From d1f763d1178c4d693fdaf954ac68fe8ecf612938 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Thu, 7 Mar 2024 16:06:04 +0100 Subject: [PATCH 01/22] testing a pyiron job --- .DS_Store | Bin 0 -> 6148 bytes .gitignore | 3 + .../datautils}/plotting.py | 0 .../datautils}/post_processing.py | 0 .../datautils}/pre_processing.py | 2 +- EXTRA_FIM/pyiron_job.py | 123 +++++++++++++++++ Examples/FIMjob.ipynb | 10 +- Examples/pyiron.log | 0 Examples/testWorkflow-main.ipynb | 17 +-- Examples/test_pyironjob.ipynb | 125 ++++++++++++++++++ pyiron.log | 0 pyproject.toml | 26 ++++ setup.py | 19 --- test_waves/.DS_Store | Bin 0 -> 6148 bytes test_waves/vElStat-eV.sxb | Bin 0 -> 25920 bytes test_waves/waves.sxb | Bin 0 -> 39297 bytes 16 files changed, 289 insertions(+), 36 deletions(-) create mode 100644 .DS_Store rename {data_utils => EXTRA_FIM/datautils}/plotting.py (100%) rename {data_utils => EXTRA_FIM/datautils}/post_processing.py (100%) rename {data_utils => EXTRA_FIM/datautils}/pre_processing.py (99%) create mode 100644 EXTRA_FIM/pyiron_job.py create mode 100644 Examples/pyiron.log create mode 100644 Examples/test_pyironjob.ipynb create mode 100644 pyiron.log create mode 100644 pyproject.toml delete mode 100644 setup.py create mode 100644 test_waves/.DS_Store create mode 100644 test_waves/vElStat-eV.sxb create mode 100644 test_waves/waves.sxb diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..c916a5117583bd226723e85ebb5f0646509f62e3 GIT binary patch literal 6148 zcmeH~PiqrF7{=d8quC@VIVki}SnwKRA_OX4wrL_1tcPw}NZ9A+3)`9=c0seL!>dul~U!xpjr4jgFHF|-DvpCNu^ zh!SST?ufceGx&X(?v*RfvDaT$yxz)$v+>X{-@{=XjVJZ`Pm$Zn-?>|G3QpO1?|+mt ze-ccl<5nl~fcRp{{n^m{b*k4rL`P2O;RkvAhEEYxQ;iJdTPP*suMIx`U4urtDpyHmFR*WEoMxh!N=87RSj(*qVc}AC7HBQ1@K7@I*FgFyT-j4ZQl}^IbYHRC& zbzt3r9sOA0^S|@=`~Ny*pR5Dcfm`K($hZA=3rjL*>&D{vtaVX7pm1>BQmaBxnd4YB f_$b~*QGqd+8&J>aQY(58_J@G7!B*CRf9k*=9-O<| literal 0 HcmV?d00001 diff --git a/.gitignore b/.gitignore index 0b24a99..b25a8a9 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,6 @@ *.swo __pycache__/ .idea/ +*.lock +.vscode/ + diff --git a/data_utils/plotting.py b/EXTRA_FIM/datautils/plotting.py similarity index 100% rename from data_utils/plotting.py rename to EXTRA_FIM/datautils/plotting.py diff --git a/data_utils/post_processing.py b/EXTRA_FIM/datautils/post_processing.py similarity index 100% rename from data_utils/post_processing.py rename to EXTRA_FIM/datautils/post_processing.py diff --git a/data_utils/pre_processing.py b/EXTRA_FIM/datautils/pre_processing.py similarity index 99% rename from data_utils/pre_processing.py rename to EXTRA_FIM/datautils/pre_processing.py index b38a5b3..5389ae0 100644 --- a/data_utils/pre_processing.py +++ b/EXTRA_FIM/datautils/pre_processing.py @@ -13,7 +13,7 @@ import scipy from .plotting import potential_figure -from ..EXTRA_FIM.potential import sx_el_potential1D_cell +from EXTRA_FIM.potential import sx_el_potential1D_cell class PreProcessingFIM: diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py new file mode 100644 index 0000000..fb3d427 --- /dev/null +++ b/EXTRA_FIM/pyiron_job.py @@ -0,0 +1,123 @@ +from pyiron_base.utils.error import ImportAlarm +from pyiron_base.jobs.master.parallel import ParallelMaster +from pyiron_base.jobs.job.jobtype import JobType +from pyiron_base import JobGenerator +from pyiron_base.jobs.job.template import TemplateJob + +from pathlib import Path +try: + import EXTRA_FIM.main as fim + from EXTRA_FIM.potential import extend_potential, sx_el_potential3D_cell + from EXTRA_FIM.datautils.pre_processing import suggest_input_dictionary + from EXTRA_FIM.sx_nc_waves_reader import sx_nc_waves_reader +except ImportError: + import_alarm = ImportAlarm("Unable to import EXTRA_FIM") + +__author__ = "Christoph Freysoldt, Shyam Katnagallu" +__copyright__ = ( + "Copyright 2021, Max-Planck-Institut für Eisenforschung GmbH - " + "Computational Materials Design (CM) Department" +) +__version__ = "0.1.0" +__maintainer__ = "Shyam Katnagallu" +__email__ = "s.katnagallu@mpie.de" +__status__ = "development" +__date__ = "March 5, 2024" + +class ExtraFimSimulatorRefJob(TemplateJob): + def __init__(self, project, job_name): + super().__init__(project, job_name) + self.__version__ = "0.1.0" + self.input['waves_directory'] = None + self.input['waves_reader'] = None + self.input['kpoint'] = None + self.input['structure'] = None + self.input['ionization_energies'] = None + self.input['extrapolate_potential'] = False + + + @property + def extrpolate_potential(self): + elec_potential,_ = sx_el_potential3D_cell (Path(self.input.simulator_dict['working_directory'])) + pot,_,_,cell = fim.potential (self.input.simulator_dict).potential_cell () + + if self.input.extrapolate_potential: + iz0 = self.input.simulator_dict['iz_ext_from'] + new_z_max=self.input.simulator_dict['z_ext'] + fig, pot_ext = extend_potential(elec_potential / fim.HARTREE_TO_EV, + iz0, pot, cell, z_max=new_z_max, + izend=self.input.simulator_dict['izend'],dv_limit=1e-4,plotG=1) + # copy extension from pot to elec_potential + elec_ext = pot_ext[:,:,:,0] * fim.HARTREE_TO_EV + elec_ext[:,:,0:iz0] = elec_potential[:,:,:iz0] + else: + pot_ext = pot + elec_ext = elec_potential + return pot_ext, elec_potential + + @property + def suggest_input_dict(self): + waves_reader = sx_nc_waves_reader(Path(self.input['waves_directory'])/ "waves.sxb") + e_fermi = waves_reader.get_fermi_energy() + fig,sim=suggest_input_dictionary(self.input.waves_directory,e_fermi, ionization_energies=self.input['ionization_energies']) + self.input['simulator_dict'] = sim + self.input['z_max'] = sim['z_max'] #rename later + self.input['izstart_min'] = sim['izstart_min'] #rename later + self.input['izend'] = sim['izend'] #rename later + self.input['limit'] = sim['limit'] #rename later + self.input['cutoff'] = sim['cutoff'] #rename later + self.input['E_fermi'] = sim['E_fermi'] #rename later + self.input['E_max'] = sim['E_max'] #rename later + return sim + + def run_static(self): + pot_ext, elec_ext = self.extrpolate_potential() + waves_reader = sx_nc_waves_reader(self.input['waves_directory'] + "/waves.sxb") + fimsim=fim.FIM_simulations(self.input['simulator_dict'],reader=waves_reader,V_total=pot_ext,V_elstat=elec_ext) + fimsim.sum_single_k(self.input['kpoint']) + self.status.finished = True + + +class ExtraFimSimulatorJobGenerator(JobGenerator): + @property + def parameter_list(self): + """ + + Returns: + (list) + """ + parameter_lst = [] + kpoints = self._master.input.get("kpoints") + if kpoints is None: + waves_reader = sx_nc_waves_reader(self._master.input['waves_directory']+ "/waves.sxb") + for k in waves_reader.nk: + parameter_lst.append(k) + return parameter_lst + + def job_name(self, parameter): + k_point = parameter[0] + return f"{self._master.job_name}_kpoint_{k_point}" + + def modify_job(self, job, parameter): + job.set= parameter[1] + return job + + +class ExtraFimSimulator(ParallelMaster): + def __init__(self, project, job_name): + super(ExtraFimSimulator).__init__(project, job_name) + self._job_generator = ExtraFimSimulatorJobGenerator(self) + + + def run_static(self): + super().run_static() + + + def collect_output(self): + for job_id in self.child_ids: + job = self.project_hdf5.inspect(job_id) + ... + return super().collect_output() + +JobType.register(ExtraFimSimulator) +JobType.register(ExtraFimSimulatorRefJob) \ No newline at end of file diff --git a/Examples/FIMjob.ipynb b/Examples/FIMjob.ipynb index f8da653..0017006 100644 --- a/Examples/FIMjob.ipynb +++ b/Examples/FIMjob.ipynb @@ -16,13 +16,11 @@ "outputs": [], "source": [ "from pyiron_base import load\n", - "import numpy as np\n", - "\n", "import sys\n", "sys.path.insert(0,'/cmmc/u/cfrey/devel/python-test')\n", - "import EXTRA_FIM.EXTRA_FIM.main as fim\n", - "from EXTRA_FIM.EXTRA_FIM.sx_nc_waves_reader import sx_nc_waves_reader\n", - "from EXTRA_FIM.EXTRA_FIM.potential import extend_potential, sx_el_potential3D_cell" + "import EXTRA_FIM.main as fim\n", + "from EXTRA_FIM.sx_nc_waves_reader import sx_nc_waves_reader\n", + "from EXTRA_FIM.potential import extend_potential, sx_el_potential3D_cell" ] }, { @@ -157,7 +155,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/Examples/pyiron.log b/Examples/pyiron.log new file mode 100644 index 0000000..e69de29 diff --git a/Examples/testWorkflow-main.ipynb b/Examples/testWorkflow-main.ipynb index 4d91926..8e7f7cf 100644 --- a/Examples/testWorkflow-main.ipynb +++ b/Examples/testWorkflow-main.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "664f67c6-6017-4604-9f0c-f43cfa72132e", "metadata": {}, "outputs": [], @@ -19,14 +19,11 @@ "import os\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches\n", - "\n", - "import netCDF4\n", - "import EXTRA_FIM.EXTRA_FIM.main as EXTRA\n", - "from EXTRA_FIM.EXTRA_FIM.sx_nc_waves_reader import sx_nc_waves_reader\n", - "from EXTRA_FIM.data_utils.plotting import potential_figure, waves_figure, BOHR_TO_AA\n", - "from EXTRA_FIM.data_utils.pre_processing import suggest_input_dictionary\n", - "from EXTRA_FIM.EXTRA_FIM.potential import extend_potential, sx_el_potential3D_cell\n", - "import scipy\n" + "import EXTRA_FIM.main as EXTRA\n", + "from EXTRA_FIM.sx_nc_waves_reader import sx_nc_waves_reader\n", + "from EXTRA_FIM.datautils.plotting import potential_figure, waves_figure, BOHR_TO_AA\n", + "from EXTRA_FIM.datautils.pre_processing import suggest_input_dictionary\n", + "from EXTRA_FIM.potential import extend_potential, sx_el_potential3D_cell\n" ] }, { @@ -1375,7 +1372,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.8" } }, "nbformat": 4, diff --git a/Examples/test_pyironjob.ipynb b/Examples/test_pyironjob.ipynb new file mode 100644 index 0000000..d8b3958 --- /dev/null +++ b/Examples/test_pyironjob.ipynb @@ -0,0 +1,125 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shyamkatnagallu/Projects/EXTRA_FIM/.venv/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 pyiron import Project\n", + "import EXTRA_FIM.pyiron_job" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "pr = Project('PyironExtraFIMSimulator')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "job = pr.create.job.ExtraFimSimulatorRefJob('test')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "ionization_energies": "None", + "kpoints": "None", + "simulator_dict": "None", + "structure": "None", + "waves_directory": "None", + "waves_reader": "None" + }, + "text/html": [ + "
DataContainer({\n",
+       "  \"waves_directory\": \"None\",\n",
+       "  \"waves_reader\": \"None\",\n",
+       "  \"simulator_dict\": \"None\",\n",
+       "  \"kpoints\": \"None\",\n",
+       "  \"structure\": \"None\",\n",
+       "  \"ionization_energies\": \"None\"\n",
+       "})
" + ], + "text/plain": [ + "DataContainer({'waves_directory': None, 'waves_reader': None, 'simulator_dict': None, 'kpoints': None, 'structure': None, 'ionization_energies': None})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job.input" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "yes\n" + ] + } + ], + "source": [ + "if 'z_ext' in sim:\n", + " print('yes')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/pyiron.log b/pyiron.log new file mode 100644 index 0000000..e69de29 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..29ab3ed --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,26 @@ +[tool.poetry] +name = "extra-fim" +version = "0.1.0" +description = "Does spatial extrapolation of wavefunctions from a DFT code. The extrapolated waves are correctly extrapolated to arbitrary precision. Allows alloes FIM image simulations based on Extrapolated waves." +authors = ["Shalini Bhatt , Shyam Katnagallu , Christoph Freysoldt "] +license = "MIT" +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.11,<3.13" +numpy = "^1.26.4" +h5py = "^3.10.0" +netCDF4 = "^1.6.5" +scipy = "^1.12.0" +matplotlib = "^3.8.3" + +[tool.poetry.group.dev.dependencies] +plotly = "^5.19.0" +pyiron = "^0.5.2" +pyiron-atomistics = "^0.4.17" +pyiron-ontology = "^0.1.4" +ipykernel = "^6.29.3" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/setup.py b/setup.py deleted file mode 100644 index 544fcfb..0000000 --- a/setup.py +++ /dev/null @@ -1,19 +0,0 @@ - -from setuptools import setup, find_packages - -setup( - author="Shalini Bhatt", - author_email='s.bhatt@mpie.de', - python_requires='>=3.8', - - description="FIM study with EXTRA", - install_requires = ['numpy', 'matplotlib', 'h5py', 'scipy', - 'netCDF4'], - - include_package_data=True, - 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zSoMa=c(~Q(+NnCRe2^2YwpMWCQt%+l_NAria`^gpI}kE59;Qf-NlkUz1k*d^6-^sf zgT}NQs~i>9fXLLqk>>?p9;=M24>UZT33%Kqivs6ofG6gXf-QClP-5H!$Gaz1z)enR z_urn%0DJpMX&Aa2ekSC8HjGxg=M%aw5f^qc<6iH;`$I%7x3Dm>%;2du^|3{cV0%j88FDYvwrY3FTgO8 z+JFA_HYgUN7jGd`4a$bhKW!_05Cnv3h}-LtCg3T#>Ac(Amy z1ej~QZQy%+5_nyyQ|pyd1nozNkAJr85S(%5K=`{Gh4_042S==}&jYK@0KsU%N+272 z*z|>27}yXd5b7hj1q@8j7k#rb1KesV7Jty54VVo#3JQkq07JJO+&kgVB(QAxQh~h- z@%!w&-}SCnYy!p6zo`T%Wq>!qP~w_TG`Rj+txQcX7HAC0-JK&A2_}kr@t01Eh7P5b$oP6{F5>9>}vf9t;W%-SMp*uC%+#}y=>omDjnf~9Pd#%h>tw)Ir+%z9`Vr!oLG-by7de3k=s3z zN97~eS;R;3$n75Sk-R)jzu3<9R66?96Wi0CgU_;QcWWp5%9H{nhw4Rqij8$51wafg zVEPjU((LN%P<8 z{r5BueZl}?#@x%J|6?#CY(d>IBWys`Z||LE)#gLg-%}qqWDP{Ld+CG)sC%5S{B(~K z79Vw!f_eYF5knu5z&pSS?MZnbSiP7+{7+v0tt%a}l(MM-YClQQTqeed2*}qA#nG2J w$W#nNdW+K0H#bl^ilcOd8fP3?nv0&*S+Y?4PpyFH+aIXcoFDv{ubi>^e_a9ucmMzZ From a070d605417c8e395ae04f039aede2d8e653d6c1 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Thu, 7 Mar 2024 17:35:10 +0100 Subject: [PATCH 03/22] changes to pyironjob to output file in the job directory changed the sum_singl_k function to work with **kwargs of path --- EXTRA_FIM/main.py | 7 +++++-- EXTRA_FIM/pyiron_job.py | 15 ++++++--------- Examples/test_pyironjob.ipynb | 6 +++--- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/EXTRA_FIM/main.py b/EXTRA_FIM/main.py index 375bca9..a670982 100644 --- a/EXTRA_FIM/main.py +++ b/EXTRA_FIM/main.py @@ -116,7 +116,7 @@ def sum_all_states(self): for ik in range(0, self.wf.nk): sum_single_k(ik) - def sum_single_k(self, ik): + def sum_single_k(self, ik, **kwargs): """compute partial fim image for several ionization energies for all eigenstates between Efermi and Emax for one k point (ik). Save the partial dos files""" @@ -175,7 +175,10 @@ def sum_single_k(self, ik): ) # --- write output file - filename = f"partial_dos{ik}.h5" + if 'path' in kwargs: + filename = kwargs['path']+'/'+f"partial_dos{ik}.h5" + else: + filename = f"partial_dos{ik}.h5" with h5py.File(filename, "w") as handle: handle.create_dataset( "ionization_energies", data=self.inputDict["ionization_energies"] diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index fb3d427..017f28d 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -3,8 +3,6 @@ from pyiron_base.jobs.job.jobtype import JobType from pyiron_base import JobGenerator from pyiron_base.jobs.job.template import TemplateJob - -from pathlib import Path try: import EXTRA_FIM.main as fim from EXTRA_FIM.potential import extend_potential, sx_el_potential3D_cell @@ -34,11 +32,9 @@ def __init__(self, project, job_name): self.input['structure'] = None self.input['ionization_energies'] = None self.input['extrapolate_potential'] = False - - - @property + def extrpolate_potential(self): - elec_potential,_ = sx_el_potential3D_cell (Path(self.input.simulator_dict['working_directory'])) + elec_potential,_ = sx_el_potential3D_cell (self.input.simulator_dict['working_directory']) pot,_,_,cell = fim.potential (self.input.simulator_dict).potential_cell () if self.input.extrapolate_potential: @@ -57,7 +53,7 @@ def extrpolate_potential(self): @property def suggest_input_dict(self): - waves_reader = sx_nc_waves_reader(Path(self.input['waves_directory'])/ "waves.sxb") + waves_reader = sx_nc_waves_reader(self.input['waves_directory']+ "/waves.sxb") e_fermi = waves_reader.get_fermi_energy() fig,sim=suggest_input_dictionary(self.input.waves_directory,e_fermi, ionization_energies=self.input['ionization_energies']) self.input['simulator_dict'] = sim @@ -74,7 +70,8 @@ def run_static(self): pot_ext, elec_ext = self.extrpolate_potential() waves_reader = sx_nc_waves_reader(self.input['waves_directory'] + "/waves.sxb") fimsim=fim.FIM_simulations(self.input['simulator_dict'],reader=waves_reader,V_total=pot_ext,V_elstat=elec_ext) - fimsim.sum_single_k(self.input['kpoint']) + self.project_hdf5.create_working_directory() + fimsim.sum_single_k(self.input['kpoint'],path=self.working_directory) self.status.finished = True @@ -99,7 +96,7 @@ def job_name(self, parameter): return f"{self._master.job_name}_kpoint_{k_point}" def modify_job(self, job, parameter): - job.set= parameter[1] + job.input.kpoint = parameter[1] return job diff --git a/Examples/test_pyironjob.ipynb b/Examples/test_pyironjob.ipynb index d8b3958..ca7d69b 100644 --- a/Examples/test_pyironjob.ipynb +++ b/Examples/test_pyironjob.ipynb @@ -103,7 +103,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -117,9 +117,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.10.8" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 8d4eeb2eb492ae0a030fedcc5bd6d00aeb991036 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Thu, 7 Mar 2024 18:03:57 +0100 Subject: [PATCH 04/22] small bugs --- Examples/test_pyironjob.ipynb | 74 +++++++++++++++++------------------ 1 file changed, 35 insertions(+), 39 deletions(-) diff --git a/Examples/test_pyironjob.ipynb b/Examples/test_pyironjob.ipynb index ca7d69b..6ff4a2b 100644 --- a/Examples/test_pyironjob.ipynb +++ b/Examples/test_pyironjob.