From ebc92c716370e731580076aefd7de6f9b0bbe653 Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Thu, 8 May 2025 23:35:17 +0800
Subject: [PATCH 001/152] Update dpnegf as an isolated package (#2)
MIME-Version: 1.0
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Content-Transfer-Encoding: 8bit

* refactor dptb_negf as dpnegf

* fix: remove old version __init__

* add loggers.py

* rename NEGF.py location

* add __init__.py

* add make_kpoints.py

* add example: atomic_chain

* force torch.float64

* fix model dtype as float32

* add atom_norbs in NEGFHamiltonianInit

* 更新 .gitignore，添加 playground 目录以排除不必要的文件

* update atom_norbs

* 在 pyproject.toml 中添加 dptb 依赖项

* 在 dpnegf/entrypoints 中添加 run.py 文件，实现运行 NEGF 和 TBtrans 计算的功能

* 在 dpnegf/runner 中添加 tbtrans_init.py 文件，实现 TBtrans 输入文件的生成和处理功能

* add unit tests

* copy argcheck from dptb

* add run_input.json

* fix: remove deprecation warning when running  unit tests

* add __init__.py in tests

* fix: set default dtype for torch model in NEGFHamiltonianInit

* refactor: 移除未使用的代码和多余的执行计数
---
 .gitignore                                    |    1 +
 {dptb_negf => dpnegf}/__init__.py             |    0
 .../entrypoints/__init__.py                   |    0
 dpnegf/entrypoints/run.py                     |  122 ++
 dpnegf/negf/__init__.py                       |    0
 .../negf/areshkin_pole_sum.py                 |    0
 {dptb_negf => dpnegf}/negf/bloch.py           |    0
 {dptb_negf => dpnegf}/negf/density.py         |    6 +-
 {dptb_negf => dpnegf}/negf/device_property.py |   10 +-
 dpnegf/negf/field.py                          |    0
 {dptb_negf => dpnegf}/negf/lead_property.py   |    8 +-
 .../negf/negf_hamiltonian_init.py             |   64 +-
 {dptb_negf => dpnegf}/negf/negf_utils.py      |    0
 {dptb_negf => dpnegf}/negf/ozaki_res_cal.py   |    0
 {dptb_negf => dpnegf}/negf/poisson.py         |    2 +-
 {dptb_negf => dpnegf}/negf/poisson_init.py    |    4 +-
 .../negf/recursive_green_cal.py               |    0
 {dptb_negf => dpnegf}/negf/scf_method.py      |    0
 {dptb_negf => dpnegf}/negf/sgf.py             |    0
 {dptb_negf => dpnegf}/negf/sort_btd.py        |    0
 {dptb_negf => dpnegf}/negf/split_btd.py       |    0
 {dptb_negf => dpnegf}/negf/surface_green.py   |    0
 {dptb_negf => dpnegf}/negf/transport.py       |   10 +-
 {dptb_negf/main => dpnegf/runner}/NEGF.py     |   40 +-
 dpnegf/runner/__init__.py                     |    0
 dpnegf/runner/tbtrans_init.py                 |  519 +++++
 dpnegf/tests/__init__.py                      |    0
 .../test_negf_hamiltonian/run_input.json      |   93 +
 .../test_negf_hamiltonian_init/.gitkeep       |    0
 .../data/test_negf/test_negf_run/chain.vasp   |   20 +
 .../data/test_negf/test_negf_run/graphene.xyz |   50 +
 .../test_negf/test_negf_run/negf_chain.json   |   78 +
 .../test_negf_run/negf_chain_new.json         |   58 +
 .../test_negf_run/negf_graphene.json          |   80 +
 .../test_negf_run/negf_graphene_new.json      |   61 +
 .../data/test_negf/test_negf_run/nnsk_C.json  |    9 +
 .../test_negf/test_negf_run/nnsk_C_new.json   |   39 +
 .../test_negf/test_negf_run/nnsk_C_newS.json  |   45 +
 .../out_negf_graphene/show.ipynb              |  661 ++++++
 dpnegf/tests/test_negf_device_property.py     |  175 ++
 dpnegf/tests/test_negf_kmesh_sampling.py      |  156 ++
 .../tests/test_negf_negf_hamiltonian_init.py  |  277 +++
 dpnegf/tests/test_negf_run.py                 |  170 ++
 dpnegf/utils/__init__.py                      |    1 +
 dpnegf/utils/argcheck.py                      | 1874 +++++++++++++++++
 {dptb_negf => dpnegf}/utils/constants.py      |    0
