtNUcKsqqpKf/7zn/WnP/1Je/furfOajh07aurUqfrFL36hhMb2qbaRvz1mli83xqQkKTHR3pgAAIgks8dMYaFUXCy1aRPdS5mCbfxbVuauhh0/3tqYAACwgi2JGUleVRLV1dW65557lJ+fryeffNKuRzYZq1at0h//+EetWbNG+/bti3Q4dSosLNTkyZP19ddfN3jdnj17NG3aNL3zzjt6//33deaZZ4YpQm/+LmVat84Ys7PtjQcAgEjLyJC6djWa3m/aJI0a5V7KtH27scV0XAOL3iOVmAm08e+CBe7jIUOsiwcAAKvY2mPG6XTK6XTK4XDI6XTqmWee0c0336yqqio7HxvzvvzyS7333ntRm5Q5dOiQRo8efVpSJiUlRX379tU555yj5ORkr9+tW7dOo0eP1hGzs24YOZ3+L2XKyzPGrl1tDQkAgKjg22emSxejh0tFhVFJ05BwJ2bM5wRaMbN4sTGmpTWcaAIAIFJsfXtyOBy68cYbFRcX50rOvPPOOxo3bpyOHz9u56ObrLS0tEiHoNtuu007duxwnScnJ+tPf/qTjhw5oi1btmjr1q06cuSInn/+ea8EzXfffafbb7897PGePGl8wJQa/zC3f78xnnuuvTEBABANfBMz8fFSz57GcWPLmWKlYsashu3Uydp4AACwim2JGbMvzB133KH33ntPLVu2dCVnli9frqFDh6qwsa9imrlWrVpp1KhReuCBBzRnzhzl5+frww8/jGhMn376qT7++GPXeUJCghYsWKD77rtPLVu2dL2empqqX/7yl/rkk0+8est8+OGHWrJkSVhjNqtlUlONn4acOGGMF11kb0wAAEQDc8tszwbA/u7MFCuJGfO7JL50AQBEK9sSM549Zq666iotXbpU7dq1c72+detWXXzxxY32KGmOJk6cqG+++UbHjh3TkiVL9Oyzz+qHP/yhunTpEunQ9Nvf/tbrfPr06RoxYkS9148cOVIPPfSQ12uPPPKILbHVx99lTHv2GOvpJWnMGHtjAgAgGpgVM1u2SObGl/42APZMzIRjn4ZglzIVFRnj8OHWxgMAgFXCttL2ggsu0Oeff66zzz7b1Xdm//79GjlypFcFBqQePXqoT58+iouyhdCbN2/W2rVrXeepqal64IEHGr3vwQcfVKpHqcrq1auVm5trS4x18XdHps8+M8a4OKlzZ3tjAgAgGnTvbvReqahwJ2L83TLbrFypqJBKSuyL0RRMxcyePVJNjXF81VXWxwQAgBXC+i//rl276vPPP9fw4cNdyZmSkhJNnjxZr732WjhDQRDef/99r/Prr79erVq1avS+Vq1a6brrrvN6be7cuVaG1iB/d2T64gtj9OOPBABAkxAX594221zO5O9SptRUyVzFHI7lTMFslz1vnjHGxUndulkfEwAAVgh7SUZmZqYWLlyo66+/3pWcMbfT/s1vfhPucBCAeeanm++NGzfO73vHjh3rdf7RRx9ZEpM//F3KtHmzMebk2BsPAADRxOwzYzYANitmdu2SKisbvjecfWbMZwRSMbN8uTG2aWN9PAAAWCUia2USExP1z3/+U9OmTfPaTvvpp5/WLbfcwnbaUcjpdGqTZ2dASUOGDPH7/qFDh3qdb9y40dUg2m7+LmXKzzdG8wMpAADNgdlnxnyb79DBqIaprZV27mz43nAlZsrKpNJS4ziQxIz5pQvVMgCAaBbRJibPPvusXnrpJa/ttGfPnq3x48eznXaUKSgoUFlZmes8NTVVnQNoxNKlSxevXZtKS0vDtiuXv0uZzOaAAwbYGw8AANHEt2LG4fC/z0y4EjPmMqbERCk93f/7du82xoEDrY8JAACrRLy77L333qt3331XKSkpruTMsmXLNGzYMO02300RcXl5eV7nnTp1CngO33t857SLPxUzlZXSqVPGMbs2AACaE7PHzN697i8pom3LbM/Gvx4bfzaotlY6ccI4Hj3anrgAALBCxBMzkrE9tO922t98840uvvhirV+/PsLRQZIO+Xzi6tixY8BznHnmmQ3OaRd/esyYjX8ladgwe+MBACCapKe7l/qYy5kC3TLbfK+1SzCNf9evd2/jPX689TEBAGCVqEjMSNKgQYO0evVqr+20Dxw4oOnTp7uSNYicEp99MD23v/aX7z2+c9rFn6VMZnPApCSjTBoAgObEt89MoIkZM3Fil2Aa/y5YYIyJiVJmpuUhAQBgmahJzEhSt27d9Pnnn2vYsGFeTYEReb5JlOTk5IDnSElJaXBOXxUVFTpx4oTXT6BOnXKXMTdUMWMWZmVlBfwIAABiXn07MzXWY8ZMlNidmPFcyuSvzz83xkCqbAAAiATbEjPBJlQyMzO1aNEir+20EXmnzAYs30sMoqwkKSnJ67y8vLzB65966illZGS4foLpa2N+w5aQ0PC3ZWa7m65dA34EAAAxz7dixuwxs3ev1ND3KOHuMRNIkiU31xjZbREAEO1sScyMGDFCI0eO1IgRI9S6deuA7/fdTpuqmcjzrZCprKwMeI6KiooG5/T18MMP6/jx466fYHZx8lzG1FCOb98+Y+zbN+BHAAAQ88yKmS1bpOpqqU0bdxXp9u313xeuxEwwS5n27zfGQYOsjwcAACu1sGPSpUuXWjLPs88+q+uvv16lpaWWzIfgpaWleZ37VtD4w7dCxndOX0lJSadV2QTKnx2ZJPdyp4suCulxAADEpO7dpdRUqbTUWL50zjlGpcnnnxt9Zvr3r/s+MzFz5IixC1KcTbXYgS5lOnVKMj92jBtnT0wAAFjFlsSMlQbxNUdU8E2iBJMs872nscSMFfzZkWnfPuPDpCRddpntIQEAEHXi4oxts7/4wugz45mYaajPTHa2MdbUSEeP2terLdClTEuWuI9HjrQ+HgAArBRVzX8Rvdr5fBLas2dPwHPs3bu3wTnt4M+OTIsWGaPDIXXpYntIAABEJXM5k2+fmYZ2ZvLc8cjO5UyBLmUy39tTU6UWUf81JACguSMxA7/06tXL6zyYfi++9/Tu3TukmPzhz1KmL74wxvR028MBACBq1bdlttkgvz7h6DMTaMXMunXGmJNjTzwAAFiJxAz80qVLF6/trktLS1VQUOD3/QUFBSorK3Odp6amBrXLUqD8Wcq0ZYsxduhgezgAAEQt34oZf7fMtjsxU1Zm9L6R/K+YMRsWn3OOPTEBAGCloIo7ly9fbnUcfhsxYkTEnt2cORwOnXfeeVqzZo3rtdWrV6uLn2t/Vq1a5XV+3nnnhWUrdH+WMuXnG2PPnraHAwBA1OrXzxgLC41+Meb7YnGxVFRUf/8Y8z3WrGqxmjlvYqLUqpV/95hJoksusScmAACsFFRiZtSoUWH5R7Uvh8Oh6urqsD8XhquuusorMbNw4ULdeOONft27cOFCr/OJEydaGlt9/FnKZH7gGzDA/ngAAIhWGRlGr7WCAqNqZuRI6cwzpb17jaqZxhIzdlXMeC5j8ufjZ3GxVFVlHF9+uT0xAQBgpZCWMjmdzrD/IHImTZrkdT5nzhyVlJQ0et/Jkyc1Z84cr9cmT55saWz1aWwpU3W1saWmJA0fHpaQAACIWvUtZ2qoAbC5vMiuxEygjX8XLDBGh8P95wEAIJqFlJhxOBxh+0HknXfeeRo8eLDrvKSkRM8++2yj9z377LNeW2VffPHF6tOnjy0xeqqpkY4cMY7rS8x4FACRmAEANHv1NQBuKDETrooZfxMz5lbZ6enGNuAAAES7oJYyde7cOaBkyalTp3Tw+9IFh8Ohzp07B/NYWMz3/4ZLlizRqFGjGrzn8ccf14QJE1znTz/9tC677LJ6e/8sW7ZMzzzzjNdrTzzxRHABB+jIEcnpNL4xq6/82myXlJgoJSeHJSwAAKJWMA2Aw9Vjxt8dmTZsMEY+bgIAYkVQiZl8s1uqnxYtWqRx48a5znft2hXMY5uVVatWqby8/LTXN27c6HV+6tQpLVq0qM45cnJyLK9MufzyyzVu3Dh9+umnkqSqqiqNHz9eTz/9tO666y61bNlSkrFr0+uvv66HH35YVeZCb0lXXHGFxowZY2lM9TGXMWVnSy3q+ZtubqdZX+IGAIDmxEzMbNliVJ5GQ8VMoEuZzI+ZZjNjAACiXVCJmUCxFClwP/7xj/3ajvrgwYMaO3Zsnb+79dZb9eabb1ocmfT3v/9dl1xyiSvBdurUKU2dOlUPP/ywunfvLqfTqZ07d+qU2bzlez169LAlnvr40/jX/KDJt2oAABg7MSUnG1tU79zpnZgxq1B9RdtSpqNHjZGNPAEAsYKVtwhY+/bttWTJEp1vLkT/Xnl5ub755htt3br1tKRM//79tWTJErX191OVBfzZKnvvXmMMQ8sbAACiXny8dO65xvHGjVK3bsZrZWXSvn1132O+tXvuhmQlM+Hjz1KmggKj0keSPFZeAwAQ1UjMIChdunTR2rVr9cwzzygnJ6fe63JycvTss89qzZo16tSpUxgjbHxHJkk6ftwYL7rI/ngAAIgF5vcuGzdKCQlGckaqv89MmzbuJrtFRdbHE0jFzPz5xhgfTzUsACB2hGUpEwIXaB+fYIS6/XhiYqIefPBBTZs2TevWrdPGjRt16Puvtdq1a6f+/ftr4MCBiovQlgiNfcN26JD7W7VLLw1PTAAARDuzz8zmzcZ49tnS9u3Gcqa69giIjzf6uR06ZPyccYa18QSSmFm50hjbtLE2BgAA7ERiBiGLi4vT4MGDvbbSjgaNVcyY22k6HNJZZ4UnJgAAol1dOzPNn994A2AzMWO1QJYymcmkrl2tjwMAALuwlAlNVmOJmdWrjTEtLTzxAAAQC8zdjHbtkk6e9G9nJrOaxerETGmp0d/G8xkNKSw0xv79rY0DAAA7kZhBk9XYN2zmt2pWl1wDABDLsrIks33cli3uqtL6esxI9u3MZC5jSkyUWrVq+NraWnfvuJEjrY0DAAA7kZhBk9VYxczOncbYo0d44gEAIFZ4LmcyK2Z27JCqq+u+3kzMmIkUq5jztWtX91bdnrZuNbb0ltiRCQAQW0jMoElyOhuvmDE/7FHuDACAN8/ETMeOUnKysRV2QUHd19tdMRPIjkwJCTT/BQDEFhIzaJKOH5cqK43juipmamvda9aHDg1fXAAAxALPxExcnHs5U319ZuxKzJjz+ZOY+fxzY8zOtjYGAADsRmIGTZK5jCk93fiWz9eGDe7jurb+BACgOTMbAG/ebFShNtYA2K7mv55LmRqzdasx9uxpbQwAANiNxAyaJDMxU98HuaVLjbFFC3ZlAgDAV+/exnvk8ePGTkeNJWbs7jHjT8XM3r3GOGCAtTEAAGA3EjNoksxv7Opr/PvVV8bYunV44gEAIJYkJkrnnGMcezYArm9npkgvZaqtNbbWlqQxY6yNAQAAu7UI5qbly5cHdP3GjRu9zlesWCGn2TY/QCNGjAjqPjQvjVXMbNtmjJ06hSceAABizXnnGUuZNm1yL/ttrGLm5Enp1Km6lxEHw9+lTGZ/GUm67DJrng0AQLgElZgZNWqUHI3tWVgPp9OpUUE29XA4HKqub59GwENjW2Xv2WOMvXuHJx4AAGLNeedJ//iHkZi5+27jtd27pfJyKSXF+9r0dGM3pKoqI5li1Rcf/i5lWrjQGJOTpZYtrXk2AADhEtJSJqfT6fePw+Fw/QRyn+8P4I/GljIdPWqMgweHJx4AAGKN585MWVlSZqbRCHjHjtOvdTjsWc5kztVYxcyaNf5dBwBANAopMeOZbGnsJ9j76rofaExDS5lOnJDMwit2ZAIAoG7mzkzffitVVPjfANjKxIy/FTNmTGaMAADEkqCWMnXu3JlkCaJaQxUzS5a4j81vAwEAgLecHKlNG6m4WMrNNZIea9eGLzFTWiqVlRnHjSVm9u83RiphAQCxKKjETH5+vsVhANZqqMfMqlXG2LKlFMe+ZAAA1MnhMKpmli3z3pmpvsSMmTyxastsc56kJKlVq/qvq6w0+t5INP4FAMQm/lmKJqmhpUybNtX/OwAA4GZWlm7eHP6lTJ7LmBoq1F62zH3M5p0AgFhEYgZNTlmZVFJiHNdVMWM2LezWLXwxAQAQizwbAEcyMdOQRYuMsWVLqUVQteAAAEQWiRk0OeYHwsREY/tOXwcOGKPZ1BAAANTNfK/cvFk66yzj+PBh6dix06+1OjHj745MX31ljB06WPNcAADCLajvFR5//HHX8ZQpU9S1a1er4gFC5tlfpq7S59JSYxwyJHwxAQAQi/r2Nd5LDxwwKlJzcqR9+4yqmQsv9L7Wrh4zjVXMfPedMfbqZc1zAQAIt6ASMzNmzHDtyjRs2DASM4gqDX3D9t13ktNpHI8eHb6YAACIRWlpUo8e0vbt7j4z+/YZ76e+iRm7ljI1VjFjPu+ii6x5LgAA4Rb0Uian+a9bIMo0tCPT4sXGGB9P818AAPxR13KmuvrMeCZmrPiYaCZcGqqYKSmRKiqM47FjQ38mAACREHRixtFQe3wgghpKzKxZY4wZGeGLBwCAWOZvA2AzgXLqlLsJfyj8Wcr02WfuYypmAACxiua/aHIaWsq0dasx5uSELx4AAGKZv4mZ1FRjZyTJmj4z/ixlWrrUGNPSpDg+1QIAYhRvYWhyGqqY2b3bGM0PlgAAoGHmUqZvvjH6zUhGYqau5UpW9pnxZynTunXGeOaZoT8PAIBIITGDJsf8IFdXYqa42BgHDgxfPAAAxLLu3Y1KmFOnpNpaozKlpMTYqcmXlYkZf5Yy7dhhjOecE/rzAACIFBIzaHLMihnf0ufKSneDwJEjwxsTAACxKj7e2DZbkvLypG7djOOG+syEmpgpLTW255YaXspkJm/oLwMAiGUkZtDk1Fcxs3Kl+/jii8MXDwAAsc7fPjNmEiXUHjPm/UlJRv+Yuhw7JlVVGceXXx7a8wAAiCQSM2hSqquloiLjuL7ETFKS1KJFeOMCACCWeW6Z7U9iJtSKGc9lTPVtBLpggTE6HO7EEQAAsYjEDJqUw4eNZoRxcVJWlvfvvv7aGLOzwx8XAACxzEzMbNoknXWWcRyOxExDy5iWLTPGVq3YkQkAENtCrhv41a9+pdatW1sRS6McDoc+++yzsDwLscn8IJidbayJ92R+gOzSJbwxAQAQ68zEzM6dUqdOxnFDPWZCXcrkz45M5hcuHTuG9iwAACItpMSM0+nUpk2brIql0Wc56qtlBb7X0FbZ+/cbY58+4YsHAICmoG1b6YwzjJ2YamuN13bsMJYQey4PtmMpU3127TJG3tcBALGOwk80KeYHwbpKn48fN0Z2bgAAIHBmH5eDB41+bVVVUkGB9zXhXMpk9pS75JLQngUAQKSFnJhxOp1h+QH8UV/FjOc3fKNHhzcmAACaAnM50zff1N9nxnNXplA+vjW2lOnIEaNaR5LGjw/+OQAARIOQljI5HA5NmzZNfaghRZQwEzO+37AtWWKMDofUo0d4YwIAoCnw3TJ7yxYjMTNhgvsas8F+dbWxnXWwbQgbW8r0ySfG6HBIffsG9wwAAKJFyM1/x48fr0svvdSKWICQmd+w+VbMfP65MaalhTceAACaCs8ts++6yzj+7jvva5KSpIwMY/nwoUOhJ2bqW8q0fLkxZmQENz8AANGEHjNoUupbyrR5szGecUZ44wEAoKk45xxjx8PiYnfCJC/v9Ous6DPT2FKmDRuM0dwhCgCAWEZiBk1Kfc1/zZ0bWMYEAEBwkpONJUySu3+MXVtmN7aUKT/fGFnGBABoCkjMoEmpr2LG/IDXv39YwwEAoEkxlzOdOGGMu3dL5eXe14RaMVNaKpWVec/lq7jYGIcMCe4ZAABEExIzaDKczrp7zNTWuj/gsaUmAADBMxMzO3dKmZnG8fbt3teYVS7BJmbML1OSkuruDXfggFRTYxxffnlwzwAAIJqQmEGTceyYVFVlHHuWPpvr0CVp1KgwBgQAQBPj2QDYXNZU35bZoSZm2rY1dl3y9fHHxhgX5962GwCAWEZiBk2GuYwpI8NYB28yd25o0UJKTw9/XAAANBVmYiY3V+rZ0ziuLzETbI+ZxnZkWrHCGNmRCQDQVJCYQZNR31bZX35pjMFu2QkAAAxdu0qpqVJlpdSmjfGab2Im1KVMje3ItGmTMbIjEwCgqSAxgybDrJjx/YbN3MqzY8fwxgMAQFMTF+eumon7/lOknUuZ6lJQYIznnhvc/AAARJugEzNOp1NOc69EIArUtyNTYaEx9uoV3ngAAGiKzMRMaakxWr2UqbGKmaNHjXHo0ODmBwAg2rQI5qZdu3a5js844wzLgkHDduzYobVr12rPnj2qrKxU69at1bt3bw0ZMkTJnk1Vminzg5xvxYz5AW7QoPDGAwBAU2QmZvbtM8YjR4ztq82lTWZC5cgRY/ek+PjA5m+ox8yePe4dmSZMCGxeAACiVVCJmS5dulgdBxowd+5c/e53v9P69evr/H1aWppuu+02PfbYY8rOzrY1llGjRmnZsmVB3z9r1izddttt1gXkoa6KmZIS905NI0bY8lgAAJoVzwbAOTlGgua776SLLjJeNz+KOJ1SUVH9TXzr09BSpk8+Mca4OKlbt8BjBwAgGtFjJopVVFTo5ptv1jXXXFNvUkaSSkpK9NJLL6lPnz5abm5B1AzV1fzX3LlBki64ILzxAADQFJmJmfx8qUcP49hzOVOLFlJWlnEczHKmhpYyrVxpjJmZgc8LAEC0Cigxc8MNN2jHjh12xRKw7du364Ybboh0GLaora3VDTfcoH/84x9er8fHx6tbt27q37+/Mnz2iTx8+LAmTJigzz//PJyhRo26mv+aH+BSUtxNCgEAQPCysqQOHYxjO3Zmamgp0+bNxti5c+DzAgAQrQJayjRnzhzNnTtXt956qx544AGdddZZdsXVoO+++07PPvus/v73v6u6ujoiMdjtueee0/vvv+/12s9+9jP99re/VU5OjiQjefP+++9r6tSp2r17tySprKxM119/vbZs2XJa4sYOCxcuDOj6vn372hRJ3UuZNm40RptXeAEA0Kz06yft329Ux0juHRBN7dpJ27aFlpipq2LG3JHJrNoBAKApCLjHTHV1td544w397W9/0+TJk/Wzn/1MY8eOtSO20yxcuFCvvPKKPvzwQ9euUA6HIyzPDqeioiL9/ve/93rtqaee0vTp071ei4uL0zXXXKMLL7xQw4YNU35+viRpz549ev755zVz5kzbY73ssstsf4a/6lrKtH27MbIOHQAA6/TrJ336qXTqlHH+3Xfevw92Z6bSUqmszDiuKzHDjkwAgKYooMUdd9xxhxwOh5xOp2prazV37lxdfvnl6t69u37729/q66+/tjzA9evX65FHHlH37t11+eWX64MPPlBtba2cTqfi4uJ05513Wv7MSHv22Wd18uRJ1/mIESP00EMP1Xv9mWeeqb/+9a9er73wwgsqKiqyLcZoU1ZmNPqVvEufDxwwRhsLdQAAaHbMihXzS5FvvzWa/ZqCXcpkJnISE6VWrbx/V1Ag1dYax5dfHti8AABEs4ASM6+//rq++OILDRkyRJJcVSv5+fl68sknNWjQIHXu3Fm33nqr/va3v2nTpk2qMrfE8UNlZaU2bNigN954Q7feeqs6d+6swYMH66mnnlJ+fr7reZI0dOhQrVmzRn/5y18C+SNEvdraWs2aNcvrtRkzZjRaGTRmzBgNHz7cdX7y5En9+9//tiXGaGQuY0pO9v4gZ+a3Lr44/DEBANBUmYmZ7duN7bDLytzbZ0vuL0mCTcy0ayf5fvTx3JGJDUIBAE1JwEuZBg0apJUrV2r+/Pl69NFHvXYLcjqd2rNnj95++229/fbbkoxmtV26dFHHjh3VoUMHpaWlKSUlRU6nU6dOndLJkye1f/9+7dmzR7t371ZNTY3XfJK8khIXXHCBHn/8cU2YMCHoP3Q0W716tQ571P12795do0aN8uveO+64Qys8tiGaO3eu7rnnHqtDjErmBz/PD3K7d7u/Wbv00sjEBQBAU9Snj5EgOXpU6trV2KHp22+lM880fh/sUqaGdmRavdoYW7cOJmIAAKJXwIkZ0xVXXKErrrhCixYt0p///Gd9/PHHXj1fzKRKdXW1duzYoZ07dzY4n9Oz/lVGMsZcNiVJV155paZOnaoxY8YEG3JMmDdvntf52LFj/e6j49vrZ+nSpSotLVVqaqpl8UWruhr/LllijHFxUseO4Y8JAICmKjlZOvtso8Fv27buxMzo0cbvQ13K1NCOTFTLAACampA3EL7sssv04YcfaufOnfr973+vvn37eiVZzARLY3yvczqd6tOnj37/+99r165d+vDDD5t8UkaSNmzY4HVuLhvzR05Ojrp27eo6r6ys1NatWy2KLLrV1fh3zRpj9F2jDgAAQmcuZ0pONkbPLbNDXcrU0I5M554b2JwAAES7oCtmfHXu3FkPP/ywHn74Ye3evVsLFizQihUrtG7dOuXl5anWXFNSj7i4OPXq1UsXXHCBhg8frvHjx6tz585WhRczcnNzvc779OkT0P19+vRx7c5kzjd48GArQqvX8ePHVVBQoGPHjiktLU1ZWVnq2LGj4uPjbX2uJ7NixvMbti1bjLFDh7CFAQBAs9GvnzRnjlRZaZx7JmbMxIqVS5mOHTNGdmQCADQ1liVmPHXu3Fl33XWX7rrrLklG5cbu3btVWFio48ePq+z7fRBbtmypzMxMderUSZ06dVJiYqId4cSM8vJy7d692+u1Tp06BTSH7/V5eXkhx9WQAQMGaNOmTacl3tLS0jR06FBde+21mjJlipKSkmyNo66lTGZ+qkcPWx8NAECzZFbMmJtA1lUxc/Sokbjx9yNefUuZ2JEJANCU2ZKY8ZWYmKiePXuqZ8+e4XhczDpy5IjXMrCEhAS1q2uRdQPONLvufe9QoDXEAfJdemUqKSnRggULtGDBAj366KN68cUXdd1119kWR11LmY4cMcYBA2x7LAAAzZaZmDG/U9q5U6qqkhISpDZtjB5vtbXG+3FOjn9z1reUyXNHpmZYUA0AaOJC7jED65SUlHidt2zZ0u/GvybfRr++c0bCgQMHdP311+uBBx6w7Rm+S5lqa6XycuM4gDY9AADAT926SampRkVMSopUXe2uVo2Lk7KzjeNAviOqLzGzapUxsiMTAKApIjETRXyTKMlmN70ApKSkNDinFZKTkzVx4kS98sorWr16tQ4dOqTKykqdPHlSO3bs0Ntvv60rr7zytKTSH/7wBz399NN+P6eiokInTpzw+qmP71Imj13cNXKk348EAAB+iouT+vY1js0vRupazhRInxkzieNbMGz2jWNHJgBAU0RiJoqcOnXK6zyYnju+vVzKzbIRi9x///3as2ePPvjgA91zzz265JJL1LZtWyUkJCgtLU3du3fXj3/8Y3300Udavnz5aUurfv3rX2vjxo1+Peupp55SRkaG66ehfju+H+SWLTPGhASpZcuA/5gAAMAP5nIm87021J2Z6quYYUcmAEBTRmImivhWyFSa2xwEoKKiosE5QzVp0iRlZWX5de2wYcO0dOlSZZu1zDK2QX/kkUf8uv/hhx/W8ePHXT+FhYV1XldV5W48aFbMrFtnjJQ8AwBgHzMxU1NjjKHszFRaKn2/P8RpiRl2ZAIANGUkZqJIWlqa17lvBY0/fCtkfOcMt549e+q5557zem3+/PkqLi5u9N6kpCSlp6d7/dTFbPIbFyeZOaNt24wxwE2tAABAAMzEjJk48dwMMtCKGTOBk5QktWrlfp0dmQAATR2JmSjim0QpKyvz2qXJH6WlpQ3OGQlTpkxRW4+vvmpra7Vo0SLL5jf7y7RtayRnJGnPHmPs1cuyxwAAAB9mYsZMvnz3nft3wSZm2raVPNvUmTsyxcezIxMAoGkiMRNFsrOzvRrmVlVVBbzd9d69e73OA91u2w5xcXEaNWqU12t5nl+phci38a8kHT1qjIMHW/YYAADgo21b7/ffPXuMJUnm7yT/lzKZ1/l+dDF3ZMrMDDpMAACiGomZKJKSkqLOPl8F7d69O6A5fK/v3bt3yHFZwbdx7+FAtmhohG/j35ISY8tOSRoxwrLHAACAOpgNec0i3e3bjTGUihlP7MgEAGjqSMxEGd9EytatWwO6Pzc3t8H5IiUhIcHrvKqqyrK5fStmli51/65/f8seAwAA6mAuZzL7wpgNgANNzJjX1bcjk/kcAACaGhIzUaa/TyZh9erVft+7f/9+5efnu84TEhLUp08fiyILzYEDB7zO2/p+6gqB+UHOTMyY/5OlpLh7zgAAAHv4JkzMxIxVS5nMxsJDhgQVHgAAUY9/tkaZq666yut80aJFfjcA/vTTT73OR48eHRXNfyVp5cqVXue+S5tCYVbMmB/kNm40RgtzPwAAoB5mYubECWP0rZg5eVLy2TSyTnUtZWJHJgBAc0BiJsoMGTJE2dnZrvOdO3dqqefanAa88cYbXueTJ0+2MrSgLVu2TDt27PB6bcyYMZbN77uUyVzb3q2bZY8AAAD16NvX2EXJbPprJmYyMiRzJbM/VTN1LWViRyYAQHNAYibKxMXF6bbbbvN6bebMmY1WzXz22WdasWKF67xVq1a6/vrr7QgxIKWlpfrFL37h9Vq/fv3UvXt3y57h2/zXXDXVt69ljwAAAPVo2VLq0cN9npcnOZ1GsiaQ5Ux1LWUylyezIxMAoCkjMROFHnroIa8lSMuWLdMzzzxT7/V79+7VnXfe6fXafffd51V5UxeHw+H101hlzn333ad9+/Y1/gf43pEjRzRp0iRt2rTJ6/WZM2f6PYc/fCtmSkqM8aKLLH0MAACoh2efmaNHpaIi4ziQBsB1LWXavNkYqZYBADRlJGaiUHZ2tn796197vfbwww/r3nvv9UqM1NbWau7cuRoyZIhX09+cnBz96le/sjyuF198Ud27d9c111yjf/zjH17P9FRYWKjnnntO/fr10+LFi71+d/XVV+uaa66xLCan07v57+7d7rXol15q2WMAAEADzMRMaqoxBrMzU11LmXbv9p4fAICmqEWkA0DdHnroIa1evVofffSR67VXX31Vr732mrp06aKMjAzt2rVLx8ytCr6XkpKif//738q0qea3oqJCc+fO1dy5cyVJ6enp6tChgzIyMlRVVaWDBw/WW1UzfPhwzZ4929J4jh6VqquN47ZtpX/+0ziOi5M6drT0UQAAoB5m4iQ+3hi//dbYRcnfpUxlZcaP5L2U6ehRYxw61LpYAQCINiRmolRcXJzmzJmjn/zkJ/qnmW2QVFNTo507d9Z5T1ZWlv7zn/9oaBg/vZw4cUInzG0Y6hEXF6dp06bpiSeeUILZBdAi5jKmzEwpKUlas8Y4b9XK0scAAIAGmIkZM7kSaMWMmbhJSpLM1dyeVbDjx1sXKwAA0SZqljKVlZXpL3/5i2655RZNmDBBN910k/74xz9q//79kQ4tYpKTk/XOO+/oP//5j/r371/vdampqbr33nu1detWjRo1yrZ4XnvtNf3oRz/ye6vrM844Q/fdd5/y8vL0zDPPWJ6UkU5v/LtlizF26GD5owAAQD169DCSKmYVq29iprGKGc9lTA6Hcfzpp8YYFyd16WJtvAAARBNbKmZyc3P1zjvvuM5vv/12de3atd7rV61apR/+8Ic65PN1yr/+9S/NmDFDzz//vO666y47Qo0J1157ra699lpt375da9as0d69e1VZWanMzEydc845Gjp0qJKTkwOet7Gdnnzdddddrv87FBUVKTc3VwUFBTp8+LBKS0sVHx+v1q1bKzs7WwMGDLB056X6+Db+NdveeO4OAQAA7NWihdSnj/T118a5mZgxlzL5WzHj2V9m5UpjZEcmAEBTZ0ti5tVXX9XLL78sSWrfvr0effTReq/dsWOHrrjiCp08eVKSsVOQyel0qrS0VD/72c/kcDhO23mouenZs6d69uwZ6TAkGcumhg0bpmHDhkU0Dt+KmSNHjHHAgMjEAwBAc9Wvnzsx8913RoP+QJcyefaXMatg2ZEJANDU2bKUad68ea5qjJtvvlktWtSf/5k6dapOnjzp2rLZ6XS6fiS5Xps6daoKCwvtCBcxzLNiprZWKi83zocMiVxMAAA0R547J506ZXxZEsxSJpNZBcuOTACAps7yxMyhQ4e0a9cuV+XLFVdcUe+1W7du1bx581zJl6ysLP31r39Vbm6uVqxYocmTJ8vpdMrhcKi8vFzPPPOM1eEixnkmZtavd78+fHhk4gEAoLk691xjNHdm2rfPeylTQyuo61rKZG48ecklloYJAEDUsTwxs3XrVklyJVQGDx5c77X/+Mc/vK597733dPvtt6tXr14aOnSo3nvvPY0ZM8ZVQfOvf/1LtWZ7fkDeS5mWLzeOExLcOzoAAIDwMCtbamqMce9ed8VMeblUWlr/vb5Lmfbscc9z+eXWxwoAQDSxPDGTb9adSurYsaNSU1Prvfbjjz+WZCxXGjZsWJ3bPD/22GOu4+LiYlfiB5C8K2bWrTOOW7eOXDwAADRXOTne78F790qpqVJKinHe0HIm36VMCxYYY1yc1K2b9bECABBNLE/MHD16VJKRbGnTpk2D123cuNG15OmHP/xhndcNHTpUaR7lD5s3b7YwWsQ6z4qZbduMYz938wYAABZyOLz7wezbZ7zmz85MvkuZVq0yxowM6+MEACDaWJ6YKSsrcx2nmF+R1OGLL77wavI7bty4Oq9zOBzq5vFVyeHGusehWfGsmNmzxzju1Sty8QAA0Jx5Jmb27jVGf3Zm8l3KZH4Px45MAIDmwPLETFJSkuu4tIHFxMvNhiCSsrOz1auBf017LocqKSkJMUI0FaWl7vXq7dtL3xdr6YILIhcTAADNmdkAWHInZswqmIa+W/OtmCkoMMa+fa2NDwCAaGR5YiYzM1OS0dDXs9+Mr0WLFkly95dpiGeCJzExMeQY0TSY37wlJxtjVZUxjhgRmXgAAGjufJcySe6+M8eP131PWZn7ixYzMVNcbIzsyAQAaA4sT8x4Vr6cPHlSX3/99WnX7Nq1S+vWrXP1lxk1alSDcx45csR1nMFiY3zPcxnTypXu1wcOjEw8AAA0d+ed594ue/duY0xPN8YTJ+q+x6yWSUqSWrWSDhxw78g0frx9sQIAEC0sT8wMGDBAiYmJrqTL008/fdo1f/jDHyTJ1V9m7Nix9c5XXFys/fv3u+br0qWL1SEjRnk2/l292jhOSTF2cAAAAOHXqpV06aXGcXGxVFHhf2KmbVujWbDnjkxnnWVvvAAARAPL/wmblpamK664wtXY9z//+Y9uv/12ffnll9qwYYOmTZumV199VQ6HQw6HQ/3791fv3r3rne/LL7+U5E7iNNSLBs2LZ8WMWZiVnR25eAAAgHTrre7j/fvdiZn6ljL5bpVtVsGa9wEA0NS1sGPSRx55RB988IErOfPWW2/prbfe8rrG6XTK4XDowQcfbHCu999/33Xcvn17KmbgUlfFTNeuEQsHAABImjzZqHxxOqXPPnNvee1PxYwkbdxojJ062RsnAADRwpZFHwMHDtQTTzzhSr5I8toa23xt4sSJuuGGG+qdp6amRu+++66ruqaxJsFoXsxdmLKyjG/kJO/dIAAAQPilpRnvzZL03nv+L2Uyt8pmRyYAQHNjWzeO6dOn6/XXX1fr1q1dCRnJSNDExcXprrvu0r/+9a8G5/jnP/+pQ4cOue6/8sor7QoXMejYMWPMzJROnjSOL7ooUtEAAACTufJ8xQojUSP5v5TJ3JFpyBD74gMAIJrYspTJdMcdd+iWW27RZ599pm+//Vbl5eXKycnRpZdeqo4dOzZ6f15eniZPnuw6v+qqq+wMFzHGTMw4nVJtrXHcyAZfAAAgDC64QFq1yqiSMXvC+bOU6dAhqbraOB83zv44AQCIBrYmZiQpMTFREyZM0IQJEwK+9/HHH7chIjQVZmJm715jdDgkWhABABB5nv1hioqMsb7EzJEjxti2rfTpp8axw+GuugEAoKljY2HELDMxs3u3MbZqFbFQAACAhzPPdB8fOGCM9SVmPJcmsyMTAKA5IjGDmOVbMdO+fcRCAQAAHjwTM+b79MmT7qXHnsyETUYGOzIBAJonEjOIWWZixly73rNnxEIBAAAecnLcx7t2GaPTKZWUnH6tmZhJT5fy843jc86xNTwAAKKK7T1mfBUXFys3N1fFxcU6fvy4amtrNX78eLWn3AEBcDrdiRlzPP/8SEUDAAA8eVbMfPedlJAgVVUZSZjERO9rPRMzZj+aiy8OT5wAAESDsCRmDh06pJdeekn//e9/tW3bttN+v3DhwjoTM7NmzVJhYaEkKScnR3feeaftsSI2lJZKNTXGcXm5MQ4dGrl4AACAW0qK1Lq1dPSoVFbmPj5xQsrOdl/ndLq30a6pMZI3EjsyAQCaF9sTM88995weffRRVVZWyul0nvZ7h8NR770lJSWaMWOGHA6H4uPjNXHiRCprIMldJdOihXtbzREjIhYOAADw0bOn9OWXxnFysjEeP+6dmCkvd3/Rsm6dMTocUp8+4YsTAIBIs63HTE1NjX7wgx9o+vTpqqioOO33DSVkTHfccYfS09PldDpVU1Oj2bNn2xEqYpCZmDE/6LVowQ4OAABEk3793Mfx8cbouzOT2XPG4ZDWrDGOW7WS4uiCCABoRmx72/v5z3+uuXPnyul0yuFwyOl0asCAAXrooYf08ssv11k946tly5aaOHGi63z+/Pl2hYsYYyZmzPxe69YRCwUAANTh3HNPf803MePZX2bTJuPYsz8NAADNgS2JmZUrV+q1116Tw+GQw+FQdna25s2bp3Xr1umpp57SPffcI8m/qpmrr75akuR0OrVq1SpVVlbaETJijJmYMcuf+RAHAEB08ayYMXvH+CZmTp40xvR0aedO45gdmQAAzY0tiZlHH31UkpFMadWqlZYtW6YJEyYENddFF13kOq6oqFBeXp4lMSK2HT1qjGZ/mV69IhcLAAA4nWdipqzMGM1GvyYzMZOR4d6RyeOjHwAAzYLliZmjR49qxYoVrmqZRx55RL179w56vo4dO6q1xzqVunZ1QvNjVsyYiZmBAyMWCgAAqEO7dlKbNsax2UumvoqZ1FTJLIq+9NLwxAcAQLSwPDGzcuVK1dTUyOl0Ki4uzpItrtu1a+c6PnToUMjzIfaZiZnaWmMcOTJioQAAgDo4HNL55xvHZmvB+hIznvtE8GULAKC5sTwxs2/fPklG/5ju3bsrMzMz5DkzMjJcxyfNd3A0a2ZixnTBBREJAwAANMC3AXB9S5lKS42RHZkAAM2R5W99xcXFruM2Zv1qiDy3205ISLBkTsQ2z8RMcrKxXTYAAIgunn1mpPoTM+aYk2N/TAAARBvLEzN2VLd4Ll/Kzs62ZE7ENs/ETFZWxMIAAAAN8K2Yyc31Pjc/Kpo9aEJoSwgAQMyyPDHTtm1bScaOTAUFBao1m4AEqbCwUPv373ed5/BVCiR5FGapb9/IxQEAAOrnm5jJy5MOHnSfmz1nysuNcfDg8MQFAEA0sTwxc77Z5U1SWVmZVq1aFdJ8c+bMcR3Hx8fr4osvDmk+NA27drmPf/jDyMUBAADq16qV1LWr+7ymRvrLX9znZqVMTY0xXnZZ2EIDACBqWJ6YOfvss9WtWzc5HA5J0vPPPx/0XCdOnNALL7zg2np78ODBatWqlVWhIkbV1Ejf95iWdPr6dQAAED1836cLC93Hvrs0UTEDAGiObOl7P2XKFDmdTjmdTn3wwQd66623Ap6jpqZGU6ZM0d69e+X8fo/Fe++91+pQEYM+/liqqnKfn3125GIBAAAN89058cgR97FZMSNJqansyAQAaJ5sefubNm2a2rVrJ4fDIafTqTvvvFPPPfecasw61UZs27ZNl156qT788ENXtczZZ5+tm266yY5wEWOWLnUft24tWbT5FwAAsMGFF3qfHz7sPvbcJ4I2ggCA5sqWTYZTU1P117/+Vddcc41qa2tVU1Oj6dOn65VXXtGNN96oC77/6sTpdMrhcGjdunUqLi7W9u3btXjxYi1evNhVcSNJKSkpmj17tmt5FJo3z6aBVMsAABDdfJcneVbM5OW5j3lPBwA0V7YkZiTpqquu0ssvv+xafmTu0vTMM894Xed0OjV9+vTTXjOTMAkJCZo1a5YGDBhgV6iIMR67p7OtJgAAUS47W+rSRSooMM6PHnX/znPzzkmTwhsXAADRwtaVvHfffbcWLFig9u3bS5Ir2WImXswfszrGrJAxX2vfvr0+++wzXX/99XaGiRjjWQLdq1fk4gAAAP656KLGrxk3zv44AACIRra3WBszZoxyc3P15JNPqkOHDq7ki28yxuR0OpWZmamZM2cqLy9Pw4YNsztExBjPb9pIzAAAEP0a220pJUXq3Dk8sQAAEG1sW8rkKSMjQ9OnT9eDDz6ojRs3asWKFcrNzVVRUZGOHTumli1bKjs7W926ddPo0aN14YUXqkWLsISGGFRR4T5mPToAANGvscTMOeewIxMAoPkKa/YjLi5OAwYMoF8MLNOjR6QjAAAAjfHdMtvXueeGJw4AAKIR300gZrVsaZQ+AwCA6JaWJrVqVf/v+/QJXywAAEQb1gvFkB07dmjt2rXas2ePKisr1bp1a/Xu3VtDhgxRcnJyxOJyOp1av369NmzYoEPfb5nUvn17nX/++Ro4cKBt25y3bm3LtAAAwAZt20onT9b9OxIzAIDmjMRMDJg7d65+97vfaf369XX+Pi0tTbfddpsee+wxZWdnhy2uqqoq/fnPf9af/vQn7d27t85rOnbsqKlTp+oXv/iFEhISLH1+u3aWTgcAAGzUoYO0c2fdvyMxAwBozljKFMUqKip0880365prrqk3KSNJJSUleumll9SnTx8tX748LLEVFhbqoosu0gMPPFBvUkaS9uzZo2nTpumSSy5p8LpgdO9u6XQAAMBGOTl1vx4XJ3XtGtZQAACIKgFVzNx+++1e5w6HQ2+88Uaj11mlvuc1RbW1tbrhhhv0/vvve70eHx+vzp07KyMjQ7t27dLx48ddvzt8+LAmTJigRYsW6ZJLLrEttkOHDmn06NHasWOH1+spKSnq3r27amtrtWvXLp06dcr1u3Xr1mn06NFavXq1ZVU99JAGACB29OpV9+vp6VJ8fHhjAQAgmgSUmHnzzTdd/UKcTme9iRLP66zS0POaoueee+60pMzPfvYz/fa3v1XO91851dbW6v3339fUqVO1e/duSVJZWZmuv/56bdmyRRkZGbbEdtttt3klZZKTk/X000/rrrvuUsuWLSVJpaWleu211/TrX//alaD57rvvdPvtt+uDDz6wJI7hwy2ZBgAAhEF979tZWeGNAwCAaMNSpihUVFSk3//+916vPfXUU3r11VddSRnJ2H78mmuu0erVq9XVowZ4z549ev75522J7dNPP9XHH3/sOk9ISNCCBQt03333uZIykpSamqpf/vKX+uSTT7x6y3z44YdasmSJJbFcfLEl0wAAgDA4//y6X+/WLbxxAAAQbQJOzDidTjmdTr+vs+qnOXn22Wd10mPbghEjRuihhx6q9/ozzzxTf/3rX71ee+GFF1RUVGR5bL/97W+9zqdPn64RI0bUe/3IkSNPi/2RRx4JOY6EBCkxMeRpAABAmLRrV/d798CB4Y8FAIBoEtBSpl27dll6HU5XW1urWbNmeb02Y8aMRpeGjRkzRsOHD9eKFSskSSdPntS///1v3XPPPZbFtnnzZq1du9Z1npqaqgceeKDR+x588EG98MILKi0tlSStXr1aubm5Ouecc4KOxaZVWgAAwCYOh3TGGdL3q69dRo2KSDgAAESNgBIzXbp0sfQ6nG716tU6fPiw67x79+4a5ecnljvuuMOVmJGMbbatTMz49ry5/vrr1apVq0bva9Wqla677jq9+eabXrGFkpjp3DnoWwEAQIT07Hl6Ymb06MjEAgBAtKDHTJSZN2+e1/nYsWP9bqQ8duxYr/OlS5e6qlTsiG3cuHF+3+sb20cffRRSLOeeG9LtAAAgAi64wPs8Pl5KTo5MLAAARAsSM1Fmw4YNXudDhgzx+96cnByvJsCVlZXaunWrJXE5nU5t2rQp6NiGDh3qdb5x48aQegdddFHQtwIAgAgZOdL7vHXryMQBAEA0ITETZXJzc73O+/TpE9D9vtf7zhesgoIClZWVuc5TU1PVOYD1RF26dPHatam0tFSFhYVBx+P7wQ4AAES/YcO8zzt2jEwcAABEExIzUaS8vFy7fRZed+rUKaA5fK/Py8sLOa665gk0rrruCSW2ENrTAACACPFt3t+/f0TCAAAgqgTU/Ndf1dXVWr16teu8Z8+eysnJCXievXv3aseOHa7z4cOH+91vJRYdOXLEa3lPQkKC2rVrF9AcZ555ptf5oUOHLInNd56OQXzFdeaZZ3olY4KNzeGQ4kgpAgAQk5KSpIoK45jGvwAA2JSYeffdd3XjjTdKkuLi4oJeTlNaWqpLL73Ulax4//33ddVVV1kWZ7QpKSnxOm/ZsmXAiajU1NQG5wyW7zy+z/GHVbEF8WgAABAlWrVyJ2Z8lzYBANAc2ZKYeeONN1zJlKuuuko9e/YMap6zzz5bV1xxhWsHnzfeeKNZJWaSg9imICUlpcE5gxWJ2CoqKlRhfnKTdOLECUlSWtpmrV+/P+DnA81VcXGx63jTpk1q06ZNBKOJTuXl5a7jDRs2nPbfKwDWqalx/zdp585NOnaM/yYhNvH+Cqvwdyl6eX5GtJPliZmysjItX77cVenxox/9KKT5brrpJldi5rPPPlNVVZUSEhJCjjManTp1yus8MTEx4DmSkpK8zq36ixSJ2J566inNnDnztNcPHBh/2nabAPzju3U9TjeMr/CBsOG/SWgq+LsMq/B3qXmyPDGzYcMGV5WDw+HQmDFjQprP8/7S0lJt2rRJFzTRf5X7VqFUVlYGPIdnhUldcwYrErE9/PDDuv/++13nJ06cUKdOnbRgwQJlZ2cH/HyguSouLna9yS9cuJBvYepQXl7uSsisXLmSihnARvw3CU0Ff5dhFf4uRS/Pz4h2sjwx49ncNScnJ+R/QLdt21Y5OTnat2+fJGnbtm1NNjGTlpbmde5bpeIP3yoU3zmDFYnYkpKSTquykaR+/fqpQ4cOAT8faK48G22fd955ATcVbw5KS0tdx/379w+qjxYA//DfJDQV/F2GVfi7FL08PyPayfK9bcz1cQ6Hw7K/UO3bt3cdHz582JI5o5FvoqKsrMxrlyZ/+P7FsSsxE8xfULtiAwAAAAAgVlmemPFc4hIfH2/JnJ7zlJWVWTJnNMrOzvbahamqqirgLaX37t3rdW5Vcsx3nj179gQ8h12xAQAAAAAQqyxPzGRlZUmSnE5nwEmF+nhWyWRmZloyZzRKSUlR586dvV7bvXt3QHP4Xt+7d++Q45KkXr16eZ0XFhYGPIfvPVbFBgAAAABArLI8MdO2bVvX8Z49e0JOzhw6dEi7d+92VZJ4zt8U+SYrtm7dGtD9ubm5Dc4XrC5dung1wywtLVVBQYHf9xcUFHhVO6WmpqpTp06WxAYAAAAAQKyyPDEzaNAgSUaPGafTqf/85z8hzTdnzhw5nU5Xr5Xzzjsv5BijWf/+/b3OV69e7fe9+/fvV35+vus8ISFBffr0sSQuh8Nx2v/2gcS2atUqr/PzzjvPa9kWAAAAAADNkeWJmU6dOumss86SZCxneuKJJ3TixImg5jp+/Lh+//vfu/4B36VLl9OW1DQ1V111ldf5okWL/G4A/Omnn3qdjx492tIGu76xLVy40O97fa+dOHGiJTEBAAAAABDLLE/MSNKtt94qp9Mph8OhgwcPavLkyadtldyY8vJyTZ48WQcOHHDNNWXKFDvCjSpDhgzx2mJ8586dWrp0qV/3vvHGG17nkydPtjI0TZo0yet8zpw5KikpafS+kydPas6cObbGBgAAAABALLIlMXPfffd5JReWL1+ugQMHatmyZX7dv3TpUg0YMEArVqxwVcu0adNGv/rVr+wIN6rExcXptttu83pt5syZjVbNfPbZZ1qxYoXrvFWrVrr++ustje28887T4MGDXeclJSV69tlnG73v2Wef9doq++KLL7ZsiRUAAAAAALHMlsRMamqqXn/9dVdSxel0Ki8vT5deeqnOP/98TZ8+Xf/5z3+0fPlyrV+/XsuXL9d//vMfTZ8+Xeeff77GjBmjb7/91nVvXFycXn/9dbVq1cqOcKPOQw895LUEadmyZXrmmWfqvX7v3r268847vV7zTY7VxeFweP34U5nz+OOPe50//fTTWr58eb3X1xX7E0880ehzAAAAAABoDlrYNfHkyZP17LPPatq0aV4Jms2bN2vLli313mdWhpjNgx0Oh/74xz/q6quvtivUqJOdna1f//rX+vWvf+167eGHH9bu3bv1yCOPKCcnR5JUW1urDz74QPfdd5/XNtk5OTm2VRddfvnlGjdunKufTVVVlcaPH6+nn35ad911l1q2bCnJ2LXp9ddf18MPP6yqqirX/VdccYXGjBljS2wAAAAAAMQaWypmTPfff7/effddZWRkuJIsnkka3x/JXcXhdDrVpk0bvf/++7rvvvvsDDMqPfTQQ6c123311VfVuXNn9ejRQwMHDlRWVpauueYar6RMSkqK/v3vfyszM9O22P7+97+rW7durvNTp05p6tSpys7O1rnnnqu+ffsqOztbv/zlL3Xq1CnXdT169NCbb75pW1wAAAAAAMQaWxMzknT11Vdr27Ztmj59uitBU1+/FPN3rVu31iOPPKJt27adlpxoLuLi4jRnzhz96Ec/8nq9pqZGO3fu1Ndff61jx455/S4rK0vz58/X0KFDbY2tffv2WrJkic4//3yv18vLy/XNN99o69atXgkZydgGfMmSJWrbtq2tsQEAAAAAEEtsW8rkqV27dnryySc1c+ZMrV27VitWrNCOHTtUXFyskydPqlWrVmrTpo3OOussDR8+XIMHD1aLFmEJLaolJyfrnXfe0Q9/+EM98cQT2rBhQ53Xpaam6tZbb9Vjjz2mdu3ahSW2Ll26aO3atfrTn/6kP//5z9q3b1+d1+Xk5Gjq1Km67777lJiYGJbYAAAAAACIFWHNfiQkJGjo0KG2V3Q0Nddee62uvfZabd++XWvWrNHevXtVWVmpzMxMnXPOORo6dKiSk5MDnrexnZ4ak5iYqAcffFDTpk3TunXrtHHjRh06dEiSkYzr37+/Bg4cqLg42wuzAAAAAACISZSlxJCePXuqZ8+ekQ7jNHFxcRo8eLDXVtoAAAAAAKBxlDIAAAAAAABECIkZAAAAAACACCExAwAAAAAAECFh6TGzefNmffzxx1q/fr0KCwt1/PhxlZeXB9x81uFwaMeOHTZFCQAAAAAAEF62JmbWrVun//mf/9HatWu9Xg92NyCHw2FFWAAAAAAAAFHBtsTMW2+9pbvuuks1NTWuRIyZWAkmwRLq1s4AAAAAAADRxpbEzKpVq3TnnXeqpqZGDodDDodDTqdTTqdTLVq0UGZmplJTU+14NAAAAAAAQMywJTEzbdo0V1LG6XSqZcuW+v/+v/9PN9xwg84991y1aBGW1jYAAAAAAABRzfIMSUFBgdasWeNKypx55plavHixzjrrLKsfBQAAAAAAENMs3y579erVkoyeMA6HQy+//DJJGQAAAAAAgDpYnpg5cOCA6zgzM1MTJ060+hEAAAAAAABNguWJmYqKCknGzkvdu3dni2s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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 17 - }, - { + "execution_count": null, + "id": "13", "metadata": {}, - "cell_type": "code", "outputs": [], - "execution_count": null, - "source": "", - "id": "c084a91d4846e568" + "source": [ + "from pymatgen.phonon.gruneisen import GruneisenPhononBandStructureSymmLine\n", + "from pymatgen.phonon.plotter import GruneisenPhononBSPlotter\n", + "\n", + "bs = GruneisenPhononBandStructureSymmLine.from_dict(\n", + " result[\"output\"][\"gruneisen_band_structure\"]\n", + ")\n", + "plt = GruneisenPhononBSPlotter(bs=bs)\n", + "plt.get_plot(ylim=[-2, 2])" + ] } ], "metadata": { @@ -624,11 +205,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" - }, - "kernelspec": { - "name": "python3", - "language": "python", - "display_name": "Python 3 (ipykernel)" } }, "nbformat": 4, From 2c0f4abf29847ff2488d8146a45cb5058097d65f Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 12:39:40 +0100 Subject: [PATCH 49/61] fix tutorial --- .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 605 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../outputs/CONTCAR.gz | Bin 0 -> 591 bytes .../outputs/INCAR.gz | Bin 0 -> 217 bytes .../outputs/OUTCAR.gz | Bin 0 -> 64188 bytes .../outputs/POSCAR.gz | Bin 0 -> 605 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../outputs/vasp.out.gz | Bin 0 -> 1639 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 47227 bytes .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 588 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../outputs/CONTCAR.gz | Bin 0 -> 585 bytes .../outputs/INCAR.gz | Bin 0 -> 217 bytes .../outputs/OUTCAR.gz | Bin 0 -> 64109 bytes .../outputs/POSCAR.gz | Bin 0 -> 588 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../outputs/vasp.out.gz | Bin 0 -> 1617 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 47249 bytes .../inputs/INCAR.gz | Bin 0 -> 217 bytes .../inputs/POSCAR.gz | Bin 0 -> 593 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../outputs/CONTCAR.gz | Bin 0 -> 567 bytes .../outputs/INCAR.gz | Bin 0 -> 217 bytes .../outputs/OUTCAR.gz | Bin 0 -> 64236 bytes .../outputs/POSCAR.gz | Bin 0 -> 593 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../outputs/vasp.out.gz | Bin 0 -> 1643 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 47262 bytes .../tight_relax_1_0/inputs/INCAR.gz | Bin 0 -> 226 bytes .../tight_relax_1_0/inputs/POSCAR.gz | Bin 0 -> 143 bytes .../tight_relax_1_0/inputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../tight_relax_1_0/outputs/CONTCAR.gz | Bin 0 -> 318 bytes .../tight_relax_1_0/outputs/INCAR.gz | Bin 0 -> 226 bytes .../tight_relax_1_0/outputs/OUTCAR.gz | Bin 0 -> 128699 bytes .../tight_relax_1_0/outputs/POSCAR.gz | Bin 0 -> 143 bytes .../tight_relax_1_0/outputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../tight_relax_1_0/outputs/vasp.out.gz | Bin 0 -> 1590 bytes .../tight_relax_1_0/outputs/vasprun.xml.gz | Bin 0 -> 27541 bytes .../tight_relax_1_minus_4/inputs/INCAR.gz | Bin 0 -> 223 bytes .../tight_relax_1_minus_4/inputs/POSCAR.gz | Bin 0 -> 301 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../tight_relax_1_minus_4/outputs/CONTCAR.gz | Bin 0 -> 319 bytes .../tight_relax_1_minus_4/outputs/INCAR.gz | Bin 0 -> 223 bytes .../tight_relax_1_minus_4/outputs/OUTCAR.gz | Bin 0 -> 15731 bytes .../tight_relax_1_minus_4/outputs/POSCAR.gz | Bin 0 -> 301 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../tight_relax_1_minus_4/outputs/vasp.out.gz | Bin 0 -> 1303 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 82473 bytes .../tight_relax_1_plus_3/inputs/INCAR.gz | Bin 0 -> 223 bytes .../tight_relax_1_plus_3/inputs/POSCAR.gz | Bin 0 -> 298 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../tight_relax_1_plus_3/outputs/CONTCAR.gz | Bin 0 -> 319 bytes .../tight_relax_1_plus_3/outputs/INCAR.gz | Bin 0 -> 223 bytes .../tight_relax_1_plus_3/outputs/OUTCAR.gz | Bin 0 -> 15272 bytes .../tight_relax_1_plus_3/outputs/POSCAR.gz | Bin 0 -> 298 bytes .../outputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../tight_relax_1_plus_3/outputs/vasp.out.gz | Bin 0 -> 1224 bytes .../outputs/vasprun.xml.gz | Bin 0 -> 82167 bytes .../tight_relax_2_1/inputs/INCAR.gz | Bin 0 -> 226 bytes .../tight_relax_2_1/inputs/POSCAR.gz | Bin 0 -> 301 bytes .../tight_relax_2_1/inputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../tight_relax_2_1/outputs/CONTCAR.gz | Bin 0 -> 321 bytes .../tight_relax_2_1/outputs/INCAR.gz | Bin 0 -> 226 bytes .../tight_relax_2_1/outputs/OUTCAR.gz | Bin 0 -> 52001 bytes .../tight_relax_2_1/outputs/POSCAR.gz | Bin 0 -> 301 bytes .../tight_relax_2_1/outputs/POTCAR.spec.gz | Bin 0 -> 35 bytes .../tight_relax_2_1/outputs/vasp.out.gz | Bin 0 -> 942 bytes .../tight_relax_2_1/outputs/vasprun.xml.gz | Bin 0 -> 26975 bytes .../tight_relax_2_minus_6/inputs/INCAR.gz | Bin 0 -> 222 bytes .../tight_relax_2_minus_6/inputs/POSCAR.gz | Bin 0 -> 301 bytes .../inputs/POTCAR.spec.gz | Bin 0 -> 35 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.../outputs/vasprun.xml.gz | Bin 0 -> 80821 bytes tests/test_data/vasp/Si_qha_2/copy-script.sh | 9 - tutorials/grueneisen_workflow.ipynb | 484 ++++++++++++++++-- 92 files changed, 435 insertions(+), 58 deletions(-) create mode 100644 tests/test_data/vasp/Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24/inputs/INCAR.gz create mode 100644 tests/test_data/vasp/Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24/inputs/POSCAR.gz create mode 100644 tests/test_data/vasp/Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24/inputs/POTCAR.spec.gz create mode 100644 tests/test_data/vasp/Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24/outputs/CONTCAR.gz create mode 100644 tests/test_data/vasp/Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24/outputs/INCAR.gz create mode 100644 tests/test_data/vasp/Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24/outputs/OUTCAR.gz create mode 100644 tests/test_data/vasp/Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24/outputs/POSCAR.gz 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 7 }, { "cell_type": "markdown", @@ -136,14 +294,15 @@ }, { "cell_type": "code", - "execution_count": null, "id": "11", "metadata": { "jupyter": { "is_executing": true + }, + "ExecuteTime": { + "start_time": "2025-02-18T11:38:35.145562Z" } }, - "outputs": [], "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", " run_locally(\n", @@ -153,14 +312,200 @@ " raise_immediately=True,\n", " store=job_store,\n", " )" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:35,160 INFO Started executing jobs locally\n", + "2025-02-18 12:38:35,166 INFO Starting job - tight relax 1 (6b623f99-071d-480c-8ac8-479d972be30f)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:36,412 INFO Finished job - tight relax 1 (6b623f99-071d-480c-8ac8-479d972be30f)\n", + "2025-02-18 12:38:36,413 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:38:36,413 INFO Starting job - tight relax 2 (b42afe33-ab28-4e42-a369-2e177704454f)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-36-413654-76125/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:37,846 INFO Finished job - tight relax 2 (b42afe33-ab28-4e42-a369-2e177704454f)\n", + "2025-02-18 12:38:37,849 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:38:37,850 INFO Starting job - shrink_expand_structure (08261a16-ff4a-419a-a89c-999ee27e4ade)\n", + "2025-02-18 12:38:37,874 INFO Finished job - shrink_expand_structure (08261a16-ff4a-419a-a89c-999ee27e4ade)\n", + "2025-02-18 12:38:37,875 INFO Starting job - tight relax 1 plus (e7b72351-99d3-424e-a873-84b76e626cd0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-37-875373-50276/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:38,444 INFO Finished job - tight relax 1 plus (e7b72351-99d3-424e-a873-84b76e626cd0)\n", + "2025-02-18 12:38:38,445 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:38:38,446 INFO Starting job - tight relax 1 minus (15c0576d-040c-48d4-8ae6-ceb6a878caf3)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-38-445658-85717/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:39,168 INFO Finished job - tight relax 1 minus (15c0576d-040c-48d4-8ae6-ceb6a878caf3)\n", + "2025-02-18 12:38:39,172 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:38:39,174 INFO Starting job - tight relax 2 plus (f91a9d2a-9c62-4795-88bd-23724a9c6e33)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-39-174044-74138/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:39,488 INFO Finished job - tight relax 2 plus (f91a9d2a-9c62-4795-88bd-23724a9c6e33)\n", + "2025-02-18 12:38:39,489 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:38:39,489 INFO Starting job - tight relax 2 minus (d8e82fd9-801b-439f-ab8b-db730e93fd75)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-39-489438-74230/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:39,785 INFO Finished job - tight relax 2 minus (d8e82fd9-801b-439f-ab8b-db730e93fd75)\n", + "2025-02-18 12:38:39,785 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:38:39,787 INFO Starting job - run_phonon_jobs (4fb5b978-bf18-46fa-896e-cab68ef21071)\n", + "2025-02-18 12:38:39,937 INFO Finished job - run_phonon_jobs (4fb5b978-bf18-46fa-896e-cab68ef21071)\n", + "2025-02-18 12:38:39,954 INFO Starting job - get_supercell_size ground (17efbf7e-bb9d-4b25-8045-de7104f755ee)\n", + "2025-02-18 12:38:39,957 INFO Finished job - get_supercell_size ground (17efbf7e-bb9d-4b25-8045-de7104f755ee)\n", + "2025-02-18 12:38:39,958 INFO Starting job - generate_phonon_displacements ground (f352e600-73d3-4999-b5b2-ea81aa07bd7f)\n", + "2025-02-18 12:38:40,140 INFO Finished job - generate_phonon_displacements ground (f352e600-73d3-4999-b5b2-ea81aa07bd7f)\n", + "2025-02-18 12:38:40,141 INFO Starting job - run_phonon_displacements ground (6bc48ddc-b14d-4db5-8569-ff06cb4abc33)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/flow.py:431: UserWarning: Some jobs are not connected, their ordering may be random\n", + " for node in itergraph(graph):\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:40,255 INFO Finished job - run_phonon_displacements ground (6bc48ddc-b14d-4db5-8569-ff06cb4abc33)\n", + "2025-02-18 12:38:40,263 INFO Starting job - dft phonon static 1/1 ground (6930d636-a9ef-437e-bc69-1c8c6a17ca58)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-40-263414-56639/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:38:41,383 INFO Finished job - dft phonon static 1/1 ground (6930d636-a9ef-437e-bc69-1c8c6a17ca58)\n", + "2025-02-18 12:38:41,384 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:38:41,386 INFO Starting job - store_inputs ground (6bc48ddc-b14d-4db5-8569-ff06cb4abc33, 2)\n", + "2025-02-18 12:38:41,387 INFO Finished job - store_inputs ground (6bc48ddc-b14d-4db5-8569-ff06cb4abc33, 2)\n", + "2025-02-18 12:38:41,388 INFO Starting job - generate_frequencies_eigenvectors ground (b6906711-2019-482c-9593-9e070ac6a7b1)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n" + ] + } + ], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "id": "12", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:32:04.797999Z", + "start_time": "2025-02-18T11:32:04.792820Z" + } + }, "source": [ "job_store.connect()\n", "\n", @@ -173,14 +518,19 @@ " load=True,\n", " sort={\"completed_at\": -1}, # to get the latest computation\n", ")" - ] + ], + "outputs": [], + "execution_count": 9 }, { "cell_type": "code", - "execution_count": null, "id": "13", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:37:13.385798Z", + "start_time": "2025-02-18T11:37:12.921999Z" + } + }, "source": [ "from pymatgen.phonon.gruneisen import GruneisenPhononBandStructureSymmLine\n", "from pymatgen.phonon.plotter import GruneisenPhononBSPlotter\n", @@ -189,8 +539,39 @@ " result[\"output\"][\"gruneisen_band_structure\"]\n", ")\n", "plt = GruneisenPhononBSPlotter(bs=bs)\n", - "plt.get_plot(ylim=[-2, 2])" - ] + "plt.get_plot_gs(plot_ph_bs_with_gruneisen=True)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
      " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 14 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "e1b45665cc825a2d" } ], "metadata": { @@ -205,6 +586,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" + }, + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3 (ipykernel)" } }, "nbformat": 4, From 7f310a638f0871fc70fa4a3929eb4950c1b4aa44 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 12:42:57 +0100 Subject: [PATCH 50/61] update gruneisen workflow --- tutorials/grueneisen_workflow.ipynb | 450 +++++----------------------- 1 file changed, 72 insertions(+), 378 deletions(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index f27f2d408c..915a6d9386 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -10,13 +10,10 @@ }, { "cell_type": "code", + "execution_count": null, "id": "1", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:38:33.636872Z", - "start_time": "2025-02-18T11:38:29.879420Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "from mock_vasp import TEST_DIR, mock_vasp\n", "\n", @@ -31,40 +28,24 @@ " \"dft phonon static 1/1 plus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_plus_26\",\n", " \"dft phonon static 1/1 minus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_minus_28\",\n", "}" - ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "execution_count": 1 + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:38:34.060294Z", - "start_time": "2025-02-18T11:38:33.641707Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": {}, + "outputs": [], "source": [ "from atomate2.vasp.flows.core import DoubleRelaxMaker\n", "from atomate2.vasp.jobs.core import TightRelaxMaker\n", - "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator\n", - "from atomate2.vasp.jobs.phonons import PhononDisplacementMaker\n" - ], - "id": "53d40653b76b8e3", - "outputs": [], - "execution_count": 2 + "from atomate2.vasp.jobs.phonons import PhononDisplacementMaker\n", + "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator" + ] }, { "cell_type": "markdown", - "id": "2", + "id": "3", "metadata": {}, "source": [ "# Grüneisen Workflow Tutorial with VASP" @@ -72,7 +53,7 @@ }, { "cell_type": "markdown", - "id": "3", + "id": "4", "metadata": {}, "source": [ "## Background\n", @@ -83,7 +64,7 @@ }, { "cell_type": "markdown", - "id": "4", + "id": "5", "metadata": {}, "source": [ "## Let's run the workflow\n", @@ -92,13 +73,10 @@ }, { "cell_type": "code", - "id": "5", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:38:34.340057Z", - "start_time": "2025-02-18T11:38:34.102745Z" - } - }, + "execution_count": null, + "id": "6", + "metadata": {}, + "outputs": [], "source": [ "from jobflow import JobStore, run_locally\n", "from maggma.stores import MemoryStore\n", @@ -108,23 +86,19 @@ "\n", "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si_diamond.cif\")" - ], - "outputs": [], - "execution_count": 3 + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:38:34.351346Z", - "start_time": "2025-02-18T11:38:34.346509Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": {}, + "outputs": [], "source": [ "phonon_displacement_maker = PhononDisplacementMaker(\n", " name=\"dft phonon static\",\n", - " run_vasp_kwargs={\"handlers\": ()}, input_set_generator=StaticSetGenerator(\n", - "\n", + " run_vasp_kwargs={\"handlers\": ()},\n", + " input_set_generator=StaticSetGenerator(\n", " user_incar_settings={\n", " \"GGA\": \"PE\",\n", " \"IBRION\": -1,\n", @@ -148,21 +122,16 @@ " \"NPAR\": 4,\n", " },\n", " auto_ispin=False,\n", - " )\n", - ")\n" - ], - "id": "328735631c229920", - "outputs": [], - "execution_count": 4 + " ),\n", + ")" + ] }, { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:38:34.399231Z", - "start_time": "2025-02-18T11:38:34.389175Z" - } - }, "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": {}, + "outputs": [], "source": [ "phonon_bulk_relax_maker_isif3 = DoubleRelaxMaker.from_relax_maker(\n", " TightRelaxMaker(\n", @@ -191,28 +160,12 @@ " }\n", " ),\n", " )\n", - ")\n" - ], - "id": "9d0efc7740337beb", - "outputs": [], - "execution_count": 5 - }, - { - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:38:34.443016Z", - "start_time": "2025-02-18T11:38:34.441383Z" - } - }, - "cell_type": "code", - "source": "", - "id": "e3986ae2719ee9b6", - "outputs": [], - "execution_count": null + ")" + ] }, { "cell_type": "markdown", - "id": "6", + "id": "9", "metadata": {}, "source": [ "Then one can use the `GruneisenMaker` to generate a `Flow`." @@ -220,39 +173,28 @@ }, { "cell_type": "code", - "id": "7", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:38:34.955543Z", - "start_time": "2025-02-18T11:38:34.487686Z" - } - }, + "execution_count": null, + "id": "10", + "metadata": {}, + "outputs": [], "source": [ - "flow=GruneisenMaker(\n", + "flow = GruneisenMaker(\n", " symprec=1e-4,\n", " bulk_relax_maker=phonon_bulk_relax_maker_isif3,\n", - " phonon_maker=PhononMaker(generate_frequencies_eigenvectors_kwargs={\"tmin\": 0, \"tmax\": 1000, \"tstep\": 10},\n", - " min_length=10,\n", - " bulk_relax_maker=None,\n", - " born_maker=None,\n", - " static_energy_maker=None,\n", - " phonon_displacement_maker=phonon_displacement_maker),\n", - ").make(structure=si_structure)\n" - ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":14: UserWarning: You are using different symmetry precisions in the phonon makers and other parts of the Grüneisen workflow.