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 33, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/shyamkatnagallu/Projects/EXTRA_FIM/.venv/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" - ] - } - ], + "outputs": [], "source": [ "from pyiron import Project\n", "import EXTRA_FIM.pyiron_job" @@ -21,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -39,40 +30,45 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "job.input.waves_directory = '../test_waves'" + ] + }, + { + "cell_type": "code", + "execution_count": 38, "metadata": {}, "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for *: 'NoneType' and 'float'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[38], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mjob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuggest_input_dict\u001b[49m()\n", + "File \u001b[0;32m~/Projects/EXTRA_FIM/EXTRA_FIM/pyiron_job.py:61\u001b[0m, in \u001b[0;36mExtraFimSimulatorRefJob.suggest_input_dict\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 59\u001b[0m waves_reader \u001b[38;5;241m=\u001b[39m sx_nc_waves_reader(Path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwaves_directory\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwaves.sxb\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 60\u001b[0m e_fermi \u001b[38;5;241m=\u001b[39m waves_reader\u001b[38;5;241m.\u001b[39mget_fermi_energy()\n\u001b[0;32m---> 61\u001b[0m fig,sim\u001b[38;5;241m=\u001b[39m\u001b[43msuggest_input_dictionary\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minput\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwaves_directory\u001b[49m\u001b[43m,\u001b[49m\u001b[43me_fermi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mionization_energies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minput\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mionization_energies\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msimulator_dict\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m sim\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz_max\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m sim[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz_max\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;66;03m#rename later\u001b[39;00m\n", + "File \u001b[0;32m~/Projects/EXTRA_FIM/EXTRA_FIM/datautils/pre_processing.py:179\u001b[0m, in \u001b[0;36msuggest_input_dictionary\u001b[0;34m(working_directory, E_fermi, ionization_energies, slope_threshold, second_derivative_threshold)\u001b[0m\n\u001b[1;32m 174\u001b[0m Simulator[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mz_max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m z[izs] \u001b[38;5;241m/\u001b[39m (\n\u001b[1;32m 175\u001b[0m scipy\u001b[38;5;241m.\u001b[39mconstants\u001b[38;5;241m.\u001b[39mphysical_constants[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBohr radius\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1e10\u001b[39m\n\u001b[1;32m 176\u001b[0m )\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mpotential_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mSimulator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43melec_potential\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 180\u001b[0m ax \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39mget_axes()[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 181\u001b[0m \u001b[38;5;66;03m# highlight the constant slope regions\u001b[39;00m\n", + "File \u001b[0;32m~/Projects/EXTRA_FIM/EXTRA_FIM/datautils/plotting.py:48\u001b[0m, in \u001b[0;36mpotential_figure\u001b[0;34m(Simulator, z, elec_potential)\u001b[0m\n\u001b[1;32m 45\u001b[0m ax\u001b[38;5;241m.\u001b[39mplot(z, elec_potential, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpotential\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 47\u001b[0m izL \u001b[38;5;241m=\u001b[39m Simulator[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mizend\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m---> 48\u001b[0m zR \u001b[38;5;241m=\u001b[39m \u001b[43mSimulator\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mz_max\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mBOHR_TO_AA\u001b[49m\n\u001b[1;32m 49\u001b[0m izR \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(zR \u001b[38;5;241m/\u001b[39m (z[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m z[\u001b[38;5;241m0\u001b[39m]))\n\u001b[1;32m 50\u001b[0m ax\u001b[38;5;241m.\u001b[39maxvline(zR, ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'NoneType' and 'float'" + ] + }, { "data": { - "application/json": { - "ionization_energies": "None", - "kpoints": "None", - "simulator_dict": "None", - "structure": "None", - "waves_directory": "None", - "waves_reader": "None" - }, - "text/html": [ - "
DataContainer({\n",
-       "  \"waves_directory\": \"None\",\n",
-       "  \"waves_reader\": \"None\",\n",
-       "  \"simulator_dict\": \"None\",\n",
-       "  \"kpoints\": \"None\",\n",
-       "  \"structure\": \"None\",\n",
-       "  \"ionization_energies\": \"None\"\n",
-       "})
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", "text/plain": [ - "DataContainer({'waves_directory': None, 'waves_reader': None, 'simulator_dict': None, 'kpoints': None, 'structure': None, 'ionization_energies': None})" + "
" ] }, - "execution_count": 5, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "job.input" + "job.suggest_input_dict()" ] }, { @@ -103,7 +99,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -117,9 +113,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.8" } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 2 } From 6a9fa17de171722c2c34968fe477990adb0aaf19 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Thu, 7 Mar 2024 18:04:26 +0100 Subject: [PATCH 05/22] deleted .vscode --- .vscode/launch.json | 18 ------------------ .vscode/settings.json | 3 --- 2 files changed, 21 deletions(-) delete mode 100644 .vscode/launch.json delete mode 100644 .vscode/settings.json diff --git a/.vscode/launch.json b/.vscode/launch.json deleted file mode 100644 index 66ae77f..0000000 --- a/.vscode/launch.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - // Use IntelliSense to learn about possible attributes. - // Hover to view descriptions of existing attributes. - // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - { - "name": "Python: Module", - "type": "python", - "request": "launch", - "module": "EXTRA_FIM", - "justMyCode": true, - "env": { - "PYTHONPATH" : "${workspaceFolder}/../EXTRA_FIM" - } - } - ] -} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 7bd6bb2..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "python.analysis.extraPaths": ["${workspaceFolder}/../EXTRA_FIM"] -} \ No newline at end of file From 6c782442771ee0d0511add61110711327af422ff Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Fri, 8 Mar 2024 09:23:15 +0100 Subject: [PATCH 06/22] fixed jobgenerator --- EXTRA_FIM/pyiron_job.py | 108 +++++++++++++++++++++++++--------------- 1 file changed, 67 insertions(+), 41 deletions(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index 017f28d..b29187b 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -3,6 +3,7 @@ from pyiron_base.jobs.job.jobtype import JobType from pyiron_base import JobGenerator from pyiron_base.jobs.job.template import TemplateJob + try: import EXTRA_FIM.main as fim from EXTRA_FIM.potential import extend_potential, sx_el_potential3D_cell @@ -22,60 +23,86 @@ __status__ = "development" __date__ = "March 5, 2024" + class ExtraFimSimulatorRefJob(TemplateJob): + """Reference pyiron job for Extra FIM simulation.""" + def __init__(self, project, job_name): super().__init__(project, job_name) self.__version__ = "0.1.0" - self.input['waves_directory'] = None - self.input['waves_reader'] = None - self.input['kpoint'] = None - self.input['structure'] = None - self.input['ionization_energies'] = None - self.input['extrapolate_potential'] = False - + self.input["waves_directory"] = None + self.input["waves_reader"] = None + self.input["kpoint"] = None + self.input["structure"] = None + self.input["ionization_energies"] = None + self.input["extrapolate_potential"] = False + def extrpolate_potential(self): - elec_potential,_ = sx_el_potential3D_cell (self.input.simulator_dict['working_directory']) - pot,_,_,cell = fim.potential (self.input.simulator_dict).potential_cell () + """Extrapolate potential if needed, to extrapolate waves to higher distances""" + + elec_potential, _ = sx_el_potential3D_cell( + self.input.simulator_dict["working_directory"] + ) + pot, _, _, cell = fim.potential(self.input.simulator_dict).potential_cell() if self.input.extrapolate_potential: - iz0 = self.input.simulator_dict['iz_ext_from'] - new_z_max=self.input.simulator_dict['z_ext'] - fig, pot_ext = extend_potential(elec_potential / fim.HARTREE_TO_EV, - iz0, pot, cell, z_max=new_z_max, - izend=self.input.simulator_dict['izend'],dv_limit=1e-4,plotG=1) + iz0 = self.input.simulator_dict["iz_ext_from"] + new_z_max = self.input.simulator_dict["z_ext"] + _, pot_ext = extend_potential( + elec_potential / fim.HARTREE_TO_EV, + iz0, + pot, + cell, + z_max=new_z_max, + izend=self.input.simulator_dict["izend"], + dv_limit=1e-4, + plotG=1, + ) # copy extension from pot to elec_potential - elec_ext = pot_ext[:,:,:,0] * fim.HARTREE_TO_EV - elec_ext[:,:,0:iz0] = elec_potential[:,:,:iz0] + elec_ext = pot_ext[:, :, :, 0] * fim.HARTREE_TO_EV + elec_ext[:, :, 0:iz0] = elec_potential[:, :, :iz0] else: pot_ext = pot elec_ext = elec_potential return pot_ext, elec_potential - + @property def suggest_input_dict(self): - waves_reader = sx_nc_waves_reader(self.input['waves_directory']+ "/waves.sxb") + """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy""" + waves_reader = sx_nc_waves_reader(self.input["waves_directory"] + "/waves.sxb") e_fermi = waves_reader.get_fermi_energy() - fig,sim=suggest_input_dictionary(self.input.waves_directory,e_fermi, ionization_energies=self.input['ionization_energies']) - self.input['simulator_dict'] = sim - self.input['z_max'] = sim['z_max'] #rename later - self.input['izstart_min'] = sim['izstart_min'] #rename later - self.input['izend'] = sim['izend'] #rename later - self.input['limit'] = sim['limit'] #rename later - self.input['cutoff'] = sim['cutoff'] #rename later - self.input['E_fermi'] = sim['E_fermi'] #rename later - self.input['E_max'] = sim['E_max'] #rename later - return sim - + _, sim = suggest_input_dictionary( + self.input.waves_directory, + e_fermi, + ionization_energies=self.input["ionization_energies"], + ) + self.input["simulator_dict"] = sim + self.input["z_max"] = sim["z_max"] # rename later + self.input["izstart_min"] = sim["izstart_min"] # rename later + self.input["izend"] = sim["izend"] # rename later + self.input["limit"] = sim["limit"] # rename later + self.input["cutoff"] = sim["cutoff"] # rename later + self.input["E_fermi"] = sim["E_fermi"] # rename later + self.input["E_max"] = sim["E_max"] # rename later + return sim + def run_static(self): pot_ext, elec_ext = self.extrpolate_potential() - waves_reader = sx_nc_waves_reader(self.input['waves_directory'] + "/waves.sxb") - fimsim=fim.FIM_simulations(self.input['simulator_dict'],reader=waves_reader,V_total=pot_ext,V_elstat=elec_ext) + waves_reader = sx_nc_waves_reader(self.input["waves_directory"] + "/waves.sxb") + fimsim = fim.FIM_simulations( + self.input["simulator_dict"], + reader=waves_reader, + V_total=pot_ext, + V_elstat=elec_ext, + ) self.project_hdf5.create_working_directory() - fimsim.sum_single_k(self.input['kpoint'],path=self.working_directory) + fimsim.sum_single_k(self.input["kpoint"], path=self.working_directory) self.status.finished = True class ExtraFimSimulatorJobGenerator(JobGenerator): + """Job generator class for extra fim simulator pyiron jobs""" + @property def parameter_list(self): """ @@ -86,7 +113,9 @@ def parameter_list(self): parameter_lst = [] kpoints = self._master.input.get("kpoints") if kpoints is None: - waves_reader = sx_nc_waves_reader(self._master.input['waves_directory']+ "/waves.sxb") + waves_reader = sx_nc_waves_reader( + self._master.input["waves_directory"] + "/waves.sxb" + ) for k in waves_reader.nk: parameter_lst.append(k) return parameter_lst @@ -101,20 +130,17 @@ def modify_job(self, job, parameter): class ExtraFimSimulator(ParallelMaster): + """ "Pyiron Extra FIM simulator job class to make subjobs for each k point""" + def __init__(self, project, job_name): super(ExtraFimSimulator).__init__(project, job_name) self._job_generator = ExtraFimSimulatorJobGenerator(self) - - def run_static(self): - super().run_static() - - def collect_output(self): for job_id in self.child_ids: job = self.project_hdf5.inspect(job_id) - ... return super().collect_output() - + + JobType.register(ExtraFimSimulator) -JobType.register(ExtraFimSimulatorRefJob) \ No newline at end of file +JobType.register(ExtraFimSimulatorRefJob) From 3dfb40e5607b8ee3dfafb3d63438723ca243d6ea Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Fri, 8 Mar 2024 10:04:18 +0100 Subject: [PATCH 07/22] fixing pyiron_job ExtraFimSimulator --- EXTRA_FIM/pyiron_job.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index b29187b..f75ce8e 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -129,17 +129,17 @@ def modify_job(self, job, parameter): return job -class ExtraFimSimulator(ParallelMaster): +class ExtraFimSimulator(ParallelMaster,ExtraFimSimulatorRefJob): """ "Pyiron Extra FIM simulator job class to make subjobs for each k point""" def __init__(self, project, job_name): - super(ExtraFimSimulator).__init__(project, job_name) + super(ExtraFimSimulatorRefJob,self).__init__(project, job_name=job_name) self._job_generator = ExtraFimSimulatorJobGenerator(self) - - def collect_output(self): - for job_id in self.child_ids: - job = self.project_hdf5.inspect(job_id) - return super().collect_output() + #TODO: collect_output + # def collect_output(self): + # for job_id in self.child_ids: + # job = self.project_hdf5.inspect(job_id) + # return super().collect_output() JobType.register(ExtraFimSimulator) From 1d7eeb19ba206bcc04fe919863ad4b62bcbe0130 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Fri, 8 Mar 2024 17:58:31 +0100 Subject: [PATCH 08/22] A pre-release working version of pyiron job for extra fim simulations --- EXTRA_FIM/pyiron_job.py | 144 +++- Examples/extra_fim_pyiron_job.ipynb | 1180 +++++++++++++++++++++++++++ 2 files changed, 1301 insertions(+), 23 deletions(-) create mode 100644 Examples/extra_fim_pyiron_job.ipynb diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index f75ce8e..86d1b8e 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -3,6 +3,10 @@ from pyiron_base.jobs.job.jobtype import JobType from pyiron_base import JobGenerator from pyiron_base.jobs.job.template import TemplateJob +import h5py +import numpy as np +from pathlib import Path +from pyiron_atomistics.sphinx.structure import read_atoms try: import EXTRA_FIM.main as fim @@ -29,32 +33,31 @@ class ExtraFimSimulatorRefJob(TemplateJob): def __init__(self, project, job_name): super().__init__(project, job_name) - self.__version__ = "0.1.0" self.input["waves_directory"] = None self.input["waves_reader"] = None self.input["kpoint"] = None - self.input["structure"] = None self.input["ionization_energies"] = None self.input["extrapolate_potential"] = False + self.input["total_kpoints"] = None def extrpolate_potential(self): """Extrapolate potential if needed, to extrapolate waves to higher distances""" elec_potential, _ = sx_el_potential3D_cell( - self.input.simulator_dict["working_directory"] + self.input['simulator_dict']["working_directory"] ) - pot, _, _, cell = fim.potential(self.input.simulator_dict).potential_cell() + pot, _, _, cell = fim.potential(self.input['simulator_dict']).potential_cell() if self.input.extrapolate_potential: - iz0 = self.input.simulator_dict["iz_ext_from"] - new_z_max = self.input.simulator_dict["z_ext"] + iz0 = self.input['simulator_dict']["iz_ext_from"] + new_z_max = self.input['simulator_dict']["z_ext"] _, pot_ext = extend_potential( elec_potential / fim.HARTREE_TO_EV, iz0, pot, cell, z_max=new_z_max, - izend=self.input.simulator_dict["izend"], + izend=self.input['simulator_dict']["izend"], dv_limit=1e-4, plotG=1, ) @@ -69,13 +72,14 @@ def extrpolate_potential(self): @property def suggest_input_dict(self): """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy""" - waves_reader = sx_nc_waves_reader(self.input["waves_directory"] + "/waves.sxb") + waves_reader = sx_nc_waves_reader(Path(self.input["waves_directory"])/"waves.sxb") e_fermi = waves_reader.get_fermi_energy() _, sim = suggest_input_dictionary( - self.input.waves_directory, + self.input['waves_directory'], e_fermi, ionization_energies=self.input["ionization_energies"], ) + self.input["total_kpoints"] = waves_reader.nk self.input["simulator_dict"] = sim self.input["z_max"] = sim["z_max"] # rename later self.input["izstart_min"] = sim["izstart_min"] # rename later @@ -86,7 +90,12 @@ def suggest_input_dict(self): self.input["E_max"] = sim["E_max"] # rename later return sim + def run_static(self): + + self.project_hdf5.create_working_directory() + #self.suggest_input_dict + pot_ext, elec_ext = self.extrpolate_potential() waves_reader = sx_nc_waves_reader(self.input["waves_directory"] + "/waves.sxb") fimsim = fim.FIM_simulations( @@ -95,11 +104,12 @@ def run_static(self): V_total=pot_ext, V_elstat=elec_ext, ) - self.project_hdf5.create_working_directory() + self.status.submitted = True fimsim.sum_single_k(self.input["kpoint"], path=self.working_directory) self.status.finished = True + class ExtraFimSimulatorJobGenerator(JobGenerator): """Job generator class for extra fim simulator pyiron jobs""" @@ -111,36 +121,124 @@ def parameter_list(self): (list) """ parameter_lst = [] - kpoints = self._master.input.get("kpoints") + kpoints = self._master.input.get("kpoint") if kpoints is None: waves_reader = sx_nc_waves_reader( - self._master.input["waves_directory"] + "/waves.sxb" + Path(self._master.input["waves_directory"])/"waves.sxb" ) - for k in waves_reader.nk: + for k in range(waves_reader.nk): parameter_lst.append(k) return parameter_lst def job_name(self, parameter): - k_point = parameter[0] + k_point = parameter return f"{self._master.job_name}_kpoint_{k_point}" def modify_job(self, job, parameter): - job.input.kpoint = parameter[1] + job.structure = self._master.get_structure + for k in self._master.input.keys(): + job.input[k] = self._master.input[k] + job.input.kpoint = parameter return job -class ExtraFimSimulator(ParallelMaster,ExtraFimSimulatorRefJob): +class ExtraFimSimulator(ParallelMaster): """ "Pyiron Extra FIM simulator job class to make subjobs for each k point""" def __init__(self, project, job_name): - super(ExtraFimSimulatorRefJob,self).