 dpnegf/utils/loggers.py                       |  114 +
 dpnegf/utils/make_kpoints.py                  |  394 ++++
 {dptb_negf => dpnegf}/utils/tools.py          |    0
 dptb_negf/negf/__init__.py                    |    1 -
 examples/atomic_chain/input_files/chain.vasp  |   20 +
 .../input_files/negf_chain_new.json           |   61 +
 .../atomic_chain/input_files/nnsk_C_new.json  |   39 +
 examples/atomic_chain/run.ipynb               |  215 ++
 pyproject.toml                                |    7 +-
 55 files changed, 5406 insertions(+), 78 deletions(-)
 rename {dptb_negf => dpnegf}/__init__.py (100%)
 rename dptb_negf/negf/field.py => dpnegf/entrypoints/__init__.py (100%)
 create mode 100644 dpnegf/entrypoints/run.py
 create mode 100644 dpnegf/negf/__init__.py
 rename {dptb_negf => dpnegf}/negf/areshkin_pole_sum.py (100%)
 rename {dptb_negf => dpnegf}/negf/bloch.py (100%)
 rename {dptb_negf => dpnegf}/negf/density.py (99%)
 rename {dptb_negf => dpnegf}/negf/device_property.py (98%)
 create mode 100644 dpnegf/negf/field.py
 rename {dptb_negf => dpnegf}/negf/lead_property.py (98%)
 rename {dptb_negf => dpnegf}/negf/negf_hamiltonian_init.py (96%)
 rename {dptb_negf => dpnegf}/negf/negf_utils.py (100%)
 rename {dptb_negf => dpnegf}/negf/ozaki_res_cal.py (100%)
 rename {dptb_negf => dpnegf}/negf/poisson.py (97%)
 rename {dptb_negf => dpnegf}/negf/poisson_init.py (99%)
 rename {dptb_negf => dpnegf}/negf/recursive_green_cal.py (100%)
 rename {dptb_negf => dpnegf}/negf/scf_method.py (100%)
 rename {dptb_negf => dpnegf}/negf/sgf.py (100%)
 rename {dptb_negf => dpnegf}/negf/sort_btd.py (100%)
 rename {dptb_negf => dpnegf}/negf/split_btd.py (100%)
 rename {dptb_negf => dpnegf}/negf/surface_green.py (100%)
 rename {dptb_negf => dpnegf}/negf/transport.py (96%)
 rename {dptb_negf/main => dpnegf/runner}/NEGF.py (96%)
 create mode 100644 dpnegf/runner/__init__.py
 create mode 100644 dpnegf/runner/tbtrans_init.py
 create mode 100644 dpnegf/tests/__init__.py
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_hamiltonian/run_input.json
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_hamiltonian/test_negf_hamiltonian_init/.gitkeep
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/chain.vasp
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/graphene.xyz
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/negf_chain.json
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/negf_graphene.json
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/negf_graphene_new.json
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/nnsk_C.json
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_new.json
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_newS.json
 create mode 100644 dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene/show.ipynb
 create mode 100644 dpnegf/tests/test_negf_device_property.py
 create mode 100644 dpnegf/tests/test_negf_kmesh_sampling.py
 create mode 100644 dpnegf/tests/test_negf_negf_hamiltonian_init.py
 create mode 100644 dpnegf/tests/test_negf_run.py
 create mode 100644 dpnegf/utils/__init__.py
 create mode 100644 dpnegf/utils/argcheck.py
 rename {dptb_negf => dpnegf}/utils/constants.py (100%)
 create mode 100644 dpnegf/utils/loggers.py
 create mode 100644 dpnegf/utils/make_kpoints.py
 rename {dptb_negf => dpnegf}/utils/tools.py (100%)
 delete mode 100644 dptb_negf/negf/__init__.py
 create mode 100644 examples/atomic_chain/input_files/chain.vasp
 create mode 100644 examples/atomic_chain/input_files/negf_chain_new.json
 create mode 100644 examples/atomic_chain/input_files/nnsk_C_new.json
 create mode 100644 examples/atomic_chain/run.ipynb