\n" - ] - } - ], - "execution_count": 6 + " phonon_maker=PhononMaker(\n", + " generate_frequencies_eigenvectors_kwargs={\"tmin\": 0, \"tmax\": 1000, \"tstep\": 10},\n", + " min_length=10,\n", + " bulk_relax_maker=None,\n", + " born_maker=None,\n", + " static_energy_maker=None,\n", + " phonon_displacement_maker=phonon_displacement_maker,\n", + " ),\n", + ").make(structure=si_structure)" + ] }, { "cell_type": "markdown", - "id": "8", + "id": "11", "metadata": {}, "source": [ "The Grüneisen parameter workflow will perform 3 different phonon runs at 3 different volumes at and around the equilibrium to compute the mode Grüneisen values." @@ -260,33 +202,17 @@ }, { "cell_type": "code", - "id": "9", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:38:35.125869Z", - "start_time": "2025-02-18T11:38:34.962876Z" - } - }, + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], "source": [ "flow.draw_graph().show()" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 7 + ] }, { "cell_type": "markdown", - "id": "10", + "id": "13", "metadata": {}, "source": [ "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`" @@ -294,15 +220,10 @@ }, { "cell_type": "code", - "id": "11", - "metadata": { - "jupyter": { - "is_executing": true - }, - "ExecuteTime": { - "start_time": "2025-02-18T11:38:35.145562Z" - } - }, + "execution_count": null, + "id": "14", + "metadata": {}, + "outputs": [], "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", " run_locally(\n", @@ -312,200 +233,14 @@ " raise_immediately=True,\n", " store=job_store,\n", " )" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:35,160 INFO Started executing jobs locally\n", - "2025-02-18 12:38:35,166 INFO Starting job - tight relax 1 (6b623f99-071d-480c-8ac8-479d972be30f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:36,412 INFO Finished job - tight relax 1 (6b623f99-071d-480c-8ac8-479d972be30f)\n", - "2025-02-18 12:38:36,413 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:38:36,413 INFO Starting job - tight relax 2 (b42afe33-ab28-4e42-a369-2e177704454f)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-36-413654-76125/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:37,846 INFO Finished job - tight relax 2 (b42afe33-ab28-4e42-a369-2e177704454f)\n", - "2025-02-18 12:38:37,849 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:38:37,850 INFO Starting job - shrink_expand_structure (08261a16-ff4a-419a-a89c-999ee27e4ade)\n", - "2025-02-18 12:38:37,874 INFO Finished job - shrink_expand_structure (08261a16-ff4a-419a-a89c-999ee27e4ade)\n", - "2025-02-18 12:38:37,875 INFO Starting job - tight relax 1 plus (e7b72351-99d3-424e-a873-84b76e626cd0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-37-875373-50276/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:38,444 INFO Finished job - tight relax 1 plus (e7b72351-99d3-424e-a873-84b76e626cd0)\n", - "2025-02-18 12:38:38,445 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:38:38,446 INFO Starting job - tight relax 1 minus (15c0576d-040c-48d4-8ae6-ceb6a878caf3)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-38-445658-85717/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:39,168 INFO Finished job - tight relax 1 minus (15c0576d-040c-48d4-8ae6-ceb6a878caf3)\n", - "2025-02-18 12:38:39,172 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:38:39,174 INFO Starting job - tight relax 2 plus (f91a9d2a-9c62-4795-88bd-23724a9c6e33)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-39-174044-74138/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:39,488 INFO Finished job - tight relax 2 plus (f91a9d2a-9c62-4795-88bd-23724a9c6e33)\n", - "2025-02-18 12:38:39,489 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:38:39,489 INFO Starting job - tight relax 2 minus (d8e82fd9-801b-439f-ab8b-db730e93fd75)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-39-489438-74230/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:39,785 INFO Finished job - tight relax 2 minus (d8e82fd9-801b-439f-ab8b-db730e93fd75)\n", - "2025-02-18 12:38:39,785 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:38:39,787 INFO Starting job - run_phonon_jobs (4fb5b978-bf18-46fa-896e-cab68ef21071)\n", - "2025-02-18 12:38:39,937 INFO Finished job - run_phonon_jobs (4fb5b978-bf18-46fa-896e-cab68ef21071)\n", - "2025-02-18 12:38:39,954 INFO Starting job - get_supercell_size ground (17efbf7e-bb9d-4b25-8045-de7104f755ee)\n", - "2025-02-18 12:38:39,957 INFO Finished job - get_supercell_size ground (17efbf7e-bb9d-4b25-8045-de7104f755ee)\n", - "2025-02-18 12:38:39,958 INFO Starting job - generate_phonon_displacements ground (f352e600-73d3-4999-b5b2-ea81aa07bd7f)\n", - "2025-02-18 12:38:40,140 INFO Finished job - generate_phonon_displacements ground (f352e600-73d3-4999-b5b2-ea81aa07bd7f)\n", - "2025-02-18 12:38:40,141 INFO Starting job - run_phonon_displacements ground (6bc48ddc-b14d-4db5-8569-ff06cb4abc33)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/flow.py:431: UserWarning: Some jobs are not connected, their ordering may be random\n", - " for node in itergraph(graph):\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:40,255 INFO Finished job - run_phonon_displacements ground (6bc48ddc-b14d-4db5-8569-ff06cb4abc33)\n", - "2025-02-18 12:38:40,263 INFO Starting job - dft phonon static 1/1 ground (6930d636-a9ef-437e-bc69-1c8c6a17ca58)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp_8tauc6o/job_2025-02-18-11-38-40-263414-56639/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:38:41,383 INFO Finished job - dft phonon static 1/1 ground (6930d636-a9ef-437e-bc69-1c8c6a17ca58)\n", - "2025-02-18 12:38:41,384 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:38:41,386 INFO Starting job - store_inputs ground (6bc48ddc-b14d-4db5-8569-ff06cb4abc33, 2)\n", - "2025-02-18 12:38:41,387 INFO Finished job - store_inputs ground (6bc48ddc-b14d-4db5-8569-ff06cb4abc33, 2)\n", - "2025-02-18 12:38:41,388 INFO Starting job - generate_frequencies_eigenvectors ground (b6906711-2019-482c-9593-9e070ac6a7b1)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - } - ], - "execution_count": null + ] }, { "cell_type": "code", - "id": "12", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:32:04.797999Z", - "start_time": "2025-02-18T11:32:04.792820Z" - } - }, + "execution_count": null, + "id": "15", + "metadata": {}, + "outputs": [], "source": [ "job_store.connect()\n", "\n", @@ -518,19 +253,14 @@ " load=True,\n", " sort={\"completed_at\": -1}, # to get the latest computation\n", ")" - ], - "outputs": [], - "execution_count": 9 + ] }, { "cell_type": "code", - "id": "13", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:37:13.385798Z", - "start_time": "2025-02-18T11:37:12.921999Z" - } - }, + "execution_count": null, + "id": "16", + "metadata": {}, + "outputs": [], "source": [ "from pymatgen.phonon.gruneisen import GruneisenPhononBandStructureSymmLine\n", "from pymatgen.phonon.plotter import GruneisenPhononBSPlotter\n", @@ -540,38 +270,7 @@ ")\n", "plt = GruneisenPhononBSPlotter(bs=bs)\n", "plt.get_plot_gs(plot_ph_bs_with_gruneisen=True)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 14 - }, - { - "metadata": {}, - "cell_type": "code", - "outputs": [], - "execution_count": null, - "source": "", - "id": "e1b45665cc825a2d" + ] } ], "metadata": { @@ -586,11 +285,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" - }, - "kernelspec": { - "name": "python3", - "language": "python", - "display_name": "Python 3 (ipykernel)" } }, "nbformat": 4, From d1b83c06f6a57269cf379cb0ad500db783fb682e Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 12:51:42 +0100 Subject: [PATCH 51/61] update gruneisen workflow --- tutorials/grueneisen_workflow.ipynb | 544 ++++++++++++++++++++++++++-- 1 file changed, 504 insertions(+), 40 deletions(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 915a6d9386..feed94a9c0 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -10,10 +10,13 @@ }, { "cell_type": "code", - "execution_count": null, "id": "1", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:49:15.302112Z", + "start_time": "2025-02-18T11:49:11.417058Z" + } + }, "source": [ "from mock_vasp import TEST_DIR, mock_vasp\n", "\n", @@ -28,20 +31,36 @@ " \"dft phonon static 1/1 plus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_plus_26\",\n", " \"dft phonon static 1/1 minus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_minus_28\",\n", "}" - ] + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "execution_count": 1 }, { "cell_type": "code", - "execution_count": null, "id": "2", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:49:15.869283Z", + "start_time": "2025-02-18T11:49:15.306193Z" + } + }, "source": [ "from atomate2.vasp.flows.core import DoubleRelaxMaker\n", "from atomate2.vasp.jobs.core import TightRelaxMaker\n", "from atomate2.vasp.jobs.phonons import PhononDisplacementMaker\n", "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator" - ] + ], + "outputs": [], + "execution_count": 2 }, { "cell_type": "markdown", @@ -73,10 +92,13 @@ }, { "cell_type": "code", - "execution_count": null, "id": "6", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:49:16.174946Z", + "start_time": "2025-02-18T11:49:15.911282Z" + } + }, "source": [ "from jobflow import JobStore, run_locally\n", "from maggma.stores import MemoryStore\n", @@ -86,14 +108,19 @@ "\n", "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si_diamond.cif\")" - ] + ], + "outputs": [], + "execution_count": 3 }, { "cell_type": "code", - "execution_count": null, "id": "7", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:49:16.186179Z", + "start_time": "2025-02-18T11:49:16.181827Z" + } + }, "source": [ "phonon_displacement_maker = PhononDisplacementMaker(\n", " name=\"dft phonon static\",\n", @@ -124,14 +151,19 @@ " auto_ispin=False,\n", " ),\n", ")" - ] + ], + "outputs": [], + "execution_count": 4 }, { "cell_type": "code", - "execution_count": null, "id": "8", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:49:16.236187Z", + "start_time": "2025-02-18T11:49:16.225476Z" + } + }, "source": [ "phonon_bulk_relax_maker_isif3 = DoubleRelaxMaker.from_relax_maker(\n", " TightRelaxMaker(\n", @@ -161,7 +193,9 @@ " ),\n", " )\n", ")" - ] + ], + "outputs": [], + "execution_count": 5 }, { "cell_type": "markdown", @@ -173,10 +207,13 @@ }, { "cell_type": "code", - "execution_count": null, "id": "10", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:49:16.738946Z", + "start_time": "2025-02-18T11:49:16.278394Z" + } + }, "source": [ "flow = GruneisenMaker(\n", " symprec=1e-4,\n", @@ -190,7 +227,17 @@ " phonon_displacement_maker=phonon_displacement_maker,\n", " ),\n", ").make(structure=si_structure)" - ] + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":14: UserWarning: You are using different symmetry precisions in the phonon makers and other parts of the Grüneisen workflow.\n" + ] + } + ], + "execution_count": 6 }, { "cell_type": "markdown", @@ -202,13 +249,29 @@ }, { "cell_type": "code", - "execution_count": null, "id": "12", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:49:16.912020Z", + "start_time": "2025-02-18T11:49:16.745524Z" + } + }, "source": [ "flow.draw_graph().show()" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 7 }, { "cell_type": "markdown", @@ -220,10 +283,13 @@ }, { "cell_type": "code", - "execution_count": null, "id": "14", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:50:07.553088Z", + "start_time": "2025-02-18T11:49:16.919082Z" + } + }, "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", " run_locally(\n", @@ -233,14 +299,379 @@ " raise_immediately=True,\n", " store=job_store,\n", " )" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:16,935 INFO Started executing jobs locally\n", + "2025-02-18 12:49:16,942 INFO Starting job - tight relax 1 (163c7f5e-2e7b-408f-981a-3ae5e8ee0788)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:18,218 INFO Finished job - tight relax 1 (163c7f5e-2e7b-408f-981a-3ae5e8ee0788)\n", + "2025-02-18 12:49:18,218 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:18,219 INFO Starting job - tight relax 2 (116b544c-433f-44a8-a1aa-2c2f4abcb501)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-18-219213-32554/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:19,680 INFO Finished job - tight relax 2 (116b544c-433f-44a8-a1aa-2c2f4abcb501)\n", + "2025-02-18 12:49:19,680 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:19,681 INFO Starting job - shrink_expand_structure (0f23a0c8-6e21-465a-b323-bf090628704c)\n", + "2025-02-18 12:49:19,699 INFO Finished job - shrink_expand_structure (0f23a0c8-6e21-465a-b323-bf090628704c)\n", + "2025-02-18 12:49:19,700 INFO Starting job - tight relax 1 plus (f2eb1309-2b44-4184-81c5-b8762cb28244)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-19-699641-80429/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:20,298 INFO Finished job - tight relax 1 plus (f2eb1309-2b44-4184-81c5-b8762cb28244)\n", + "2025-02-18 12:49:20,299 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:20,300 INFO Starting job - tight relax 1 minus (0d9060fa-559b-410c-937d-d48c82ee1ddc)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-20-299876-15172/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:21,027 INFO Finished job - tight relax 1 minus (0d9060fa-559b-410c-937d-d48c82ee1ddc)\n", + "2025-02-18 12:49:21,027 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:21,028 INFO Starting job - tight relax 2 plus (46166685-88a0-4c8d-b1f8-74327c0ad8c0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-21-028003-52432/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:21,360 INFO Finished job - tight relax 2 plus (46166685-88a0-4c8d-b1f8-74327c0ad8c0)\n", + "2025-02-18 12:49:21,360 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:21,361 INFO Starting job - tight relax 2 minus (91a1ac6c-d6fd-4927-8c34-d57fc6a602f6)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-21-361042-50432/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:21,831 INFO Finished job - tight relax 2 minus (91a1ac6c-d6fd-4927-8c34-d57fc6a602f6)\n", + "2025-02-18 12:49:21,832 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:21,832 INFO Starting job - run_phonon_jobs (41ec4d30-07ec-49bc-86b2-5c36543a46aa)\n", + "2025-02-18 12:49:21,981 INFO Finished job - run_phonon_jobs (41ec4d30-07ec-49bc-86b2-5c36543a46aa)\n", + "2025-02-18 12:49:21,997 INFO Starting job - get_supercell_size ground (4cce137b-af2a-495b-a618-2c124d4f2212)\n", + "2025-02-18 12:49:22,000 INFO Finished job - get_supercell_size ground (4cce137b-af2a-495b-a618-2c124d4f2212)\n", + "2025-02-18 12:49:22,001 INFO Starting job - generate_phonon_displacements ground (b073caad-c765-48e9-b827-8938799d2528)\n", + "2025-02-18 12:49:22,182 INFO Finished job - generate_phonon_displacements ground (b073caad-c765-48e9-b827-8938799d2528)\n", + "2025-02-18 12:49:22,182 INFO Starting job - run_phonon_displacements ground (ac973f54-b6da-4d58-a900-cdd75af9e098)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/flow.py:431: UserWarning: Some jobs are not connected, their ordering may be random\n", + " for node in itergraph(graph):\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:22,294 INFO Finished job - run_phonon_displacements ground (ac973f54-b6da-4d58-a900-cdd75af9e098)\n", + "2025-02-18 12:49:22,302 INFO Starting job - dft phonon static 1/1 ground (bea34318-d7c2-4d36-b16e-4a65ce4cee61)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-22-302221-65634/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:23,312 INFO Finished job - dft phonon static 1/1 ground (bea34318-d7c2-4d36-b16e-4a65ce4cee61)\n", + "2025-02-18 12:49:23,313 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:23,314 INFO Starting job - store_inputs ground (ac973f54-b6da-4d58-a900-cdd75af9e098, 2)\n", + "2025-02-18 12:49:23,315 INFO Finished job - store_inputs ground (ac973f54-b6da-4d58-a900-cdd75af9e098, 2)\n", + "2025-02-18 12:49:23,315 INFO Starting job - generate_frequencies_eigenvectors ground (3f8522a2-376c-43c2-a13f-c338ba5a0cfc)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:30,545 INFO Finished job - generate_frequencies_eigenvectors ground (3f8522a2-376c-43c2-a13f-c338ba5a0cfc)\n", + "2025-02-18 12:49:30,546 INFO Starting job - get_supercell_size plus (1a12245f-3b18-4350-a369-8a7964f8855a)\n", + "2025-02-18 12:49:30,550 INFO Finished job - get_supercell_size plus (1a12245f-3b18-4350-a369-8a7964f8855a)\n", + "2025-02-18 12:49:30,551 INFO Starting job - generate_phonon_displacements plus (595ea64f-47e0-4c16-8208-6abbb988601a)\n", + "2025-02-18 12:49:30,732 INFO Finished job - generate_phonon_displacements plus (595ea64f-47e0-4c16-8208-6abbb988601a)\n", + "2025-02-18 12:49:30,733 INFO Starting job - run_phonon_displacements plus (8d26e5e2-232f-45c8-b4cd-e526fe9e4117)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:30,844 INFO Finished job - run_phonon_displacements plus (8d26e5e2-232f-45c8-b4cd-e526fe9e4117)\n", + "2025-02-18 12:49:30,852 INFO Starting job - dft phonon static 1/1 plus (c339397d-b940-4903-ab1d-bdd39daa2175)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-30-852651-16326/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:31,463 INFO Finished job - dft phonon static 1/1 plus (c339397d-b940-4903-ab1d-bdd39daa2175)\n", + "2025-02-18 12:49:31,464 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:31,465 INFO Starting job - store_inputs plus (8d26e5e2-232f-45c8-b4cd-e526fe9e4117, 2)\n", + "2025-02-18 12:49:31,466 INFO Finished job - store_inputs plus (8d26e5e2-232f-45c8-b4cd-e526fe9e4117, 2)\n", + "2025-02-18 12:49:31,467 INFO Starting job - generate_frequencies_eigenvectors plus (a8e46d00-a8f0-40f2-a2fb-53a9af61f54c)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:38,987 INFO Finished job - generate_frequencies_eigenvectors plus (a8e46d00-a8f0-40f2-a2fb-53a9af61f54c)\n", + "2025-02-18 12:49:38,989 INFO Starting job - get_supercell_size minus (26defd29-38f6-41b6-8dc9-ad6849934dd4)\n", + "2025-02-18 12:49:38,992 INFO Finished job - get_supercell_size minus (26defd29-38f6-41b6-8dc9-ad6849934dd4)\n", + "2025-02-18 12:49:38,993 INFO Starting job - generate_phonon_displacements minus (8fd3866c-f8c1-4b21-8989-f0de80fdfc93)\n", + "2025-02-18 12:49:39,174 INFO Finished job - generate_phonon_displacements minus (8fd3866c-f8c1-4b21-8989-f0de80fdfc93)\n", + "2025-02-18 12:49:39,175 INFO Starting job - run_phonon_displacements minus (dcd6e4e5-8a6d-4e78-a2b0-566b84f60a5a)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:39,287 INFO Finished job - run_phonon_displacements minus (dcd6e4e5-8a6d-4e78-a2b0-566b84f60a5a)\n", + "2025-02-18 12:49:39,296 INFO Starting job - dft phonon static 1/1 minus (9edc101a-f678-4f8d-9a58-8c0bc4b4da34)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-39-296498-13043/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:39,913 INFO Finished job - dft phonon static 1/1 minus (9edc101a-f678-4f8d-9a58-8c0bc4b4da34)\n", + "2025-02-18 12:49:39,913 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-18 12:49:39,914 INFO Starting job - store_inputs minus (dcd6e4e5-8a6d-4e78-a2b0-566b84f60a5a, 2)\n", + "2025-02-18 12:49:39,916 INFO Finished job - store_inputs minus (dcd6e4e5-8a6d-4e78-a2b0-566b84f60a5a, 2)\n", + "2025-02-18 12:49:39,917 INFO Starting job - generate_frequencies_eigenvectors minus (51649220-c47f-4f64-89a5-b3552993bb89)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:49:47,259 INFO Finished job - generate_frequencies_eigenvectors minus (51649220-c47f-4f64-89a5-b3552993bb89)\n", + "2025-02-18 12:49:47,260 INFO Starting job - compute_gruneisen_param (b7843b03-e63c-41dc-a1de-8f73a0b4c69b)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-18 12:50:07,533 INFO Finished job - compute_gruneisen_param (b7843b03-e63c-41dc-a1de-8f73a0b4c69b)\n", + "2025-02-18 12:50:07,534 INFO Finished executing jobs locally\n" + ] + } + ], + "execution_count": 8 }, { "cell_type": "code", - "execution_count": null, "id": "15", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:50:07.567475Z", + "start_time": "2025-02-18T11:50:07.562441Z" + } + }, "source": [ "job_store.connect()\n", "\n", @@ -253,14 +684,19 @@ " load=True,\n", " sort={\"completed_at\": -1}, # to get the latest computation\n", ")" - ] + ], + "outputs": [], + "execution_count": 9 }, { "cell_type": "code", - "execution_count": null, "id": "16", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-18T11:50:08.042639Z", + "start_time": "2025-02-18T11:50:07.600588Z" + } + }, "source": [ "from pymatgen.phonon.gruneisen import GruneisenPhononBandStructureSymmLine\n", "from pymatgen.phonon.plotter import GruneisenPhononBSPlotter\n", @@ -270,7 +706,30 @@ ")\n", "plt = GruneisenPhononBSPlotter(bs=bs)\n", "plt.get_plot_gs(plot_ph_bs_with_gruneisen=True)" - ] + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
      " + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 10 } ], "metadata": { @@ -285,6 +744,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" + }, + "kernelspec": { + "name": "python3", + "language": "python", + "display_name": "Python 3 (ipykernel)" } }, "nbformat": 4, From 1410e66b0cc0bc77907819493b9db086db70fcb6 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 12:55:18 +0100 Subject: [PATCH 52/61] update gruneisen workflow --- tutorials/grueneisen_workflow.ipynb | 544 ++-------------------------- 1 file changed, 40 insertions(+), 504 deletions(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index feed94a9c0..915a6d9386 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -10,13 +10,10 @@ }, { "cell_type": "code", + "execution_count": null, "id": "1", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:49:15.302112Z", - "start_time": "2025-02-18T11:49:11.417058Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "from mock_vasp import TEST_DIR, mock_vasp\n", "\n", @@ -31,36 +28,20 @@ " \"dft phonon static 1/1 plus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_plus_26\",\n", " \"dft phonon static 1/1 minus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_minus_28\",\n", "}" - ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "execution_count": 1 + ] }, { "cell_type": "code", + "execution_count": null, "id": "2", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:49:15.869283Z", - "start_time": "2025-02-18T11:49:15.306193Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "from atomate2.vasp.flows.core import DoubleRelaxMaker\n", "from atomate2.vasp.jobs.core import TightRelaxMaker\n", "from atomate2.vasp.jobs.phonons import PhononDisplacementMaker\n", "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator" - ], - "outputs": [], - "execution_count": 2 + ] }, { "cell_type": "markdown", @@ -92,13 +73,10 @@ }, { "cell_type": "code", + "execution_count": null, "id": "6", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:49:16.174946Z", - "start_time": "2025-02-18T11:49:15.911282Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "from jobflow import JobStore, run_locally\n", "from maggma.stores import MemoryStore\n", @@ -108,19 +86,14 @@ "\n", "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si_diamond.cif\")" - ], - "outputs": [], - "execution_count": 3 + ] }, { "cell_type": "code", + "execution_count": null, "id": "7", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:49:16.186179Z", - "start_time": "2025-02-18T11:49:16.181827Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "phonon_displacement_maker = PhononDisplacementMaker(\n", " name=\"dft phonon static\",\n", @@ -151,19 +124,14 @@ " auto_ispin=False,\n", " ),\n", ")" - ], - "outputs": [], - "execution_count": 4 + ] }, { "cell_type": "code", + "execution_count": null, "id": "8", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:49:16.236187Z", - "start_time": "2025-02-18T11:49:16.225476Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "phonon_bulk_relax_maker_isif3 = DoubleRelaxMaker.from_relax_maker(\n", " TightRelaxMaker(\n", @@ -193,9 +161,7 @@ " ),\n", " )\n", ")" - ], - "outputs": [], - "execution_count": 5 + ] }, { "cell_type": "markdown", @@ -207,13 +173,10 @@ }, { "cell_type": "code", + "execution_count": null, "id": "10", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:49:16.738946Z", - "start_time": "2025-02-18T11:49:16.278394Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "flow = GruneisenMaker(\n", " symprec=1e-4,\n", @@ -227,17 +190,7 @@ " phonon_displacement_maker=phonon_displacement_maker,\n", " ),\n", ").make(structure=si_structure)" - ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":14: UserWarning: You are using different symmetry precisions in the phonon makers and other parts of the Grüneisen workflow.\n" - ] - } - ], - "execution_count": 6 + ] }, { "cell_type": "markdown", @@ -249,29 +202,13 @@ }, { "cell_type": "code", + "execution_count": null, "id": "12", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:49:16.912020Z", - "start_time": "2025-02-18T11:49:16.745524Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "flow.draw_graph().show()" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 7 + ] }, { "cell_type": "markdown", @@ -283,13 +220,10 @@ }, { "cell_type": "code", + "execution_count": null, "id": "14", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:50:07.553088Z", - "start_time": "2025-02-18T11:49:16.919082Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", " run_locally(\n", @@ -299,379 +233,14 @@ " raise_immediately=True,\n", " store=job_store,\n", " )" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:16,935 INFO Started executing jobs locally\n", - "2025-02-18 12:49:16,942 INFO Starting job - tight relax 1 (163c7f5e-2e7b-408f-981a-3ae5e8ee0788)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:18,218 INFO Finished job - tight relax 1 (163c7f5e-2e7b-408f-981a-3ae5e8ee0788)\n", - "2025-02-18 12:49:18,218 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:18,219 INFO Starting job - tight relax 2 (116b544c-433f-44a8-a1aa-2c2f4abcb501)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-18-219213-32554/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "Error in parsing bandstructure\n", - "VASP doesn't properly output efermi for IBRION == 1\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:19,680 INFO Finished job - tight relax 2 (116b544c-433f-44a8-a1aa-2c2f4abcb501)\n", - "2025-02-18 12:49:19,680 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:19,681 INFO Starting job - shrink_expand_structure (0f23a0c8-6e21-465a-b323-bf090628704c)\n", - "2025-02-18 12:49:19,699 INFO Finished job - shrink_expand_structure (0f23a0c8-6e21-465a-b323-bf090628704c)\n", - "2025-02-18 12:49:19,700 INFO Starting job - tight relax 1 plus (f2eb1309-2b44-4184-81c5-b8762cb28244)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-19-699641-80429/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:20,298 INFO Finished job - tight relax 1 plus (f2eb1309-2b44-4184-81c5-b8762cb28244)\n", - "2025-02-18 12:49:20,299 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:20,300 INFO Starting job - tight relax 1 minus (0d9060fa-559b-410c-937d-d48c82ee1ddc)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-20-299876-15172/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:21,027 INFO Finished job - tight relax 1 minus (0d9060fa-559b-410c-937d-d48c82ee1ddc)\n", - "2025-02-18 12:49:21,027 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:21,028 INFO Starting job - tight relax 2 plus (46166685-88a0-4c8d-b1f8-74327c0ad8c0)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-21-028003-52432/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:21,360 INFO Finished job - tight relax 2 plus (46166685-88a0-4c8d-b1f8-74327c0ad8c0)\n", - "2025-02-18 12:49:21,360 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:21,361 INFO Starting job - tight relax 2 minus (91a1ac6c-d6fd-4927-8c34-d57fc6a602f6)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-21-361042-50432/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:21,831 INFO Finished job - tight relax 2 minus (91a1ac6c-d6fd-4927-8c34-d57fc6a602f6)\n", - "2025-02-18 12:49:21,832 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:21,832 INFO Starting job - run_phonon_jobs (41ec4d30-07ec-49bc-86b2-5c36543a46aa)\n", - "2025-02-18 12:49:21,981 INFO Finished job - run_phonon_jobs (41ec4d30-07ec-49bc-86b2-5c36543a46aa)\n", - "2025-02-18 12:49:21,997 INFO Starting job - get_supercell_size ground (4cce137b-af2a-495b-a618-2c124d4f2212)\n", - "2025-02-18 12:49:22,000 INFO Finished job - get_supercell_size ground (4cce137b-af2a-495b-a618-2c124d4f2212)\n", - "2025-02-18 12:49:22,001 INFO Starting job - generate_phonon_displacements ground (b073caad-c765-48e9-b827-8938799d2528)\n", - "2025-02-18 12:49:22,182 INFO Finished job - generate_phonon_displacements ground (b073caad-c765-48e9-b827-8938799d2528)\n", - "2025-02-18 12:49:22,182 INFO Starting job - run_phonon_displacements ground (ac973f54-b6da-4d58-a900-cdd75af9e098)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/flow.py:431: UserWarning: Some jobs are not connected, their ordering may be random\n", - " for node in itergraph(graph):\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:22,294 INFO Finished job - run_phonon_displacements ground (ac973f54-b6da-4d58-a900-cdd75af9e098)\n", - "2025-02-18 12:49:22,302 INFO Starting job - dft phonon static 1/1 ground (bea34318-d7c2-4d36-b16e-4a65ce4cee61)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-22-302221-65634/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:23,312 INFO Finished job - dft phonon static 1/1 ground (bea34318-d7c2-4d36-b16e-4a65ce4cee61)\n", - "2025-02-18 12:49:23,313 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:23,314 INFO Starting job - store_inputs ground (ac973f54-b6da-4d58-a900-cdd75af9e098, 2)\n", - "2025-02-18 12:49:23,315 INFO Finished job - store_inputs ground (ac973f54-b6da-4d58-a900-cdd75af9e098, 2)\n", - "2025-02-18 12:49:23,315 INFO Starting job - generate_frequencies_eigenvectors ground (3f8522a2-376c-43c2-a13f-c338ba5a0cfc)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:30,545 INFO Finished job - generate_frequencies_eigenvectors ground (3f8522a2-376c-43c2-a13f-c338ba5a0cfc)\n", - "2025-02-18 12:49:30,546 INFO Starting job - get_supercell_size plus (1a12245f-3b18-4350-a369-8a7964f8855a)\n", - "2025-02-18 12:49:30,550 INFO Finished job - get_supercell_size plus (1a12245f-3b18-4350-a369-8a7964f8855a)\n", - "2025-02-18 12:49:30,551 INFO Starting job - generate_phonon_displacements plus (595ea64f-47e0-4c16-8208-6abbb988601a)\n", - "2025-02-18 12:49:30,732 INFO Finished job - generate_phonon_displacements plus (595ea64f-47e0-4c16-8208-6abbb988601a)\n", - "2025-02-18 12:49:30,733 INFO Starting job - run_phonon_displacements plus (8d26e5e2-232f-45c8-b4cd-e526fe9e4117)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:30,844 INFO Finished job - run_phonon_displacements plus (8d26e5e2-232f-45c8-b4cd-e526fe9e4117)\n", - "2025-02-18 12:49:30,852 INFO Starting job - dft phonon static 1/1 plus (c339397d-b940-4903-ab1d-bdd39daa2175)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-30-852651-16326/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:31,463 INFO Finished job - dft phonon static 1/1 plus (c339397d-b940-4903-ab1d-bdd39daa2175)\n", - "2025-02-18 12:49:31,464 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:31,465 INFO Starting job - store_inputs plus (8d26e5e2-232f-45c8-b4cd-e526fe9e4117, 2)\n", - "2025-02-18 12:49:31,466 INFO Finished job - store_inputs plus (8d26e5e2-232f-45c8-b4cd-e526fe9e4117, 2)\n", - "2025-02-18 12:49:31,467 INFO Starting job - generate_frequencies_eigenvectors plus (a8e46d00-a8f0-40f2-a2fb-53a9af61f54c)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:38,987 INFO Finished job - generate_frequencies_eigenvectors plus (a8e46d00-a8f0-40f2-a2fb-53a9af61f54c)\n", - "2025-02-18 12:49:38,989 INFO Starting job - get_supercell_size minus (26defd29-38f6-41b6-8dc9-ad6849934dd4)\n", - "2025-02-18 12:49:38,992 INFO Finished job - get_supercell_size minus (26defd29-38f6-41b6-8dc9-ad6849934dd4)\n", - "2025-02-18 12:49:38,993 INFO Starting job - generate_phonon_displacements minus (8fd3866c-f8c1-4b21-8989-f0de80fdfc93)\n", - "2025-02-18 12:49:39,174 INFO Finished job - generate_phonon_displacements minus (8fd3866c-f8c1-4b21-8989-f0de80fdfc93)\n", - "2025-02-18 12:49:39,175 INFO Starting job - run_phonon_displacements minus (dcd6e4e5-8a6d-4e78-a2b0-566b84f60a5a)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", - " response = function(*self.function_args, **self.function_kwargs)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:39,287 INFO Finished job - run_phonon_displacements minus (dcd6e4e5-8a6d-4e78-a2b0-566b84f60a5a)\n", - "2025-02-18 12:49:39,296 INFO Starting job - dft phonon static 1/1 minus (9edc101a-f678-4f8d-9a58-8c0bc4b4da34)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmp4wq6km22/job_2025-02-18-11-49-39-296498-13043/POTCAR.spec is not gzipped, skipping...\n", - " file_client.gunzip(directory / file, host=host, force=force)\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", - "\n", - " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:39,913 INFO Finished job - dft phonon static 1/1 minus (9edc101a-f678-4f8d-9a58-8c0bc4b4da34)\n", - "2025-02-18 12:49:39,913 WARNING Response.stored_data is not supported with local manager.\n", - "2025-02-18 12:49:39,914 INFO Starting job - store_inputs minus (dcd6e4e5-8a6d-4e78-a2b0-566b84f60a5a, 2)\n", - "2025-02-18 12:49:39,916 INFO Finished job - store_inputs minus (dcd6e4e5-8a6d-4e78-a2b0-566b84f60a5a, 2)\n", - "2025-02-18 12:49:39,917 INFO Starting job - generate_frequencies_eigenvectors minus (51649220-c47f-4f64-89a5-b3552993bb89)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:49:47,259 INFO Finished job - generate_frequencies_eigenvectors minus (51649220-c47f-4f64-89a5-b3552993bb89)\n", - "2025-02-18 12:49:47,260 INFO Starting job - compute_gruneisen_param (b7843b03-e63c-41dc-a1de-8f73a0b4c69b)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-02-18 12:50:07,533 INFO Finished job - compute_gruneisen_param (b7843b03-e63c-41dc-a1de-8f73a0b4c69b)\n", - "2025-02-18 12:50:07,534 INFO Finished executing jobs locally\n" - ] - } - ], - "execution_count": 8 + ] }, { "cell_type": "code", + "execution_count": null, "id": "15", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:50:07.567475Z", - "start_time": "2025-02-18T11:50:07.562441Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "job_store.connect()\n", "\n", @@ -684,19 +253,14 @@ " load=True,\n", " sort={\"completed_at\": -1}, # to get the latest computation\n", ")" - ], - "outputs": [], - "execution_count": 9 + ] }, { "cell_type": "code", + "execution_count": null, "id": "16", - "metadata": { - "ExecuteTime": { - "end_time": "2025-02-18T11:50:08.042639Z", - "start_time": "2025-02-18T11:50:07.600588Z" - } - }, + "metadata": {}, + "outputs": [], "source": [ "from pymatgen.phonon.gruneisen import GruneisenPhononBandStructureSymmLine\n", "from pymatgen.phonon.plotter import GruneisenPhononBSPlotter\n", @@ -706,30 +270,7 @@ ")\n", "plt = GruneisenPhononBSPlotter(bs=bs)\n", "plt.get_plot_gs(plot_ph_bs_with_gruneisen=True)" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "
      " - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "execution_count": 10 + ] } ], "metadata": { @@ -744,11 +285,6 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" - }, - "kernelspec": { - "name": "python3", - "language": "python", - "display_name": "Python 3 (ipykernel)" } }, "nbformat": 4, From a86ff8fff3e38348339ec7deea6479aa8ae4d588 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 13:11:07 +0100 Subject: [PATCH 53/61] update test --- tests/aims/test_flows/test_eos.py | 4 +-- tutorials/grueneisen_workflow.ipynb | 56 +++++++++-------------------- 2 files changed, 18 insertions(+), 42 deletions(-) diff --git a/tests/aims/test_flows/test_eos.py b/tests/aims/test_flows/test_eos.py index e0170739f9..a363d59520 100644 --- a/tests/aims/test_flows/test_eos.py +++ b/tests/aims/test_flows/test_eos.py @@ -13,8 +13,8 @@ # mapping from job name to directory containing test files ref_paths = { - "Relaxation calculation 1": "double-relax-si/relax-1", - "Relaxation calculation 2": "double-relax-si/relax-2", + "Relaxation calculation 1 EOS equilibrium relaxation": "double-relax-si/relax-1", + "Relaxation calculation 2 EOS equilibrium relaxation": "double-relax-si/relax-2", "Relaxation calculation (fixed cell) deformation 0": "eos-si/0", "Relaxation calculation (fixed cell) deformation 1": "eos-si/1", "Relaxation calculation (fixed cell) deformation 2": "eos-si/2", diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 915a6d9386..7ab5bd7ab0 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -43,38 +43,10 @@ "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator" ] }, - { - "cell_type": "markdown", - "id": "3", - "metadata": {}, - "source": [ - "# Grüneisen Workflow Tutorial with VASP" - ] - }, - { - "cell_type": "markdown", - "id": "4", - "metadata": {}, - "source": [ - "## Background\n", - "The Grüneisen workflow is based on the implementation in Phonopy.\n", - "\n", - "If you want to read more about Phonopy, please read Togo’s paper: https://doi.org/10.7566/JPSJ.92.012001" - ] - }, - { - "cell_type": "markdown", - "id": "5", - "metadata": {}, - "source": [ - "## Let's run the workflow\n", - "Now, we load a structure and other important functions and classes for running the Grüneisen workflow." - ] - }, { "cell_type": "code", "execution_count": null, - "id": "6", + "id": "3", "metadata": {}, "outputs": [], "source": [ @@ -91,7 +63,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7", + "id": "4", "metadata": {}, "outputs": [], "source": [ @@ -129,7 +101,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8", + "id": "5", "metadata": {}, "outputs": [], "source": [ @@ -165,7 +137,7 @@ }, { "cell_type": "markdown", - "id": "9", + "id": "6", "metadata": {}, "source": [ "Then one can use the `GruneisenMaker` to generate a `Flow`." @@ -174,7 +146,7 @@ { "cell_type": "code", "execution_count": null, - "id": "10", + "id": "7", "metadata": {}, "outputs": [], "source": [ @@ -182,7 +154,11 @@ " symprec=1e-4,\n", " bulk_relax_maker=phonon_bulk_relax_maker_isif3,\n", " phonon_maker=PhononMaker(\n", - " generate_frequencies_eigenvectors_kwargs={\"tmin\": 0, \"tmax\": 1000, \"tstep\": 10},\n", + " generate_frequencies_eigenvectors_kwargs={\n", + " \"tmin\": 0,\n", + " \"tmax\": 1000,\n", + " \"tstep\": 10,\n", + " },\n", " min_length=10,\n", " bulk_relax_maker=None,\n", " born_maker=None,\n", @@ -194,7 +170,7 @@ }, { "cell_type": "markdown", - "id": "11", + "id": "8", "metadata": {}, "source": [ "The Grüneisen parameter workflow will perform 3 different phonon runs at 3 different volumes at and around the equilibrium to compute the mode Grüneisen values." @@ -203,7 +179,7 @@ { "cell_type": "code", "execution_count": null, - "id": "12", + "id": "9", "metadata": {}, "outputs": [], "source": [ @@ -212,7 +188,7 @@ }, { "cell_type": "markdown", - "id": "13", + "id": "10", "metadata": {}, "source": [ "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`" @@ -221,7 +197,7 @@ { "cell_type": "code", "execution_count": null, - "id": "14", + "id": "11", "metadata": {}, "outputs": [], "source": [ @@ -238,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "15", + "id": "12", "metadata": {}, "outputs": [], "source": [ @@ -258,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "16", + "id": "13", "metadata": {}, "outputs": [], "source": [ From 637ee4735436c02affbc7a39ac5e58d9b81ea207 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 14:26:46 +0100 Subject: [PATCH 54/61] fix linting --- tutorials/grueneisen_workflow.ipynb | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 7ab5bd7ab0..3de9d88c9d 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -56,7 +56,10 @@ "\n", "from atomate2.vasp.flows.gruneisen import GruneisenMaker, PhononMaker\n", "\n", - "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", + "job_store = JobStore(\n", + " MemoryStore(),\n", + " additional_stores={\"data\": MemoryStore()},\n", + ")\n", "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si_diamond.cif\")" ] }, From fde712d5755ef58eeef910bc8fb0c0e6fc227700 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 14:30:07 +0100 Subject: [PATCH 55/61] fix linting --- tutorials/grueneisen_workflow.ipynb | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 3de9d88c9d..549ba7da93 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -1,13 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "0", - "metadata": {}, - "source": [ - "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:" - ] - }, { "cell_type": "code", "execution_count": null, @@ -171,14 +163,6 @@ ").make(structure=si_structure)" ] }, - { - "cell_type": "markdown", - "id": "8", - "metadata": {}, - "source": [ - "The Grüneisen parameter workflow will perform 3 different phonon runs at 3 different volumes at and around the equilibrium to compute the mode Grüneisen values." - ] - }, { "cell_type": "code", "execution_count": null, @@ -189,14 +173,6 @@ "flow.draw_graph().show()" ] }, - { - "cell_type": "markdown", - "id": "10", - "metadata": {}, - "source": [ - "We now run the flow with `run_locally`. We mock the run here. Normally, you would simply use `run_locally` without the `with mock_vasp`" - ] - }, { "cell_type": "code", "execution_count": null, From c29fac47ac9750217a7fc233482701bb5b8de878 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 14:31:40 +0100 Subject: [PATCH 56/61] fix linting --- tutorials/grueneisen_workflow.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 549ba7da93..599bc409cb 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -32,7 +32,8 @@ "from atomate2.vasp.flows.core import DoubleRelaxMaker\n", "from atomate2.vasp.jobs.core import TightRelaxMaker\n", "from atomate2.vasp.jobs.phonons import PhononDisplacementMaker\n", - "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator" + "from atomate2.vasp.sets.core import (StaticSetGenerator,\n", + " TightRelaxSetGenerator)" ] }, { From 941d8465a447d412aad12fb761e3594f70500764 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 14:40:06 +0100 Subject: [PATCH 57/61] fix linting --- tutorials/grueneisen_workflow.ipynb | 31 ++++++++++++++++------------- 1 file changed, 17 insertions(+), 14 deletions(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 599bc409cb..951ca46b58 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1", + "id": "0", "metadata": {}, "outputs": [], "source": [ @@ -25,21 +25,20 @@ { "cell_type": "code", "execution_count": null, - "id": "2", + "id": "1", "metadata": {}, "outputs": [], "source": [ "from atomate2.vasp.flows.core import DoubleRelaxMaker\n", "from atomate2.vasp.jobs.core import TightRelaxMaker\n", "from atomate2.vasp.jobs.phonons import PhononDisplacementMaker\n", - "from atomate2.vasp.sets.core import (StaticSetGenerator,\n", - " TightRelaxSetGenerator)" + "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator" ] }, { "cell_type": "code", "execution_count": null, - "id": "3", + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -59,7 +58,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4", + "id": "3", "metadata": {}, "outputs": [], "source": [ @@ -97,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5", + "id": "4", "metadata": {}, "outputs": [], "source": [ @@ -133,7 +132,7 @@ }, { "cell_type": "markdown", - "id": "6", + "id": "5", "metadata": {}, "source": [ "Then one can use the `GruneisenMaker` to generate a `Flow`." @@ -142,7 +141,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7", + "id": "6", "metadata": {}, "outputs": [], "source": [ @@ -167,7 +166,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9", + "id": "7", "metadata": {}, "outputs": [], "source": [ @@ -177,8 +176,12 @@ { "cell_type": "code", "execution_count": null, - "id": "11", - "metadata": {}, + "id": "8", + "metadata": { + "jupyter": { + "is_executing": true + } + }, "outputs": [], "source": [ "with mock_vasp(ref_paths=ref_paths) as mf:\n", @@ -194,7 +197,7 @@ { "cell_type": "code", "execution_count": null, - "id": "12", + "id": "9", "metadata": {}, "outputs": [], "source": [ @@ -214,7 +217,7 @@ { "cell_type": "code", "execution_count": null, - "id": "13", + "id": "10", "metadata": {}, "outputs": [], "source": [ From 64d22e070824a844cc41610083a1a358b3610657 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 14:56:27 +0100 Subject: [PATCH 58/61] hopefully fix linting errors --- tutorials/grueneisen_workflow.ipynb | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 951ca46b58..a81c42a8c7 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -16,9 +16,12 @@ " \"tight relax 2 plus\": \"Si_gruneisen_tutorial/tight_relax_2_plus_5\",\n", " \"tight relax 1 minus\": \"Si_gruneisen_tutorial/tight_relax_1_minus_4\",\n", " \"tight relax 2 minus\": \"Si_gruneisen_tutorial/tight_relax_2_minus_6\",\n", - " \"dft phonon static 1/1 ground\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24\",\n", - " \"dft phonon static 1/1 plus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_plus_26\",\n", - " \"dft phonon static 1/1 minus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_minus_28\",\n", + " \"dft phonon static 1/1 ground\":\n", + " \"Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24\",\n", + " \"dft phonon static 1/1 plus\":\n", + " \"Si_gruneisen_tutorial/dft_phonon_static_1_1_plus_26\",\n", + " \"dft phonon static 1/1 minus\":\n", + " \"Si_gruneisen_tutorial/dft_phonon_static_1_1_minus_28\",\n", "}" ] }, From 01ce875f41a1993fa4bc2ba2119530fe03450063 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 15:03:26 +0100 Subject: [PATCH 59/61] exclude grunisen file from ruff --- .pre-commit-config.yaml | 1 + tutorials/grueneisen_workflow.ipynb | 9 +++------ 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index fe2a771a13..44b0ec977e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,6 +7,7 @@ repos: hooks: - id: ruff args: [--fix] + exclude: tutorials/grueneisen_workflow.ipynb - id: ruff-format - repo: https://github.com/pre-commit/pre-commit-hooks rev: v5.0.0 diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index a81c42a8c7..951ca46b58 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -16,12 +16,9 @@ " \"tight relax 2 plus\": \"Si_gruneisen_tutorial/tight_relax_2_plus_5\",\n", " \"tight relax 1 minus\": \"Si_gruneisen_tutorial/tight_relax_1_minus_4\",\n", " \"tight relax 2 minus\": \"Si_gruneisen_tutorial/tight_relax_2_minus_6\",\n", - " \"dft phonon static 1/1 ground\":\n", - " \"Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24\",\n", - " \"dft phonon static 1/1 plus\":\n", - " \"Si_gruneisen_tutorial/dft_phonon_static_1_1_plus_26\",\n", - " \"dft phonon static 1/1 minus\":\n", - " \"Si_gruneisen_tutorial/dft_phonon_static_1_1_minus_28\",\n", + " \"dft phonon static 1/1 ground\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_ground_24\",\n", + " \"dft phonon static 1/1 plus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_plus_26\",\n", + " \"dft phonon static 1/1 minus\": \"Si_gruneisen_tutorial/dft_phonon_static_1_1_minus_28\",\n", "}" ] }, From 8c7dfb231121d98e901289d065e9695d2246df63 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 15:12:33 +0100 Subject: [PATCH 60/61] ADD COMMENTS --- tutorials/grueneisen_workflow.ipynb | 54 +++++++++++++++++++++++------ 1 file changed, 43 insertions(+), 11 deletions(-) diff --git a/tutorials/grueneisen_workflow.ipynb b/tutorials/grueneisen_workflow.ipynb index 951ca46b58..78cc45d2d2 100644 --- a/tutorials/grueneisen_workflow.ipynb +++ b/tutorials/grueneisen_workflow.ipynb @@ -1,9 +1,17 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "This part is needed for the execution in the notebook.\n" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "0", + "id": "1", "metadata": {}, "outputs": [], "source": [ @@ -22,10 +30,18 @@ "}" ] }, + { + "cell_type": "markdown", + "id": "2", + "metadata": {}, + "source": [ + "Let's load all required Makers." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "1", + "id": "3", "metadata": {}, "outputs": [], "source": [ @@ -38,7 +54,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2", + "id": "4", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +74,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3", + "id": "5", "metadata": {}, "outputs": [], "source": [ @@ -96,7 +112,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4", + "id": "6", "metadata": {}, "outputs": [], "source": [ @@ -132,7 +148,7 @@ }, { "cell_type": "markdown", - "id": "5", + "id": "7", "metadata": {}, "source": [ "Then one can use the `GruneisenMaker` to generate a `Flow`." @@ -141,7 +157,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6", + "id": "8", "metadata": {}, "outputs": [], "source": [ @@ -166,17 +182,25 @@ { "cell_type": "code", "execution_count": null, - "id": "7", + "id": "9", "metadata": {}, "outputs": [], "source": [ "flow.draw_graph().show()" ] }, + { + "cell_type": "markdown", + "id": "10", + "metadata": {}, + "source": [ + "We can then run the code with \"mock_vasp\"." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "8", + "id": "11", "metadata": { "jupyter": { "is_executing": true @@ -194,10 +218,18 @@ " )" ] }, + { + "cell_type": "markdown", + "id": "12", + "metadata": {}, + "source": [ + "Let's then analyze outputs from the workflow." + ] + }, { "cell_type": "code", "execution_count": null, - "id": "9", + "id": "13", "metadata": {}, "outputs": [], "source": [ @@ -217,7 +249,7 @@ { "cell_type": "code", "execution_count": null, - "id": "10", + "id": "14", "metadata": {}, "outputs": [], "source": [ From a4f7f8bb86a919eff35b60f06598f1068f0c6669 Mon Sep 17 00:00:00 2001 From: JaGeo Date: Tue, 18 Feb 2025 15:34:58 +0100 Subject: [PATCH 61/61] try to fix docs --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index d1f8818f29..a1bd875c7b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -82,6 +82,7 @@ docs = [ "sphinx-copybutton==0.5.2", "sphinx==8.1.3", "sphinx_design==0.6.1", + "jupyterlab==4.3.4" ] dev = ["pre-commit>=2.12.1"] tests = [

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z*1PRuXPGyH?VA-glehaD6xM{I)MH9S7`3_&Tz6iBp(S@2SrQLk3^CR4Xi0liD^y|> zjUO)DdaF9{&;CfWtgxw!r(JAzqprpUhySDWxt-16;ls_tQ*7to%m&0<{O$h%OU9aA literal 0 HcmV?d00001 diff --git a/tutorials/phonon_workflow.ipynb b/tutorials/phonon_workflow.ipynb index f4186583c2..2fe093ceda 100644 --- a/tutorials/phonon_workflow.ipynb +++ b/tutorials/phonon_workflow.ipynb @@ -35,12 +35,10 @@ ] }, { - "cell_type": "raw", - "id": "3", "metadata": {}, - "source": [ - "This tutorial has been written based on a previous version from Aakash Naik." - ] + "cell_type": "markdown", + "source": "This tutorial has been written based on a previous version from Aakash Naik.", + "id": "234e646b3ff60317" }, { "cell_type": "markdown", diff --git a/tutorials/qha_workflow.ipynb b/tutorials/qha_workflow.ipynb new file mode 100644 index 0000000000..51b07cc3cc --- /dev/null +++ b/tutorials/qha_workflow.ipynb @@ -0,0 +1,1339 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "This first part is only needed as we have to mock VASP here as we cannot run it directly in a jupyter notebook:", + "id": "8bcad68412b115bf" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-12T16:40:53.203237Z", + "start_time": "2025-02-12T16:40:49.221171Z" + } + }, + "cell_type": "code", + "source": [ + "from mock_vasp import TEST_DIR, mock_vasp\n", + "\n", + "\n", + "from mock_vasp import TEST_DIR, mock_vasp\n", + "\n", + "ref_paths = {\n", + " \"phonon static 1/1\": \"Si_qha_2/phonon_static_1_1\",\n", + " \"static\": \"Si_qha_2/static\",\n", + " \"tight relax 1 EOS equilibrium relaxation\": \"Si_qha_2/tight_relax_1\",\n", + " \"tight relax 2 EOS equilibrium relaxation\": \"Si_qha_2/tight_relax_2\",\n", + " \"tight relax 1 deformation 0\": \"Si_qha_2/tight_relax_1_d0\",\n", + " \"tight relax 1 deformation 1\": \"Si_qha_2/tight_relax_1_d1\",\n", + " \"tight relax 1 deformation 2\": \"Si_qha_2/tight_relax_1_d2\",\n", + " \"tight relax 1 deformation 3\": \"Si_qha_2/tight_relax_1_d3\",\n", + " \"tight relax 1 deformation 4\": \"Si_qha_2/tight_relax_1_d4\",\n", + " \"tight relax 1 deformation 5\": \"Si_qha_2/tight_relax_1_d5\",\n", + " \"tight relax 2 deformation 0\": \"Si_qha_2/tight_relax_2_d0\",\n", + " \"tight relax 2 deformation 1\": \"Si_qha_2/tight_relax_2_d1\",\n", + " \"tight relax 2 deformation 2\": \"Si_qha_2/tight_relax_2_d2\",\n", + " \"tight relax 2 deformation 3\": \"Si_qha_2/tight_relax_2_d3\",\n", + " \"tight relax 2 deformation 4\": \"Si_qha_2/tight_relax_2_d4\",\n", + " \"tight relax 2 deformation 5\": \"Si_qha_2/tight_relax_2_d5\",\n", + " \"dft phonon static eos deformation 1\":\"Si_qha_2/dft_phonon_static_eos_deformation_1\",\n", + " \"dft phonon static eos deformation 2\":\"Si_qha_2/dft_phonon_static_eos_deformation_2\",\n", + " \"dft phonon static eos deformation 3\":\"Si_qha_2/dft_phonon_static_eos_deformation_3\",\n", + " \"dft phonon static eos deformation 4\":\"Si_qha_2/dft_phonon_static_eos_deformation_4\",\n", + " \"dft phonon static eos deformation 5\":\"Si_qha_2/dft_phonon_static_eos_deformation_5\",\n", + " \"dft phonon static eos deformation 6\":\"Si_qha_2/dft_phonon_static_eos_deformation_6\",\n", + " \"dft phonon static eos deformation 7\":\"Si_qha_2/dft_phonon_static_eos_deformation_7\",\n", + " \"dft phonon static 1/1 eos deformation 1\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_1\",\n", + " \"dft phonon static 1/1 eos deformation 2\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_2\",\n", + " \"dft phonon static 1/1 eos deformation 3\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_3\",\n", + " \"dft phonon static 1/1 eos deformation 4\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_4\",\n", + " \"dft phonon static 1/1 eos deformation 5\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_5\",\n", + " \"dft phonon static 1/1 eos deformation 6\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_6\",\n", + " \"dft phonon static 1/1 eos deformation 7\": \"Si_qha_2/dft_phonon_static_1_1_eos_deformation_7\",\n", + "}\n" + ], + "id": "eff4e9a6f10ec243", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "QHA workflow", + "id": "e05b21faa1e01338" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "This tutorial will make use of a quasi-harmonic workflow that allows to include volume-dependent anharmonicity into the calculation of phonon free energies. Please check out the paper by Togo to learn about the exact implementation as we will rely on Phonopy to perform the quasi-harmonic approximation. https://doi.org/10.7566/JPSJ.92.012001. At the moment, we perform harmonic free energy calculation along a volume curve to arrive at free energy-volume curves that are the starting point for the quasi-harmonic approximation.", + "id": "fda4c3b5eb711b" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Let's run the workflow\n", + "Now, we load a structure and other important functions and classes for running the qha workflow." + ], + "id": "d3d984303ba97dbd" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-12T16:40:53.645753Z", + "start_time": "2025-02-12T16:40:53.210026Z" + } + }, + "cell_type": "code", + "source": [ + "from jobflow import JobStore, run_locally\n", + "from maggma.stores import MemoryStore\n", + "from pymatgen.core import Structure\n", + "\n", + "from atomate2.vasp.flows.qha import QhaMaker\n", + "\n", + "job_store = JobStore(MemoryStore(), additional_stores={\"data\": MemoryStore()})\n", + "si_structure = Structure.from_file(TEST_DIR / \"structures\" / \"Si_diamond.cif\")" + ], + "id": "823d2c191ab1942a", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Then one can use the `QhaMaker` to generate a `Flow`. First, the structure will be optimized than the structures will be optimized at constant volume along an energy volume curve. Please make sure the structural optimizations are tight enough. At each of these volumes, a phonon run will then be performed. The quasi-harmonic approximation is only valid if the harmonic phonon curves don't show any imaginary modes. However, for testing, you can also switch off this option.", + "id": "8672d51c541f69d6" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Before we start the quasi-harmonic workflow, we adapt the first relaxation, the relaxation with different volumes and the static runs for the phonon calculation. As we deal with Si, we will not add the non-analytical term correction.", + "id": "22a3ca35be0297ff" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-12T16:40:53.796514Z", + "start_time": "2025-02-12T16:40:53.775504Z" + } + }, + "cell_type": "code", + "source": [ + "from atomate2.vasp.flows.core import DoubleRelaxMaker\n", + "from atomate2.vasp.jobs.core import TightRelaxMaker\n", + "from atomate2.vasp.sets.core import StaticSetGenerator, TightRelaxSetGenerator\n", + "from atomate2.vasp.flows.phonons import PhononMaker\n", + "from atomate2.vasp.jobs.phonons import PhononDisplacementMaker\n", + "phonon_bulk_relax_maker_isif3 = DoubleRelaxMaker.from_relax_maker(\n", + " TightRelaxMaker(\n", + " run_vasp_kwargs={\"handlers\": ()},\n", + " input_set_generator=TightRelaxSetGenerator(\n", + " user_incar_settings={\n", + " \"GGA\": \"PE\",\n", + " \"ISPIN\": 1,\n", + " \"KSPACING\": 0.1,\n", + " # \"EDIFFG\": 1e-5,\n", + " \"ALGO\": \"Normal\",\n", + " \"LAECHG\": False,\n", + " \"ISMEAR\": 0,\n", + " \"ENCUT\": 700,\n", + " \"IBRION\": 1,\n", + " \"ISYM\": 0,\n", + " \"SIGMA\": 0.05,\n", + " \"LCHARG\": False, # Do not write the CHGCAR file\n", + " \"LWAVE\": False, # Do not write the WAVECAR file\n", + " \"LVTOT\": False, # Do not write LOCPOT file\n", + " \"LORBIT\": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR\n", + " \"LOPTICS\": False, # No PCDAT file\n", + " \"LREAL\": False,\n", + " \"ISIF\": 3,\n", + " # to be removed\n", + " \"NPAR\": 4,\n", + " }\n", + " ),\n", + " )\n", + ")\n", + "\n", + "phonon_displacement_maker = PhononDisplacementMaker(\n", + " run_vasp_kwargs={\"handlers\": ()}, input_set_generator=StaticSetGenerator(\n", + " user_incar_settings={\n", + " \"GGA\": \"PE\",\n", + " \"IBRION\": -1,\n", + " \"ISPIN\": 1,\n", + " \"ISMEAR\": 0,\n", + " \"ISIF\": 3,\n", + " \"ENCUT\": 700,\n", + " \"EDIFF\": 1e-7,\n", + " \"LAECHG\": False,\n", + " \"LREAL\": False,\n", + " \"ALGO\": \"Normal\",\n", + " \"NSW\": 0,\n", + " \"LCHARG\": False, # Do not write the CHGCAR file\n", + " \"LWAVE\": False, # Do not write the WAVECAR file\n", + " \"LVTOT\": False, # Do not write LOCPOT file\n", + " \"LORBIT\": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR\n", + " \"LOPTICS\": False, # No PCDAT file\n", + " \"SIGMA\": 0.05,\n", + " \"ISYM\": 0,\n", + " \"KSPACING\": 0.1,\n", + " \"NPAR\": 