__init__(project, job_name=job_name) + super(ExtraFimSimulator,self).__init__(project, job_name) + self.__version__ = "0.1.0" + self.input["waves_directory"] = None + self.input["waves_reader"] = None + self.input["kpoint"] = None + self.input["ionization_energies"] = None + self.input["extrapolate_potential"] = False + self.input["total_kpoints"] = None self._job_generator = ExtraFimSimulatorJobGenerator(self) - #TODO: collect_output - # def collect_output(self): - # for job_id in self.child_ids: - # job = self.project_hdf5.inspect(job_id) - # return super().collect_output() + self.ref_job = ExtraFimSimulatorRefJob(project=project,job_name=job_name) + + def extrpolate_potential(self): + """Extrapolate potential if needed, to extrapolate waves to higher distances""" + + elec_potential, _ = sx_el_potential3D_cell( + self.input['simulator_dict']["working_directory"] + ) + pot, _, _, cell = fim.potential(self.input['simulator_dict']).potential_cell() + + if self.input.extrapolate_potential: + iz0 = self.input['simulator_dict']["iz_ext_from"] + new_z_max = self.input['simulator_dict']["z_ext"] + _, pot_ext = extend_potential( + elec_potential / fim.HARTREE_TO_EV, + iz0, + pot, + cell, + z_max=new_z_max, + izend=self.input['simulator_dict']["izend"], + dv_limit=1e-4, + plotG=1, + ) + # copy extension from pot to elec_potential + elec_ext = pot_ext[:, :, :, 0] * fim.HARTREE_TO_EV + elec_ext[:, :, 0:iz0] = elec_potential[:, :, :iz0] + else: + pot_ext = pot + elec_ext = elec_potential + return pot_ext, elec_potential + + @property + def suggest_input_dict(self): + """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy and populates input""" + waves_reader = sx_nc_waves_reader(Path(self.input["waves_directory"])/"waves.sxb") + e_fermi = waves_reader.get_fermi_energy() + _, sim = suggest_input_dictionary( + self.input['waves_directory'], + e_fermi, + ionization_energies=self.input["ionization_energies"], + ) + self.input["total_kpoints"] = waves_reader.nk + self.input["simulator_dict"] = sim + self.input["z_max"] = sim["z_max"] # rename later + self.input["izstart_min"] = sim["izstart_min"] # rename later + self.input["izend"] = sim["izend"] # rename later + self.input["limit"] = sim["limit"] # rename later + self.input["cutoff"] = sim["cutoff"] # rename later + self.input["E_fermi"] = sim["E_fermi"] # rename later + self.input["E_max"] = sim["E_max"] # rename later + self.get_structure + return sim + + @property + def get_structure(self): + if (Path(self.input['waves_directory'])/'relaxedStr.sx').exists(): + self.structure = read_atoms(Path(self.input['waves_directory'])/'relaxedStr.sx') + + def collect_output(self): + FIM_total ={} + zFIM_total = {} + for job_id in self.child_ids: + subjob = self.project_hdf5.load(job_id) + subjobdir = subjob.working_directory + ik = subjob.input.kpoint + IEs = subjob.input.ionization_energies + with h5py.File(f'{subjobdir}/partial_dos{ik}.h5') as handle: + for IE in IEs: + fim_k = np.asarray(handle[f'IE={IE}']) + zfim_k = np.asarray(handle[f'zIE={IE}']) + if IE not in FIM_total: + FIM_total[IE] = np.zeros_like(fim_k) + zFIM_total[IE] = np.zeros_like(zfim_k) + FIM_total[IE] += fim_k + zFIM_total[IE] += zfim_k + + for IE in IEs: + self._output[f'total_FIM/{IE}'] = FIM_total[IE] + self._output[f'z_resolved_FIM/{IE}'] = zFIM_total[IE] + + with self.project_hdf5.open("output") as hdf5_out: + for key,val in self._output.items(): + hdf5_out[key] = val JobType.register(ExtraFimSimulator) -JobType.register(ExtraFimSimulatorRefJob) + diff --git a/Examples/extra_fim_pyiron_job.ipynb b/Examples/extra_fim_pyiron_job.ipynb new file mode 100644 index 0000000..b12db77 --- /dev/null +++ b/Examples/extra_fim_pyiron_job.ipynb @@ -0,0 +1,1180 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "f0ddbe05-048b-48c3-b6d0-afc3d9e648b7", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d35a6d7167df4d0abd93ff7cc44868bb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pyiron import Project\n", + "import EXTRA_FIM.pyiron_job\n", + "import matplotlib.pyplot as plt\n", + "import EXTRA_FIM.main as fim\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "9539e683-6005-4918-860a-7d561a4137a0", + "metadata": {}, + "source": [ + "# Setting up and running PyironnExtraFIMSimulator jobs" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "241f7c8a-148d-46f2-9cea-d6f8be3b3945", + "metadata": {}, + "outputs": [], + "source": [ + "pr = Project('Test_PyironExtraFIMSimulator')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ba51d04c-3543-4c75-96e4-30f42b36aaff", + "metadata": {}, + "outputs": [], + "source": [ + "job = pr.create.job.ExtraFimSimulator('test_Pt_210',delete_existing_job=True,delete_aborted_job=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "13903d06-79f9-4a05-b1d0-9536a0c4e1f1", + "metadata": {}, + "outputs": [], + "source": [ + "job.input['waves_directory'] = '/cmmc/u/skatnagallu/pyiron/projects/HEA_FIM/PtIrAu_FIM_contrast/Pt_210_relax_wf_doubley_4_0_VA_hdf5/Pt_210_relax_wf_doubley_4_0_VA'\n", + "job.input['ionization_energies'] = [21.5]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "2b415d1c-fbe1-4fab-a212-34feba156b63", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "constant slope start vals(izend): [100 156]\n", + "constant slope end vals(izstart): [152 206]\n", + "constant slope potential drop: [31.56595517 -0.07491849]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'working_directory': '/cmmc/u/skatnagallu/pyiron/projects/HEA_FIM/PtIrAu_FIM_contrast/Pt_210_relax_wf_doubley_4_0_VA_hdf5/Pt_210_relax_wf_doubley_4_0_VA',\n", + " 'z_max': 43.71867072219179,\n", + " 'izstart_min': 147,\n", + " 'izend': 100,\n", + " 'cutoff': 10,\n", + " 'limit': 1e-06,\n", + " 'E_fermi': 52.11577191472367,\n", + " 'E_max': 57.11577191472367,\n", + " 'ionization_energies': [21.5]}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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CCFHK2VriwVrmoati2qPj7Zr7LtC2trPF7NmzWbJkCT/++COOjo7ExMQQExNDamoqdnZ2XL9+nSlTphAUFES5cuUAuHTpkjlZURSF5cuX89BDD9GkSRM2btyIk5MT8fHxaDQaxo8fb36szPcriSTREUKIUszWEg95cXGwI/z9xwh//zEc7EwfMfpiuuqqQ7UOVHWriobsV51p0ODr5kuHah0K9HEVReHDDz/k2rVrtGnTBm9vb/Pl2LFjuLq6cvLkSZo0aUL//v35448/uHTpElWqVDGfY+XKlZw7d46dO3dy/Phx3NzcqF+/PqdOnbKoF3nv/UoiSXSEEKKUylziIaCy7SUe8lLcd0bWaXUs6rkIIEuyk3F9Yc+F6LS27RidF41GQ3x8PIqiZLm0bt2a0NBQXF1def755+nQoQOpqamEhYXh4+NjPkdQUBDt2rXD2dmZRYsWYTQa8fDw4NSpUzRq1Mjc7t77lUSS6AghRCllUeJhSDObSzzk5W6tqwI97QPVr14/1g1YRxU3y16Pqm5VWTdgXaHuo5OTWbNmUadOHZo1a4aTkxNPPfUUDRs2JDQ0lEaNGnHmzBmef/55Zs2aRadOnbh+/bo5uQkKCrJIdO69X0kkk5GFEKIUurfEQ10vtwI5b0q6gUk/HTNdudORU1xXXWXoV68ffev0ZVfELqITo/F29aZDtQ4F3pNjrW+//TbLMQ8PD44ePWpxLDw8PEu7Tz/9NM/7lTSS6AghRClzPyUe8mJUFDadjAHg0QaepmPFdNVVZjqtrtCWkIvCJUNXQghRyhRUiYe8ZMzR0ZeAREcUX5LoCCFEKVKQJR7yUtx3RhYlgyQ6QghRShR0iYe8FPedkUXJIImOEEKUAoVR4iEvGZsrF9edkUXJIImOEEKUAoVR4iEvMnQligJJdIQQooQrrBIPeTHXupKhK6EiWV4uhBAlWEGXeMiLs72O4JmPAvD+JtM+PTJ0JdQkiY4QQpRQhV3iITsajQYXB9NHi+7O8JgkOkJNMnQlhBAlVGGXeMiLTlZdiSJAenSEEKIEKqwSD3lJ1Rt4a4NpCbuHiz0gk5GFuqRHRwghSpjCLPGQF4NRYf2RS6w/comMgt/FuainmcEAO3bAmjWmfw2GQn/IuXPnotFoslwWLFhQ6I9dkkiiI4QQJcyDKvGQFztz9fJinuls2AB+ftClCwwZYvrXz890vBC9+uqrREdHmy//+9//qF69OgMGDCjUxy1p8pXoREZGsmvXLrZs2cKRI0dITU0t6LiEEELkw4Ms8ZCXErEz8oYN0L8/XLpkeTwqynS8EJMdV1dXvLy88PLyYtmyZWzatInAwECqVq163+fu1KlTttXNSyKr5+hcvHiRpUuXsmbNGiIjI1EyvXAdHBzo0KEDL774Ik8//TRarXQUCSHEg/agSzzkxbyPTnHt0DEYYPx4yC5RUxTQaGDCBOjbF3SFN9F7xowZrFixgsDAQKpXr14g5wwPD8fPz69AzlXUWZWRjB8/nkaNGhEaGsrMmTMJCgoiPj6etLQ0YmJi2LRpEw8//DDvvvsujRs35uDBg4UdtxBCiEzUKPGQl4wenWI7GXnXrqw9OZkpCkRGmtoVkpySnJCQEHr37k2LFi3o3Lkz165dA6BXr15MmzaNNm3aUL16dYKDgwEICgqiTZs2NGnShAULFuDr61toMRc1ViU6Dg4OnD9/nnXr1jF06FDq1q2Lq6srdnZ2VK5cma5duzJt2jTOnDnDBx98wMWLFws7biGEEJmoUeIhL8V+6Co6umDb2SinJCc1NZVXXnmF5cuXc/jwYfr3789XX30FwKlTp/D392ffvn2MHj2a33//neTkZAYNGsQ333zD8ePH2bFjB40bNy6UmIsiq4auPvzwQ6tP2Lt373wHI4QQwnZqlXjIi11xr3XlbeUu0ta2s8Hs2bNZsmQJf/zxB46OjsTExADg4eHBL7/8QnBwMH369AFMic+oUaOIj4/H3t6e4cOHA6ZOCnd3dzZu3Ejnzp2pX9+0YWSdOnWoWbNmgcdcVFk9R6dp06aMGjWKZ599Fg8Pj8KMSQghhJUedImHvDjb6zj8ziMAbDwaBYC+uCY6HTpA1aqmicfZ9UppNKbbO3Qo0IdVFIUPP/yQhIQE2rRpY3Hbvn37OHnyJB9//DGDBw+2uG3Pnj20atXKfP3UqVO8+OKL/PXXXxY9OEeOHOGpp54q0JiLMqv7Nlu3bs0777yDj48PgwcP5p9//inMuIQQQuRBURTeeMAlHvKi0WioUNaRCmUd7y4vL65DVzodLFpk+vneJfoZ1xcuLPCJyBqNhvj4eBRFyXJp3bo1Xl5ebNmyxdz+5MmTgCmxadSokcXxhg0bUqFCBfNcna1bt7Jjxw6LdiWd1YnOsmXLiImJYfny5Vy5coUePXrg5+fHzJkziYiIKMwYhRBCZOObPeFsV7HEQ150xX3oCqBfP1i3DqpUsTxetarpeL9+DzykESNGEBcXR926dWnSpAmrV68GTBOOMxIYvV7PrVu3KFeuHM899xw7d+6kefPmfP/99/j7++Pq6vrA41aLRlHyl2qHhYXxzTff8O233xIVFUW3bt144YUXitxGRgkJCbi7uxMfH4+b24PZAl0IIQrbqah4nvp8D+kGhVl9G/B8Wz+1QwJMJSBm/2EqPVGrchmm/RZMj/qeLB/aUpV4UlJSCAsLw9/fHycnp/yfyGAwra6KjjbNyenQoVCXlAuT3P7/rP18z/e0fH9/f2bNmkV4eDhr167l0KFDWcYLhRBCFLzbqXrGZS7x0KZg9lYpCAajwnf7LvLdvoto7tSAMBbXoavMdDro3BkGDzb9K0lOsXFfRT23b9/OihUr2LBhA3Z2dowePbqg4hJCCJGDab8FcaEIlHjIS8bQVbGdjCxKBJsTnYiICFauXMnKlSsJDw+nQ4cOfP755zzzzDM4OxeNJY1CCFFSFaUSD3nJ2CTfIImOUJHVic7q1atZsWIF27dvx9PTk6FDh/LCCy9Qq5b6u28KIURpUNRKPOTFPBm5JAxdiWLL6kRn+PDhPPbYY/zyyy/07t1b6lkJIcQDlLnEQ8vqRaPEQ17MOyNLj45QkdWJzqVLl6hcuXJhxiKEECIHliUemhaJEg95ubu8XOVAAGNRCELYrCD+36xOdDInOd999x1Lly4lLCyM//77j+rVq7Nw4UL8/f3p27fvfQclhBDirntLPFT1cFE5IusUhVpXDg4OaLVaLl++TKVKlXBwcCiyk7fFXYqikJaWxtWrV9FqtTg45H8ums2Tkb/44gvee+89JkyYwJw5czAYDACUK1eOhQsX2pTo6PV6pk+fzg8//EBMTAze3t4MHz6cd955xzw0pigKM2bMYPny5dy8eZPWrVvz2Wef0aBBA1tDF0KIYqeolXjIi5Odjl2TuwBwOjoBUHfVlVarxd/fn+joaC5fvqxaHCJ/XFxcqFat2n1Nl7E50Vm8eDFffvklTz75JO+//775eMuWLXn99ddtOtf8+fNZunQpq1atokGDBhw6dIgRI0bg7u7O+PHjAfjggw9YsGABK1eupHbt2syePZvu3bsTEhJSqnZ2FEKUPkajwutFrMRDXrRaDb7lTT1O52JvAervjOzg4EC1atXQ6/XmL+ei6NPpdNjZ2d13D5zNiU5YWBjNmjXLctzR0ZHbt2/bdK7//vuPvn378thjjwHg5+fHmjVrOHToEGDqzVm4cCFvv/02/e5ss71q1So8PT1ZvXo1Y8aMsTV8IYQoNlbsDWdHyFUcimiJh7xotUVnMrJGo8He3h57e3u1QxEPmM19Qf7+/hw7dizL8b/++stcAt5aDz/8MP/88w9nz54F4Pjx4+zevZvevXsDpqQqJiaGHj16mO/j6OhIp06d2Lt3b7bnTE1NJSEhweIihBDFzamoeN7/y1RK4d3H6lHXq3iUsEnTG5m76TRzN51GuZPgyPJyoSabe3TeeOMNXnnlFVJSUlAUhQMHDrBmzRrmzZvHV199ZdO5pkyZQnx8PHXr1kWn02EwGJgzZ465lERMTAwAnp6eFvfz9PTk4sWL2Z5z3rx5zJgxw9ZfSwghiozMJR66F7ESD3nRG40s33kBgDY1ygNFo0dHlF42JzojRoxAr9czefJkkpKSGDJkCFWqVGHRokUMGjTIpnP9+OOPfP/996xevZoGDRpw7NgxJkyYgI+PD8OGDTO3u3d8TlGUHMfspk6dyqRJk8zXExIS8PX1tSkuIYRQ0/TMJR6eLrolHvJid2cCqSQ6Qk35qnU1evRoRo8ezbVr1zAajfneX+eNN97gzTffNCdIjRo14uLFi8ybN49hw4bh5eUFYF6RlSE2NjZLL08GR0dHHB0d8xWPEEKo7ddjUfycqcSDR5miW+IhLxlb/ai5vFyI+9pxqmLFive1iWBSUlKWJWM6nc68QZC/vz9eXl5s27bNfHtaWhqBgYG0a9cu348rhBBFUXEr8ZAX2RlZFAVWJTo9e/bMcfJvZomJicyfP5/PPvvMqgd//PHHmTNnDn/++Sfh4eFs3LiRBQsW8NRTTwGmIasJEyYwd+5cNm7cyKlTpxg+fDguLi4MGTLEqscQQojioDiWeMjL3Z2RJdER6rFq6OqZZ55hwIABuLq68sQTT9CyZUt8fHxwcnLi5s2bBAcHs3v3bjZt2kSfPn348MMPrXrwxYsX8+677/Lyyy8TGxuLj48PY8aM4b333jO3mTx5MsnJybz88svmDQO3bt0qe+gIIUqU4ljiIS9FYWdkITSKYt0rMC0tjXXr1vHjjz+ya9cu4uLiTCfQaKhfvz6PPvooo0ePpk6dOoUZr80SEhJwd3cnPj4eN7fisTxTCFG67A69xvPf7EdR4PNnm9O7iO9+nJukND3139sCwMaX2/HU53upWNaRQ+88onJkoqSx9vPd6snIDg4ODBkyxDxkFB8fT3JyMhUqVJANmIQQIp+uZyrxMLhVtWKd5ICpBMTWiR0B0wpZAIMU1BQqyteqKwB3d3fc3d0LMhYhhChVFMWyxMN7fYp+iYe8aLUaanuaphacv2oqASGTkYWaiv8gsBBCFFMr9oSzvRiXeMiL7s4cHclzhJry3aMjhBAi/0wlHs4AxavEQ17S9EY+234OgL5NfQDp0RHqkkRHCCEesIwSD2kGY7Er8ZAXvdHIon9CAXi8yZ1ER1ZdCRXJ0JUQQjxgGSUevNyKd4mHvNgVoerlovSSREcIIR6gjBIPGg0sHFS8SzzkRXZGFkVBgSY6/v7+vPDCC0RFRRXkaYUQokSIuJ7EO3dKPLzapVaxL/GQl4ydkUF2RxbqKdBEZ9iwYRiNRjp27FiQpxVCiGIv3WBk3NqjJGaUeOgWoHZIhS5TniPzdIRqCnQy8vTp0wvydEIIUWIs2HaWYyWsxENeMtdsNhgV7EvW6nlRTJT8vzQhhFDZ7tBrLA08D8D7TzemqoeLyhE9GBZDV9KjI1RiVY/OpEmTrD7hggUL8h2MEEKUNCWtxENeHO10/PpKewBcHO5+xOhljo5QiVWJztGjR606WUldIimEEPmRucRDrRJS4iEvOq2GJr7lANAb7ta4ksnIQi1WJTrbt28v7DiEEKLEsSjxMLjklXjIS+ahK1liLtQiOyMLIUQhyFzi4Z3H6lHPu2SUeMhLmt7Iij1hAIxo749GA4oiq66EevKV6Bw8eJCff/6ZiIgI0tLSLG7bsGFDgQQmhBDFVeYSD4/U8+T5ElTiIS96o5F5dxK859tWR6fRoFcUjMY87ihEIbF51dXatWtp3749wcHBbNy4kfT0dIKDg/n3339xd3cvjBiFEKJYyVzi4cP+JbfEgzUyhq/0kukIldic6MydO5dPPvmEP/74AwcHBxYtWsTp06cZMGAA1apVK4wYhRCi2ChNJR6skZHoSJ4j1GJzonP+/Hkee+wxABwdHbl9+zYajYaJEyeyfPnyAg9QCCGKi9JW4sEauox6VzJHR6jE5kSnfPnyJCYmAlClShVOnTL9UcfFxZGUlFSw0QkhRDGRucRDi1JS4sEaWqlgLlRm82TkDh06sG3bNho1asSAAQMYP348//77L9u2baNbt26FEaMQQhR5n9wp8eDqZMeiUlLiwRrmoSvp0REqsTnRWbJkCSkpKQBMnToVe3t7du/eTb9+/Xj33XcLPEAhhCjq9py7xhcZJR76lZ4SD9bQaqRHR6jL5kSnfPny5p+1Wi2TJ09m8uTJBRqUEEIUF9dvpTLxx4wSD7481rhkl3jIi6OdjjWj25h/tpOhK6EyqxKdhIQE3NzczD/nJqOdEEKUdIqi8Ma6E8SaSzw0UDsk1em0GtrWrGBxHSTREeqxKtHx8PAgOjqaypUrU65cuWz3hFAUBY1Gg8FgKPAghRCiKFqxJ5x/z8SW2hIP1tDemaokq66EWqxKdP7991/zkJXUvRJCiNJb4iEv6QYjaw5EAKZq7RnLy6Wop1CLVYlOp06dzD/7+/vj6+ubpVdHURQiIyMLNjohhCiCSnOJh7ykG4y892sQAP1bVJXl5UJ1Nq9/9Pf35+rVq1mO37hxA39//wIJSgghirIZv0uJB2vpZNWVUJnNiU7GXJx73bp1CycnpwIJSgghiqrfjl/mp0OmEg+fDJQSD3kxT0aWOTpCJVYvL580aRIAGo2Gd999FxeXu/tEGAwG9u/fT9OmTQs8QCGEKCoibyTx9oaTAIztUstidZHInqy6EmqzOtE5evQoYOrROXnyJA4Od7/FODg40KRJE15//fWCj1AIIYqAdIORV9fcLfEwXko8WEV2RhZqszrRyVhtNWLECBYtWiT75QghShUp8ZA/d3dGVjkQUWrZvDPyihUrCiMOIYQosqTEQ/7J0JVQm82Jzu3bt3n//ff5559/iI2NxWi0TNMvXLhQYMEJIYTapMSDbRx0Wr4Z3tL8syQ6Qm02JzqjRo0iMDCQ559/Hm9vb1lWKYQosaTEg+3sdFq61vU0XzcvL5c5OkIlNic6f/31F3/++Sft27cvjHiEEKLIWLn3bomHTwdJiYf8ME9Glh4doRKbEx0PDw+LCuZCCFESnYqKZ94mU4mHt3vXo76PLMCwRrrByC9HowB4slkV2RlZqM7mZQOzZs3ivffeIykpqTDiEUII1d1b4mFoWynxYK10g5E31p3gjXUnSDcY0d2Z3SBDV0ItNvfofPzxx5w/fx5PT0/8/Pywt7e3uP3IkSMFFpwQQqhBSjwUHJmMLNRmc6Lz5JNPFkIYQghRNEiJh4IliY5Qm82JzrRp0wojDiGEUJ2UeCh4sjOyUFu+tvaMi4vjq6++YurUqdy4cQMwDVlFRUXZdB4/Pz80Gk2WyyuvvAKYlnZOnz4dHx8fnJ2d6dy5M0FBQfkJWQghcpVuMDJuranEQ/Nq5aTEQwHRSvVyoTKbE50TJ05Qu3Zt5s+fz0cffURcXBwAGzduZOrUqTad6+DBg0RHR5sv27ZtA+CZZ54B4IMPPmDBggUsWbKEgwcP4uXlRffu3UlMTLQ1bCGEyNXCv89yNCKjxEMzKfFQQGToSqjN5r/kSZMmMXz4cEJDQ3FycjIf79WrFzt37rTpXJUqVcLLy8t8+eOPP6hZsyadOnVCURQWLlzI22+/Tb9+/WjYsCGrVq0iKSmJ1atX2xq2EELkaO+5a3y+426JB9/yUuKhoGRsGChDV0ItNic6Bw8eZMyYMVmOV6lShZiYmHwHkpaWxvfff8/IkSPRaDSEhYURExNDjx49zG0cHR3p1KkTe/fuzfE8qampJCQkWFyEECIn12+lMuFOiYdBD0mJh/w6deoUiqLgoNPy2ZDmfDakuUUJCL306AiV2JzoODk5ZZs8hISEUKlSpXwH8ssvvxAXF8fw4cMBzEmTp6enRTtPT89cE6p58+bh7u5uvvj6+uY7JiFEyXZviYdpj0uJh/z66KOPeOqpp0hNSeaxxt481tgbu0yJjuyMLNRic6LTt29fZs6cSXp6OgAajYaIiAjefPNNnn766XwH8vXXX9OrVy98fHwsjt+7f4WiKLnuaTF16lTi4+PNl8jIyHzHJIQo2aTEQ8FZsWIFL774Ii4ulsN+d3dGViMqIfKR6Hz00UdcvXqVypUrk5ycTKdOnahVqxaurq7MmTMnX0FcvHiRv//+m1GjRpmPeXl5AWTpvYmNjc3Sy5OZo6Mjbm5uFhchhLhX0GUp8VAQlDtzbzQaDb1798ZgVPjzRDR/nohGbzBKUU+hOpv30XFzc2P37t38+++/HDlyBKPRSPPmzXnkkUfyHcSKFSuoXLkyjz32mPmYv78/Xl5ebNu2jWbNmgGmeTyBgYHMnz8/348lhBBJaXpeNZd4qCwlHvJp/fr1rFmzhi+//BIPDw8A0gxGXllt2iE/eOajMnQlVGdzovPtt98ycOBAunbtSteuXc3H09LSWLt2LUOHDrXpfEajkRUrVjBs2DDs7O6Go9FomDBhAnPnziUgIICAgADmzp2Li4sLQ4YMsTVsIYQwm/5bEBeu3sbTzZEP+jeREg/5EBERwahRo4iLi6N58+a89dZb2bbTSo+OUJnNQ1cjRowgPj4+y/HExERGjBhhcwB///03ERERjBw5MsttkydPZsKECbz88su0bNmSqKgotm7diqurq82PI4QQkLXEQ3kp8WAzvV7Ps88+S1xcHK1ateKNN97Isa2dTvbREeqyuUcnp8nAly5dwt3d3eYAevToYR7jvZdGo2H69OlMnz7d5vMKIcS9Mpd4eKVzLdrVrKhyRMXTrFmz2L17N66urqxZsyZLcefMZGdkoTarE51mzZqZSzR069bNYpjJYDAQFhZGz549CyVIIYS4X/eWeJjwiJR4yI/AwEBmz54NwNKlS6lRo0au7TM2mJZER6jF6kQno2r5sWPHePTRRylbtqz5NgcHB/z8/O5rebkQQhQmKfFw/65fv85zzz2H0Whk+PDhVs2XlJ2RhdqsTnQyqpb7+fkxcOBAi/IPQghRlEmJh4IRFRWFRqMhICCAxYsXW3UfrdS6EiqzeY7OsGHDADh8+DCnT59Go9FQv3598xJwIYQoSm7cTpMSDwWkcePGHD9+nNjYWIte/czsdVo+7N/Y/LNO5ugIldmc6MTGxjJo0CB27NhBuXLlUBSF+Ph4unTpwtq1a++rDIQQQhQkRVF44+fjxCamUrNSGd57vL7aIRVLRqMRrdY01Ofh4WHeMyc79jotz7S8W3pHJ6uuhMpsHqR+9dVXSUhIICgoiBs3bnDz5k1OnTpFQkIC48aNK4wYhRAiX1buDeefM7E46LQsHtwcFwebv9uVeklJSbRt25avv/46xxWyuZGdkYXabP6r37x5M3///Tf16tUzH6tfvz6fffaZRaVxIYRQU+YSD2/1rislHvJp0qRJHDhwgIiICJ5++mnKlSuXa3u9wcjO0KsAdAyoJDsjC9XZnOgYjcZs90ywt7fHaJSqbUII9d1b4mFYOz+1QyqW1q9fz7Jly9BoNHz33Xd5JjlgKgExcuUhwFQC4u7OyIUZqRA5s3noqmvXrowfP57Lly+bj0VFRTFx4kS6detWoMEJIUR+zPgtWEo83KeMEg9g2qU+v/UMpUdHqM3mRGfJkiUkJibi5+dHzZo1qVWrFv7+/iQmJlq93FAIIQrL78cv8+OhSCnxcB/uLfEwa9asfJ8rI9HRS4+/UInNQ1e+vr4cOXKEbdu2cebMGRRFoX79+vdVvVwIIQpC5I0k3pISD/fNlhIPedGZ99EpqOiEsE2+lyB0796d7t27F2QsQgiRb/eWeBgvJR7yLaPcz7Jly/Is8ZAX2RlZqC1fe6D/888/9OnTxzx01adPH/7++++Cjk0IIaxmLvHgaCrxYC8lHvJt+vTpBAUFMXjw4Ps+l+yMLNSWrzk6PXv2xNXVlfHjxzNu3Djc3Nzo3bs3S5YsKYwYhRAiV5lLPMzt10hKPOSDoiikpaWZr2feQuR+ZOSb0qMj1GLz0NW8efP45JNPGDt2rPnYuHHjaN++PXPmzLE4LoQQhS1ziYeBLX15vImP2iEVS0uXLuWrr75izZo11K5dO9/nsddpmdm3gfnnjOXlellfLlRic49OQkICPXv2zHK8R48eJCQkFEhQQghhjXtLPEx7Qko85MfJkyeZOHEiR44c4a+//rqvc9nrtAxt68fQtn7Y67TY3SkdITsjC7XYnOg88cQTbNy4McvxX3/9lccff7xAghJCCGuskhIP9y0pKYlBgwaRmppK7969C7yUj3noSuboCJXY/K5Qr1495syZw44dO2jbti0A+/btY8+ePbz22mt8+umn5rZS+0oIUViCLycwV0o83LdJkyYRHByMl5cXK1asuO/NFQ1GhQNhNwBo5V8+087IkugIddic6Hz99dd4eHgQHBxMcHCw+Xi5cuX4+uuvzdc1Go0kOkKIQpGUpmfsmiOkGYx0qyslHvLr3hIPlStXvu9zpuoNDP5yH2AqASE7Iwu12ZzohIWFFUYcQghhtYwSD5VdHfnwGSnxkB8XL140l3h44403Cm3TV/PycunRESqRAW0hRLGSucTDQinxkG+KolCnTh0URWH27NmF9jh2Wll1JdQliY4QotjIXOLh5c41aVdLSjzkl5+fH7t27eLGjRv3VeIhL7IzslCbbB0qhCgW7i3xMOGR/O/1UpolJiaaf7a3t8fT07NQH092RhZqk0RHCFEsLPo7VEo83Kfr16/TsGFDpkyZQnp6+gN5TPNkZMlzhErknUIIUeTtPXeNz3acA6TEQ34pisKoUaOIiIhg48aNpKamPpDHNS8vl0xHqMTmOTorVqygbNmyPPPMMxbHf/75Z5KSkhg2bFiBBSeEEFLioWAsXbqUX375BXt7e9auXUvZsmUL5XHstFqm9qpr/lknQ1dCZTb36Lz//vtUrJh1AmDlypWZO3dugQQlhBBgWeKhhpR4yLeMEg8A8+fPp3nz5oX2WA52WsZ0qsmYTjVxsNOaV11JoiPUYnOic/HiRfz9/bMcr169OhEREQUSlBBCwL0lHppJiYd8SEpKYvDgweYSDxMmTHigjy87Iwu12ZzoVK5cmRMnTmQ5fvz4cSpUqFAgQQkhROYSD1N716WBj7vKERVPr732GkFBQQVW4iEvBqPC8cg4jkfGYTAqsjOyUJ3Nic6gQYMYN24c27dvx2AwYDAY+Pfffxk/fjyDBg0qjBiFEKVMcpqBVzOVeBguJR7yrV27dri5uRVYiYe8pOoN9P1sD30/20Oq3mAu6ik9OkItNvcDz549m4sXL9KtWzfs7Ex3NxqNDB06VOboCCEKxKw/gzl/p8TDB/0bS4mH+/D888/Tp08fPDw8VHl8WXUl1GZzouPg4MCPP/7IrFmzOH78OM7OzjRq1Ijq1asXRnxCiFJmS1AMq/eb5vstGNCUCmUdVY6o+NHr9cTHx5unE6iV5IBp5RVIoiPUk++ZfbVr16Z2bdmZVAhRcGLiU5iy3jQH8MWONXg4QEo85Mfs2bP58ssv+f777+nSpYuqsWgzhq4k0REqsSrRmTRpErNmzaJMmTJMmjQp17YLFiwokMCEEKWL0agw6adjxCWl07CKG6/3qKN2SMXSzp07mTVrFkajkZiYGLXDybQzsiQ6Qh1WJTpHjx41bxd+9OjRQg1ICFE6Ld91gb3nr+Nsr2PRoGY42MnG7ba6fv06zz77LEajkWHDhjF48GC1QzIX9ZQeHaEWqxKd7du3Z/uzEEIUhJOX4vloSwgA7z1en5qVCmfX3pIso8TDpUuXqF27NkuWLFE7JOBuUU+jYopRJpaLB83mr0wjR460qH6b4fbt24wcObJAghJClB63U/WMW3sUvVGhZwMvBj3kq3ZIxVJGiQcHB4dCLfGQFzutlvHdAhjfLcBUAiJTYiO9OkINNic6q1atIjk5Ocvx5ORkvv322wIJSghResz8PZiwa7fxcnPi/acbyTf+fDh16pRFiYdmzZqpFouDnZaJ3WszsXttHOy06HSZEh2ZpyNUYPWqq4SEBBRFQVEUEhMTcXJyMt9mMBjYtGnTA9mMSghRzBkMsGsXREezSVOJH4+lotHAgoFNKOfioHZ0xZKvry9PPfUU8fHxjBs3Tu1wLGTu0TEaVQxElFpWJzrlypVDo9Gg0WiyXVau0WiYMWNGgQYnhChhNmyA8ePh0iUuu1bkzZFLwKksL3npaVdTlpLnl7u7O6tXryYlJQWtVt1J3EajwrmrtwCoVamsedUVSI+OUIfVic727dtRFIWuXbuyfv16ypcvb77NwcGB6tWr4+PjUyhBCiFKgA0boH9/UBQMGi0T+7xGglNZmkSfZdJHk8H3R+jXT+0oi5ULFy7g7+9v/hLq7Oysdkik6A30+GQnAMEzHzVvGAgyR0eow+rUv1OnTnTu3JmwsDD69u1Lp06dzJe2bdvmO8mJioriueeeo0KFCri4uNC0aVMOHz5svl1RFKZPn46Pjw/Ozs507tyZoKCgfD2WEEIlBoOpJ+fON/qlrZ9mf7VGuKQls+i3D7E3GmDCBFM7YZWIiAhatGhBv379SEhIUDucHGXu0ZHCnkINNu+MnFHqISkpiYiICNLS0ixub9y4sdXnunnzJu3bt6dLly789ddfVK5cmfPnz1OuXDlzmw8++IAFCxawcuVKateuzezZs+nevTshISG4urraGr4QQg27dsGlSwCc8qzJJw8/C8CMbUvxi4s2tYmMNLXr3FmlIIsPvV7Ps88+S1xcHJcvXy4SPTk5yZTnoJdER6jA5kTn6tWrjBgxgr/++ivb2w02fCObP38+vr6+rFixwnzMz8/P/LOiKCxcuJC3336bfne6tFetWoWnpyerV69mzJgxtoYvhFBDtCmZSdXZ8dpjE9Hr7OgZsof+p/7Jtp3I3ezZs9m9ezeurq6sWbMGe3t7tUPKkUajQafVYDAqsjuyUIXNs9YmTJjAzZs32bdvH87OzmzevJlVq1YREBDAb7/9ZtO5fvvtN1q2bMkzzzxD5cqVadasGV9++aX59rCwMGJiYujRo4f5mKOjI506dWLv3r22hi6EUIu3NwCL2g8hpJIfFW7HMWfLZ2RZSH6nnchZYGAgs2bNAkx759SoUUPliPImuyMLNdnco/Pvv//y66+/8tBDD6HVaqlevTrdu3fHzc2NefPm8dhjj1l9rgsXLvDFF18wadIk3nrrLQ4cOMC4ceNwdHRk6NCh5jotnp6eFvfz9PTk4sWL2Z4zNTWV1NRU8/WiPHYtRKnRoQNHmzzM0tZPAzBn62dUSM70t6nRQNWq0KGDSgEWD9evX+e5557DaDQyfPhwhgwZonZIVtFqAYMkOkIdNvfo3L5927xfTvny5bl69SoAjRo14siRIzady2g00rx5c+bOnUuzZs0YM2YMo0eP5osvvrBod+8GYrltIz5v3jzc3d3NF19f2WVVCLWlGOG1Jydj1OroG7yDnmf/u3tjxt/ywoWg06kSX3Hx0ksvcenSJQICAli8eLHa4Vgto0dHhq6EGmxOdOrUqUNIiKkmTdOmTVm2bBlRUVEsXboUbxu7nb29valfv77FsXr16hEREQGAl5cXQJYKvLGxsVl6eTJMnTqV+Ph48yUyMtKmmIQQBe+jLSFcSNFS2V5hRtA9Q9xVq8K6dbK03AoTJkwgICBA1RIPebHTanmxYw1e7FjDvLQ8o96VTEYWarB56GrChAlE35kwOG3aNB599FF++OEHHBwcWLlypU3nat++vTlpynD27Fnzyi5/f3+8vLzYtm2beUvztLQ0AgMDmT9/frbndHR0xNHRMcfH7NixI7t27cpyfMiQIfzwww82xS+EyNuBsBt8vScMgPnPtqLc9GDzzsh4e5uGq6Qnxyrt27cnODgYOzub37ofGAc7LW/1rmdxzC6jsKckOkIFNv+1PPvss+afmzVrRnh4OGfOnKFatWpUrGjbzqYTJ06kXbt2zJ07lwEDBnDgwAGWL1/O8uXLAdOQ1YQJE5g7dy4BAQEEBAQwd+5cXFxc8jU2rSgKx44d46OPPrL4PYAi++1IiOIsTW/krY0nURQY0LIqXereKRMjS8itlpSURGRkJHXq1AEo0klOTjL20pGdkYUabB66mjlzJklJSebrLi4uNG/enDJlyjBz5kybzvXQQw+xceNG1qxZQ8OGDZk1axYLFy60SEImT57MhAkTePnll2nZsiVRUVFs3bo1X3vohIaGkpiYSMeOHfHy8rK4SKIjRMH7Zk8Y52JvUaGMA2/3rp/3HUQWr732Gs2aNWP16tVqh2IVo1Eh8kYSkTeSzD04Wll1JVSkURTbUmydTkd0dHSWAp7Xr1+ncuXKNu2j8yAkJCTg7u5OfHw8f/75J0OHDuXWrVu5Dm8JIe7f5bhkun0cSHK6gY+eaUL/FlXVDqnYWb9+Pf3790ej0bB161YeeeQRtUPKU1KanvrvbQFMJSBcHOxoO+8fouNT+H3swzSq6q5yhKKkyPz57ubmlmM7m3t0clrxdPz4cYv6V0XRkSNHMBgMVKhQgbJly5ovo0ePVjs0IUqcmb8Hk5xu4CE/D55uXkXtcIqdiIgIRo0aBZh6totDkpMTc4+ODF0JFVg92Ovh4WFRvTxzsmMwGLh16xYvvfRSoQRZUA4fPswzzzzDnDlzLI57eHioFJEQJdOOkFg2B8Wg02qY9WTDHLeDENnLXOKhVatW5g0CiyvzHB2jUeVIRGlkdaKzcOFCFEVh5MiRzJgxA3f3u92PDg4O+Pn50bZt20IJsqAcPXqUmTNnUqtWrWxv79WrF/Xq1WPv3r3ExcXx3XffMWvWLI4fP87bb7/Niy++CMB3333HkiVLSEpKwt/fn3Xr1uHg4EDr1q357LPPaNmyJcOGDaNNmzb873//e5C/ohCqS0k3MO03U+HdEe38qOuVc5eyyF5xKvFgDTtzoqNyIKJUsjrRGTZsGGBa8t2+fftiN/M/LCyMuLg48zL17Jw6dYqBAweyYMEChg4dypQpU/j9998JDQ3lf//7nznR6d27N88//zwAI0eOZNeuXXTr1o13332XuXPn0r59e8qWLStJjiiVlgVe4OL1JDzdHJnQvbba4RQ7hw4dMvfgLFu2rFiUeMiLVnvPZGSDQbYYKCEMRgO7InYRnRiNt6s3Hap1QKctWv+XNmcrrq6unD59mkaNGgHw66+/smLFCurXr8/06dNxcHAo8CALwrFjxwBT+Yh7NyCsXLkyiYmJODg4MHz4cACcnJwYP348ZcqUwdHR0dyDpSgKy5cvZ8OGDaSlpREREcELL7wAQJ8+fXjnnXe4desWmzZtemC/mxBFRVRcMp/vOAfAO4/Vp6xj8fpCVBQ0bdqUd955h6ioKAYPHqx2OAXCYmfkDRtg/HhzNXvAtGnkokWyaWQxs+H0BsZvHs+lhLv/l1XdqrKo5yL61Ss6/5c2T0YeM2YMZ8+eBUy1qgYOHIiLiws///wzkydPLvAAC8rx48cBqF27Nt7e3uZLtWrVSE9P59SpUzz00EPm9idPnqR169bmnxs2bAjAypUrOXfuHDt37uT48eO4ubmZd3c+cOAAcXFxlCtXrtj1eAlREJbuOE+q3khr//L0aSwFOvPDzs6OGTNmWBQ4Lu7MPTq7dkH//pZJDkBUlOn4hg0qRCfyY8PpDfT/qb9FkgMQlRBF/5/6s+F00fm/tDnROXv2LE2bNgXg559/plOnTqxevZqVK1eyfv36go6vwEyfPh1FUbJc0tLScHR05NSpU+ZeKkVRuHLlirkERebbgoKCaNeuHc7OzixatAij0YiHhwdRUVGMGjWK7du3ExoayunTp1X7XYVQw5WEFH48ZCq5MuGR2jIB2UZ79uyxKEhcXJ8/nVbD822q83yb6uZJyLo7nzQJX60i3sGFeMcyFpcEe2cSHFxIeONNEm+nkpiSzq1UvflyO9MlKc10SU4zmC8p6Vkvqfq7lzS90XxJN5gu+kwXg1ExX4x3Lpk/J4Qlg9HA+M3jUcj63GQcm7B5AgZj0dhuxuZuB0VRMN6ZOf/333/Tp08fAHx9fbl27VrBRleAktL02KXpsxzXajQ42esICgrikUceISlNT3hYGL7Vq5N0p/3xEyd59LE+pKQbeP755+nbty/ffvst7R7uQP0GDbken0i/p5/mo08W4VnFl/GTXmPGzFmsXXN3g6/kNEO2LwoADRqcHXT5apuSbsi1UJ6Lg53qbZ3tdeY37VS9IddNw2xp62SnM39TTNMb0eeyosOWto52OvMbtC1tM95Ac+Kg02J35x3flrZ6g5G0XNra67TY56OtwaiQqs/5jchOq8XBzvq2ywIvkKY30rJ6ORpXdTP//eR2XqNRISWX8+q0GhztTK93RVFITi+Ythl/9xlyivVBtT116hSPPPIIderW5fdNm6lQoYJF2+L2HjG1d12Lthl/02M7jIYOeWznMevv3G8vIjLyUEUBzT3H4O4xuNujZfFflk1bjSZTgpupbcahzOfMWAVtDiKHc2Zuq9FgTtzuvd0yjsxtNZnamf7VG9MwJr2DF8ko2mSMpGAkHr3mJgbNdfSa61y5eY0toYF09u8I2PZ3b23b3P6+MrN5w8CuXbvi6+vLI488wgsvvEBwcDC1atUiMDCQYcOGER4ebsvpCl3GhkK+E35C6+iS5fZOtSvy1fPNzdcbz/yb5PTsPyha+Xnwwwt3h7dazdvOzaT0bNs2quLGhpfamK93/ngnUXEp2batVakMf41rb77e69M9nLt6O9u2Vco5seO1jubr/Zbu42RUQrZtPVzsOTC1i/n6s18f5ED4zWzbOttrOfHe3X06Rn13hMCzOSeuobN6mH9+de1xNgddybHt8Xe7mt/0Jm84xcajl3Nsu+/NzlQoY5rnNf330/xwIOeirNsndaCqhzMA728O4es9F3Nsu2lsOwI8Tbtff/rvORZvv5Bj2/VjWtP4zqZmX+4O44MtoTm2/X5kS1r7m/aP+n5/BDP+OJNj2+XPNaNLnUqmxzgSxZsbg3Js++nAxvRqaOpR/OtUDON+PJFj2/efamDep2Z7yFVe/P5ojm2n9anLc62rAbA/7AbPfXMox7aTHw1g9MP+AJy4FM/Ty/bn2PaF9tX54UAkKelGZj1Rj3d/y7lH84X21Xmzp6mcwaWbyXRZkLX2XIZnW/ky/XFT3aTrt9No8/6OHNs+1cyHD/qZhpiT0vQ0mfVvjm17NvBk8aAm5usB727NsW1hv0ckJSXRrl07goODcarRgsr9p2fpzSmO7xHn5/REd2eCcfcFgYTG3sqxrSj5BrfyZe5TjdBoNJy9kkiPT3bm2PbFjjXM9dIibyTR4YPt2bYzpiYRuXBAnhsG2tyjk1Gi4ZdffuHtt982L9Vet24d7dq1s/V0qktOSiIsLMx8PbdvMykpKZZtc/mmn5qaatFWr88580xPT7dom56e/Rtjxnkyt83c1X0vo9Fo0TYlJftEC0y/d+a2yZnKfGQnc9vbt7N/w80QHn4RZ3tTdn4rMTHXthEXL5LgbHpZJiRk/+acITIykvQ407Lb+Pj4XNteirqEXZJpN+ybN+NybXv58mVc028AcONG9m/6GaKjownD9NjXr+V+3itXrhDmYHqzv3Yt998tNjaWsLDkOz/n/pxdu3aNsLC0O4+R+//F9WvXCQsz3Ik99//jGzdukvHffPlqzq8dgIPnr5CSbqRORUcqa3P/QIuPjze/fmISc36tg+k1kNE2Ljn3b2+3EhPNbXNKRDLcvn3b4jWcm8J+j5g2bRrBwcHYl/WgYu+J2Q5ZFcf3iJNnw2gY4IednR0BnmVzTXSOLRyIs94U55SPf+eX6Jy/7e+e0oXyd74Mzfw9mLUHc/4y9Nf4DlS582Xo4y0hrPov5y9D68a0pWZl05ehLwLPs3xnzl+GVo54iAY+7igofPffRRb/ey7Htp8NaU6L6h4oKKw7fImPt57Nse0H/RvRrmZFFAU2nYxm3l85f3Ga/kR9Otc2VSjYERLL9N+Dc2z7xqN1eLSB6YvTvgvXeeeXUzm2HdulFo838QHgWORNpqw/mWPbRN1WUrRH0eKMvbEmbobHcmy75kAk+y7c4OXONWng82C3nLC5RycnKSkp6HS6IrffQ0aPzn8Hj1CuXLkst2s14Gh3d6pSbm+Q99M2Jd2YQ0ezqWPQyT5/bVP1RnIrH+NcBNo62d3tYk0zGHPdS8OWto52GvOOq+kGI/oCauug01gMR1nbVm9QSM/libDXacz7idjU1qiQbsilrVaDnc72tgajQloube20WAxz5db2lQ3nOX8jlTe7VOHROuVI1Vt3XqOi5NpWpzUN44GpGz2lgNo+qL/7vNpu/3ureaPVr1esot3DHbJtW5zeI1LSjTy5yvQBffzdrriXcTYNc+n1UK8+RF++O9SScd70VNPffdWqpIaex5DL/CQZ3s7a9kEPbxuMBup/Xp+ohEgUzZ2kW9GiwfT5r0FDFdeq/D5gPycuJbDj7FX2hF4j5c6bafXyzkztXY+OtStlG4O1Q1cJCQl4V6pQ8D06GQ4fPszp06fRaDTUq1eP5s2b530nFZV1ssfNJe/6VrbkadJW2t7b1rkw2lJ4bZ0KoO3l+FTO30hFp4FOtcrj6GCHow27TNjS1pbdK2xpq8brJyoqiilTpgDw8ssv07OH9SUeisrrPdvj2cyNcrLXgb0OFnxoWl0FlslORmKzcCGOjtYHkTEnyxoOdlocrFx/U1htMycRBdnWLlPSU5BtdVqNxVyrTGdhUa8P6f9TfzRoTPPFNEYUUu9ch4W959PEtzxNfMvzfFs/bqfq+X7fRZbtvMDFG8m89P0Rnmzqw7THG+BRJuc/Vm2OMYA+h+NZzmFVq0xiY2Pp0qULDz30EOPGjWPs2LG0bNmSbt26cfXqVVtPJ4Qo5gLPm4bumlQpi7uzbKtgralTpxIfH0+zZs2K9NYcBapfP1i3DqrcU/usalXTcdlHp9joV68f6waso4qb5f9lVbeqrBuwLss+OmUc7RjTqSa7JnfhhYf90Wrgl2OXGbR8H/E5zGMrKDa/K7366qskJiYSFBREvXqmyULBwcEMGzaMcePGsWbNmgIPUghRdO28EAdAxxpSldoWs2fPRq/XM3fu3CI35F+o+vWDvn1lZ+QSoF+9fvSt09emnZHLONrxbp/69GnszZjvDhNyJZHR3x7i2xdaWaxYLEg2z9Fxd3fn77//tthcD0yb5fXo0YO4uLiCjO++ZczROXnyZJGvri5EcXMjKZ3HvzqFAmwY0QAv16K5M7p4cJLTDXT7wrRKMGOOjhDZOR2dwICl/5GYqqd3Iy8+f7aFTffP+HzPa46OzUNXRqMx228f9vb2ua4wEEKUPLsuxKMAdSs7S5JjhZs3b7JzZ87LaoUoTep5u/HlsJbY6zRsOhnD0YjcV7nml82JTteuXRk/fjyXL9/dCyUqKoqJEyfSrVu3Ag1OCFG07bpgmp/TsWY5dQMpBhRF4fXXX2fw4MEsW7ZM7XCEKBLa1KjAE01M83xW7g0vlMewOdFZsmQJiYmJ+Pn5UbNmTWrVqoW/vz+JiYksXry4MGIUQhRBBqPCsSjT3ihtqz/YfTGKo2+//ZbNmzdjb29fLPccs5ZOo6FnnXJ0r+WKTmvzR4wohUa09wPgzxPRXEnIfc+u/LB5MrKvry9Hjhxh27ZtnDlzBkVRqF+/Po88Yv3SSCFE8XfhejJJ6UZc7LXUqijzMHJz+vRpZsyYAcBbb71lrp1XEjnYaZnatSqpqakWewoJkZOGVdx5yM+Dg+E3+X7fRV7rUadAz29ToqPX63FycuLYsWN0796d7t27F2gwQoji40S0aRfmBl5lzBuiiaySk5N55ZVXSE1NpWvXrowaNUrtkIQocka09+dg+E1W74/glS61CnQFlk3ptp2dHdWrV8dgKBoVSYUQ6jl5J9Fp7FNG5UiKthkzZhASEkLlypX55JNP0Jbw4RxTQVUjyelGqfwtrNajvic+7k5cv53Gv2diC/TcNv/FvfPOO0ydOpUbN24UaCBCiOLlxGVTotPIWxKdnBw7dozvvvsOjUbDokWLqFixotohFboUvZGeXwXT97vzuVaQFyIzO52WXo28Adh5tmA3H7Z5js6nn37KuXPn8PHxoXr16pQpY/kmd+TIkQILrjD069eP/fuzVmF+6qmnWLJkiQoRCVH8XL2VRkxiGlqNaehKZK9p06YsXryYixcv0rFjx7zvIEQp1iGgIl/vDmNX6DUURcm2wG1+2Jzo9O3bt8Ae/EFTFIWgoCDeffdd+t2z1fi9CZsQImcZvTk1KzhTxkF2tM3Nve81QojstfavgINOS1RcMheu3aZmpbIFcl6bE53p06cXyAOr4cKFC9y6dYs2bdpQuXJltcMRotg6IfNzcvXrr7/y8MMPU6FCBbVDEaLYcHbQ8ZC/B3vOXWfX2asFlujYPEenRo0aXL9+PcvxuLg4atSoUSBBFZaTJ09iZ2dnrtElhMifk9Gm/XNkfk5W+/btY+zYsXTv3p1r166pHY4QxUrHgEoA7AwtuL8dmxOd8PDwbFddpaamcunSpQIJqrCcPHkSg8FAw4YNCQgIMF/eeOMNAJYuXUqLFi3MS+c/+uijAo9h586dLF++vMDPK8SDkpxuIPRqMgCNfQrmG1dJcfPmTcaOHYvRaKRjx46lYvKxEAWpw51E57/z10nVF8xkdquHrn777Tfzz1u2bMHd/W6lYoPBwD///IO/v3+BBFVYTpw4QZ8+fZgyZYrF8YzfJSQkhDlz5tCzZ0+bz20wGNBZUX23Y8eOMilRFGuhV5MxKFCxjJ3Ut8oko8RDdHQ0/v7+zJkzR+2QhCh26nq5UrGsI9dupXLkYhxta97/8K/Vic6TTz4JgEajYdiwYRa32dvb4+fnx8cff3zfARWmoKAgXn/99RwTspCQECZMmJDl+Llz55gxYwZXr16lbNmyLF++nPLly/Pcc89Rt25djhw5wsCBA/n999+pVasWhw4dIiEhgU8//ZSFCxcSHBzMuHHjeO6553juueeYNm0aAQEBhfzbClE4zsQmAVCnkovKkRQtmUs8fPHFF6V2gYNWo6FTDTeMRiO6YrpwRahHq9XQIaAiG49GsSv0aoEkOlYPXRmNRoxGI9WqVSM2NtZ83Wg0kpqaSkhICH369LnvgApLZGQk8fHxNGzYMNvbFUXhwoULjBo1iu7duzNx4kTANCT39ttvM3/+fDZv3sxjjz3G6tWrAThz5gxeXl5s2LCBgQMHcubMGerVq8cff/xB06ZNmTNnDp9//jnffPMNP/74IwBhYWFFvudLiNycvTNsVaeyJDoZzpw5w8yZM4GSX+IhL452WmY+Wo13u3rjWIC724rSIyO5ORReMNXMbV51FRYWViAP/KAFBwcDULFiRWJjLXddrFixIhEREQQEBPD7779b3LZ582ZCQ0PNvVhpaWkMHjyYhIQENBqNeTv3hIQEHBwcGDhwIABOTk6MGjUKFxcXHB0dcXNzIyEhARcXF+zsbH7ahSgyzD06kuiYvf/++6SkpNC1a1dGjx6tdjhCFGvNq3kAcCIqjnSDEXvd/e0mbvMn7rhx46hVqxbjxo2zOL5kyRLOnTvHwoUL7yugwnL69GkAOnToYHHc3t6ekJAQQkJCsh1OOnPmDO+995556C7DwYMHadmypfl6SEgITZo0sXi8119/3fxznTp1CAkJoU6dgi1WJsSDlJJuJPyGqbpw3cpSyDPD4sWLmTdvHpMmTSq2+4wJUVTUqFgGNyc7ElL0hMQk0rCKe953yoXNadL69etp3759luPt2rVj3bp19xVMYZowYQJRUVFZLuHh4Tg6OnLmzJlsE53KlSuzY8cO8/WMhOnMmTPUrVvXfDzzdUVRuHbtmnmvnpCQEOrWrcuZM2ck0RHF2rlryRgVKO9iR8Uy9mqHU2S4uroyd+5cWWWFaVVepy9O0eObUJLS9GqHI4ohrVZD0zu9Okcj7n/4yuYenevXr1usuMrg5uZWpPeMSEk3Zlt3RavR4Gin5ezZszz55JNZ2jzRrz87AnfSoUNHHBwdeKRbN+rVq8fZs2dp1baduX3wmTO0a/8wyekGIiMjqFKlqvkcISEhdHmkB+vX/UT7hztkeQwNGpzs7+acKelGFLIvhndv21S9EWMuhfOcM42Rq9XWyU5r/pabpjdiKKC2jnZatHfaphuM6I0F09ZBpzVX47alrd6gkG405tjWXqfFLj9tjQrphlzaarXY6WxvazAqpOXS1k6rMXcZZ7Q9dWf/nFoVnUnRG7Nta1QUUvXWnTevtjqNBgc7U1tFUSwe837aZvzdZ8itJlNObS9HRbFj+78MfvY582v2fs5ry999UX6PSE7P+rynpBvyPG/Gc5iqN2DI5W/OlrZOdjq02rvvJ/pc/uZsaetopzP/3dvSNt1gzPXv00Gnxe7O34YtbfUGY65/y/Y6rflvzpa2BqOS6xJvO63W/DdnS1ujUSElj7bNfMux8+xVjly8ydMtqmbbztpEWqPYWF62YcOGvPTSS4wdO9bi+OLFi/niiy/Mc2GKioSEBNzd3fGd8BNax6xzCtpUK8v8x/zM1x/9MogUffZPSVMfFxb1vbsp4hMrThOfkv1/Vp1KzizvX9N8feD3IcQkpmfb1s/DkVWD7vYmDVsbSvjN1Gzberna8+Nzd3uFXlx3npA7k0Pv5e6k47cRdzdHHP/rBY5dTsq2rZOdhi2jG5ivT/kznH0Rt7JtCxD4v7uTut/bEkHghYQc224eVR/nO2+88/69xOaQuBzb/jq8LuWcTfn3Jzsv80tQzsVj1z5bG2830/LmL/bGsPZ4zon2yoG18C/vBMCKg1dYeSjnonFLn65BvTvzT9YcvcrSfVdybLvwCT+aVTHtJbPx1HUW7orOse37vavTtrorAH+ducn726NybDu9hy9dapq+UGw/H8/0rZE5tn2zSxV61TV9+/nvYiJvbrqYY9sJHbx5qqFpot/RqFtM+C08x7YvtfFkcDPTnhanY5N4af2FHNsOb1mJEQ95AhB2I4XhP57Lse2gJhX5XzsvAKIT0hj0w9kc2z7ZoDwTO/oAEJesp+/KMzm27VmnHFO7mt4Qk9NNhSVz0qmGGzMfrXb3+hencmyb3XtEcpqeK2umknopGLe2A/DoOBSQ94h7HX+3K+5lnHn5h8NsOhmTY7vgmY/i4mD6u3/tp+OsP5LznmyH33mECmUdAXj3l1N8ty/n1/uuyV3wLW/6W5676TTLd+b8Gt46sSO1PU1/n59sO8uif0JzbPvrK+1p4lsOgGWB55n3V86vyzWj25gn1377Xzjv/RqUY9tvhreka13T39HPhyJ5Y92JHNt+NqQ5jzU2FcH880Q0r6zOuc7kh/0b80xLXwD+PXOFkSsP5dh2Zt8GDG3rB5j2shn85b4c207tVZcxnUyv4eORcfT9bE+Obcd3C2Bi99oAnL2SSI9PdubY9sWONWhXswLDVxykqoczl25m//o1piYRuXAA8fHxuLm55Xg+m3t0Jk2axNixY7l69Spdu3YF4J9//uHjjz8usvNzcpOxaixDblmf0ahYtM2Notxz3lzySUVR7qNtzpk5YNHWmMs3HyVLW1vOm3dbrfFO1p/NZpP3tk3VGqxqm5aWRmqq6XfSG3LP7E1t7/R65LEJVXpauvn30+cRQ3p6prbpucdg0Vafe1u9xXmz//Azt9XrzW3T82qbbkNbg+Fu27S8YrjbNi0tLY/z6jO1zf28hkwxpKbm/pxZtM2mVyGze//ubWmrAPF715J6KRiNgzNlG3XP1FbeIzLTau9vEqkovZr5mr685ZTk2MLmHh2AL774gjlz5nD58mUA/Pz8mD59OkOHDr3vgApaRo9OxOWYbDM+nUZjsQQyt64wrUaDUz7bJqcZcu1qdnbIX9u8uoQzviXZ2jY13ZDrsJEtbS27mo0YcnmDtKWtdEubPKhu6YTkdNrM34FRgS3j2uPl7phtW2u6pa1tq9NqzcM7iqLkOhRkU1sb/u7vbbvt3x083rsnRqOR5V9/w4CBg8y3yXsEJKUZaDs/ELjbUyNDV6XjPaIgh64c7LR0+3gH56/e5otnm9GpTtb6lAkJCXhXqlDwPToA//vf//jf//7H1atXcXZ2pmzZor8NvHsZZ9zK5L1KxN3e+gmWtrS1l7Z32lrdVNpmamvt+iab2gJOjnk2M7c9E5uEUYEKZRyo7e2e6+oiRyvPa2tbBxs2YralrbV/y9evX2fMqJEYjUaGDRvG6JEjCuS8UFT+Pu+/rb191uTOyYb9dBztCqetg50WByvX3xRW28xJREG2tcuU9BRkW51WY5HcFlRbrZVtm1Xz4PzV2wRHJ9KrkU+W2/XWPp5Vre49uV7P33//zYYNG8xdqJcvX+bWrdzHa4UQxdfJqHgAGlTJPckpqRRFYdSoUVy6dImAgACWLFmidkhClGjNqpUD4GhE3H2dx+YenYsXL9KzZ08iIiJITU2le/fuuLq68sEHH5CSksLSpUvvKyAhRNEUdCfRaVQl5y7ikuzo0aP8/vvv2Nvbs3bt2mLRk60GrUZDlzqVzD8LkV8Z83SORcZhMCrmIUBb2ZzojB8/npYtW3L8+HEqVLhbg+Kpp54y7xIshCh5TpoTnfvbvKu4at68Obt27eLMmTM0b95c7XCKLCd7HStGtFI7DFEC1PYsi4uDjlupes5fvWVeFWcrmxOd3bt3s2fPHhzuGQCvXr06UVE5L5cVQhRfqXoDZ68kAtDAp3QmOgBt27albdu2aochRKlgp9PSpGo5/rtwnSMXb+Y70bF5jo7RaMx22e+lS5dwdbUtiOnTp6PRaCwuXl5e5tsVRWH69On4+Pjg7OxM586dCQrKeQ8CIUThCIlJJN2gUM7Fnqoepav0w/z58+V9RwiVFMQ8HZsTne7du1vsl6PRaLh16xbTpk2jd+/eNgfQoEEDoqOjzZeTJ0+ab/vggw9YsGABS5Ys4eDBg3h5edG9e3cSExNtfhwhRP6dijJtCNmolE1EXr9+PW+++SatWrUiJibnDe/EXUlpeuq9u5l6726WEhDivjXLKAURmf9SEDYPXX3yySd06dKF+vXrk5KSwpAhQwgNDaVixYqsWbPG9gDs7Cx6cTIoisLChQt5++236devHwCrVq3C09OT1atXM2bMGJsfSwiRPxnzc+63uF5xEhERYZ53OHbs2Gzfp0T2ctvDSAhbNL2zA3Vo7C0SUtJxc7K9xp7NPTo+Pj4cO3aM119/nTFjxtCsWTPef/99jh49ai5iaYvQ0FB8fHzw9/dn0KBBXLhg2qI7LCyMmJgYevToYW7r6OhIp06d2Lt3b47nS01NJSEhweIihLg/pzISnVIyP0ev1/Pss88SFxdHq1atmD17ttohCVEqVXJ1xLe8M4oCJyLj83WOfG0Y6OzszMiRIxk5cmS+HjRD69at+fbbb6lduzZXrlxh9uzZtGvXjqCgIHM3saenp8V9PD09uXgx59om8+bNY8aMGfcVlxDirjS9kZAY03BxaVlxNXv2bHbv3o2rqyurV6+2aSM9IUTBal7Ng8gbyRyNuMnDARVtvr9Vic5vv/1m9QmfeOIJq9v26tXL/HOjRo1o27YtNWvWZNWqVbRp0wYgy3wARVFynSMwdepUJk2aZL6ekJCAr6+v1TEJISydvZJImsGIu7M9vuVL/kTknTt3MmvWLACWLVtGzZo187iHEKIwNfMtx6/HLnMkIn/zdKxKdJ588kmrTqbRaPIsxJibMmXK0KhRI0JDQ82PGRMTg7e3t7lNbGxsll6ezBwdHXG0ZU95IUSuzMNWVdxKxUTkpUuXYjQaGT58OIMHD1Y7HCFKvbsTkuPy7OzIjlVzdIxGo1WX+0lywDS/5vTp03h7e+Pv74+Xlxfbtm0z356WlkZgYCDt2rW7r8cRQljvZCmbn/Ptt9/yySefsHjxYrVDEUIA9bzdcLDTEpeUTti12zbfP1+1rgrK66+/TmBgIGFhYezfv5/+/fuTkJDAsGHD0Gg0TJgwgblz57Jx40ZOnTrF8OHDcXFxYciQIWqGLUSpcuLSnR2Rq5aORMfOzo4JEyZIiYd80mo0tPYvT2v/8lICQhQIBzstTe68/xwMv2Hz/a1OdHr37k18/N0Zz3PmzCEuLs58/fr169SvX9+mB7906RKDBw+mTp069OvXDwcHB/bt20f16tUBmDx5MhMmTODll1+mZcuWREVFsXXrVps3JhRC5E9ymoHT0aaVixndxyXRqVOnmDRpEikpKWqHUuw52ev4cUxbfhzT1qaq5ULkprW/qeTU/gu2JzoaJaP8eB50Oh3R0dHmJeRubm4cO3aMGjVqAHDlyhV8fHzue/iqoCUkJODu7k58fDxubqWzGKEQ+XUw/AbPLP2Pyq6O7H+rW4mco5OUlESrVq0ICgpi/PjxFhuiCiGKhl2hV3n+6wNUKefMnje7AtZ/vlvdo3NvPmRlfiSEKMaO3lnl0KxauRKZ5AC89tprBAUF4eXlxVtvvaV2OEKIbLSo7oGdVkNUXDKRN5Jsuq+qc3SEEEVbRn2ZkjpstWHDBpYuXYpGo+G7777L16anwlJSmp7ms7bRfNY2KQEhCoyLg515nuD+MNuGr6xOdDKKbt57TAhRcpkTnTvbsJckERERvPDCC4BpPuAjjzyickQlx43bady4naZ2GKKEuTtP57pN97N6Z2RFURg+fLh5j5qUlBReeuklypQpA5iWhgshSo7o+GRiElLQaTUlbsXVvSUeMjYIFEIUXa1rlGdp4Hmbe3SsTnSGDRtmcf25557L0mbo0KE2PbgQoujK6M2p6+WKi0O+qsUUWadPn+bkyZO4urqyZs0aKfEgRDHQsroHWg1E3EgiOj6ZMlYOKln97rVixYr8xiaEKIYyT0QuaRo1asSxY8cICQkxrxwVQhRtrk72NKzizolL8ey/cIOuNa3bakYmIwshsnV3fk7JnIjs5+fHo48+qnYYQggbtKlhmqezK/Sa1feRREcIkUWa3mgu/dC0hPToKIrCmDFj2Lx5s9qhCCHyqXPtSgAEnr2K0WjdNjeS6Aghsgi6HE+q3kg5F3tqVCyjdjgFYunSpSxfvpwnn3ySy5cvqx1OiaXVaGhc1Z3GVd2lBIQocC39ylPGQce1W6kER8fnfQdsmKMjhCg9Dl80zc9pUc2jRGwjkVHiAWDu3Ln4+PioHFHJ5WSv47exD6sdhiihHOy0PBxQkS1BV9h11rpl5tKjI4TI4sidicgt/Ir//Jzk5GQGDRpESkoKvXr1YsKECWqHJIS4D13rmjb23Bl61ar2kugIISwoisKh8Ls9OsXdpEmTzCUeVq5ciVYrb3tCFGed65gSnVOXrRu6kr94IYSFSzeTiU1MxU6roXHVcmqHc18ySjwAfPvtt1Li4QFITjPQ/v1/af/+vySnFa0iz6Jk8HRzor63G9aW3JRERwhhIWPYqkEVd5wddCpHc3927doFwJQpU+jevbvK0ZQOCgpRcclExSWjIMWfReHoUreS1W1lMrIQwkJJGrb65JNP6Natm+yXI0QJ07VuZRZbuVOE9OgIISyYV1xVL/6JDkCfPn2kxIMQJUxTXw9m9W1gVVtJdIQQZrdS9ZyJSQCgZTFdcbVz504ef/xxYmNj1Q5FCFFIdFoNTzWvalVbSXSEEGbHIuIwKlClnDOebk5qh2OzGzdu8Oyzz/LHH38wd+5ctcMRQhQBkugIIcyK87CVoiiMGjWKS5cuERAQwOzZs9UOSQhRBMhkZCGE2eE7K66K47DVsmXL2LhxI/b29qxdu5ayZcuqHVKppEFDQOWy5p+FUJskOkIIAAxGhaN3enSaF7MVV6dOnWLixIkAzJ8/n+bNm6scUenl7KBj26ROaochhFmpGbpKSElXOwQhirTQ2EQSU/W4OOio6+WqdjhWS0pKsijxMH78eLVDEkIUIaUm0QkMkRUYQuQmY35Os2rlsNMVn7eGy5cvk5KSgqenp5R4EEJkUWreEbYGX1E7BCGKtMwVy4uTWrVqcfToUTZv3iwlHoqA5DQD3RcE0n1BoJSAEEVCqZmjs/vcdW6l6inrWGp+ZSFskpHoNC8mK64URUGjMU12dXV1pWnTpuoGJABTCYjQ2Fvmn4VQW6np0UnXG9l+RoavhMjO1cRULl5PQqOBZsWgR0ev19OjRw8WL16MYm1lPyFEqVRqEh2Av05Fqx2CEEVSRiHP2pVdcXcu+uUS5syZw99//83bb7/N5cuX1Q5HCFGElapEZ/uZqySl6dUOQ4gipzgNW+3cuZOZM2cC8MUXX1ClShWVIxJCFGWlJtHxKedEcrqBf07L8JVQn9FYtIZb9p6/BkBr//IqR5K7jBIPRqORoUOH8uyzz6odkhCiiCs1ic7jjX0A+H7fRZUjEaVZYko68zadpv60zQz75gCnoxPUDokbt9MIumyKo12tCipHk7PMJR5q1arFkiVL1A5JCFEMlJpE55mWVdFpNewPu2GuzizEg7T3/DW6fhzIsp0XSEk3Enj2Kr0/3cWb608Qn6Tehpb/nb+OokAdT1cquxbdQp73lnhwdS0+mxqWJho0VCnnTJVyzlICQhQJpWattZe7M4828GTTyRi+/e8ic59qpHZIohRJStMz8cdjXE1Mxa+CC+MfCeDv07H8eSKatQcj+ft0LK/1qE35Mg6k6Y1cu5XK9VtptKlRgYcDKhZqbLvPmYat2tcq3Me5X0lJSeh0OubNm0eLFi3UDkfkwNlBx543u6odhhBmGqWEr81MSEjA3d2d+Ph4gq+lM2j5Ppztdex7q1uxWF0iSoZP/wllwbazVPVw5u9JnXCy1wFwKPwGU9af4PzV2zned1zXWkx4pDZabeF8O+704XYuXk/i62Et6VbPs1Aeo6CcOnWK+vXry+7HQgiLz3c3N7cc25Wqd4vW/uWp4+lKcrqBNQcism2TbjASFZfM8cg42dXzAbt5O43pvwXx4reHisTclYISm5DC0sDzAEzpWdec5AC09CvPpvEdmNS9Ns2qlaN5tXK08i9P70Ze9GzgBcCn/57j5R+OoDcYCzy2yBtJXLyehE6roXWNojk/R6+/u1KyYcOGkuQIIWxSaoauADQaDSMf9mPK+pMs2HaWVv7lzVWaDUaFxf+G8sWO86TqTR8onm6OrBrZirpeOWeKIntf7w7j+30XKetoh7e7EyMf9qdNDh+kiqLw8+FLzNt0mpt35qr8cyaWoW2rM/nRujg76LK9X0FITjPgaKcttN4SgE/+PktSmoGmvuXo09g7y+2OdjrGdQtgXLeALLetO3yJtzacZHNQDBuPRvFMS98CjW3PnWGrZr7liuSu4b/88gvvvPMOa9eupWHDhmqHI6yQkm5gwLL/APhpTFuLxF4INZS6r0bPtPCle31P0vRGXvz2MMGXEzgUfoNnv9rHwr9DSdUbsddpKOtox5WEVJ5Z+h/7Lly36twGo8LN22mF/BsUfV/uvMCsP4IJu3abk1HxbA2+wtCvD7AlKCbb9ksDLzB53QluJqVTx9OVHvU9MRgVVuwJZ8r6E3nufHv2SiLDVxxg9LeHOHzxhtVx/ngwgiYzt9Jq7t+88fNxq/6fgy8n8NbGk7R//18WbA0hTZ97L0tITCI/HowE4J3H6plLFlirf4uqvNajNgBLtp8r8F6djPk57Yrg/JzIyEhGjhxJUFAQ3333ndrhCCsZFYUTl+I5cSkeY8meGSGKiVI1RydjDO9Wqp7+X+zlTEyiRdsyDjpmP9WQvk2qkJiiZ/S3hzgQfgMHOy3rXmpL46rlsn2M345fZumO85y/eotUvZGudSvz6eBmeX5DDr2SyN+nY7l2KxUNMLy9H1U9XPL8nfZfuM7ZK4m4ONjhX6mMuVcqNynpBtYfuYTRqFDP242GVdyt+qa1PSSW5YEXqO/jRvf6njzkVx5dLr0fq/dH8NbGkwC80qUmzat58NOhSLYEXUGn1TD/6cb0b1HV3P7HgxFMWW9qP65