diff --git a/.gitignore b/.gitignore
index 06abc03..ba4135a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,6 +10,7 @@ dptb/tests/**/out*/*
 dptb/tests/**/*lmdb
 dptb/tests/**/*h5
 examples/_*
+playground/*
 *.dat
 *log*
 dptb/tests/data/**/out*/config_*.json
diff --git a/dptb_negf/__init__.py b/dpnegf/__init__.py
similarity index 100%
rename from dptb_negf/__init__.py
rename to dpnegf/__init__.py
diff --git a/dptb_negf/negf/field.py b/dpnegf/entrypoints/__init__.py
similarity index 100%
rename from dptb_negf/negf/field.py
rename to dpnegf/entrypoints/__init__.py
diff --git a/dpnegf/entrypoints/run.py b/dpnegf/entrypoints/run.py
new file mode 100644
index 0000000..7f8b8b3
--- /dev/null
+++ b/dpnegf/entrypoints/run.py
@@ -0,0 +1,122 @@
+import os
+import logging
+import json
+from typing import Optional
+from pathlib import Path
+from dptb.nn.build import build_model
+from dpnegf.utils.loggers import set_log_handles
+from dpnegf.utils.argcheck import normalize_run
+from dpnegf.utils.tools import j_loader
+from dpnegf.utils.tools import j_must_have
+from dpnegf.runner.NEGF import NEGF
+from dpnegf.runner.tbtrans_init import TBTransInputSet,sisl_installed
+
+
+
+
+
+log = logging.getLogger(__name__)
+
+def run(
+        INPUT: str,
+        init_model: str,
+        structure: str,
+        output: str,
+        log_level: int,
+        log_path: Optional[str],
+        **kwargs
+        ):
+
+    run_opt = {
+        "init_model":init_model,
+        "structure":structure,
+        "log_path": log_path,
+        "log_level": log_level,
+    }
+
+    if output:
+        Path(output).parent.mkdir(exist_ok=True, parents=True)
+        Path(output).mkdir(exist_ok=True, parents=True)
+        results_path = os.path.join(str(output), "results")
+        Path(results_path).mkdir(exist_ok=True, parents=True)
+        if not log_path:
+            log_path = os.path.join(str(output), "log/log.txt")
+        Path(log_path).parent.mkdir(exist_ok=True, parents=True)
+
+        run_opt.update({
+                        "output": str(Path(output).absolute()),
+                        "results_path": str(Path(results_path).absolute()),
+                        "log_path": str(Path(log_path).absolute())
+                    })
+
+    set_log_handles(log_level, Path(log_path) if log_path else None)
+
+    jdata = j_loader(INPUT)
+    jdata = normalize_run(jdata)
+
+    task_options = j_must_have(jdata, "task_options")
+    task = task_options["task"]
+    use_gui = jdata.get("use_gui", False)
+    task_options.update({"use_gui": use_gui})
+    results_path = run_opt.get("results_path", None)
+
+    in_common_options = {}
+    if jdata.get("device", None):
+        in_common_options.update({"device": jdata["device"]})
+    
+    if jdata.get("dtype", None):
+        in_common_options.update({"dtype": jdata["dtype"]})
+
+    model = build_model(checkpoint=init_model, common_options=in_common_options)
+    
+    if  run_opt['structure'] is None:
+        log.warning(msg="Warning! structure is not set in run option, read from input config file.")
+        structure = j_must_have(jdata, "structure")
+        run_opt.update({"structure":structure})
+
+    struct_file = run_opt["structure"]
+
+
+    if task=='negf':
+
+        # try:
+        #     from pyinstrument import Profiler
+        # except ImportWarning:
+        #     log.warning(msg="pyinstrument is not installed, no profiling will be done.")
+        #     Profiler = None
+        # if Profiler is not None:
+        #     profiler = Profiler()
+        #     profiler.start()
+        
+        negf = NEGF(
+            model=model,
+            AtomicData_options=jdata['AtomicData_options'],
+            structure=structure,
+            results_path=results_path,  
+            **task_options
+            )
+   
+        negf.compute()
+        log.info(msg='negf calculation successfully completed.')
+
+        # if Profiler is not None:
+        #     profiler.stop()
+        #     with open(results_path+'/profile_report.html', 'w') as report_file:
+        #         report_file.write(profiler.output_html())
+
+    elif task == 'tbtrans_negf':
+        if not(sisl_installed):
+            log.error(msg="sisl is required to perform tbtrans calculation !")
+            raise RuntimeError
+        basis_dict = json.load(open(init_model))['common_options']['basis']
+        tbtrans_init = TBTransInputSet(
+            model=model, 
+            AtomicData_options=jdata['AtomicData_options'],
+            structure=structure,
+            basis_dict=basis_dict,
+            results_path=results_path,
+            **task_options)
+        tbtrans_init.hamil_get_write(write_nc=True)
+        log.info(msg='TBtrans input files are successfully generated.')
+    
+
diff --git a/dpnegf/negf/__init__.py b/dpnegf/negf/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dptb_negf/negf/areshkin_pole_sum.py b/dpnegf/negf/areshkin_pole_sum.py
similarity index 100%
rename from dptb_negf/negf/areshkin_pole_sum.py
rename to dpnegf/negf/areshkin_pole_sum.py
diff --git a/dptb_negf/negf/bloch.py b/dpnegf/negf/bloch.py
similarity index 100%
rename from dptb_negf/negf/bloch.py
rename to dpnegf/negf/bloch.py
diff --git a/dptb_negf/negf/density.py b/dpnegf/negf/density.py
similarity index 99%
rename from dptb_negf/negf/density.py
rename to dpnegf/negf/density.py
index d8af05d..15c4462 100644
--- a/dptb_negf/negf/density.py
+++ b/dpnegf/negf/density.py
@@ -1,8 +1,8 @@
 import torch
-from dptb_negf.negf.ozaki_res_cal import ozaki_residues
-from dptb_negf.negf.areshkin_pole_sum import pole_maker
+from dpnegf.negf.ozaki_res_cal import ozaki_residues
+from dpnegf.negf.areshkin_pole_sum import pole_maker
 import numpy as np
-from dptb_negf.negf.negf_utils import gauss_xw
+from dpnegf.negf.negf_utils import gauss_xw
 import logging
 import matplotlib as mpl
 import matplotlib.pyplot as plt
diff --git a/dptb_negf/negf/device_property.py b/dpnegf/negf/device_property.py
similarity index 98%
rename from dptb_negf/negf/device_property.py
rename to dpnegf/negf/device_property.py
index a53c4f8..a6414e5 100644
--- a/dptb_negf/negf/device_property.py
+++ b/dpnegf/negf/device_property.py
@@ -1,10 +1,10 @@
-from dptb_negf.negf.recursive_green_cal import recursive_gf
+from dpnegf.negf.recursive_green_cal import recursive_gf
 import logging
 import torch
 import os
-from dptb_negf.negf.negf_utils import update_kmap, update_temp_file,gauss_xw, leggauss
-from dptb_negf.negf.density import Ozaki
-from dptb_negf.utils.constants import Boltzmann, eV2J,pi
+from dpnegf.negf.negf_utils import update_kmap, update_temp_file,gauss_xw, leggauss
+from dpnegf.negf.density import Ozaki
+from dpnegf.utils.constants import Boltzmann, eV2J,pi
 import numpy  as np
 from scipy.integrate import simpson
 import matplotlib.pyplot as plt
@@ -292,7 +292,7 @@ def _cal_current_nscf_(self, energy_grid, tc):
         cc = []
 