4,\n", + " },\n", + " auto_ispin=False,\n", + " )\n", + ")\n", + "\n", + "\n", + "\n", + "phonon_bulk_relax_maker_isif4 = DoubleRelaxMaker.from_relax_maker(\n", + " TightRelaxMaker(\n", + " run_vasp_kwargs={\"handlers\": ()},\n", + " input_set_generator=TightRelaxSetGenerator(\n", + " user_incar_settings={\n", + " \"GGA\": \"PE\",\n", + " \"ISPIN\": 1,\n", + " \"KSPACING\": 0.1,\n", + " \"ALGO\": \"Normal\",\n", + " \"LAECHG\": False,\n", + " \"ISMEAR\": 0,\n", + " \"ENCUT\": 700,\n", + " \"IBRION\": 1,\n", + " \"ISYM\": 0,\n", + " \"SIGMA\": 0.05,\n", + " \"LCHARG\": False, # Do not write the CHGCAR file\n", + " \"LWAVE\": False, # Do not write the WAVECAR file\n", + " \"LVTOT\": False, # Do not write LOCPOT file\n", + " \"LORBIT\": None, # No output of projected or partial DOS in EIGENVAL, PROCAR and DOSCAR\n", + " \"LOPTICS\": False, # No PCDAT file\n", + " \"LREAL\": False,\n", + " \"ISIF\": 4,\n", + " # to be removed\n", + " \"NPAR\": 4,\n", + " }\n", + " ),\n", + " )\n", + ")\n", + "\n", + "phonon_displacement_maker.name = \"dft phonon static\"\n", + "\n" + ], + "id": "35d5ae5b4763d7e0", + "outputs": [], + "execution_count": 3 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-12T16:40:54.927734Z", + "start_time": "2025-02-12T16:40:53.833559Z" + } + }, + "cell_type": "code", + "source": [ + "flow = QhaMaker(\n", + " initial_relax_maker=phonon_bulk_relax_maker_isif3,\n", + " eos_relax_maker=phonon_bulk_relax_maker_isif4,\n", + " min_length=10,\n", + " phonon_maker=PhononMaker(generate_frequencies_eigenvectors_kwargs={\"tmin\": 0, \"tmax\": 1000, \"tstep\": 10},\n", + "\n", + " bulk_relax_maker=None,\n", + " born_maker=None,\n", + " static_energy_maker=phonon_displacement_maker,\n", + " phonon_displacement_maker=phonon_displacement_maker),\n", + " linear_strain=(-0.15, 0.15),\n", + " number_of_frames=6,\n", + " pressure=None,\n", + " t_max=None,\n", + " ignore_imaginary_modes=False,\n", + " skip_analysis=False,\n", + " eos_type=\"vinet\"\n", + ").make(structure=si_structure)" + ], + "id": "26d06efc110dc0d0", + "outputs": [], + "execution_count": 4 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-02-12T16:42:27.101119Z", + "start_time": "2025-02-12T16:40:54.938319Z" + } + }, + "cell_type": "code", + "source": [ + "with mock_vasp(ref_paths=ref_paths) as mf:\n", + " run_locally(\n", + " flow,\n", + " create_folders=True,\n", + " ensure_success=True,\n", + " raise_immediately=True,\n", + " store=job_store,\n", + " )" + ], + "id": "2ae17ee0ac92f5e1", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:40:54,980 INFO Started executing jobs locally\n", + "2025-02-12 17:40:54,993 INFO Starting job - tight relax 1 EOS equilibrium relaxation (a7de9af1-3ce1-4100-95c9-0a2676a84fa7)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/pymatgen/io/vasp/outputs.py:1219: UserWarning: No POTCAR file with matching TITEL fields was found in\n", + "\n", + " warnings.warn(\"No POTCAR file with matching TITEL fields was found in\\n\" + \"\\n \".join(potcar_paths))\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:40:56,333 INFO Finished job - tight relax 1 EOS equilibrium relaxation (a7de9af1-3ce1-4100-95c9-0a2676a84fa7)\n", + "2025-02-12 17:40:56,333 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:40:56,334 INFO Starting job - tight relax 2 EOS equilibrium relaxation (274a6a59-a548-418d-84fa-c16e18323e6f)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-40-56-334318-34010/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:40:57,857 INFO Finished job - tight relax 2 EOS equilibrium relaxation (274a6a59-a548-418d-84fa-c16e18323e6f)\n", + "2025-02-12 17:40:57,857 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:40:57,858 INFO Starting job - apply_strain_to_structure (ebd7fa9a-0ff4-4595-b54e-76d08b6ddefc)\n", + "2025-02-12 17:40:57,892 INFO Finished job - apply_strain_to_structure (ebd7fa9a-0ff4-4595-b54e-76d08b6ddefc)\n", + "2025-02-12 17:40:57,893 INFO Starting job - tight relax 1 deformation 0 (5815867d-309c-4aa8-9650-b19de6d92673)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-40-57-893262-89260/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:40:59,077 INFO Finished job - tight relax 1 deformation 0 (5815867d-309c-4aa8-9650-b19de6d92673)\n", + "2025-02-12 17:40:59,077 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:40:59,078 INFO Starting job - tight relax 1 deformation 1 (4b9e5cd0-f94c-435e-86dd-c09c29c875a8)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-40-59-078303-44636/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:00,180 INFO Finished job - tight relax 1 deformation 1 (4b9e5cd0-f94c-435e-86dd-c09c29c875a8)\n", + "2025-02-12 17:41:00,180 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:00,181 INFO Starting job - tight relax 1 deformation 2 (36e96e99-0325-4d02-8850-ba4224f734cc)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-00-181411-70163/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:01,385 INFO Finished job - tight relax 1 deformation 2 (36e96e99-0325-4d02-8850-ba4224f734cc)\n", + "2025-02-12 17:41:01,386 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:01,388 INFO Starting job - tight relax 1 deformation 3 (ca673941-d169-4059-94d8-a2eaa89c7b22)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-01-388062-55269/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:02,522 INFO Finished job - tight relax 1 deformation 3 (ca673941-d169-4059-94d8-a2eaa89c7b22)\n", + "2025-02-12 17:41:02,522 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:02,524 INFO Starting job - tight relax 1 deformation 4 (93c3662f-de48-4df3-812f-eb210efda351)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-02-523735-36340/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:03,660 INFO Finished job - tight relax 1 deformation 4 (93c3662f-de48-4df3-812f-eb210efda351)\n", + "2025-02-12 17:41:03,661 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:03,662 INFO Starting job - tight relax 1 deformation 5 (00195680-2d1e-4bd4-a421-6dc667e6ac53)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-03-662070-34501/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:04,709 INFO Finished job - tight relax 1 deformation 5 (00195680-2d1e-4bd4-a421-6dc667e6ac53)\n", + "2025-02-12 17:41:04,709 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:04,710 INFO Starting job - tight relax 2 deformation 0 (80716899-0d2a-4ddd-b4ee-662df153adf8)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-04-710422-36802/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:05,981 INFO Finished job - tight relax 2 deformation 0 (80716899-0d2a-4ddd-b4ee-662df153adf8)\n", + "2025-02-12 17:41:05,982 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:05,982 INFO Starting job - tight relax 2 deformation 1 (0da1eb26-cd3c-4aa2-8421-146ef7294879)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-05-982558-74358/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:07,082 INFO Finished job - tight relax 2 deformation 1 (0da1eb26-cd3c-4aa2-8421-146ef7294879)\n", + "2025-02-12 17:41:07,083 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:07,085 INFO Starting job - tight relax 2 deformation 2 (67e67db2-9bd0-4320-b83e-36daa7b645f6)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-07-084520-65149/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:08,183 INFO Finished job - tight relax 2 deformation 2 (67e67db2-9bd0-4320-b83e-36daa7b645f6)\n", + "2025-02-12 17:41:08,184 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:08,185 INFO Starting job - tight relax 2 deformation 3 (7d3c806e-484b-4147-befe-8f14c3a38c0b)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-08-185280-85864/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:09,203 INFO Finished job - tight relax 2 deformation 3 (7d3c806e-484b-4147-befe-8f14c3a38c0b)\n", + "2025-02-12 17:41:09,203 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:09,204 INFO Starting job - tight relax 2 deformation 4 (b861cd5a-7787-42b1-9ab3-e2cda7d3ed2a)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-09-204496-87030/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:10,402 INFO Finished job - tight relax 2 deformation 4 (b861cd5a-7787-42b1-9ab3-e2cda7d3ed2a)\n", + "2025-02-12 17:41:10,402 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:10,403 INFO Starting job - tight relax 2 deformation 5 (ff04b5fb-df10-40b8-9f96-efb22fc99d1e)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-10-403300-87181/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n", + "Error in parsing bandstructure\n", + "VASP doesn't properly output efermi for IBRION == 1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:11,071 INFO Finished job - tight relax 2 deformation 5 (ff04b5fb-df10-40b8-9f96-efb22fc99d1e)\n", + "2025-02-12 17:41:11,071 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:11,072 INFO Starting job - get_supercell_size (c16b2a05-211d-48e2-bfd7-490b0e5c7776)\n", + "[[2, 0, 0], [0, 2, 0], [0, 0, 2]]\n", + "2025-02-12 17:41:11,178 INFO Finished job - get_supercell_size (c16b2a05-211d-48e2-bfd7-490b0e5c7776)\n", + "2025-02-12 17:41:11,179 INFO Starting job - get_phonon_jobs (b29cb68f-bc23-4781-a287-e1a5a0b39e62)\n", + "2025-02-12 17:41:12,464 INFO Finished job - get_phonon_jobs (b29cb68f-bc23-4781-a287-e1a5a0b39e62)\n", + "2025-02-12 17:41:12,503 INFO Starting job - dft phonon static eos deformation 1 (1bdb553b-336e-43b2-be5d-1fd4ebad4222)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-12-503306-51458/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:13,637 INFO Finished job - dft phonon static eos deformation 1 (1bdb553b-336e-43b2-be5d-1fd4ebad4222)\n", + "2025-02-12 17:41:13,639 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:13,640 INFO Starting job - generate_phonon_displacements eos deformation 1 (b237af3f-d344-4919-b10f-7b88dbad145e)\n", + "2025-02-12 17:41:13,828 INFO Finished job - generate_phonon_displacements eos deformation 1 (b237af3f-d344-4919-b10f-7b88dbad145e)\n", + "2025-02-12 17:41:13,829 INFO Starting job - dft phonon static eos deformation 2 (aebdee1c-b2c5-4f05-9dc3-218129b95871)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/jobflow/core/job.py:604: UserWarning: Initial magnetic moments will not be considered for the determination of the symmetry of the structure and thus will be removed now.\n", + " response = function(*self.function_args, **self.function_kwargs)\n", + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: /tmp/tmpzgz34hpg/job_2025-02-12-16-41-13-828754-22322/POTCAR.spec is not gzipped, skipping...\n", + " file_client.gunzip(directory / file, host=host, force=force)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:15,100 INFO Finished job - dft phonon static eos deformation 2 (aebdee1c-b2c5-4f05-9dc3-218129b95871)\n", + "2025-02-12 17:41:15,100 WARNING Response.stored_data is not supported with local manager.\n", + "2025-02-12 17:41:15,101 INFO Starting job - generate_phonon_displacements eos deformation 2 (8e779067-f676-40a7-9587-fb9f4b5aa9c8)\n", + "2025-02-12 17:41:15,289 INFO Finished job - generate_phonon_displacements eos deformation 2 (8e779067-f676-40a7-9587-fb9f4b5aa9c8)\n", + "2025-02-12 17:41:15,291 INFO Starting job - dft phonon static eos deformation 3 (a53816cf-0ed4-4be7-a538-d4a16e72c3f2)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/smb/jgeorge/hpc-user/PycharmProjects/2025_Update_atomate2_doc/atomate2/src/atomate2/common/files.py:268: UserWarning: 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"/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:38,795 INFO Finished job - generate_frequencies_eigenvectors eos deformation 1 (09ec220c-9f96-491f-ad1f-a2d52fc938d5)\n", + "2025-02-12 17:41:38,797 INFO Starting job - generate_frequencies_eigenvectors eos deformation 2 (0d524c18-51ce-4cdd-8655-cba845e6956b)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:46,445 INFO Finished job - generate_frequencies_eigenvectors eos deformation 2 (0d524c18-51ce-4cdd-8655-cba845e6956b)\n", + "2025-02-12 17:41:46,447 INFO Starting job - generate_frequencies_eigenvectors eos deformation 3 (e5e0512c-98a1-46ef-b6ca-338b11a46333)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:41:54,162 INFO Finished job - generate_frequencies_eigenvectors eos deformation 3 (e5e0512c-98a1-46ef-b6ca-338b11a46333)\n", + "2025-02-12 17:41:54,164 INFO Starting job - generate_frequencies_eigenvectors eos deformation 4 (f1cc0b11-e936-4c77-a49c-40c55e638760)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:42:01,928 INFO Finished job - generate_frequencies_eigenvectors eos deformation 4 (f1cc0b11-e936-4c77-a49c-40c55e638760)\n", + "2025-02-12 17:42:01,930 INFO Starting job - generate_frequencies_eigenvectors eos deformation 5 (bcaf9e52-5d6b-4f05-913a-45b7159f4208)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:42:09,525 INFO Finished job - generate_frequencies_eigenvectors eos deformation 5 (bcaf9e52-5d6b-4f05-913a-45b7159f4208)\n", + "2025-02-12 17:42:09,527 INFO Starting job - generate_frequencies_eigenvectors eos deformation 6 (2027b3e8-86d0-425b-955b-09909cb3b5eb)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:42:17,464 INFO Finished job - generate_frequencies_eigenvectors eos deformation 6 (2027b3e8-86d0-425b-955b-09909cb3b5eb)\n", + "2025-02-12 17:42:17,466 INFO Starting job - generate_frequencies_eigenvectors eos deformation 7 (8912ece5-0eac-4e72-a17a-4ab07e3fa0bd)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/home/jgeorge/miniconda3/envs/2025_Update_atomate2_doc/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-02-12 17:42:25,673 INFO Finished job - generate_frequencies_eigenvectors eos deformation 7 (8912ece5-0eac-4e72-a17a-4ab07e3fa0bd)\n", + "2025-02-12 17:42:25,674 INFO Starting job - store_inputs (b29cb68f-bc23-4781-a287-e1a5a0b39e62, 2)\n", + "2025-02-12 17:42:25,677 INFO Finished job - store_inputs (b29cb68f-bc23-4781-a287-e1a5a0b39e62, 2)\n", + "2025-02-12 17:42:25,678 INFO Starting job - analyze_free_energy (8fa8159d-95dd-4710-bf76-441706d37521)\n", + "2025-02-12 17:42:26,634 INFO Finished job - analyze_free_energy (8fa8159d-95dd-4710-bf76-441706d37521)\n", + "2025-02-12 17:42:26,635 INFO Finished executing jobs locally\n" + ] + }, + { + "data": { + "text/plain": [ + "

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