bAK92rYW9Tsu24Cu89P1hDEaF+U83YuBD1bI8VrrByKf/hLI08Dzphrsv44drVeTTwc0oX8Yh2xgNRoW5m07z9e4wi+MaDSwc2JS+TbNuQGcwKryx7jgbjkRZHK/v7cYnA5tSxyv7FUDDvjlA4Nmr9GroxRfP5W8CbVKanofnb+fG7TQ+eqaJxfN3P1LSDbSe+w/xyen8NKYtrYrQHjoGg4Fu3boRGBhIixYt2Lt3Lw4O2f9/iqIlKU1P/fe2ABA881FcHIpeT6EoGaydo1MqEx0wzU14Zul/XElMwdvNifo+brz9WH38K5Yxt0lJN/DS94fZEXKVqh7O/PlqB9xdLCcwZ/5gz6yulysrRjyEt7tztnHtPX+NESsOmofJACq7OvLdC61z/NAEWL7zPHM3nbE4Nq5bABMfCcixt+DarVRGf3uIoxFx5mNebk78MLo1NSuVzfY+eoORBdvO8vmO8xbHm1crx6qRrXB1yjqR+/DFmwxY9h8Go8JLnWoypWcdNBoNeoORKetPsv7IJQB6NfRiWDs//jwRzQ/7L2JU4KVONXmzV12L832+4xwfbA7ByV7Lb2Mfprbn3efFYFQYt/Yof54wlfV4pJ4nFco4sOHoJdINCq39y/PdC61xsLPstFQUhakbTrL2Ti/LhEcCaOVXnh/2R/DnyWh0Wg3LnmvBI/XvTso1GhUmrz/BusOX0Gk19GzoRYtqHiz+N5SbSem4O9vz6yvt8cv02gHYefYqQ785gL1Ow7aJnbLcboulged5/68zVK/gwj+TOmGnu//O2N+OX2bcmqP4uDuxa0rXXBPYB2327Nm8++67lC1bliNHjhAQkHVYTxRNkuiIB0USnTtyeyL0BiNGhSwfhpnFJ6XTZ8kuIm8k072+J8ufb4FGo0FRFL7ZE86sP4IBGNq2Oi887M+N22mM/vYw126lUqWcMxteboenm+XeJPsvXGf4ioMkpxtoXq0cD/mVZ3tILGev3MLNyY4VI1rRIput+D/bfo4Pt4QA0L5WBYxG+O/OcMu4rrWY2L12lmTnwtVbDFtxgMgbybg52dG8ugcnLsVz43Ya3u5O/DSmLb7lLXuR9AYjL31/mL/v7CL9TIuqGBSFrUFXuJWqp7V/eVaNbGXRI5SYkk7vT03P0xNNfFg0qKlFLEajwsJ/Qvls+zkM9+wKPLiVL3OfapQldqNRYdiKA+wKvYa3uxMrRjxEXS83i8TDXqfh4wFNebyxNxqNhpCYRJ7+Yi+3UvXZnvf7fRd555dTaDWwaFAzHm/iY36s138+zoajUTjYaZn2eH2GtKpGYqqeuX+eZu3BSHRaDZ8NaU7PhqZJwrGJKYxedYjjl+KpWakMG15ub17JZzAqPPbpLs7EJDKyvT/vPV4/64vLBrdT9XT4wNSrs2BAE/o1v/9enWe/2seec9cZ3y2Aid1r3/f5CsrevXvp2LEjBoOBVatWMXToULVDEjaQREc8KJLo3GHtE5GbE5fi6P/Ff6QZjLSvVYExHWvy7X/h5kTgxY41mNqrrvkDNfJGEkO/OUDYtds0rOLGjy+2pcydYazdodd48btDJKUZ6FynEsueb4GjnY64pDRGrjzIkYg4XB3tWPNiGxpWcTfHsOjvUD75+ywAk7rXNk9c/XLnBeZsOg3A2C61eK3H3WQnKi6Z/l/sJTo+hWrlXVgx4iFqVirL9VupDFy+j3Oxt6hW3oU1L7ahSjlTz5OiKLy18SRrDkTiZK/lw/5NzMnAyUvxDPlyH4mpejrXqcTiwc1wdbJHURQm/XScjUejqOrhzKbxHXDLpscHIOhyPG+uP8np6AR6NPBkSKvqtK9VIdfeqIHL/uP81du4OtoxpHU19py/xqmohCyJR4btZ2IZueogyp2eojcerYNOq+Fg+A2GfLmPdIPCm73q8lKnmhb30xuMvLL6CFuCrgDQxLccYVdvkZCiR6OBTwY05clmlsNasQkp9P1sD9HxKbSvVYH3+zXGzcmecWuPEnj2Km5Oduyc3IVyLvc/7LLk31A+2nqWJr7l+PWV9vd1rojrSXT8cDsaDeya3MWqIdMHIT4+niZNmnDx4kWGDBnC999/b/O8JqEuSXTEgyKJzh0FkegA/Hwokrc2nrSYC+Kg0/LGo3UY1cE/y5vxxeu36ff5Xq7fTqNDQEVe6VKLmPgU3lh3nHSDQoeAinw5tKVFr0hSmp7h3xzkQPgNKpRx4McxbalZqQwLtp1l8b/nAJjcsw4vd65l8Vhf7brA7D9Nyc7LnU0f7Ddup/HMsv+4cPU2NSuV4ccxbalY1tF8n5j4FJ5ZtpfIG8lUcnXkm2EP4VfRhaWB5/ls+3k0Glj2XAt6NLBMIg6E3WDoN/tJSTdSpZwzY7vWYsORSxwMv4lWY1pl0dIv97keiqKQqjdavRojPimd0d8d4kDY3YnG9joNH/RvzFPNsu/ZyPyctK1RAU83R34/EW3qaWnkzZIhzbL9ADUYFVbuDefDLWdISTcNK9aqXJY3e9a1GM7K7FRUPP2X7iUl3YhWA+XLOHDtVhpO9loWDWrGo/c8h/l17VYqbef9Q7pB4bex7XOcM2aNj7aEsGT7OTrWrsS3I1sVSHwFISYmhkGDBhEREcGxY8fu629WqEMSHfGgFLtEZ968ebz11luMHz+ehQsXAqYPxBkzZrB8+XJu3rxJ69at+eyzz2jQoIHV5y2oRAdMPTUL/w5l49FL1PVyY8HAJrkuPT8ScZPBy/dZzMMBeKyRNwsGNsHRLusHfUJKOkO+3MepKNM+MvY6jTm5ert3PUZ3rJHtY32zO4yZd4bRvN2duJqYit6o4OPuxLr/tcOnXNa5Qpfjkhm58iBnYhJxtNNiVBTzY814ogHD2vll+1iHL95gwo/HiLyRbD7mYKfl3cfq8Xzb7O9zv1L1BuZtOkNMfAqd61Sia73KeZYr+O34Zd5cf4KkTPshdbszUbxMHhPFw6/dZuXecFpU96B3I+88568cjbjJgm1n2RVqWsVU1cOZZc+3oIGPe673s9X4tUf59dhlBrSsygf9m+TrHOkGIw/P/5crCal8/mxzejfKuuRdTQaDgaioKKpVyzoBXRR9GZPnAXZP6SKJjig0xSrROXjwIAMGDMDNzY0uXbqYE5358+czZ84cVq5cSe3atZk9ezY7d+4kJCTE6jo3BZnoZIhPSsfVyc6qvVcOht9g5Z5wDl28wZWEVIa2rc60xxvk+sF5/VYqI1Ye5MSleAB0Wg3vPFaPEe39c32slXvCmP57sPm6b3lnVgxvRa3K2U84BtPcmpd/OGL+gK5RsQzD2/sxNI+E5XaqnrmbTvPrscv0berDq10D8HIvenWSzsXeYtpvp6hQxpEXO9awGA4sDKejE9h/4Tp9m1bBI4dVX/fjYPgNnln6H072WvZPfSTL5Pi86A1GJvx4jD9ORFOhjAP/Te2W6xy1ByUlJQUnp6L3+hFCFF1Wf74rKktMTFQCAgKUbdu2KZ06dVLGjx+vKIqiGI1GxcvLS3n//ffNbVNSUhR3d3dl6dKlVp8/Pj5eAZT4+PiCDt0mRqNRSUhOs6l9XFKaEnUzSblxK9Xq+52LTVT2X7iuXI5LUgwGo1X3SdMblC2nopXQKwlWP07mOMWDYzQalUc/CVSqT/lD+WrXBZvuezs1XZm49qhSfcofSq23/lT+PX2lkKK0TWpqqvLQQw8pY8eOVZKSkixuGzZsmNK3b1/zz0CWy6OPPpqvxz127JgyaNAgpWrVqoqTk5NSt25dZeHChRZtkpOTlWHDhikNGzZUdDqdOZa8VK9ePUucU6ZMyVecQojsWfv5rnqf4iuvvMJjjz3GI488wuzZs83Hw8LCiImJoUePHuZjjo6OdOrUib179zJmzJhsz5eamkpqaqr5ekJC0SgloNFosl2SnVt7d2d7m+tx1axUlpqVbIvNXqfNMhfHWjJR9MHSaDQ816Y67/xyii92nOPxJt7ZDuEpisKRiDiORcZx4eotTl1OICgqHr1RwU6rYcmQ5nSpW1mF3yCradOmcfDgQc6dO8eUKVOoWjXnFWU9e/ZkxYoVFsccHR1zaJ27w4cPU6lSJb7//nt8fX3Zu3cvL774IjqdjrFjxwKmYTRnZ2fGjRvH+vXrbTr/zJkzGT16tPl62bI596wKIQqPqonO2rVrOXLkCAcPHsxyW0yMaRddT0/LCaCenp5cvHgxx3POmzePGTNmFGygQhQh/VtU5ft9FzkTk8hrPx1n1YhW5mHUpDQ96w9f4rt9Fzl75VaW+1Yp58y7feoX2ATp+7V9+3bmz58PwJdffplrkgOmpMbLq2BiHzlypMX1GjVq8N9//7FhwwZzolOmTBm++OILAPbs2UNcXJzV53d1dS2wWIuTlHQDw745AJBlGwoh1KBaohMZGcn48ePZunVrrmPz9/YYKIqSay/C1KlTmTRpkvl6QkICvr4FWx9ICDU52etYPLgZjy/Zza7Qa8z+8zQt/Tw4E5PIt/+FE3enXpizvY6OtSsSUNmVAM+ytKjuUWSWkQPcvHmToUOHoigKo0aN4umnn77vc/bq1Ytdu3bl2ubWrawJYIb4+HjKly+YHaLnz5/PrFmz8PX15ZlnnuGNN94oFbs7GxWF/XdWSEoJCFEUqJboHD58mNjYWFq0uLstvsFgYOfOnSxZsoSQENPGeDExMXh7310VEhsbm6WXJzNHR8d8d2ULUVwEeLoy7fEGTN1wkm/2hPHNnrvlLKpXcGF4Oz+eblE1x/2M1KYoCi+99BKXLl2iVq1afPLJJ1bd748//sgyBDRlyhTeffddAL766iuSk5Ozu2ue/vvvP3766Sf+/PPPfN0/s/Hjx9O8eXM8PDw4cOAAU6dOJSwsjK+++uq+zy2EsI1qiU63bt04edKydMKIESOoW7cuU6ZMoUaNGnh5ebFt2zaaNWsGQFpaGoGBgeaubiFKs0EP+XIrRc8/Z65gNIKzg45nWlalV8O8l8Or7bvvvuOnn35Cp9Pxww8/WD1/pUuXLuahpAyZe2CqVMlap8waQUFB9O3bl/fee4/u3bvn6xyZTZw40fxz48aN8fDwoH///syfP58KFSrc9/mFENZTLdFxdXWlYcOGFsfKlClDhQoVzMcnTJjA3LlzCQgIICAggLlz5+Li4sKQIUPUCFmIIkWj0TC6Y40c91YqysqWLUu5cuV47bXXaNXK+g0Ly5QpQ61atXK8PT9DV8HBwXTt2pXRo0fzzjvvWB2LLdq0aQPAuXPnJNER4gFTfdVVbiZPnkxycjIvv/yyecPArVu3Wr2HjhCiaOrXrx9t2rShcuWCXfll69BVUFAQXbt2ZdiwYcyZM6dAY8ns6NGjABbD8EKIB6NIJTo7duywuK7RaJg+fTrTp09XJR4hROHx8fGx+T6pqanmFZkZ7OzsqFixImDb0FVQUBBdunShR48eTJo0yXxenU5HpUp392gIDg4mLS2NGzdukJiYyLFjxwBo2rQpAAcOHGDo0KH8888/VKlShf/++499+/bRpUsX3N3dOXjwIBMnTuSJJ56Q3Z6FUEGRSnSEECI3mzdvztIrUqdOHc6cOWPzuX7++WeuXr3KDz/8wA8//GA+Xr16dcLDw83Xe/fubbGlRcacQeXOiqKkpCRCQkJITzetdnN0dOTHH39kxowZpKamUr16dUaPHs3kyZNtjrG4cpYl5aIIKRIlIApTYZSAEEIIIYS6rP18V7/IjRBCCCFEIZFERwghhBAlliQ6QgghCkxKuoERKw4wYsUBUtINaocjhExGFkIIUXCMisL2kKvmn4VQm/ToCCGEEKLEkkRHCCGEECWWJDpCCCGEKLEk0RFCCCFEiSWJjhBCCCFKrBK/6ipj4+eEhASVIxFCiJIvKU2PMTUJML3v6h1K/MeMUEnG53peBR5K/Cvw+vXrAPj6+qociRBClC7eC9WOQJQGiYmJuLu753h7iU90ypcvD0BERESuT4QwSUhIwNfXl8jISKkNlgd5rmwjz5f15LmyjTxf1itJz5WiKCQmJuLj45NruxKf6Gi1pmlI7u7uxf4/9UFyc3OT58tK8lzZRp4v68lzZRt5vqxXUp4razowZDKyEEIIIUosSXSEEEIIUWKV+ETH0dGRadOm4ejoqHYoxYI8X9aT58o28nxZT54r28jzZb3S+FxplLzWZQkhhBBCFFMlvkdHCCGEEKWXJDpCCCGEKLEk0RFCCCFEiSWJjhBCCCFKrBKd6Hz++ef4+/vj5OREixYt2LVrl9ohFUnTp09Ho9FYXLy8vNQOq8jYuXMnjz/+OD4+Pmg0Gn755ReL2xVFYfr06fj4+ODs7Eznzp0JCgpSJ1iV5fVcDR8+PMtrrU2bNuoEq7J58+bx0EMP4erqSuXKlXnyyScJCQmxaCOvrbuseb7k9XXXF198QePGjc0bA7Zt25a//vrLfHtpem2V2ETnxx9/ZMKECbz99tscPXqUDh060KtXLyIiItQOrUhq0KAB0dHR5svJkyfVDqnIuH37Nk2aNGHJkiXZ3v7BBx+wYMEClixZwsGDB/Hy8qJ79+4kJiY+4EjVl9dzBdCzZ0+L19qmTZseYIRFR2BgIK+88gr79u1j27Zt6PV6evTowe3bt81t5LV1lzXPF8jrK0PVqlV5//33OXToEIcOHaJr16707dvXnMyUqteWUkK1atVKeemllyyO1a1bV3nzzTdViqjomjZtmtKkSRO1wygWAGXjxo3m60ajUfHy8lLef/9987GUlBTF3d1dWbp0qQoRFh33PleKoijDhg1T+vbtq0o8RV1sbKwCKIGBgYqiyGsrL/c+X4oir6+8eHh4KF999VWpe22VyB6dtLQ0Dh8+TI8ePSyO9+jRg71796oUVdEWGhqKj48P/v7+DBo0iAsXLqgdUrEQFhZGTEyMxWvN0dGRTp06yWstBzt27KBy5crUrl2b0aNHExsbq3ZIRUJ8fDxwtxCxvLZyd+/zlUFeX1kZDAbWrl3L7du3adu2bal7bZXIROfatWsYDAY8PT0tjnt6ehITE6NSVEVX69at+fbbb9myZQtffvklMTExtGvXjuvXr6sdWpGX8XqS15p1evXqxQ8//MC///7Lxx9/zMGDB+natSupqalqh6YqRVGYNGkSDz/8MA0bNgTktZWb7J4vkNfXvU6ePEnZsmVxdHTkpZdeYuPGjdSvX7/UvbZKdPVyjUZjcV1RlCzHhOnNIUOjRo1o27YtNWvWZNWqVUyaNEnFyIoPea1ZZ+DAgeafGzZsSMuWLalevTp//vkn/fr1UzEydY0dO5YTJ06we/fuLLfJayurnJ4veX1ZqlOnDseOHSMuLo7169czbNgwAgMDzbeXltdWiezRqVixIjqdLktmGhsbmyWDFVmVKVOGRo0aERoaqnYoRV7G6jR5reWPt7c31atXL9WvtVdffZXffvuN7du3U7VqVfNxeW1lL6fnKzul/fXl4OBArVq1aNmyJfPmzaNJkyYsWrSo1L22SmSi4+DgQIsWLdi2bZvF8W3bttGuXTuVoio+UlNTOX36NN7e3mqHUuT5+/vj5eVl8VpLS0sjMDBQXmtWuH79OpGRkaXytaYoCmPHjmXDhg38+++/+Pv7W9wury1LeT1f2SnNr6/sKIpCampq6XttqTYNupCtXbtWsbe3V77++mslODhYmTBhglKmTBklPDxc7dCKnNdee03ZsWOHcuHCBWXfvn1Knz59FFdXV3mu7khMTFSOHj2qHD16VAGUBQsWKEePHlUuXryoKIqivP/++4q7u7uyYcMG5eTJk8rgwYMVb29vJSEhQeXIH7zcnqvExETltddeU/bu3auEhYUp27dvV9q2batUqVKlVD5X//vf/xR3d3dlx44dSnR0tPmSlJRkbiOvrbvyer7k9WVp6tSpys6dO5WwsDDlxIkTyltvvaVotVpl69atiqKUrtdWiU10FEVRPvvsM6V69eqKg4OD0rx5c4tliOKugQMHKt7e3oq9vb3i4+Oj9OvXTwkKClI7rCJj+/btCpDlMmzYMEVRTMuAp02bpnh5eSmOjo5Kx44dlZMnT6obtEpye66SkpKUHj16KJUqVVLs7e2VatWqKcOGDVMiIiLUDlsV2T1PgLJixQpzG3lt3ZXX8yWvL0sjR440f/5VqlRJ6datmznJUZTS9drSKIqiPLj+IyGEEEKIB6dEztERQgghhABJdIQQQghRgkmiI4QQQogSSxIdIYQQQpRYkugIIYQQosSSREcIIYQQJZYkOkIIIYQosSTREUIIIUSJJYmOEEIIIUosSXSEECXeH3/8QY0aNXjooYc4e/as2uEIIR4gKQEhhCjxateuzeeff05QUBD//fcfa9euVTskIcQDIj06Qohi7/r161SuXJnw8PBsb69YsSK1atWiRo0auLu7m4/379+fBQsWPKAohRBqkB4dIUSx9/rrr3Pz5k2+/vrrbG//8ssveemll6hUqRJBQUFUqFABgBMnTtClSxfCwsJwc3N7kCELIR4Q6dERQhRrycnJfP3114waNSrb2/V6PYsWLWLy5MkkJibi4eFhvq1x48b4+fnxww8/PKhwhRAPmCQ6QogiJTw8HI1Gk+XSuXPnbNv/9ddf2NnZ0bZt22xvX7p0KTVq1OCVV14hKSmJ0NBQi9ufeOIJ1qxZU9C/hhCiiJBERwhRpPj6+hIdHW2+HD16lAoVKtCxY8ds2+/cuZOWLVtme9vNmzeZNWsW8+fPp2rVqri7u3Ps2DGLNq1ateLAgQOkpqYW9K8ihCgCJNERQhQpOp0OLy8vvLy8KFeuHC+99BJt27Zl+vTp2bYPDw/Hx8cn29vee+89nnrqKerVqwdA/fr1OX78uEWbKlWqkJqaSkxMTIH+HkKIosFO7QCEECInL7zwAomJiWzbtg2tNvvvZcnJyTg5OWU5HhwczPfff8/p06fNxxo2bJilR8fZ2RmApKSkggtcCFFkSKIjhCiSZs+ezebNmzlw4ACurq45tqtYsSI3b97McnzixInExcVRtWpV8zGj0Yi3t7dFuxs3bgBQqVKlAopcCFGUSKIjhChy1q9fz8yZM/nrr7+oWbNmrm2bNWvG999/b3Hsjz/+4PDhwxw9ehQ7u7tvcwcPHmTkyJFcvXrVnNicOnWKqlWrUrFixYL/RYQQqpNERwhRpJw6dYqhQ4cyZcoUGjRoYJ474+DgQPny5bO0f/TRR5k6dSo3b97Ew8OD9PR0XnvtNd544w2aNm1q0TZjr5zjx4/zyCOPALBr1y569OhRuL+UEEI1MhlZCFGkHDp0iKSkJGbPno23t7f50q9fv2zbN2rUiJYtW/LTTz8BsHjxYuLi4hg7dmyWtr6+vri4uJjn6aSkpLBx40ZGjx5daL+PEEJdsjOyEKLY27RpE6+//jqnTp3KcdJydj777DN+/fVXtm7dWojRCSHUJENXQohir3fv3oSGhhIVFYWvr6/V97O3t2fx4sWFGJkQQm3SoyOEEEKIEkvm6AghhBCixJJERwghhBAlliQ6QgghhCixJNERQgghRIkliY4QQgghSixJdIQQQghRYkmiI4QQQogSSxIdIYQQQpRYkugIIYQQosSSREcIIYQQJdb/AbPVYzgt8iZeAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "job.suggest_input_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c5ffd483-39d6-4944-bb83-769e50b90ba5", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "91828b9d5d614b1f996285e30dec4fb7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "job.structure.plot3d()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "1682b3cf-063e-483d-83a9-46a8b02c1d1d", + "metadata": {}, + "outputs": [], + "source": [ + "# uncomment this for testing\n", + "#job.input['simulator_dict']['E_max']=52.12" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "c54141e4-10e6-4e7c-ae11-dde88bbcdf44", + "metadata": {}, + "outputs": [], + "source": [ + "job.server.queue = 'cmti'\n", + "job.server.cores = job.input['total_kpoints']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "caeacdc4-a63a-44a9-b1d0-935802bb8920", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The job test_Pt_210 was saved and received the ID: 21796604\n", + "Queue system id: 7075752\n" + ] + } + ], + "source": [ + "job.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1da29398-4406-4b3f-a695-f94519b1da0d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idstatuschemicalformulajobsubjobprojectpathprojecttimestarttimestoptotalcputimecomputerhamiltonhamversionparentidmasterid
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" + ], + "text/plain": [ + " id status chemicalformula job \\\n", + "0 21796604 finished None test_Pt_210 \n", + "9 21796605 finished None test_Pt_210_kpoint_0 \n", + "12 21796606 finished None test_Pt_210_kpoint_1 \n", + "2 21796607 finished None test_Pt_210_kpoint_2 \n", + "19 21796608 finished None test_Pt_210_kpoint_3 \n", + "3 21796609 finished None test_Pt_210_kpoint_4 \n", + "17 21796610 finished None test_Pt_210_kpoint_5 \n", + "5 21796611 finished None test_Pt_210_kpoint_6 \n", + "13 21796612 finished None test_Pt_210_kpoint_7 \n", + "8 21796613 finished None test_Pt_210_kpoint_8 \n", + "4 21796614 finished None test_Pt_210_kpoint_9 \n", + "23 21796615 finished None test_Pt_210_kpoint_10 \n", + "7 21796616 finished None test_Pt_210_kpoint_11 \n", + "6 21796617 finished None test_Pt_210_kpoint_12 \n", + "21 21796618 finished None test_Pt_210_kpoint_13 \n", + "24 21796619 finished None test_Pt_210_kpoint_14 \n", + "22 21796620 finished None test_Pt_210_kpoint_15 \n", + "16 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/test_Pt_210_kpoint_10 /cmmc/u/ \n", + "7 /test_Pt_210_kpoint_11 /cmmc/u/ \n", + "6 /test_Pt_210_kpoint_12 /cmmc/u/ \n", + "21 /test_Pt_210_kpoint_13 /cmmc/u/ \n", + "24 /test_Pt_210_kpoint_14 /cmmc/u/ \n", + "22 /test_Pt_210_kpoint_15 /cmmc/u/ \n", + "16 /test_Pt_210_kpoint_16 /cmmc/u/ \n", + "15 /test_Pt_210_kpoint_17 /cmmc/u/ \n", + "10 /test_Pt_210_kpoint_18 /cmmc/u/ \n", + "18 /test_Pt_210_kpoint_19 /cmmc/u/ \n", + "25 /test_Pt_210_kpoint_20 /cmmc/u/ \n", + "11 /test_Pt_210_kpoint_21 /cmmc/u/ \n", + "20 /test_Pt_210_kpoint_22 /cmmc/u/ \n", + "1 /test_Pt_210_kpoint_23 /cmmc/u/ \n", + "14 /test_Pt_210_kpoint_24 /cmmc/u/ \n", + "\n", + " project \\\n", + "0 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/ \n", + "9 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "12 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "2 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "19 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "3 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "17 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "5 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "13 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "8 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "4 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "23 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "7 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "6 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "21 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "24 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "22 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "16 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "15 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "10 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "18 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "25 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "11 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "20 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "1 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "14 skatnagallu/pyiron/projects/Extra_FIM_pyiron/Test_PyironExtraFIMSimulator/test_Pt_210_hdf5/ \n", + "\n", + " timestart timestop totalcputime \\\n", + "0 2024-03-08 17:43:24.865557 2024-03-08 17:44:24.704155 59.0 \n", + "9 2024-03-08 17:43:32.049827 NaT NaN \n", + "12 2024-03-08 17:43:32.537242 NaT NaN \n", + "2 2024-03-08 17:43:33.025201 NaT NaN \n", + "19 2024-03-08 17:43:33.507807 NaT NaN \n", + "3 2024-03-08 17:43:33.995958 NaT NaN \n", + "17 2024-03-08 17:43:34.489102 NaT NaN \n", + "5 2024-03-08 17:43:34.983260 NaT NaN \n", + "13 2024-03-08 17:43:35.440192 NaT NaN \n", + "8 2024-03-08 17:43:35.906689 NaT NaN \n", + "4 2024-03-08 17:43:36.369823 NaT NaN \n", + "23 2024-03-08 17:43:36.822454 NaT NaN \n", + "7 2024-03-08 17:43:37.277802 NaT NaN \n", + "6 2024-03-08 17:43:37.743402 NaT NaN \n", + "21 2024-03-08 17:43:38.251601 NaT NaN \n", + "24 2024-03-08 17:43:38.753940 NaT NaN \n", + "22 2024-03-08 17:43:39.270307 NaT NaN \n", + "16 2024-03-08 17:43:39.787152 NaT NaN \n", + "15 2024-03-08 17:43:40.297269 NaT NaN \n", + "10 2024-03-08 17:43:40.827211 NaT NaN \n", + "18 2024-03-08 17:43:41.356049 NaT NaN \n", + "25 2024-03-08 17:43:41.909335 NaT NaN \n", + "11 2024-03-08 17:43:42.442997 NaT NaN \n", + "20 2024-03-08 17:43:42.973955 NaT NaN \n", + "1 2024-03-08 17:43:43.505111 NaT NaN \n", + "14 2024-03-08 17:43:44.048403 NaT NaN \n", + "\n", + " computer hamilton hamversion parentid \\\n", + "0 skatnagallu@cmti001#25#0/25 ExtraFimSimulator 0.1.0 None \n", + "9 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "12 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "2 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "19 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "3 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "17 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "5 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "13 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "8 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "4 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "23 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "7 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "6 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "21 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "24 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "22 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "16 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "15 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "10 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "18 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "25 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "11 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "20 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "1 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "14 skatnagallu@cmti339#1 ExtraFimSimulatorRefJob 0.4 None \n", + "\n", + " masterid \n", + "0 NaN \n", + "9 21796604.0 \n", + "12 21796604.0 \n", + "2 21796604.0 \n", + "19 21796604.0 \n", + "3 21796604.0 \n", + "17 21796604.0 \n", + "5 21796604.0 \n", + "13 21796604.0 \n", + "8 21796604.0 \n", + "4 21796604.0 \n", + "23 21796604.0 \n", + "7 21796604.0 \n", + "6 21796604.0 \n", + "21 21796604.0 \n", + "24 21796604.0 \n", + "22 21796604.0 \n", + "16 21796604.0 \n", + "15 21796604.0 \n", + "10 21796604.0 \n", + "18 21796604.0 \n", + "25 21796604.0 \n", + "11 21796604.0 \n", + "20 21796604.0 \n", + "1 21796604.0 \n", + "14 21796604.0 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pr.job_table()" + ] + }, + { + "cell_type": "markdown", + "id": "dcdbf432-3fa6-43a3-9bc6-60500e25d891", + "metadata": {}, + "source": [ + "# Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "69cac4aa-7083-4372-ab3b-c87ecded16cf", + "metadata": {}, + "outputs": [], + "source": [ + "pj = pr.load('test_Pt_210')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "241006e0-3172-4785-aca3-b652e1954776", + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "total_FIM": { + "21.5": "[[2.94212018e-20 2.93407688e-20 2.91044637e-20 ... 