         for dv in vv * 0.5:
-            I = simpson((f(energy_grid+self.mu, self.lead_L.efermi-vm+dv) - f(energy_grid+self.mu, self.lead_R.efermi-vm-dv)) * tc, energy_grid)
+            I = simpson(y=(f(energy_grid+self.mu, self.lead_L.efermi-vm+dv) - f(energy_grid+self.mu, self.lead_R.efermi-vm-dv)) * tc, x=energy_grid)
             cc.append(I)
 
         return vv, cc
diff --git a/dpnegf/negf/field.py b/dpnegf/negf/field.py
new file mode 100644
index 0000000..e69de29
diff --git a/dptb_negf/negf/lead_property.py b/dpnegf/negf/lead_property.py
similarity index 98%
rename from dptb_negf/negf/lead_property.py
rename to dpnegf/negf/lead_property.py
index 7e9b011..1786842 100644
--- a/dptb_negf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -1,12 +1,12 @@
 import torch
 from typing import List
-from dptb_negf.negf.surface_green import selfEnergy
+from dpnegf.negf.surface_green import selfEnergy
 import logging
-from dptb_negf.negf.negf_utils import update_kmap, update_temp_file
+from dpnegf.negf.negf_utils import update_kmap, update_temp_file
 import os
-from dptb_negf.utils.constants import Boltzmann, eV2J
+from dpnegf.utils.constants import Boltzmann, eV2J
 import numpy as np
-from dptb_negf.negf.bloch import Bloch
+from dpnegf.negf.bloch import Bloch
 import torch.profiler
 import ase
 
diff --git a/dptb_negf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
similarity index 96%
rename from dptb_negf/negf/negf_hamiltonian_init.py
rename to dpnegf/negf/negf_hamiltonian_init.py
index 01a2e70..c2d8e5d 100644
--- a/dptb_negf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -1,18 +1,18 @@
 from typing import List
 import torch
-from dptb_negf.negf.areshkin_pole_sum import pole_maker
-from dptb_negf.negf.recursive_green_cal import recursive_gf
-from dptb_negf.negf.surface_green import selfEnergy
-from dptb_negf.negf.negf_utils import quad, gauss_xw,update_kmap,leggauss
-from dptb_negf.negf.ozaki_res_cal import ozaki_residues
-from dptb_negf.negf.areshkin_pole_sum import pole_maker
+from dpnegf.negf.areshkin_pole_sum import pole_maker
+from dpnegf.negf.recursive_green_cal import recursive_gf
+from dpnegf.negf.surface_green import selfEnergy
+from dpnegf.negf.negf_utils import quad, gauss_xw,update_kmap,leggauss
+from dpnegf.negf.ozaki_res_cal import ozaki_residues
+from dpnegf.negf.areshkin_pole_sum import pole_maker
 from ase.io import read,write
-from dptb_negf.negf.poisson import Density2Potential, getImg
-from dptb_negf.negf.scf_method import SCFMethod
+from dpnegf.negf.poisson import Density2Potential, getImg
+from dpnegf.negf.scf_method import SCFMethod
 import logging
 import os
 import torch.optim as optim
-from dptb_negf.utils.tools import j_must_have
+from dpnegf.utils.tools import j_must_have
 import numpy as np
 
 import ase
@@ -23,10 +23,10 @@
 from dptb.nn.hr2hk import HR2HK
 from ase import Atoms
 from ase.build import sort
-from dptb_negf.negf.bloch import Bloch
-from dptb_negf.negf.sort_btd import sort_lexico, sort_projection, sort_capacitance
-from dptb_negf.negf.split_btd import show_blocks,split_into_subblocks,split_into_subblocks_optimized
-from dptb_negf.negf.negf_utils import natsorted
+from dpnegf.negf.bloch import Bloch
+from dpnegf.negf.sort_btd import sort_lexico, sort_projection, sort_capacitance
+from dpnegf.negf.split_btd import show_blocks,split_into_subblocks,split_into_subblocks_optimized
+from dpnegf.negf.negf_utils import natsorted
 from scipy.spatial import KDTree
 import h5py
 import re
@@ -76,7 +76,9 @@ def __init__(self,
                  ) -> None:
         
         # TODO: add dtype and device setting to the model
-        #torch.set_default_dtype(torch.float64)
+        # torch.set_default_dtype(torch.float64)
+        
+        torch.set_default_dtype(model.dtype)
 
         if isinstance(torch_device, str):
             torch_device = torch.device(torch_device)
@@ -123,17 +125,6 @@ def __init__(self,
             device=self.torch_device,
             )
 
-        # if overlap:
-        #     self.s2k = HR2HK(
-        #         idp=model.idp, 
-        #         overlap=True, 
-        #         edge_field=AtomicDataDict.EDGE_OVERLAP_KEY, 
-        #         node_field=AtomicDataDict.NODE_OVERLAP_KEY, 
-        #         out_field=AtomicDataDict.OVERLAP_KEY, 
-        #         dtype=model.dtype, 
-        #         device=self.torch_device,
-        #         )   
-        
         self.device_id = [int(x) for x in self.stru_options['device']["id"].split("-")]
         self.lead_ids = {}
         for kk in self.stru_options:
@@ -156,6 +147,12 @@ def __init__(self,
             log.error("The unit name is not correct !")
             raise ValueError
 
+        # obtain atom_norbs
+        atom_norbs = []
+        for atom in self.structase:
+            atom_norbs.append(int(self.model.idp.atom_norb[model.idp.chemical_symbol_to_type[atom.symbol]]))
+        self.atom_norbs = atom_norbs
+
     def initialize(self, kpoints, block_tridiagnal=False,useBloch=False,bloch_factor=None,\
                    use_saved_HS=False, saved_HS_path=None):
         '''This function initializes the structure and Hamiltonian for a system with optional block tridiagonal
@@ -220,7 +217,7 @@ def initialize(self, kpoints, block_tridiagnal=False,useBloch=False,bloch_factor
             log.info(msg=f"The Hamiltonian has been initialized by model.")
             log.info(msg="=="*40)
 
-        torch.set_default_dtype(torch.float32)
+        # torch.set_default_dtype(torch.float32)
         return  structure_device, structure_leads, structure_leads_fold, \
                 bloch_sorted_indices, bloch_R_lists
 
@@ -303,9 +300,8 @@ def Hamiltonian_initialized(self,kpoints:List[List[float]],useBloch:bool,bloch_f
         else:
             SK = torch.eye(HK.shape[1], dtype=self.model.dtype, device=self.torch_device).unsqueeze(0).repeat(HK.shape[0], 1, 1)          
       
-        # H, S = self.apiH.get_HK(kpoints=kpoints)
-        d_start = int(np.sum(self.h2k.atom_norbs[:self.device_id[0]]))
-        d_end = int(np.sum(self.h2k.atom_norbs)-np.sum(self.h2k.atom_norbs[self.device_id[1]:]))
+        d_start = int(np.sum(self.atom_norbs[:self.device_id[0]]))
+        d_end = int(np.sum(self.atom_norbs)-np.sum(self.atom_norbs[self.device_id[1]:]))
         HD, SD = HK[:,d_start:d_end, d_start:d_end], SK[:, d_start:d_end, d_start:d_end]
         Hall, Sall = HK, SK
 
@@ -327,8 +323,8 @@ def Hamiltonian_initialized(self,kpoints:List[List[float]],useBloch:bool,bloch_f
                     HS_leads["kpoints_bloch"] = None
                     HS_leads["bloch_factor"] = None
                         
-                l_start = int(np.sum(self.h2k.atom_norbs[:lead_atom_range[kk][0]]))
-                l_end = int(l_start + np.sum(self.h2k.atom_norbs[lead_atom_range[kk][0]:lead_atom_range[kk][1]]) / 2)
+                l_start = int(np.sum(self.atom_norbs[:lead_atom_range[kk][0]]))
+                l_end = int(l_start + np.sum(self.atom_norbs[lead_atom_range[kk][0]:lead_atom_range[kk][1]]) / 2)
                 # lead hamiltonian in the first principal layer(the layer close to the device)
                 HL, SL = HK[:,l_start:l_end, l_start:l_end], SK[:, l_start:l_end, l_start:l_end]
                 # device and lead's hopping
@@ -593,10 +589,10 @@ def get_block_tridiagonal(self,HK,SK,structase:ase.Atoms,leftmost_size:int,right
 
         if leftmost_size is None:
             leftmost_atoms_index = np.where(structase.positions[:,2]==min(structase.positions[:,2]))[0]
-            leftmost_size = sum([self.h2k.atom_norbs[leftmost_atoms_index[i]] for i in range(len(leftmost_atoms_index))])
+            leftmost_size = sum([self.atom_norbs[leftmost_atoms_index[i]] for i in range(len(leftmost_atoms_index))])
         if rightmost_size is None:   
             rightmost_atoms_index = np.where(structase.positions[:,2]==max(structase.positions[:,2]))[0]
-            rightmost_size = sum([self.h2k.atom_norbs[rightmost_atoms_index[i]] for i in range(len(rightmost_atoms_index))])
+            rightmost_size = sum([self.atom_norbs[rightmost_atoms_index[i]] for i in range(len(rightmost_atoms_index))])
         
         subblocks = split_into_subblocks_optimized(HK[0],leftmost_size,rightmost_size)
         if subblocks[0] < leftmost_size or subblocks[-1] < rightmost_size:
@@ -867,7 +863,7 @@ def device_norbs(self):
         """ 
         return the number of atoms in the device Hamiltonian
         """
-        return self.h2k.atom_norbs[self.device_id[0]:self.device_id[1]]
+        return self.atom_norbs[self.device_id[0]:self.device_id[1]]
 
     # def get_hs_block_tridiagonal(self, HD, SD):
 
diff --git a/dptb_negf/negf/negf_utils.py b/dpnegf/negf/negf_utils.py
similarity index 100%
rename from dptb_negf/negf/negf_utils.py
rename to dpnegf/negf/negf_utils.py
diff --git a/dptb_negf/negf/ozaki_res_cal.py b/dpnegf/negf/ozaki_res_cal.py
similarity index 100%
rename from dptb_negf/negf/ozaki_res_cal.py
rename to dpnegf/negf/ozaki_res_cal.py
diff --git a/dptb_negf/negf/poisson.py b/dpnegf/negf/poisson.py
similarity index 97%
rename from dptb_negf/negf/poisson.py
rename to dpnegf/negf/poisson.py
index fa19453..e4e8a77 100644
--- a/dptb_negf/negf/poisson.py
+++ b/dpnegf/negf/poisson.py
@@ -1,6 +1,6 @@
 from fmm3dpy import lfmm3d
 import torch
-from dptb_negf.utils.constants import pi
+from dpnegf.utils.constants import pi
 