2.87268124e-20\n 2.91044570e-20 2.93407654e-20]\n [3.11937322e-20 3.10777529e-20 3.07368002e-20 ... 3.01912285e-20\n 3.07367948e-20 3.10777501e-20]\n [3.36985632e-20 3.35437869e-20 3.30886930e-20 ... 3.23602074e-20\n 3.30886894e-20 3.35437851e-20]\n ...\n [2.79334456e-20 2.79524845e-20 2.80095467e-20 ... 2.81042918e-20\n 2.80095393e-20 2.79524808e-20]\n [2.78673994e-20 2.78531201e-20 2.78118227e-20 ... 2.77478817e-20\n 2.78118149e-20 2.78531162e-20]\n [2.83352440e-20 2.82882655e-20 2.81505313e-20 ... 2.79313060e-20\n 2.81505237e-20 2.82882617e-20]]" + }, + "z_resolved_FIM": { + "21.5": "[[[0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]]\n\n [[0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]]\n\n [[0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n ...\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]\n [0. 0. 0. ... 0. 0. 0.]]\n\n ...\n\n [[0. 0. 0. ... 0. 0. 0.]\n [0. 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(pj['output/total_FIM/21.5'])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a17e8f98-8a7b-43be-8f5a-56e88c73737d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'FIM contribution')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.mean(pj['output/z_resolved_FIM/21.5'],axis=(0,1)))\n", + "plt.ylabel(\"FIM contribution\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "675d40c2-c75f-4d3b-85af-9ba4a054a8ec", + "metadata": {}, + "outputs": [], + "source": [ + "pot,_,_,cell = fim.potential(pj.input['simulator_dict']).potential_cell()\n", + "ex = fim.extra_waves(pj.input['simulator_dict'],pot=pot)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c0de0d17-1cb8-49fa-b11f-b882de8e83f0", + "metadata": {}, + "outputs": [], + "source": [ + "xp =np.linspace(0,50,250)\n", + "yp= np.linspace(0,50,250)\n", + "#xp = np.linspace(0, fim_simulation.cell[0, 0], fim_simulation.Nx)\n", + "#yp = np.linspace(0, fim_simulation.cell[1, 1], fim_simulation.Ny)\n", + "FIM_image_ex_all=np.zeros([xp.shape[0],yp.shape[0]],dtype=np.complex128)\n", + "\n", + "prho_rec_ex_all= np.fft.ifft2(pj['output/total_FIM/21.5'])\n", + "\n", + "for ix in range(xp.shape[0]):\n", + " for iy in range(yp.shape[0]):\n", + " x=xp[ix]\n", + " y=yp[iy]\n", + " phase_1=np.exp(-1j*(ex.gk_1[:,0]*x+ex.gk_1[:,1]*y))\n", + " phase_2= np.exp(-1j*(ex.gk_2[:,0]*x+ex.gk_2[:,1]*y))\n", + " phase=np.outer(phase_1,phase_2)\n", + " FIM_image_ex_all[ix,iy] =np.sum(prho_rec_ex_all.flatten()*phase.flatten())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "cad29638-6812-4135-8fe5-bfacb98115d2", + "metadata": {}, + "outputs": [], + "source": [ + "BOHR_TO_AA = 0.529177210903" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "7b559141-7a65-4dc7-8e51-3850861517b6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vmax_lev=np.max(FIM_image_ex_all.real)\n", + "xp_AA = xp*BOHR_TO_AA\n", + "yp_AA = yp*BOHR_TO_AA\n", + "plt.contourf(xp_AA,yp_AA,FIM_image_ex_all.real.T,vmin=0,vmax=vmax_lev,levels=np.linspace(0,vmax_lev,41))\n", + "plt.rcParams['font.size'] = '16'\n", + "plt.rcParams['font.family'] ='serif'\n", + "plt.xlabel('x ($\\AA$)')\n", + "#plt.xticks([0,5,10,15,20,25])\n", + "plt.ylabel('y ($\\AA$)')\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "aa8ee5e6-f3aa-4197-818d-eacb158c1628", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "81c8d82f38c34e8f831895ec1b90a5b4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pj.get_structure\n", + "struct = pj.structure\n", + "struct.plot3d()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "2c201d23-6b5c-42cc-b5cd-a2aae35bc87f", + "metadata": {}, + "outputs": [], + "source": [ + "new_str = struct.repeat([int(np.ceil(xp_AA.max()/struct.positions[:,0].max())),int(np.ceil(yp_AA.max()/struct.positions[:,1].max())),1])\n", + "scatter_mask = new_str.positions[:, 2] > 11 " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "f7460f04-2b42-4a7d-8458-bcfc4b5b0f8f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vmax_lev=np.max(FIM_image_ex_all.real)\n", + "xp_AA = xp*BOHR_TO_AA\n", + "yp_AA = yp*BOHR_TO_AA\n", + "plt.contourf(xp_AA,yp_AA,FIM_image_ex_all.real.T,vmin=0,vmax=vmax_lev,levels=np.linspace(0,vmax_lev,41))\n", + "plt.rcParams['font.size'] = '16'\n", + "plt.rcParams['font.family'] ='serif'\n", + "plt.xlabel('x ($\\AA$)')\n", + "#plt.xticks([0,5,10,15,20,25])\n", + "plt.ylabel('y ($\\AA$)')\n", + "plt.colorbar()\n", + "plt.scatter(new_str.positions[scatter_mask, 0], new_str.positions[scatter_mask, 1],\n", + " c=new_str.get_atomic_numbers()[scatter_mask], cmap='rainbow', edgecolors='k', s=5*new_str.positions[scatter_mask,2])\n", + "plt.xlim([xp_AA.min(),xp_AA.max()])\n", + "plt.ylim([yp_AA.min(),yp_AA.max()])\n", + "plt.show();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cf273be-1e37-4b64-965e-ad0aa53c723c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pyiron/latest (Python3.10)", + "language": "python", + "name": "pyiron_latest" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 77c1b7e2fa6e701373a3a2d8d422890ab03c91d8 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Fri, 8 Mar 2024 18:03:05 +0100 Subject: [PATCH 09/22] deleted unnecessary files --- .DS_Store | Bin 6148 -> 6148 bytes Examples/test_pyironjob.ipynb | 121 ---------------------------------- 2 files changed, 121 deletions(-) delete mode 100644 Examples/test_pyironjob.ipynb diff --git a/.DS_Store b/.DS_Store index c916a5117583bd226723e85ebb5f0646509f62e3..3695456f814b885fb74f76f3bef0bf434bc1b64d 100644 GIT binary patch delta 67 zcmZoMXfc=|#>B)qu~2NHo+2ar#(>?7jO?3vSh5*6i*X3DOl+v%%+A5j0aUVCkmEb^ VWPTAx4hA4#WME*~93irX82~VX4+H=J delta 375 zcmZoMXfc=|#>B!ku~2NHo+2aH#(>?7izhHMF>-C@VajH#=VB;fNM$GnlJN}X42cY7 zAf9JVesWSyei8!%g8&da0`a{6U;tz>Fksb_R9;*F)w3h1ASbi9#K7PhBNH 1\u001b[0m \u001b[43mjob\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuggest_input_dict\u001b[49m()\n", - "File \u001b[0;32m~/Projects/EXTRA_FIM/EXTRA_FIM/pyiron_job.py:61\u001b[0m, in \u001b[0;36mExtraFimSimulatorRefJob.suggest_input_dict\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 59\u001b[0m waves_reader \u001b[38;5;241m=\u001b[39m sx_nc_waves_reader(Path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwaves_directory\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwaves.sxb\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 60\u001b[0m e_fermi \u001b[38;5;241m=\u001b[39m 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sim\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz_max\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m sim[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz_max\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;66;03m#rename later\u001b[39;00m\n", - "File \u001b[0;32m~/Projects/EXTRA_FIM/EXTRA_FIM/datautils/pre_processing.py:179\u001b[0m, in \u001b[0;36msuggest_input_dictionary\u001b[0;34m(working_directory, E_fermi, ionization_energies, slope_threshold, second_derivative_threshold)\u001b[0m\n\u001b[1;32m 174\u001b[0m Simulator[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mz_max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m z[izs] \u001b[38;5;241m/\u001b[39m (\n\u001b[1;32m 175\u001b[0m scipy\u001b[38;5;241m.\u001b[39mconstants\u001b[38;5;241m.\u001b[39mphysical_constants[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBohr 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ls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m--\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'NoneType' and 'float'" - ] - }, - { - "data": { - "image/png": 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nUFnNJohEf8fih6gF5Ksq8cTXJ1ByvRq9fZywanJfWFrw141anpejDdbPGAh7a0ucunwNCzbHsgki0d/wb2OiZlamrsGM9SeRXXwd/q52WPtEf9goeINSaj2Bnvb4Ymp/KCzk2HUhF29tvwgzXN5JVC8WP0TNqFqja2J44YoKrm0U2DBjIO/VRUKEdnTByn/2AgCsP5KOLw6mCk5EZDhY/BA1E0mS8NIv5/BHUiFsFRb4evoAdHDh1TYkzv29vPHqhGAAQMTOBGyLzRaciMgwsPghaiYr91zCr6ezYSGXYfXjfdGzvZPoSESYNSwATw7Rdc5/4aezOJJcKDgRkXgsfoiawabjl/FJpO7echEP9sCoQHfBiYj+9OqEYEzo4YVqjYSnv41BfI5KdCQioVj8EN2lvRfz8NrWOADAgrDO+OcAH8GJiGqTy2VY+c9eGOjvjFJ1DWasO4krxddvvyORiWLxQ3QXTmdcw/zvT0MrAY8O8MFzY9i9mQyTtZUFvpzaH53d2yBXVYnp63StGIjMEYsfojuUVniziZwWowLd8J9J3dnEkAyao60V1j85EB4OSlzKK8Psb05BXcMmiGR+WPwQ3YGCUjWe+PoEisqr0LO9Iz55jE0MyTi0c7LBuukD0UZpieNpRVj441lo2QSRzAz/tiZqonJ1DWZuOImMogp0cLbF19MHwE5pKToWUaN19XbA51P7wcpCht/P5eDtHfGiIxG1KhY/RE1Qo9Fi3nencS6rBM52Cmx4ciBc2cSQjNCQTq547x+6JohfHUrD2kNpghMRtR4WP0SNJEkSXtkSh8jEAlhbybH2if7wd7UTHYvojk3q0w4vjQsCAPzn94v4/VyO4ERErYPFD1Ejfbw/CT+cyoRcBnwyuS/6dGgrOhLRXXtmRACmhfpCkoDnf4jF8dSroiMRtTgWP0SN8MPJDHy0LwkA8Nak7gjr6iE4EVHzkMlkWHp/N4R380CVRounvjmFS3mlomMRtSgWP0S3EZmQj5e36JoYzhvVCY+H+ApORNS8LOQyfPxoH/TzbQtVZQ2mf30CuSWVomMRtRgWP0QNOJdVjH9tOg2NVsJDfdtj0dguoiMRtQhrKwt8Na0/AtzscKVE1wRRVckmiGSaWPwQ1SPjagWeXH8S16s1GNbZFSse6sEmhmTS2topsGGG7grGhNxSzNkYg6oarehYRM2OxQ9RHa6WqfHEuhMoLKtCN28HfDalH6zYxJDMgI+zLdbPGAA7hQUOJ1/Fiz+zCSKZHv5tTvQ316s0mLnhFNIKy290wx2ANmxiSGakeztHfDqlHyzlMmyNvYJ3dieIjkTUrFj8EP1FjUaL+d+fRmxmMZxsrbDhyYFwd7AWHYuo1Y3o4oaI/+sBAPg8OhUbjqSLDUTUjFj8EN0gSRJe/+0C9sXnQ2kpx1fT+qOTexvRsYiEebi/Dxbdo1vkv+x/F7Arjk0QyTSw+CG6YXVkMr47ngGZDPj40T7o7+csOhKRcPNGd8LkgR0gScBzm2NxKr1IdCSiu8bihwjAzzFZeH/PJQDAsvu7YVx3T8GJiAyDTCbDWxO7ISzYHeoaLWZuOIXk/DLRsYjuCosfMnsHLxVg8S/nAABPjwjAE4P9xAYiMjCWFnL8d3If9PJxQsn1ajzx9Qnkq9gEkYwXix8ya3HZJZizMQY1WgmTenvjpfAg0ZGIDJKtwhJfP9Effi62yC6+jhnrT6JMXSM6FtEdYfFDZiuzqAIz1p9EeZUGgzu64N1/9IJcziaGRPVxaaPEhicHwsVOgQtXVJizMQbVGjZBJOPD4ofM0rXyKjyx7gQKStUI8rTHmqn9oLDkrwPR7fi62OHr6QNgY2WBP5IK8dIv5yBJbIJIxsUg/rZfvXo1/Pz8YG1tjZCQEJw4caLB8T/99BOCgoJgbW2NHj16YMeOHa2UlExBZbUGs745hdSCcng7WmP9jIFwsLYSHYvIaPTyccLqx/vAQi7Dr6ezsfLGxQJExkJ48fPDDz9g4cKFWLp0KU6fPo1evXohPDwc+fn5dY4/cuQIJk+ejJkzZ+LMmTOYNGkSJk2ahLi4uFZOTsbqs6gUxFy+BgdrS2x4ciA8HdnEkKipRgd54O0HuwMAPolMRszla4ITETWeTBJ8vjIkJAQDBgzAJ598AgDQarXw8fHB/PnzsXjx4lvGP/LIIygvL8f27dv1jw0aNAi9e/fGmjVrGvU9VSoVHB0dUVJSAgcHh+aZCBmFPFUlRr4XhevVGnzyWB/c19NbdCQio/bCT2fxc0wW+vm2xc/PhPLmvyRUY9/fhZ75qaqqQkxMDMLCwvSPyeVyhIWF4ejRo3Xuc/To0VrjASA8PLze8QCgVquhUqlqbWSePthzCderNejbwQkTeniJjkNk9F4YGwhrKzliLl/Drrhc0XGIGkVo8VNYWAiNRgMPD49aj3t4eCA3t+5fotzc3CaNB4CIiAg4OjrqNx8fn7sPT0YnPkeFH2MyAQCvTAjmv1CJmoGnozVmDwsAAKzYlYCqGl79RYZP+Jqf1rBkyRKUlJTot8zMTNGRSICInQmQJGB8D0/08+WtK4iay+wRHeHaRonLVyuw6fhl0XGIbkto8ePq6goLCwvk5eXVejwvLw+ennXfXsDT07NJ4wFAqVTCwcGh1kbmJfpSAQ5eKoCVhQwvspEhUbNqo7TE8/d0BgB8vD8JJderBSciapjQ4kehUKBfv37Yv3+//jGtVov9+/cjNDS0zn1CQ0NrjQeAvXv31jueSKOVELEjHgAwdZAf/FztBCciMj2P9PdBJ/c2KK6oxqeRyaLjEDVI+MdeCxcuxJdffokNGzYgPj4ec+bMQXl5OWbMmAEAmDZtGpYsWaIf/9xzz2HXrl1YuXIlEhISsGzZMpw6dQrz5s0TNQUycL+czkJCbikcrC0xf3Qn0XGITJKlhRwvj9edVV13OB2ZRRWCExHVT3jx88gjj+D999/H66+/jt69eyM2Nha7du3SL2rOyMhATk6OfvzgwYPx3Xff4YsvvkCvXr3w888/Y+vWrejevbuoKZABq6iqwco9iQCA+aM7o62dQnAiItM1KtAdgzu6oEqjxXu7E0XHIaqX8D4/IrDPj/n47/4kfLD3Etq3tcH+RSOgtLQQHYnIpMVll+D+Tw5BkoCtc4egt4+T6EhkRoyizw9RS8ovrcSa6BQAwEvjglj4ELWC7u0c8WCfdgCAt3+P532/yCCx+CGT9eHeJFRUadDbxwn39WRDQ6LW8sLYQCgt5TiRXoQ9F/NuvwNRK2PxQybpUl4pfjiZAYANDYlam7eTDWYN8wcArNiZgGoNGx+SYWHxQyYpYkc8tBIwrpsnBvixoSFRa3tmREe42CmQVliO745niI5DVAuLHzI5h5IKEZlYAEu5DC/dy4aGRCLYW1thwT1dAAAf7bsEVSUbH5LhYPFDJkWjlbD8RkPDKYN84c+GhkTCPDrABx3d7HCtohqfRqaIjkOkx+KHTMqWM9mIz1HB3toSz47pLDoOkVmzspBjyb3BAICvD6ch6xobH5JhYPFDJuN6lQbv32isNm9UJzizoSGRcGOC3TEowBlVNVr97yeRaCx+yGSsPZSKXFUl2jnZ4InBfqLjEBEAmUyGV8Z3BQBsjb2Cc1nFYgMRgcUPmYiCUjU+i9KtKXhxXCCsrdjQkMhQ9Gj/Z+PD5Wx8SAaAxQ+ZhI/2XUJ5lQa92jvi/p7eouMQ0d+8EB4IhaUcx9OKsC8+X3QcMnMsfsjoJeWVYvPJTADAy+ODIZezoSGRoWnnZIOZQ3WNDyN2xrPxIQnF4oeM3oqdCdBoJYzt6oGQABfRcYioHnNGdoSznQKpBeXYfIKND0kcFj9k1I4kF2J/Qj4s5TIsZkNDIoPmYG2FBWG6FhQf7kti40MShsUPGS3tXxoaPh7SAQFubQQnIqLbmTywAwJc7VBUXoU1UWx8SGKw+CGjtTU2GxeuqGCvZENDImNhZSHXn6VdeygN2cXXBScic8Tih4xSZbUG791omPavUZ3g0kYpOBERNdY9XT0w0N8Z6hotVrLxIQnA4oeM0tpDacgp0TU0nDHET3QcImoCXeND3W0vfj2TjbjsEsGJyNyw+CGjU1j2Z0PDf4ezoSGRMerl44SJvXU9uf7z+0U2PqRWxeKHjM7H+5JQpq5Bj3aOeKAXGxoSGasXxuoaHx5LLcKBBDY+pNbD4oeMSnJ+Gb670R+EDQ2JjJuPs63+Y+u3d8Sjho0PqZWw+CGjcrOhYViwB0I7sqEhkbH718hOaGtrhZSCcn2ndqKWxuKHjMbRlKvYF58HCzY0JDIZjjZWeO5Gq4qP9l1CKRsfUitg8UNGQauV8PaNhoaPDeyATu5saEhkKh4L8YW/qx0Ky6rweXSq6DhkBlj8kFH47ewVnM8uQRulJZ4LY0NDIlOisJTjpXG6s7lfHUpFTgkbH1LLYvFDBu+vDQ3njOwIVzY0JDI54d08MMCvLSqrtVi555LoOGTiWPyQwVt3OB3Zxdfh5WiNmUP9RcchohYgk8nw8o3Gh7+czsKFK2x8SC2HxQ8ZtKtlanwamQyADQ2JTF2fDm1xfy9vSJLu0nc2PqSWwuKHDNp/9yehVF2Dbt4OmNS7neg4RNTCXgwPhMJCjsPJVxF1qUB0HDJRLH7IYKUUlGHTcV1Dw1fY0JDILPg422L6zcaHv7PxIbUMYcVPeno6Zs6cCX9/f9jY2KBjx45YunQpqqqqGtxv5MiRkMlktbZnnnmmlVJTa3pnZwJqtBLGBLljcCdX0XGIqJXMHdkJTrZWSMovw08xWaLjkAkSVvwkJCRAq9Xi888/x4ULF/Dhhx9izZo1ePnll2+771NPPYWcnBz99u6777ZCYmpNx1OvYs9FXUPDJePZ0JDInDjaWuHZ0bqWFiv3XEK5ukZwIjI1lqK+8bhx4zBu3Dj91wEBAUhMTMRnn32G999/v8F9bW1t4enp2dIRSZC/NjR8dIAPOrnbC05ERK1tyiBfbDiajstXK/D5wVQsvKeL6EhkQgxqzU9JSQmcnZ1vO27Tpk1wdXVF9+7dsWTJElRUVDQ4Xq1WQ6VS1drIcP3v3BWczSqBncICC8L4Fx6ROVJYyrH4RuPDLw6mILekUnAiMiUGU/wkJydj1apVePrppxsc99hjj2Hjxo2IjIzEkiVL8O2332LKlCkN7hMREQFHR0f95uPj05zRqRlVVmvw7q4/Gxq62bOhIZG5GtfdE/18dY0PP9ibKDoOmRCZ1MyNFBYvXox33nmnwTHx8fEICvpzHUd2djZGjBiBkSNH4quvvmrS9ztw4ADGjBmD5ORkdOzYsc4xarUaarVa/7VKpYKPjw9KSkrg4ODQpO9HLevz6BRE7EyAp4M1Il8YCRsF+/oQmbPTGdfwf58egUwG7Hh2GIK9+Hc21U+lUsHR0fG27+/NvuZn0aJFmD59eoNjAgIC9P9/5coVjBo1CoMHD8YXX3zR5O8XEhICAA0WP0qlEkolzyAYuqLyKnxyo6HhorFdWPgQEfp2aIsJPb3w+7kcvL0jHt/ODBEdiUxAsxc/bm5ucHNza9TY7OxsjBo1Cv369cO6desglzf9U7jY2FgAgJeXV5P3JcPy3/1JKK2sQbCXA/6vb3vRcYjIQLwUHoQ9F3LxR1Ihoi8VYESXxr3HENVH2Jqf7OxsjBw5Eh06dMD777+PgoIC5ObmIjc3t9aYoKAgnDhxAgCQkpKCt956CzExMUhPT8dvv/2GadOmYfjw4ejZs6eoqVAzSCssx8ZjlwHoGhpasKEhEd3QwcUWT4T6AdA1PtRoedsLujvCLnXfu3cvkpOTkZycjPbta/8r/+YypOrqaiQmJuqv5lIoFNi3bx8++ugjlJeXw8fHBw899BBeffXVVs9PzetmQ8ORgW4Y2pkNDYmotnmjO+GnmCwk5pXi55hMPDKgg+hIZMSafcGzMWjsgihqHSfTi/DwmqOQy4Cdzw1HoCf7+hDRrb76IxX/+T0e7vZKRL4wEnZKYf9+JwPV2Pd3g7nUncyTJEn4z++6hoaPDPBh4UNE9Zoa6osOzrbIL1Xjyz9SRcchI8bih4Tafi4HZzOLYauwwPNsaEhEDVBaWuClG40PP49ORb6KjQ/pzrD4IWHUNRq8sysBAPD08I5wd7AWnIiIDN34Hp7o08EJ16s1+GDvJdFxyEix+CFhvjlyGVnXrsPdXomnhvuLjkNERkAmk+HVCcEAgB9PZSIhl7croqZj8UNCXCuvwqoDSQCAF8YGwlbBhYtE1Dj9fJ0xvocntBIQsSNBdBwyQix+SIhVB5KhqqxBkKc9HurHhoZE1DQvhgfBykKG6EsFOHipQHQcMjIsfqjVpReW49tj6QCAl9nQkIjugJ+rHaYO8gMAvL2DjQ+paVj8UKt7d3cCqjUShndxw3C2qSeiOzR/dCc4WFsiIbcUv5zOEh2HjAiLH2pVMZeLsON8LuQy4OXxQaLjEJERa2unwPzRnQEAK/ckoqKqRnAiMhYsfqjV/LWh4cP9fBDkye7aRHR3pg32Rfu2NshTqfHVH2mi45CRYPFDrWbH+VycySiGjZUFFo5lQ0Miunt/bXy4JjoF+aVsfEi3x+KHWsVfGxrOHh4ADzY0JKJmcl9PL/T2cUJFlQYf7k0SHYeMAIsfahWbjmUgo6gCbvZKzB4eIDoOEZkQmUyGV240PvzhZAaS80sFJyJDx+KHWlxltQafRacAABaEdeadmImo2Q3wc8bYrh7QSro+YkQNYfFDLe7HU5koKFWjnZMNHu7nIzoOEZmoZ8forvz639krSCssF5yGDBmLH2pRVTVarInSnfV5ZkQAFJb8kSOiltG9nSPGBLlDKwGfRvLsD9WP70TUon49nYUrJZVwt1fi4f4860NELWve6E4AgC1nspFZVCE4DRkqFj/UYmo0Wnx646zP7OEBsLayEJyIiExdnw5tMayzK2q0EtbcWGtI9HcsfqjFbIu9goyiCjjbKfBYSAfRcYjITMwbpTv789OpLOSWsO8P3YrFD7UIjVbC6ijdZ+6zhvnDVsErvIiodYQEuGCgvzOqNFp8fpBnf+hWLH6oRew4n4PUgnI42lhh6iBf0XGIyMw8e+OeX98dz0BBqVpwGjI0LH6o2Wm1Ej650WdjxhA/2FtbCU5EROZmSCcX9PZxgrpGi6/+SBUdhwwMix9qdnvj85CYV4o2SkvMGOwvOg4RmSGZTIZnx+jW/nx77DKulVcJTkSGhMUPNStJkrDqgO7eOtNCfeFoy7M+RCTGqEB3dPN2QEWVBl8f5h3f6U8sfqhZRV0qQFy2CjZWFpg5lGd9iEgcmUyG+Tf6/qw/nI6S69WCE5GhYPFDzUaSJKzarzvrM2VQB7i0UQpORETmbmxXT3TxaINSdQ2+OZIuOg4ZCBY/1GyOpFzF6YxiKCzleGoY79xOROLJ5TLMvdH3Z+3hNJSpawQnIkPA4oeazc21PpMH+MDdwVpwGiIinft6eiPA1Q7FFdXYdOyy6DhkAFj8ULM4mV6EY6lFsLKQ4ekRHUXHISLSs5DL8K8bZ3++/CMV16s0ghORaCx+qFmsutHX5x/92sPbyUZwGiKi2ib29kb7tjYoLKvC9ycyRMchwYQWP35+fpDJZLW2FStWNLhPZWUl5s6dCxcXF7Rp0wYPPfQQ8vLyWikx1SU2sxgHLxXAQi7DnBGdRMchIrqFlYUc/xqp+/vp84MpUNfw7I85E37m580330ROTo5+mz9/foPjn3/+efzvf//DTz/9hOjoaFy5cgX/93//10ppqS6f3FjrM7G3Nzq42ApOQ0RUt4f6tYOXozXyVGr8dCpLdBwSSHjxY29vD09PT/1mZ2dX79iSkhKsXbsWH3zwAUaPHo1+/fph3bp1OHLkCI4dO9aKqemmC1dKsC8+HzIZ9FdUEBEZIqWlBZ4errsS9bOoFFRrtIITkSjCi58VK1bAxcUFffr0wXvvvYeamvovQ4yJiUF1dTXCwsL0jwUFBaFDhw44evRovfup1WqoVKpaGzWP1ZG6tT739fRGR7c2gtMQETXs0YEd4NpGiezi69hyJlt0HBJEaPHz7LPPYvPmzYiMjMTTTz+Nt99+Gy+++GK943Nzc6FQKODk5FTrcQ8PD+Tm5ta7X0REBBwdHfWbj49Pc03BrCXllWJnnO7Pfe4oXuFFRIbP2soCs4frus9/GpmMGp79MUvNXvwsXrz4lkXMf98SEhIAAAsXLsTIkSPRs2dPPPPMM1i5ciVWrVoFtVrdrJmWLFmCkpIS/ZaZmdmsr2+uVkcmQ5KA8G4eCPJ0EB2HiKhRHg/xRVtbK6RfrcDv53NExyEBLJv7BRctWoTp06c3OCYgoO7uvyEhIaipqUF6ejoCAwNved7T0xNVVVUoLi6udfYnLy8Pnp6e9X4/pVIJpZK3WmhOaYXl+O3sFQDA/NGdBachImo8O6UlZg0LwHu7E/HJgWTc39MbcrlMdCxqRc1e/Li5ucHNze2O9o2NjYVcLoe7u3udz/fr1w9WVlbYv38/HnroIQBAYmIiMjIyEBoaeseZqek+i0qGVgJGBbqheztH0XGIiJpkaqgvPo9OQVJ+GXZdyMX4Hl6iI1ErErbm5+jRo/joo49w9uxZpKamYtOmTXj++ecxZcoUtG3bFgCQnZ2NoKAgnDhxAgDg6OiImTNnYuHChYiMjERMTAxmzJiB0NBQDBo0SNRUzE5mUQV+Pa1bKDiPZ32IyAg5WFth+hDd2p9VB5IhSZLgRNSamv3MT2MplUps3rwZy5Ytg1qthr+/P55//nksXLhQP6a6uhqJiYmoqKjQP/bhhx9CLpfjoYceglqtRnh4OD799FMRUzBba6JTUKOVMKSTC/r5thUdh4jojjw5xA9r/0hFfI4K++PzEdbVQ3QkaiUyyQzLXZVKBUdHR5SUlMDBgQt1myK3pBLD341ElUaLzbMHYVCAi+hIRER3bMXOBKyJTkGv9o7YOncIZDKu/TFmjX1/F97nh4zL5wdTUKXRYqCfMwsfIjJ6s4b5w9pKjrNZJfgjqVB0HGolLH6o0QpK1fobAs4bzW7ORGT8XNso8dhAXwDAqgNJXPtjJlj8UKN9dSgVldVa9PJxwrDOrqLjEBE1i6dHBEBhKcfJ9Gs4nlYkOg61AhY/1CjXyqvw7dHLAIBnR3fi5+JEZDI8HKzxSH9d5/9VN27UTKaNxQ81yrrDaaio0qCrlwNGB9Xdh4mIyFg9PSIAlnIZDidfRczla6LjUAtj8UO3paqsxroj6QCA+TzrQ0QmqH1bWzzUtz0A4BOe/TF5LH7otjYcTkdpZQ06u7dBeLf6byNCRGTM5ozsCLkMiEwswPmsEtFxqAWx+KEGlatrsPZwGgDdFV68/w0RmSo/VztM7N0OANf+mDoWP9Sgjccuo7iiGv6udrivp7foOERELWruqI6QyYA9F/OQkKsSHYdaCIsfqldltQZf/pEKQHc62IJnfYjIxHVyt8f47rqbnH5yIFlwGmopLH6oXt+fyEBhWRXaOdngwT7tRMchImoVN5u4/n4+B8n5ZYLTUEtg8UN1Utdo8Hn0n2d9rCz4o0JE5iHYywFhwR6QJODTKJ79MUV8R6M6/RyThVxVJTwdrPFw//ai4xARtapnx+jO/myLvYKMqxWC01BzY/FDt6jWaPFZVAoAXeMvpaWF4ERERK2rZ3snjOjiBo1WwmfRPPtjalj80C22nslG1rXrcG2jwKMDOoiOQ0QkxPwba39+jslCdvF1wWmoObH4oVo0Wgmf3jjr89SwANgoeNaHiMxTfz9nhAa4oFoj4fPoFNFxqBmx+KFatp+7grTCcjjZWmHKIF/RcYiIhJp/Y+3P5pOZyFdVCk5DzYXFD+lptZK+r8XMIf6wU1oKTkREJFZogAv6+bZFVY0WXxxMFR2HmgmLH9LbfSEXSfllsLe2xBND/ETHISISTiaT6df+bDqegatlasGJqDmw+CEAgCRJWHXjrM/0wX5wsLYSnIiIyDCM6OKGnu0dcb1ag7WH0kTHoWbA4ocAAAcS8nExRwU7hQWeHOIvOg4RkcGQyWSYN0p39uebo5dRXFElOBHdLRY/BEmS8N8bZ32mhPqirZ1CcCIiIsMSFuyBIE97lKlrsP5Iuug4dJdY/BAOJRfibGYxrK3kmDU0QHQcIiKDI5fL9Pf8+vpQGkorqwUnorvB4oewar/urM/kgR3gZq8UnIaIyDDd290LHd3soKqswTdHL4uOQ3eBxY+ZO5Z6FSfSi6CwkOPp4R1FxyEiMlgWfzn7s/ZQGiqqagQnojvF4sfM3ezr83D/9vB0tBachojIsN3f0xu+LrYoKq/Cd8czRMehO8Tix4ydzriGQ8mFsJTL8MwInvUhIrodSws5/jVS9/fl5wdTUVmtEZyI7gSLHzN286zPg33awcfZVnAaIiLj8GCf9mjnZIOCUjV+PJUpOg7dARY/ZiouuwQHEvIhlwFzb/SvICKi21NYyvHMCN2VsWuiUlBVoxWciJqKxY+ZWnUgCQDwQC9v+LnaCU5DRGRcHu7vA3d7Ja6UVOLX01mi41ATCSt+oqKiIJPJ6txOnjxZ734jR468ZfwzzzzTismNX2JuKXZfyIOMZ32IiO6ItZUFZg/Xnf35NCoFNRqe/TEmwoqfwYMHIycnp9Y2a9Ys+Pv7o3///g3u+9RTT9Xa7913322l1Kbhk0jdWp97u3uis4e94DRERMbp8RBfuNgpkFFUgW2xV0THoSYQVvwoFAp4enrqNxcXF2zbtg0zZsyATCZrcF9bW9ta+zo4OLRSauOXUlCG7ed0v6TzRnUWnIaIyHjZKCwwa5ju7M/qqGRotJLgRNRYBrPm57fffsPVq1cxY8aM247dtGkTXF1d0b17dyxZsgQVFRUNjler1VCpVLU2c/VpZAokCQgLdkdXbxaNRER3Y2qoLxxtrJBaUI4d53NEx6FGMpjiZ+3atQgPD0f79u0bHPfYY49h48aNiIyMxJIlS/Dtt99iypQpDe4TEREBR0dH/ebj49Oc0Y1GxtUKbI3NBgDMG82zPkREd6uN0hJPDvEHoGsfouXZH6PQ7MXP4sWL613IfHNLSEiotU9WVhZ2796NmTNn3vb1Z8+ejfDwcPTo0QOPP/44vvnmG2zZsgUpKSn17rNkyRKUlJTot8xM8+zL8Fl0CjRaCcM6u6K3j5PoOEREJmH6YD+0UVoiMa8Ue+PzRMehRrBs7hdctGgRpk+f3uCYgIDadw5ft24dXFxc8MADDzT5+4WEhAAAkpOT0bFj3V2KlUollErzvmHnleLr+DlGV/Q9O4ZnfYiImoujrRWeGOyL1ZEpWHUgCWO7etx27SqJ1ezFj5ubG9zc3Bo9XpIkrFu3DtOmTYOVlVWTv19sbCwAwMvLq8n7mpPPo1NQrZEwKMAZA/ycRcchIjIpM4cG4OtD6YjLViHqUgFGBbqLjkQNEL7m58CBA0hLS8OsWbNueS47OxtBQUE4ceIEACAlJQVvvfUWYmJikJ6ejt9++w3Tpk3D8OHD0bNnz9aObjTySyvx/UndWZ/5XOtDRNTsnO0UmDKoAwBg1f4kSBLX/hgy4cXP2rVrMXjwYAQFBd3yXHV1NRITE/VXcykUCuzbtw9jx45FUFAQFi1ahIceegj/+9//Wju2UfnyYCqqarTo28EJgzu6iI5DRGSSnhoeAIWlHKczinEk5aroONSAZv/Yq6m+++67ep/z8/OrVT37+PggOjq6NWKZjKLyKmw8lgFAd9aHn0MTEbUMd3trTB7ggw1HL2PVgSQM6eQqOhLVQ/iZH2pZaw+l4nq1Bj3aOWJkYOPXYhERUdM9PaIjrCxkOJZahJPpRaLjUD1Y/JiwkopqbDhyGQAwb3QnnvUhImph3k42+Ec/XS+5/+5PEpyG6sPix4StP5KOMnUNAj3scU+wh+g4RERmYc6IjrCQy/BHUiFiM4tFx6E6sPgxUTkl1/HFQV3jx3mjO0Eu51kfIqLW0MHFFpN6twMA/Gf7RXZ9NkAsfkzUm/+7iPIqDfp2cMKEHuyBRETUmhaN7QJbhQVOXb6Gn2LM864ChozFjwk6kJCHnXG5sJDLsPzBHjzrQ0TUyrydbLDwni4AgIidCbhaphaciP6KxY+JuV6lwevbLgAAZg71R7AX79xORCTC9MF+CPZyQHFFNSJ2Jtx+B2o1LH5MzKoDSci6dh3ejtZ4jvfwIiISxtJCjuUPdodMBvwck4VjqWx8aChY/JiQS3ml+OJgKgBg2QPdYKcU3sOSiMis9e3QFpMH6m578erWOFTVaAUnIoDFj8mQJAmvbolDjVZCWLAHxnbzFB2JiIgAvBQeBBc7BZLzy/DlH6mi4xBY/JiMn2OycCK9CDZWFlj2QFfRcYiI6AZHWyu8el8wAF3jw4yrFYITEYsfE3CtvApv74gHACwI64z2bW0FJyIior+a1LsdQgNcoK7R4vXf4njXd8FY/JiAFTsTcK2iGoEe9nhyqL/oOERE9DcymQz/ebA7FBZyRCUWYFdcruhIZo3Fj5E7mV6EH07pGmgtf7A7rCx4SImIDFFHtzZ4ZkQAAGDZ/y6gTF0jOJH54julEavWaPHKlvMAgEcH+KC/n7PgRERE1JB/jeoEXxdb5KnU+GDPJdFxzBaLHyO29lAaLuWVwdlOgZfGBYmOQ0REt2FtZYE3J3YHAKw/koa47BLBicwTix8jlVlUgY/26f7V8PL4YLS1UwhOREREjTGiixvu6+kFrQS8suU8NLzxaatj8WOEJEnCst8uoLJaixB/ZzzUt53oSERE1ASv3dcV9kpLnM0qwXcnMkTHMTssfozQnot52J+QDysL2Y3W6bxxKRGRMfFwsMYL4YEAgHd3JSC/tFJwIvPC4sfIlKtrsOw33Y1LZw8PQCd3e8GJiIjoTkwZ5Ise7RxRWlmD5b/Hi45jVlj8GJmP9l1CTkklfJxtMG8Ub1xKRGSsLOQyvP1gD8hlwLbYKziUVCg6ktlg8WNELl5R4evD6QCANx/oDhuFhdhARER0V3q0d8S0UD8AwGvb4lBZrREbyEyw+DESWq2EV7bqrgoY38MTo4LcRUciIqJmsHBsF7jbK5FWWI410Smi45gFFj9G4vuTGTiTUQw7hQVev6+b6DhERNRMHKyt8Pr9uhtSfxqZgrTCcsGJTB+LHyNQUKrGOzsTAACLxgbC09FacCIiImpOE3p4YXgXN1RptHhtK2982tJY/BiBt3fEQ1VZg27eDpgW6is6DhERNTOZTIa3JnaDwlKOQ8mF+O3sFdGRTBqLHwN3JLkQW85kQyYDlj/YA5a8cSkRkUnydbHD/FGdAABvbY9HyfVqwYlMF99JDZi6RoNXt8YBAKaE+KK3j5PYQERE1KJmjwhAgJsdCsvUeG93gug4JovFjwH7PDoVqYXlcG2j1HcCJSIi06W0tMB/JulufLrpeAbOZFwTnMg0sfgxUOmF5fgkMhkA8Np9wXC0sRKciIiIWsPgjq74vz7tIEnAK1viUKPRio5kclqs+Fm+fDkGDx4MW1tbODk51TkmIyMDEyZMgK2tLdzd3fHvf/8bNTU1Db5uUVERHn/8cTg4OMDJyQkzZ85EWVlZC8xAHEmS8Nq2OFTVaDG0kyse6OUtOhIREbWilycEw8HaEhdzVNhw9LLoOCanxYqfqqoqPPzww5gzZ06dz2s0GkyYMAFVVVU4cuQINmzYgPXr1+P1119v8HUff/xxXLhwAXv37sX27dtx8OBBzJ49uyWmIMz2czn4I6kQCks53prEG5cSEZkb1zZKLL43GADwwZ5E5JRcF5zItMikFm4msH79eixYsADFxcW1Ht+5cyfuu+8+XLlyBR4eHgCANWvW4KWXXkJBQQEUCsUtrxUfH4+uXbvi5MmT6N+/PwBg165dGD9+PLKysuDt3bgzJCqVCo6OjigpKYGDg8PdTbCZqSqrMWZlNApK1VgQ1hkLwrqIjkRERAJotRL+seYITmcU497unvhsSj/RkQxeY9/fha35OXr0KHr06KEvfAAgPDwcKpUKFy5cqHcfJycnfeEDAGFhYZDL5Th+/Hi930utVkOlUtXaDNXK3YkoKFXD39UOz4zoKDoOEREJIpfLsPzBHrCQy7AzLhcHEvJERzIZwoqf3NzcWoUPAP3Xubm59e7j7l77nlaWlpZwdnaudx8AiIiIgKOjo37z8fG5y/Qt41xWMb45pvts962J3WFtxRuXEhGZs2AvB8wc6g8AeH3bBVyv4o1Pm0OTip/FixdDJpM1uCUkGF5fgiVLlqCkpES/ZWZmio50C41WwstbzkOSgIm9vTG0s6voSEREZACeG9MZ3o7WyLp2HasOJImOYxIsmzJ40aJFmD59eoNjAgICGvVanp6eOHHiRK3H8vLy9M/Vt09+fn6tx2pqalBUVFTvPgCgVCqhVCoblUuUb4+mIy5bBXtrS7wyIVh0HCIiMhB2Sksse6AbZn8bgy8OpmJSn3bo4mEvOpZRa1Lx4+bmBjc3t2b5xqGhoVi+fDny8/P1H2Xt3bsXDg4O6Nq1a737FBcXIyYmBv366RZ+HThwAFqtFiEhIc2SS4Q8VSXe33MJAPDSuCC42/PGpURE9Kex3TwRFuyBffF5eHVLHH54ehCvBL4LLbbmJyMjA7GxscjIyIBGo0FsbCxiY2P1PXnGjh2Lrl27YurUqTh79ix2796NV199FXPnztWfpTlx4gSCgoKQnZ0NAAgODsa4cePw1FNP4cSJEzh8+DDmzZuHRx99tNFXehmiN7dfRJm6Br19nPDYwA6i4xARkQFa9kBX2FhZ4ER6EX6OyRIdx6i1WPHz+uuvo0+fPli6dCnKysrQp08f9OnTB6dOnQIAWFhYYPv27bCwsEBoaCimTJmCadOm4c0339S/RkVFBRITE1Fd/efN3TZt2oSgoCCMGTMG48ePx9ChQ/HFF1+01DRaXFRiPn4/lwO5DFj+YHfI5azkiYjoVu3b2mJBWGcAwNs74nGtvEpwIuPV4n1+DJGh9PmprNZg7IcHkVFUgZlD/fHafXV/3EdERAQA1Rot7vvvISTmleKR/j545x89RUcyKAbf54eA1ZHJyCiqgKeDNZ6/h80MiYioYVYWcix/UHfj0x9OZeJkepHgRMaJxY8gyfllWBOdAkD3OW4bZZPWnhMRkZnq7+eMRwfo+tW9suU8qnnj0yZj8SOAJEl4det5VGskjA5yR3i3+i/TJyIi+ruXxgXB2U6BS3llWHsoTXQco8PiR4AtZ7JxLLUI1lZyvPFAN16uSERETdLWToGXx+t6wn207xIyiyoEJzIuLH5aWXFFFZb/Hg8AeHZMZ/g42wpORERExuihvu0Q4u+Mymotlv12AWZ4/dIdY/HTyt7ZlYir5VXo7N4Gs4Y2rhs2ERHR38lkMix/sDusLGTYn5CPPRd549PGYvHTimIuF+H7ExkAgP9M6g6FJf/4iYjoznVyt8fs4bp/SC/77QLK1TWCExkHvvu2kmqNFq9siQMAPNyvPUICXAQnIiIiUzBvVGf4ONsgp6QSH+27JDqOUWDx00rWH05HQm4pnGytsGQ8b1xKRETNw0ZhgTcf0PX++fpwOi5eUQlOZPhY/LSC7OLr+PBGNf7yvcFwtlMITkRERKZkVJA7xvfwhEYr4ZWt56HVcvFzQ1j8tII3fruAiioNBvi1xT/6tRcdh4iITNDr93WDncICZzKKsflkpug4Bo3FTwvbezEPey7mwVIuw38m9eCNS4mIqEV4Olpj0dhAAMCKnfEoLFMLTmS4WPy0oIqqGiz77QIAYNawAAR62gtOREREpmxaqC+6eTtAVVmDt2/0lKNbsfhpQR/vT0J28XW0c7LBs2M6iY5DREQmztJCjuUP9oBMBvx6JhtHUgpFRzJILH5aSEKuCmv/0N1v5c2J3WCr4I1LiYio5fX2ccKUEF8AwKtb46Cu0QhOZHhY/LQArVbCq1viUKOVEN7NA2OCPURHIiIiM/JCeCBc2yiRWlCOL6JTRccxOCx+WsBPMZk4dfkabBUWWHp/N9FxiIjIzDjaWOG1+3Q95VZFJiO9sFxwIsPC4qeZXS1TI2JnAgBg4T1d4O1kIzgRERGZowd6eWNoJ1dU1Wjx2rY43vj0L1j8NLOInQkorqhGsJcDpg/2Ex2HiIjMlEwmw1s37iP5R1Ihfo7JEh3JYLD4aUaxmcX4OSYLMhmw/MHusLTgHy8REYnj72qHf43sCAD498/nsGDzGRSUsv8PL0FqRj3bOWL5g92RcbUCfTu0FR2HiIgIc0d1QnFFNTYcTcfW2Cs4kJCPl+4NwuQBHcy28a5MMsMPAVUqFRwdHVFSUgIHBwfRcYiIiFrcuaxivLzlPOKydTc+7dPBCcsn9UBXb9N5H2zs+zs/lyEiIjIDPds7YdvcoVh6f1e0UVriTEYx7v/kEJb/fhHl6hrR8VoVix8iIiIzYSGXYcYQf+xbOAITenhBo5Xw5R9puOeDaOy5kCs6Xqth8UNERGRmPB2tsfrxvlg3fQB8nG1wpaQSs7+NwawNp5B1rUJ0vBbH4oeIiMhMjQpyx54FIzB3VEdYWciwLz4P93xwEF8cTEG1Ris6Xoth8UNERGTGbBQW+Hd4EHY8OwwD/ZxxvVqDt3ck4P5VhxBz+ZroeC2CxQ8RERGhs4c9fnh6EN77R0+0tbVCQm4pHvrsCJb8eh4lFdWi4zUrFj9EREQEQNcV+uH+Pti/aCT+2b89AOD7ExkYvTIKW85kmcwtMlqs+Fm+fDkGDx4MW1tbODk53fL82bNnMXnyZPj4+MDGxgbBwcH4+OOPb/u6fn5+kMlktbYVK1a0wAyIiIjMk7OdAu/+oxd+mD0Ind3b4Gp5FZ7/4Swe+/I4UgrKRMe7ay1W/FRVVeHhhx/GnDlz6nw+JiYG7u7u2LhxIy5cuIBXXnkFS5YswSeffHLb137zzTeRk5Oj3+bPn9/c8YmIiMxeSIALfn92GF4cFwhrKzmOpl7FvR/9gQ/2XkJltUZ0vDvW4h2e169fjwULFqC4uPi2Y+fOnYv4+HgcOHCg3jF+fn5YsGABFixYcMeZ2OGZiIioaTKLKvDatjhEJRYAAPxcbPHWpO4Y1tlNcLI/GWWH55KSEjg7O9923IoVK+Di4oI+ffrgvffeQ02NeXWmJCIiam0+zrZYN30APnu8LzwclEi/WoGpa0/g2e/PIL+0UnS8JjGYG5seOXIEP/zwA37//fcGxz377LPo27cvnJ2dceTIESxZsgQ5OTn44IMP6t1HrVZDrf7zLrYqlarZchMREZkLmUyGe3t4YWhnV3yw9xI2HEnHb2evIDIxHy+OC8JjAzvAwghultqkMz+LFy++ZbHx37eEhIQmh4iLi8PEiROxdOlSjB07tsGxCxcuxMiRI9GzZ08888wzWLlyJVatWlWruPm7iIgIODo66jcfH58mZyQiIiIde2srLL2/G36bNxQ92zuitLIGr22Nw/99dgRx2SWi491Wk9b8FBQU4OrVqw2OCQgIgEKh0H99uzU/Fy9exKhRozBr1iwsX768sVH0Lly4gO7duyMhIQGBgYF1jqnrzI+Pjw/X/BAREd0ljVbCpuOX8d6uRJSqayCXATOG+OP5e7qgjbJ1P2Bq7JqfJqVyc3ODm1vzLWy6cOECRo8ejSeeeOKOCh8AiI2NhVwuh7u7e71jlEollErlncYkIiKieljIZZgW6ofwbp54a/tFbD+Xg7WH0vD7uRwse6Arwrt5QiYzrI/CWmzBc0ZGBmJjY5GRkQGNRoPY2FjExsairEzXHyAuLg6jRo3C2LFjsXDhQuTm5iI3NxcFBQX61zhx4gSCgoKQnZ0NADh69Cg++ugjnD17Fqmpqdi0aROef/55TJkyBW3btm2pqRAREdFteDhY45PH+mLDkwPRwdkWuapKPLPxNGZtOIXMIsO6WWqLXeo+ffp0bNiw4ZbHIyMjMXLkSCxbtgxvvPHGLc/7+voiPT0dABAVFYVRo0YhLS0Nfn5+OH36NP71r38hISEBarUa/v7+mDp1KhYuXNikMzu81J2IiKjlVFZrsDoyGWuiU1CtkWBjZYHnwjpj5lB/WFm03IXmjX1/b/E+P4aIxQ8REVHLS84vw6tbz+NYahEAINDDHssf7I7+frdva3MnjLLPDxEREZmOTu5t8P1Tg7Dy4V5wtlMgMa8U/1hzFIt/OYfiiiphuVj8EBERUYuRyWR4qF977F84Ao8O0LWa+f1cDqpqtMIyGUyTQyIiIjJdbe0UWPFQT/yjX3vkqirh7mAtLAuLHyIiImo1LbXepyn4sRcRERGZFRY/REREZFZY/BAREZFZYfFDREREZoXFDxEREZkVFj9ERERkVlj8EBERkVlh8UNERERmhcUPERERmRUWP0RERGRWzPL2FpIkAQBUKpXgJERERNRcbr6v33yfr49ZFj+lpaUAAB8fH8FJiIiIqLmVlpbC0dGx3udl0u3KIxOk1Wpx5coV2NvbQyaTNetrq1Qq+Pj4IDMzEw4ODs362obC1Odo6vMDTH+Opj4/wPTnaOrzAzjHliBJEkpLS+Ht7Q25vP6VPWZ55kcul6N9+/Yt+j0cHBxM9of5JlOfo6nPDzD9OZr6/ADTn6Opzw/gHJtbQ2d8buKCZyIiIjIrLH6IiIjIrLD4aWZKpRJLly6FUqkUHaXFmPocTX1+gOnP0dTnB5j+HE19fgDnKJJZLngmIiIi88UzP0RERGRWWPwQERGRWWHxQ0RERGaFxQ8RERGZFRY/jbB69Wr4+fnB2toaISEhOHHiRIPjf/rpJwQFBcHa2ho9evTAjh07aj0vSRJef/11eHl5wcbGBmFhYUhKSmrJKTSoKfP78ssvMWzYMLRt2xZt27ZFWFjYLeOnT58OmUxWaxs3blxLT6NBTZnj+vXrb8lvbW1da4wxH8ORI0feMj+ZTIYJEyboxxjSMTx48CDuv/9+eHt7QyaTYevWrbfdJyoqCn379oVSqUSnTp2wfv36W8Y09fe6JTV1jr/++ivuueceuLm5wcHBAaGhodi9e3etMcuWLbvlGAYFBbXgLBrW1DlGRUXV+XOam5tba5yhHMemzq+u3zGZTIZu3brpxxjSMYyIiMCAAQNgb28Pd3d3TJo0CYmJibfdz1DfD1n83MYPP/yAhQsXYunSpTh9+jR69eqF8PBw5Ofn1zn+yJEjmDx5MmbOnIkzZ85g0qRJmDRpEuLi4vRj3n33Xfz3v//FmjVrcPz4cdjZ2SE8PByVlZWtNS29ps4vKioKkydPRmRkJI4ePQofHx+MHTsW2dnZtcaNGzcOOTk5+u37779vjenUqalzBHTdSP+a//Lly7WeN+Zj+Ouvv9aaW1xcHCwsLPDwww/XGmcox7C8vBy9evXC6tWrGzU+LS0NEyZMwKhRoxAbG4sFCxZg1qxZtYqDO/mZaElNnePBgwdxzz33YMeOHYiJicGoUaNw//3348yZM7XGdevWrdYxPHToUEvEb5SmzvGmxMTEWnNwd3fXP2dIx7Gp8/v4449rzSszMxPOzs63/B4ayjGMjo7G3LlzcezYMezduxfV1dUYO3YsysvL693HoN8PJWrQwIEDpblz5+q/1mg0kre3txQREVHn+H/+85/ShAkTaj0WEhIiPf3005IkSZJWq5U8PT2l9957T/98cXGxpFQqpe+//74FZtCwps7v72pqaiR7e3tpw4YN+seeeOIJaeLEic0d9Y41dY7r1q2THB0d6309UzuGH374oWRvby+VlZXpHzO0Y3gTAGnLli0NjnnxxRelbt261XrskUcekcLDw/Vf3+2fWUtqzBzr0rVrV+mNN97Qf7106VKpV69ezResGTVmjpGRkRIA6dq1a/WOMdTjeCfHcMuWLZJMJpPS09P1jxnyMczPz5cASNHR0fWOMeT3Q575aUBVVRViYmIQFhamf0wulyMsLAxHjx6tc5+jR4/WGg8A4eHh+vFpaWnIzc2tNcbR0REhISH1vmZLuZP5/V1FRQWqq6vh7Oxc6/GoqCi4u7sjMDAQc+bMwdWrV5s1e2Pd6RzLysrg6+sLHx8fTJw4ERcuXNA/Z2rHcO3atXj00UdhZ2dX63FDOYZNdbvfweb4MzM0Wq0WpaWlt/weJiUlwdvbGwEBAXj88ceRkZEhKOGd6927N7y8vHDPPffg8OHD+sdN7TiuXbsWYWFh8PX1rfW4oR7DkpISALjlZ+6vDPn9kMVPAwoLC6HRaODh4VHrcQ8Pj1s+d74pNze3wfE3/9uU12wpdzK/v3vppZfg7e1d64d33Lhx+Oabb7B//3688847iI6Oxr333guNRtOs+RvjTuYYGBiIr7/+Gtu2bcPGjRuh1WoxePBgZGVlATCtY3jixAnExcVh1qxZtR43pGPYVPX9DqpUKly/fr1Zfu4Nzfvvv4+ysjL885//1D8WEhKC9evXY9euXfjss8+QlpaGYcOGobS0VGDSxvPy8sKaNWvwyy+/4JdffoGPjw9GjhyJ06dPA2iev78MxZUrV7Bz585bfg8N9RhqtVosWLAAQ4YMQffu3esdZ8jvh2Z5V3dqHitWrMDmzZsRFRVVa0Hwo48+qv//Hj16oGfPnujYsSOioqIwZswYEVGbJDQ0FKGhofqvBw8ejODgYHz++ed46623BCZrfmvXrkWPHj0wcODAWo8b+zE0J9999x3eeOMNbNu2rdZ6mHvvvVf//z179kRISAh8fX3x448/YubMmSKiNklgYCACAwP1Xw8ePBgpKSn48MMP8e233wpM1vw2bNgAJycnTJo0qdbjhnoM586di7i4OKFryO4Wz/w0wNXVFRYWFsjLy6v1eF5eHjw9Pevcx9PTs8HxN//blNdsKXcyv5vef/99rFixAnv27EHPnj0bHBsQEABXV1ckJyffdeamups53mRlZYU+ffro85vKMSwvL8fmzZsb9ZeoyGPYVPX9Djo4OMDGxqZZfiYMxebNmzFr1iz8+OOPt3y88HdOTk7o0qWLURzD+gwcOFCf31SOoyRJ+PrrrzF16lQoFIoGxxrCMZw3bx62b9+OyMhItG/fvsGxhvx+yOKnAQqFAv369cP+/fv1j2m1Wuzfv7/WmYG/Cg0NrTUeAPbu3asf7+/vD09Pz1pjVCoVjh8/Xu9rtpQ7mR+gW53/1ltvYdeuXejfv/9tv09WVhauXr0KLy+vZsndFHc6x7/SaDQ4f/68Pr8pHENAdwmqWq3GlClTbvt9RB7Dprrd72Bz/EwYgu+//x4zZszA999/X6tNQX3KysqQkpJiFMewPrGxsfr8pnIco6OjkZyc3Kh/hIg8hpIkYd68ediyZQsOHDgAf3//2+5j0O+HLbqc2gRs3rxZUiqV0vr166WLFy9Ks2fPlpycnKTc3FxJkiRp6tSp0uLFi/XjDx8+LFlaWkrvv/++FB8fLy1dulSysrKSzp8/rx+zYsUKycnJSdq2bZt07tw5aeLEiZK/v790/fp1g5/fihUrJIVCIf38889STk6OfistLZUkSZJKS0ulF154QTp69KiUlpYm7du3T+rbt6/UuXNnqbKystXndydzfOONN6Tdu3dLKSkpUkxMjPToo49K1tbW0oULF/RjjPkY3jR06FDpkUceueVxQzuGpaWl0pkzZ6QzZ85IAKQPPvhAOnPmjHT58mVJkiRp8eLF0tSpU/XjU1NTJVtbW+nf//63FB8fL61evVqysLCQdu3apR9zuz+z1tbUOW7atEmytLSUVq9eXev3sLi4WD9m0aJFUlRUlJSWliYdPnxYCgsLk1xdXaX8/PxWn58kNX2OH374obR161YpKSlJOn/+vPTcc89Jcrlc2rdvn36MIR3Hps7vpilTpkghISF1vqYhHcM5c+ZIjo6OUlRUVK2fuYqKCv0YY3o/ZPHTCKtWrZI6dOggKRQKaeDAgdKxY8f0z40YMUJ64oknao3/8ccfpS5dukgKhULq1q2b9Pvvv9d6XqvVSq+99prk4eEhKZVKacyYMVJiYmJrTKVOTZmfr6+vBOCWbenSpZIkSVJFRYU0duxYyc3NTbKyspJ8fX2lp556Stibyk1NmeOCBQv0Yz08PKTx48dLp0+frvV6xnwMJUmSEhISJADSnj17bnktQzuGNy95/vt2c05PPPGENGLEiFv26d27t6RQKKSAgABp3bp1t7xuQ39mra2pcxwxYkSD4yVJd3m/l5eXpFAopHbt2kmPPPKIlJyc3LoT+4umzvGdd96ROnbsKFlbW0vOzs7SyJEjpQMHDtzyuoZyHO/k57S4uFiysbGRvvjiizpf05COYV1zA1Drd8uY3g9lNyZFREREZBa45oeIiIjMCosfIiIiMissfoiIiMissPghIiIis8Lih4iIiMwKix8iIiIyKyx+iIiIyKyw+CEiIiKzwuKHiIiIzAqLHyIiIjIrLH6IiIjIrLD4ISIiIrPy/yVesO3p0x82AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "job.suggest_input_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "yes\n" - ] - } - ], - "source": [ - "if 'z_ext' in sim:\n", - " print('yes')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From e8423551a9ae56f900ae0144b5c28de97eaed819 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Mon, 11 Mar 2024 09:35:16 +0100 Subject: [PATCH 10/22] Some minor changes to import orders and statements. --- EXTRA_FIM/datautils/plotting.py | 2 +- ...fim_pyiron_job.ipynb => extra_fim_pyiron_job_workflow.ipynb} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename Examples/{extra_fim_pyiron_job.ipynb => extra_fim_pyiron_job_workflow.ipynb} (100%) diff --git a/EXTRA_FIM/datautils/plotting.py b/EXTRA_FIM/datautils/plotting.py index 2b35379..5953a3c 100644 --- a/EXTRA_FIM/datautils/plotting.py +++ b/EXTRA_FIM/datautils/plotting.py @@ -159,7 +159,7 @@ def waves_figure( # --- compute and plot extrapolated wave if compute_extra: print("Computing EXTRA wave...") - from ..EXTRA_FIM.extra import extra_waves + from EXTRA_FIM.extra import extra_waves waves_extra = extra_waves(Simulator, reader=waves_reader, pot=pot) _, psi_extra = waves_extra.get_psi(istate, ispin, ik) diff --git a/Examples/extra_fim_pyiron_job.ipynb b/Examples/extra_fim_pyiron_job_workflow.ipynb similarity index 100% rename from Examples/extra_fim_pyiron_job.ipynb rename to Examples/extra_fim_pyiron_job_workflow.ipynb From 0a2c331e2108f78b87818d707cba150e3d52f77a Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Mon, 11 Mar 2024 09:35:43 +0100 Subject: [PATCH 11/22] changes to import statements --- EXTRA_FIM/extra.py | 8 ++--- EXTRA_FIM/main.py | 4 +-- EXTRA_FIM/pyiron_job.py | 73 +++++++++++++++++++++-------------------- 3 files changed, 43 insertions(+), 42 deletions(-) diff --git a/EXTRA_FIM/extra.py b/EXTRA_FIM/extra.py index 5e21685..1d35224 100644 --- a/EXTRA_FIM/extra.py +++ b/EXTRA_FIM/extra.py @@ -1,11 +1,9 @@ +from pathlib import Path import numpy as np -import netCDF4 import scipy.constants import scipy.optimize -from .waves_reader_abc import waves_reader_abc -from .sx_nc_waves_reader import sx_nc_waves_reader -from pathlib import Path - +from EXTRA_FIM.waves_reader_abc import waves_reader_abc +from EXTRA_FIM.sx_nc_waves_reader import sx_nc_waves_reader __author__ = "Shalini Bhatt" __copyright__ = ( diff --git a/EXTRA_FIM/main.py b/EXTRA_FIM/main.py index a670982..5ebae90 100644 --- a/EXTRA_FIM/main.py +++ b/EXTRA_FIM/main.py @@ -1,10 +1,10 @@ +from pathlib import Path import numpy as np import h5py import netCDF4 import scipy.constants import scipy.optimize -from pathlib import Path -from .extra import extra_waves +from EXTRA_FIM.extra import extra_waves __author__ = "Shalini Bhatt" diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index 86d1b8e..abc0b77 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -1,3 +1,4 @@ +from pathlib import Path from pyiron_base.utils.error import ImportAlarm from pyiron_base.jobs.master.parallel import ParallelMaster from pyiron_base.jobs.job.jobtype import JobType @@ -5,7 +6,6 @@ from pyiron_base.jobs.job.template import TemplateJob import h5py import numpy as np -from pathlib import Path from pyiron_atomistics.sphinx.structure import read_atoms try: @@ -44,20 +44,20 @@ def extrpolate_potential(self): """Extrapolate potential if needed, to extrapolate waves to higher distances""" elec_potential, _ = sx_el_potential3D_cell( - self.input['simulator_dict']["working_directory"] + self.input["simulator_dict"]["working_directory"] ) - pot, _, _, cell = fim.potential(self.input['simulator_dict']).potential_cell() + pot, _, _, cell = fim.potential(self.input["simulator_dict"]).potential_cell() if self.input.extrapolate_potential: - iz0 = self.input['simulator_dict']["iz_ext_from"] - new_z_max = self.input['simulator_dict']["z_ext"] + iz0 = self.input["simulator_dict"]["iz_ext_from"] + new_z_max = self.input["simulator_dict"]["z_ext"] _, pot_ext = extend_potential( elec_potential / fim.HARTREE_TO_EV, iz0, pot, cell, z_max=new_z_max, - izend=self.input['simulator_dict']["izend"], + izend=self.input["simulator_dict"]["izend"], dv_limit=1e-4, plotG=1, ) @@ -72,10 +72,12 @@ def extrpolate_potential(self): @property def suggest_input_dict(self): """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy""" - waves_reader = sx_nc_waves_reader(Path(self.input["waves_directory"])/"waves.sxb") + waves_reader = sx_nc_waves_reader( + Path(self.input["waves_directory"]) / "waves.sxb" + ) e_fermi = waves_reader.get_fermi_energy() _, sim = suggest_input_dictionary( - self.input['waves_directory'], + self.input["waves_directory"], e_fermi, ionization_energies=self.input["ionization_energies"], ) @@ -90,12 +92,10 @@ def suggest_input_dict(self): self.input["E_max"] = sim["E_max"] # rename later return sim - def run_static(self): self.project_hdf5.create_working_directory() - #self.suggest_input_dict - + # self.suggest_input_dict pot_ext, elec_ext = self.extrpolate_potential() waves_reader = sx_nc_waves_reader(self.input["waves_directory"] + "/waves.sxb") fimsim = fim.FIM_simulations( @@ -109,7 +109,6 @@ def run_static(self): self.status.finished = True - class ExtraFimSimulatorJobGenerator(JobGenerator): """Job generator class for extra fim simulator pyiron jobs""" @@ -124,7 +123,7 @@ def parameter_list(self): kpoints = self._master.input.get("kpoint") if kpoints is None: waves_reader = sx_nc_waves_reader( - Path(self._master.input["waves_directory"])/"waves.sxb" + Path(self._master.input["waves_directory"]) / "waves.sxb" ) for k in range(waves_reader.nk): parameter_lst.append(k) @@ -146,7 +145,7 @@ class ExtraFimSimulator(ParallelMaster): """ "Pyiron Extra FIM simulator job class to make subjobs for each k point""" def __init__(self, project, job_name): - super(ExtraFimSimulator,self).__init__(project, job_name) + super(ExtraFimSimulator, self).__init__(project, job_name) self.