 
 
diff --git a/dptb_negf/negf/poisson_init.py b/dpnegf/negf/poisson_init.py
similarity index 99%
rename from dptb_negf/negf/poisson_init.py
rename to dpnegf/negf/poisson_init.py
index 1599509..60ea742 100644
--- a/dptb_negf/negf/poisson_init.py
+++ b/dpnegf/negf/poisson_init.py
@@ -1,8 +1,8 @@
 import numpy as np 
 # import pyamg #TODO: later add it to optional dependencies,like sisl
 # from pyamg.gallery import poisson
-from dptb_negf.utils.constants import elementary_charge
-from dptb_negf.utils.constants import Boltzmann, eV2J
+from dpnegf.utils.constants import elementary_charge
+from dpnegf.utils.constants import Boltzmann, eV2J
 from scipy.constants import epsilon_0 as eps0  #TODO:later add to untils.constants.py
 from scipy.sparse import csr_matrix
 from scipy.sparse.linalg import spsolve
diff --git a/dptb_negf/negf/recursive_green_cal.py b/dpnegf/negf/recursive_green_cal.py
similarity index 100%
rename from dptb_negf/negf/recursive_green_cal.py
rename to dpnegf/negf/recursive_green_cal.py
diff --git a/dptb_negf/negf/scf_method.py b/dpnegf/negf/scf_method.py
similarity index 100%
rename from dptb_negf/negf/scf_method.py
rename to dpnegf/negf/scf_method.py
diff --git a/dptb_negf/negf/sgf.py b/dpnegf/negf/sgf.py
similarity index 100%
rename from dptb_negf/negf/sgf.py
rename to dpnegf/negf/sgf.py
diff --git a/dptb_negf/negf/sort_btd.py b/dpnegf/negf/sort_btd.py
similarity index 100%
rename from dptb_negf/negf/sort_btd.py
rename to dpnegf/negf/sort_btd.py
diff --git a/dptb_negf/negf/split_btd.py b/dpnegf/negf/split_btd.py
similarity index 100%
rename from dptb_negf/negf/split_btd.py
rename to dpnegf/negf/split_btd.py
diff --git a/dptb_negf/negf/surface_green.py b/dpnegf/negf/surface_green.py
similarity index 100%
rename from dptb_negf/negf/surface_green.py
rename to dpnegf/negf/surface_green.py
diff --git a/dptb_negf/negf/transport.py b/dpnegf/negf/transport.py
similarity index 96%
rename from dptb_negf/negf/transport.py
rename to dpnegf/negf/transport.py
index cee0cbd..ef540e5 100644
--- a/dptb_negf/negf/transport.py
+++ b/dpnegf/negf/transport.py
@@ -1,15 +1,15 @@
 import ase.transport
 import torch
 from dptb.utils.constants import Boltzmann, eV2J,pi
-from dptb_negf.negf.recursive_green_cal import recursive_gf
+from dpnegf.negf.recursive_green_cal import recursive_gf
 from fmm3dpy import lfmm3d
-from dptb_negf.negf.areshkin_pole_sum import pole_maker
-from dptb_negf.negf.surface_green import selfEnergy
-from dptb_negf.negf.negf_utils import finite_difference
+from dpnegf.negf.areshkin_pole_sum import pole_maker
+from dpnegf.negf.surface_green import selfEnergy
+from dpnegf.negf.negf_utils import finite_difference
 import numpy as np
 from tqdm import tqdm
 import time
-from dptb_negf.negf.negf_utils import quad
+from dpnegf.negf.negf_utils import quad
 
 
 '''
diff --git a/dptb_negf/main/NEGF.py b/dpnegf/runner/NEGF.py
similarity index 96%
rename from dptb_negf/main/NEGF.py
rename to dpnegf/runner/NEGF.py
index 2d84fe3..66e657d 100644
--- a/dptb_negf/main/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -1,25 +1,25 @@
 from typing import List
 import torch
-from dptb_negf.negf.recursive_green_cal import recursive_gf
-from dptb_negf.negf.surface_green import selfEnergy
-from dptb_negf.negf.negf_utils import quad, gauss_xw,leggauss,update_kmap
-from dptb_negf.negf.ozaki_res_cal import ozaki_residues
-from dptb_negf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
-from dptb_negf.negf.density import Ozaki,Fiori
-from dptb_negf.negf.areshkin_pole_sum import pole_maker
-from dptb_negf.negf.device_property import DeviceProperty
-from dptb_negf.negf.lead_property import LeadProperty
+from dpnegf.negf.recursive_green_cal import recursive_gf
+from dpnegf.negf.surface_green import selfEnergy
+from dpnegf.negf.negf_utils import quad, gauss_xw,leggauss,update_kmap
+from dpnegf.negf.ozaki_res_cal import ozaki_residues
+from dpnegf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
+from dpnegf.negf.density import Ozaki,Fiori
+from dpnegf.negf.areshkin_pole_sum import pole_maker
+from dpnegf.negf.device_property import DeviceProperty
+from dpnegf.negf.lead_property import LeadProperty
 from ase.io import read
 import ase
-from dptb_negf.negf.poisson import Density2Potential, getImg
-from dptb_negf.negf.scf_method import SCFMethod
-from dptb_negf.utils.constants import Boltzmann, eV2J
+from dpnegf.negf.poisson import Density2Potential, getImg
+from dpnegf.negf.scf_method import SCFMethod
+from dpnegf.utils.constants import Boltzmann, eV2J
 import os
-from dptb_negf.utils.tools import j_must_have
+from dpnegf.utils.tools import j_must_have
 import numpy as np
-from dptb_negf.utils.make_kpoints import kmesh_sampling_negf
+from dpnegf.utils.make_kpoints import kmesh_sampling_negf
 import logging
-from dptb_negf.negf.poisson_init import Grid,Interface3D,Dirichlet,Dielectric
+from dpnegf.negf.poisson_init import Grid,Interface3D,Dirichlet,Dielectric
 from typing import Optional, Union
 # from pyinstrument import Profiler
 