__version__ = "0.1.0" self.input["waves_directory"] = None self.input["waves_reader"] = None @@ -155,26 +154,26 @@ def __init__(self, project, job_name): self.input["extrapolate_potential"] = False self.input["total_kpoints"] = None self._job_generator = ExtraFimSimulatorJobGenerator(self) - self.ref_job = ExtraFimSimulatorRefJob(project=project,job_name=job_name) + self.ref_job = ExtraFimSimulatorRefJob(project=project, job_name=job_name) def extrpolate_potential(self): """Extrapolate potential if needed, to extrapolate waves to higher distances""" elec_potential, _ = sx_el_potential3D_cell( - self.input['simulator_dict']["working_directory"] + self.input["simulator_dict"]["working_directory"] ) - pot, _, _, cell = fim.potential(self.input['simulator_dict']).potential_cell() + pot, _, _, cell = fim.potential(self.input["simulator_dict"]).potential_cell() if self.input.extrapolate_potential: - iz0 = self.input['simulator_dict']["iz_ext_from"] - new_z_max = self.input['simulator_dict']["z_ext"] + iz0 = self.input["simulator_dict"]["iz_ext_from"] + new_z_max = self.input["simulator_dict"]["z_ext"] _, pot_ext = extend_potential( elec_potential / fim.HARTREE_TO_EV, iz0, pot, cell, z_max=new_z_max, - izend=self.input['simulator_dict']["izend"], + izend=self.input["simulator_dict"]["izend"], dv_limit=1e-4, plotG=1, ) @@ -189,10 +188,12 @@ def extrpolate_potential(self): @property def suggest_input_dict(self): """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy and populates input""" - waves_reader = sx_nc_waves_reader(Path(self.input["waves_directory"])/"waves.sxb") + waves_reader = sx_nc_waves_reader( + Path(self.input["waves_directory"]) / "waves.sxb" + ) e_fermi = waves_reader.get_fermi_energy() _, sim = suggest_input_dictionary( - self.input['waves_directory'], + self.input["waves_directory"], e_fermi, ionization_energies=self.input["ionization_energies"], ) @@ -210,35 +211,37 @@ def suggest_input_dict(self): @property def get_structure(self): - if (Path(self.input['waves_directory'])/'relaxedStr.sx').exists(): - self.structure = read_atoms(Path(self.input['waves_directory'])/'relaxedStr.sx') + """Tries to get the relaxed sphinx structure if available""" + if (Path(self.input["waves_directory"]) / "relaxedStr.sx").exists(): + self.structure = read_atoms( + Path(self.input["waves_directory"]) / "relaxedStr.sx" + ) def collect_output(self): - FIM_total ={} + FIM_total = {} zFIM_total = {} for job_id in self.child_ids: subjob = self.project_hdf5.load(job_id) subjobdir = subjob.working_directory ik = subjob.input.kpoint IEs = subjob.input.ionization_energies - with h5py.File(f'{subjobdir}/partial_dos{ik}.h5') as handle: - for IE in IEs: - fim_k = np.asarray(handle[f'IE={IE}']) - zfim_k = np.asarray(handle[f'zIE={IE}']) + with h5py.File(f"{subjobdir}/partial_dos{ik}.h5") as handle: + for IE in IEs: + fim_k = np.asarray(handle[f"IE={IE}"]) + zfim_k = np.asarray(handle[f"zIE={IE}"]) if IE not in FIM_total: FIM_total[IE] = np.zeros_like(fim_k) zFIM_total[IE] = np.zeros_like(zfim_k) - FIM_total[IE] += fim_k - zFIM_total[IE] += zfim_k + FIM_total[IE] += fim_k + zFIM_total[IE] += zfim_k for IE in IEs: - self._output[f'total_FIM/{IE}'] = FIM_total[IE] - self._output[f'z_resolved_FIM/{IE}'] = zFIM_total[IE] + self._output[f"total_FIM/{IE}"] = FIM_total[IE] + self._output[f"z_resolved_FIM/{IE}"] = zFIM_total[IE] with self.project_hdf5.open("output") as hdf5_out: - for key,val in self._output.items(): + for key, val in self._output.items(): hdf5_out[key] = val JobType.register(ExtraFimSimulator) - From 02f71274a665f39ac640789085dc19ad11b74704 Mon Sep 17 00:00:00 2001 From: Shyam <85609781+skatnagallu@users.noreply.github.com> Date: Tue, 19 Mar 2024 08:23:44 +0100 Subject: [PATCH 12/22] Update EXTRA_FIM/datautils/plotting.py relative imports Co-authored-by: freyso <63301887+freyso@users.noreply.github.com> --- EXTRA_FIM/datautils/plotting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EXTRA_FIM/datautils/plotting.py b/EXTRA_FIM/datautils/plotting.py index 5953a3c..273cc98 100644 --- a/EXTRA_FIM/datautils/plotting.py +++ b/EXTRA_FIM/datautils/plotting.py @@ -159,7 +159,7 @@ def waves_figure( # --- compute and plot extrapolated wave if compute_extra: print("Computing EXTRA wave...") - from EXTRA_FIM.extra import extra_waves + from ..extra import extra_waves waves_extra = extra_waves(Simulator, reader=waves_reader, pot=pot) _, psi_extra = waves_extra.get_psi(istate, ispin, ik) From e1bafd9aa4d25435f22db8ae1d584deb51d40ca7 Mon Sep 17 00:00:00 2001 From: Shyam <85609781+skatnagallu@users.noreply.github.com> Date: Tue, 19 Mar 2024 08:23:54 +0100 Subject: [PATCH 13/22] Update EXTRA_FIM/datautils/pre_processing.py relative imports Co-authored-by: freyso <63301887+freyso@users.noreply.github.com> --- EXTRA_FIM/datautils/pre_processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EXTRA_FIM/datautils/pre_processing.py b/EXTRA_FIM/datautils/pre_processing.py index 5389ae0..be4b666 100644 --- a/EXTRA_FIM/datautils/pre_processing.py +++ b/EXTRA_FIM/datautils/pre_processing.py @@ -13,7 +13,7 @@ import scipy from .plotting import potential_figure -from EXTRA_FIM.potential import sx_el_potential1D_cell +from ..potential import sx_el_potential1D_cell class PreProcessingFIM: From a508d4e9fda348a1c1352488bb820eac7e68c9b1 Mon Sep 17 00:00:00 2001 From: Shyam <85609781+skatnagallu@users.noreply.github.com> Date: Tue, 19 Mar 2024 08:24:03 +0100 Subject: [PATCH 14/22] Update EXTRA_FIM/extra.py relative imports Co-authored-by: freyso <63301887+freyso@users.noreply.github.com> --- EXTRA_FIM/extra.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/EXTRA_FIM/extra.py b/EXTRA_FIM/extra.py index 1d35224..3ec37da 100644 --- a/EXTRA_FIM/extra.py +++ b/EXTRA_FIM/extra.py @@ -2,8 +2,8 @@ import numpy as np import scipy.constants import scipy.optimize -from EXTRA_FIM.waves_reader_abc import waves_reader_abc -from EXTRA_FIM.sx_nc_waves_reader import sx_nc_waves_reader +from .waves_reader_abc import waves_reader_abc +from .sx_nc_waves_reader import sx_nc_waves_reader __author__ = "Shalini Bhatt" __copyright__ = ( From 2abc181b4fa1cc5b2fd597a2bcef035c69977b8e Mon Sep 17 00:00:00 2001 From: Shyam <85609781+skatnagallu@users.noreply.github.com> Date: Tue, 19 Mar 2024 08:24:15 +0100 Subject: [PATCH 15/22] Update EXTRA_FIM/main.py relative imports Co-authored-by: freyso <63301887+freyso@users.noreply.github.com> --- EXTRA_FIM/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EXTRA_FIM/main.py b/EXTRA_FIM/main.py index 5ebae90..acdf104 100644 --- a/EXTRA_FIM/main.py +++ b/EXTRA_FIM/main.py @@ -4,7 +4,7 @@ import netCDF4 import scipy.constants import scipy.optimize -from EXTRA_FIM.extra import extra_waves +from .extra import extra_waves __author__ = "Shalini Bhatt" From 0ff181476b3e96097df315d6f92eefa1e49f6845 Mon Sep 17 00:00:00 2001 From: Shyam <85609781+skatnagallu@users.noreply.github.com> Date: Tue, 19 Mar 2024 08:24:27 +0100 Subject: [PATCH 16/22] Update EXTRA_FIM/pyiron_job.py relative imports Co-authored-by: freyso <63301887+freyso@users.noreply.github.com> --- EXTRA_FIM/pyiron_job.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index abc0b77..1e304b5 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -40,7 +40,7 @@ def __init__(self, project, job_name): self.input["extrapolate_potential"] = False self.input["total_kpoints"] = None - def extrpolate_potential(self): + def extrapolate_potential(self): """Extrapolate potential if needed, to extrapolate waves to higher distances""" elec_potential, _ = sx_el_potential3D_cell( From 3df38506f2a1c351746c9bc3b55537afe04e4297 Mon Sep 17 00:00:00 2001 From: Shyam <85609781+skatnagallu@users.noreply.github.com> Date: Tue, 19 Mar 2024 08:26:27 +0100 Subject: [PATCH 17/22] Update EXTRA_FIM/pyiron_job.py typo Co-authored-by: freyso <63301887+freyso@users.noreply.github.com> --- EXTRA_FIM/pyiron_job.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index 1e304b5..48fdf4f 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -156,7 +156,7 @@ def __init__(self, project, job_name): self._job_generator = ExtraFimSimulatorJobGenerator(self) self.ref_job = ExtraFimSimulatorRefJob(project=project, job_name=job_name) - def extrpolate_potential(self): + def extrapolate_potential(self): """Extrapolate potential if needed, to extrapolate waves to higher distances""" elec_potential, _ = sx_el_potential3D_cell( From 7eb8e1328838f0aafca8af89568007083aead2a6 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Wed, 20 Mar 2024 08:15:08 +0100 Subject: [PATCH 18/22] removed repeated functions --- EXTRA_FIM/pyiron_job.py | 54 ++++---------------- Examples/extra_fim_pyiron_job_workflow.ipynb | 24 ++++----- 2 files changed, 21 insertions(+), 57 deletions(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index 48fdf4f..9bced72 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -1,3 +1,4 @@ +'''Class to create pyiron jobs for ExtraFIM''' from pathlib import Path from pyiron_base.utils.error import ImportAlarm from pyiron_base.jobs.master.parallel import ParallelMaster @@ -69,9 +70,9 @@ def extrapolate_potential(self): elec_ext = elec_potential return pot_ext, elec_potential - @property def suggest_input_dict(self): - """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy""" + """Suggests a input dictionary based on the electrostatic potential, + Fermi and ionization energy""" waves_reader = sx_nc_waves_reader( Path(self.input["waves_directory"]) / "waves.sxb" ) @@ -96,7 +97,7 @@ def run_static(self): self.project_hdf5.create_working_directory() # self.suggest_input_dict - pot_ext, elec_ext = self.extrpolate_potential() + pot_ext, elec_ext = self.extrapolate_potential() waves_reader = sx_nc_waves_reader(self.input["waves_directory"] + "/waves.sxb") fimsim = fim.FIM_simulations( self.input["simulator_dict"], @@ -155,61 +156,28 @@ def __init__(self, project, job_name): self.input["total_kpoints"] = None self._job_generator = ExtraFimSimulatorJobGenerator(self) self.ref_job = ExtraFimSimulatorRefJob(project=project, job_name=job_name) + self.structure = None def extrapolate_potential(self): - """Extrapolate potential if needed, to extrapolate waves to higher distances""" - - elec_potential, _ = sx_el_potential3D_cell( - self.input["simulator_dict"]["working_directory"] - ) - pot, _, _, cell = fim.potential(self.input["simulator_dict"]).potential_cell() - - if self.input.extrapolate_potential: - iz0 = self.input["simulator_dict"]["iz_ext_from"] - new_z_max = self.input["simulator_dict"]["z_ext"] - _, pot_ext = extend_potential( - elec_potential / fim.HARTREE_TO_EV, - iz0, - pot, - cell, - z_max=new_z_max, - izend=self.input["simulator_dict"]["izend"], - dv_limit=1e-4, - plotG=1, - ) - # copy extension from pot to elec_potential - elec_ext = pot_ext[:, :, :, 0] * fim.HARTREE_TO_EV - elec_ext[:, :, 0:iz0] = elec_potential[:, :, :iz0] - else: - pot_ext = pot - elec_ext = elec_potential + '''returns extrapolated potential if true''' + pot_ext, elec_potential = self.ref_job.extrapolate_potential() return pot_ext, elec_potential - @property def suggest_input_dict(self): - """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy and populates input""" + """Suggests a input dictionary based on the electrostatic potential, + Fermi and ionization energy and populates input""" waves_reader = sx_nc_waves_reader( Path(self.input["waves_directory"]) / "waves.sxb" ) e_fermi = waves_reader.get_fermi_energy() - _, sim = suggest_input_dictionary( + _, sim = self.ref_job.suggest_input_dictionary( self.input["waves_directory"], e_fermi, ionization_energies=self.input["ionization_energies"], ) - self.input["total_kpoints"] = waves_reader.nk - self.input["simulator_dict"] = sim - self.input["z_max"] = sim["z_max"] # rename later - self.input["izstart_min"] = sim["izstart_min"] # rename later - self.input["izend"] = sim["izend"] # rename later - self.input["limit"] = sim["limit"] # rename later - self.input["cutoff"] = sim["cutoff"] # rename later - self.input["E_fermi"] = sim["E_fermi"] # rename later - self.input["E_max"] = sim["E_max"] # rename later - self.get_structure + self.get_structure() return sim - @property def get_structure(self): """Tries to get the relaxed sphinx structure if available""" if (Path(self.input["waves_directory"]) / "relaxedStr.sx").exists(): diff --git a/Examples/extra_fim_pyiron_job_workflow.ipynb b/Examples/extra_fim_pyiron_job_workflow.ipynb index b12db77..daf4fcb 100644 --- a/Examples/extra_fim_pyiron_job_workflow.ipynb +++ b/Examples/extra_fim_pyiron_job_workflow.ipynb @@ -7,16 +7,12 @@ "metadata": {}, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d35a6d7167df4d0abd93ff7cc44868bb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shyamkatnagallu/Projects/EXTRA_FIM/.venv/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": [ @@ -32,7 +28,7 @@ "id": "9539e683-6005-4918-860a-7d561a4137a0", "metadata": {}, "source": [ - "# Setting up and running PyironnExtraFIMSimulator jobs" + "# Setting up and running Pyiron ExtraFIMSimulator jobs" ] }, { @@ -1158,9 +1154,9 @@ ], "metadata": { "kernelspec": { - "display_name": "pyiron/latest (Python3.10)", + "display_name": ".venv", "language": "python", - "name": "pyiron_latest" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1172,7 +1168,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.8" } }, "nbformat": 4, From 6cb5d337cc2dec9b92b33736b371b7a28a9c6efc Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Wed, 20 Mar 2024 08:21:16 +0100 Subject: [PATCH 19/22] bug in method name --- EXTRA_FIM/pyiron_job.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index 9bced72..0205d06 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -69,7 +69,7 @@ def extrapolate_potential(self): pot_ext = pot elec_ext = elec_potential return pot_ext, elec_potential - + def suggest_input_dict(self): """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy""" @@ -170,7 +170,7 @@ def suggest_input_dict(self): Path(self.input["waves_directory"]) / "waves.sxb" ) e_fermi = waves_reader.get_fermi_energy() - _, sim = self.ref_job.suggest_input_dictionary( + _, sim = self.ref_job.suggest_input_dict( self.input["waves_directory"], e_fermi, ionization_energies=self.input["ionization_energies"], From 5f8bda8894cd4d594c75f9b79351619e0fa32b0b Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Wed, 20 Mar 2024 09:28:32 +0100 Subject: [PATCH 20/22] bug fix for suggest_input_dict --- EXTRA_FIM/pyiron_job.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index 0205d06..2ef81d7 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -1,4 +1,5 @@ -'''Class to create pyiron jobs for ExtraFIM''' +"""Class to create pyiron jobs for ExtraFIM""" + from pathlib import Path from pyiron_base.utils.error import ImportAlarm from pyiron_base.jobs.master.parallel import ParallelMaster @@ -71,7 +72,7 @@ def extrapolate_potential(self): return pot_ext, elec_potential def suggest_input_dict(self): - """Suggests a input dictionary based on the electrostatic potential, + """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy""" waves_reader = sx_nc_waves_reader( Path(self.input["waves_directory"]) / "waves.sxb" @@ -143,7 +144,7 @@ def modify_job(self, job, parameter): class ExtraFimSimulator(ParallelMaster): - """ "Pyiron Extra FIM simulator job class to make subjobs for each k point""" + """ Pyiron Extra FIM simulator job class to make subjobs for each k point""" def __init__(self, project, job_name): super(ExtraFimSimulator, self).__init__(project, job_name) @@ -159,22 +160,21 @@ def __init__(self, project, job_name): self.structure = None def extrapolate_potential(self): - '''returns extrapolated potential if true''' + """returns extrapolated potential if true""" pot_ext, elec_potential = self.ref_job.extrapolate_potential() return pot_ext, elec_potential def suggest_input_dict(self): - """Suggests a input dictionary based on the electrostatic potential, + """Suggests a input dictionary based on the electrostatic potential, Fermi and ionization energy and populates input""" waves_reader = sx_nc_waves_reader( Path(self.input["waves_directory"]) / "waves.sxb" ) e_fermi = waves_reader.get_fermi_energy() - _, sim = self.ref_job.suggest_input_dict( - self.input["waves_directory"], - e_fermi, - ionization_energies=self.input["ionization_energies"], - ) + self.ref_job.iput["waves_directory"] = self.input["waves_directory"] + self.ref_job.input["ionization_energies"] = self.input["ionization_energies"] + self.ref_job.input["E_fermi"] = e_fermi + _, sim = self.ref_job.suggest_input_dict() self.get_structure() return sim From 21baa10d75d9ba913ee14532db5869c3ac217a14 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Wed, 20 Mar 2024 11:06:00 +0100 Subject: [PATCH 21/22] bugfix --- EXTRA_FIM/pyiron_job.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index 2ef81d7..cdd0052 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -171,7 +171,7 @@ def suggest_input_dict(self): Path(self.input["waves_directory"]) / "waves.sxb" ) e_fermi = waves_reader.get_fermi_energy() - self.ref_job.iput["waves_directory"] = self.input["waves_directory"] + self.ref_job.input["waves_directory"] = self.input["waves_directory"] self.ref_job.input["ionization_energies"] = self.input["ionization_energies"] self.ref_job.input["E_fermi"] = e_fermi _, sim = self.ref_job.suggest_input_dict() From b7d4840838b0da202f412b9a3b22b0c588108817 Mon Sep 17 00:00:00 2001 From: skatnagallu Date: Wed, 20 Mar 2024 11:07:06 +0100 Subject: [PATCH 22/22] bugfix in suggest_input_dict --- EXTRA_FIM/pyiron_job.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EXTRA_FIM/pyiron_job.py b/EXTRA_FIM/pyiron_job.py index cdd0052..e2901f8 100644 --- a/EXTRA_FIM/pyiron_job.py +++ b/EXTRA_FIM/pyiron_job.py @@ -174,7 +174,7 @@ def suggest_input_dict(self): self.ref_job.input["waves_directory"] = self.input["waves_directory"] self.ref_job.input["ionization_energies"] = self.input["ionization_energies"] self.ref_job.input["E_fermi"] = e_fermi - _, sim = self.ref_job.suggest_input_dict() + sim = self.ref_job.suggest_input_dict() self.get_structure() return sim