@@ -43,14 +43,14 @@ def __init__(self,
                 structure: Union[AtomicData, ase.Atoms, str],
                 ele_T: float,e_fermi: float,
                 emin: float, emax: float, espacing: float,
-                use_saved_HS: bool, saved_HS_path: str,
                 density_options: dict,
                 unit: str,
                 scf: bool, poisson_options: dict,
                 stru_options: dict,eta_lead: float,eta_device: float,
                 block_tridiagonal: bool,
                 sgf_solver: str,
-                self_energy_save: bool, self_energy_save_path: str, se_info_display: bool,
+                use_saved_HS: bool=False, saved_HS_path: str=None,
+                self_energy_save: bool=False, self_energy_save_path: str=None, se_info_display: bool=False,
                 out_tc: bool=False,out_dos: bool=False,out_density: bool=False,out_potential: bool=False,
                 out_current: bool=False,out_current_nscf: bool=False,out_ldos: bool=False,out_lcurrent: bool=False,
                 results_path: Optional[str]=None,
@@ -182,13 +182,13 @@ def __init__(self,
             raise ValueError
 
         # number of orbitals on atoms in device region
-        self.device_atom_norbs = self.negf_hamiltonian.h2k.atom_norbs[self.negf_hamiltonian.device_id[0]:self.negf_hamiltonian.device_id[1]]
+        self.device_atom_norbs = self.negf_hamiltonian.atom_norbs[self.negf_hamiltonian.device_id[0]:self.negf_hamiltonian.device_id[1]]
         left_connected_atom_mask = abs(struct_device.positions[:,2]-min(struct_device.positions[:,2]))<1e-6
         right_connected_atom_mask = abs(struct_device.positions[:,2]-max(struct_device.positions[:,2]))<1e-6
 
-        self.left_connected_orb_mask = torch.tensor( [p for p, norb in zip(left_connected_atom_mask, self.device_atom_norbs) \
+        self.left_connected_orb_mask = torch.tensor( [bool(p) for p, norb in zip(left_connected_atom_mask, self.device_atom_norbs) \
                                                       for _ in range(norb)],dtype=torch.bool)
-        self.right_connected_orb_mask = torch.tensor( [p for p, norb in zip(right_connected_atom_mask, self.device_atom_norbs) \
+        self.right_connected_orb_mask = torch.tensor( [bool(p) for p, norb in zip(right_connected_atom_mask, self.device_atom_norbs) \
                                                         for _ in range(norb)],dtype=torch.bool)
 
 
diff --git a/dpnegf/runner/__init__.py b/dpnegf/runner/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dpnegf/runner/tbtrans_init.py b/dpnegf/runner/tbtrans_init.py
new file mode 100644
index 0000000..8264c29
--- /dev/null
+++ b/dpnegf/runner/tbtrans_init.py
@@ -0,0 +1,519 @@
+import numpy as np
+import logging
+import shutil
+import re
+from dptb.utils.tools import j_loader,j_must_have
+from typing import Optional, Union
+from ase.io import read,write
+from ase.build import sort
+import ase.atoms
+import torch
+
+from dptb.utils.constants import atomic_num_dict_r
+from dptb.data import AtomicData, AtomicDataDict
+from  dptb.data.interfaces.ham_to_feature import feature_to_block
+
+log = logging.getLogger(__name__)
+
+try:
+    import sisl
+    from sisl.orbital import Orbital
+    
+    sisl_installed = True
+
+except ImportError:
+    log.error('sisl is not installed.Thus the input for TBtrans can not be generated, please install it first!')
+    sisl_installed = False
+
+
+if  shutil.which('tbtrans') is None:
+    log.error('tbtrans is not in the Environment PATH. Thus the input for TBtrans can be generated but not run.')
+ 
+#  TBTransInputSet is used to transform input data for DeePTB-negf into TBtrans input files.
+#  TBtrans (Tight-Binding transport) is a generic computer program which calculates transport and other physical quantities
+#  using the Green function formalism. It is a stand-alone program which allows extreme scale tight-binding calculations. 
+#  For details, see https://www.sciencedirect.com/science/article/pii/S001046551630306X?via%3Dihub.
+#  To run TBTransInputSet, user need sisl package(https://zerothi.github.io/sisl/index.html)
+
+
+class TBTransInputSet(object):
+
+
+    def __init__(self, 
+                model: torch.nn.Module,
+                AtomicData_options: dict, 
+                structure: Union[AtomicData, ase.Atoms, str],
+                stru_options: dict,
+                basis_dict:dict,
+                results_path: Optional[str]=None,
+                unit: str='eV',
+                nel_atom: Optional[dict]=None,
+                **kwargs): 
+        '''
+        This function initializes  properties and calculations for TBtrans input files.
+        
+        Parameters
+        ----------
+        model : torch.nn.Module
+            The `model` parameter in the `__init__` method is expected to be an instance of
+        `torch.nn.Module`. This parameter is used to store a neural network model that has been loaded
+        in the `run.py` script.
+        AtomicData_options : dict
+            The `AtomicData_options` parameter is a dictionary containing options for atomic data.
+         These options are used to provide necessary atomic information for the model.
+        structure : Union[AtomicData, ase.Atoms, str]
+            The `structure` parameter in the `__init__` method is used to specify the structure of the
+        system. 
+        stru_options : dict
+            The `stru_options` parameter in the `__init__` method is a dictionary containing options for
+        the structure. This dictionary includes pbc, kmesh and the regions division of the structure.
+        basis_dict : dict
+            The `basis_dict` parameter in the `__init__` method is used in the `orbitals_get`
+        method. It is  a dictionary that contains the basis functions used in the calculations. 
+        results_path : Optional[str]
+            The `results_path` parameter is a string that specifies the path where the results of the
+        calculations will be saved. 
+        unit : str, optional
+            The `unit` parameter in the `__init__` function is used to specify the energy unit for TBtrans
+        calculation. It can take two possible values: eV or Hartree. The default value is eV.
+        nel_atom : Optional[dict]
+            The `nel_atom` parameter in the `__init__` function is an optional dictionary that represents
+        the number of electrons per atom. 
+        '''  
+        
+        self.model = model  #apiHrk has been loaded in run.py
+        self.AtomicData_options = AtomicData_options
+        # self.jdata = jdata    #jdata has been loaded in run.py, jdata is written in negf.json    
+
+        self.results_path = results_path
+        if not self.results_path.endswith('/'):self.results_path += '/'             
+        self.stru_options = stru_options
+        self.energy_unit_option = unit  # enenrgy unit for TBtrans calculation
+        self.nel_atom = nel_atom
+
+       
+        self.geom_all,self.geom_lead_L,self.geom_lead_R,self.all_tbtrans_stru,self.lead_L_tbtrans_stru,self.lead_R_tbtrans_stru\
+                   = self.read_rewrite_structure(structure,self.stru_options,self.results_path)
+        
+        if nel_atom is not None:
+            self.orbitals_get(self.geom_all,self.geom_lead_L,self.geom_lead_R,basis_dict)
+            log.warning('nel_atom is none!')
+        
+        self.H_all = sisl.Hamiltonian(self.geom_all)
+        self.H_lead_L = sisl.Hamiltonian(self.geom_lead_L)
+        self.H_lead_R = sisl.Hamiltonian(self.geom_lead_R)
+
+
+        #important properties for later use
+
+        ##allbonds matrx, hamiltonian matrix, overlap matrix for the whole structure
+        self.allbonds_all = None
+        self.hamil_block_all = None
+        self.overlap_block_all = None
+        ##allbonds matrx, hamiltonian matrix, overlap matrix for lead_L
+        self.allbonds_lead_L = None
+        self.hamil_block_lead_L = None
+        self.overlap_block_lead_L = None
+        ##allbonds matrx, hamiltonian matrix, overlap matrix for lead_R
+        self.allbonds_lead_R = None
+        self.hamil_block_lead_R = None
+        self.overlap_block_lead_R = None
+
+        if self.energy_unit_option=='Hartree':
+            self.unit_constant = 1.0000/13.605662285137 /2
+           
+        elif self.energy_unit_option=='eV':
+            self.unit_constant = 1.0000000000
+            
+        else:
+            raise RuntimeError("energy_unit_option should be 'Hartree' or 'eV'")
+
+   
+ 
+    def hamil_get_write(self,write_nc:bool=True):
+        
+        '''The function `hamil_get_write` loads models for different structure.retrieves the Hamiltonian and overlap matrices /
+        for the device and left and right leads, then writes the contents of `self.H_all`, `self.H_lead_L`, and `self.H_lead_R` to nc files for
+        TBtrans calculations.
+
+            `all` refers to the entire system, including the device and leads.
+
+
+        Returns
+        -------
+        - allbonds_all: all of the bond information 
+        - hamil_block_all: Hamiltonian block for the entire system, which is a tensor that contains 
+                            the values of the Hamiltonian matrix elements for each specific bond in allbonds_all
+        - overlap_block_all: overlap block for the entire system,  which is a tensor that contains 
+                             the values of the overlap matrix elements for each specific basis
+        '''
+
+
+        # get the Hamiltonian matrix for the entire system
+        self.allbonds_all,self.hamil_block_all,self.overlap_block_all\
+                    =self._load_model(self.model,self.AtomicData_options,self.all_tbtrans_stru)
+        self.hamiltonian_get(self.allbonds_all,self.hamil_block_all,self.overlap_block_all,self.H_all)    
+
+        # get the Hamiltonian matrix for the left lead
+        self.allbonds_lead_L,self.hamil_block_lead_L,self.overlap_block_lead_L\
+                        =self._load_model(self.model,self.AtomicData_options,self.lead_L_tbtrans_stru)
+        self.hamiltonian_get(self.allbonds_lead_L,self.hamil_block_lead_L,self.overlap_block_lead_L,self.H_lead_L)
+        
+        # get the Hamiltonian matrix for the right lead
+        self.allbonds_lead_R,self.hamil_block_lead_R,self.overlap_block_lead_R\
+                        =self._load_model(self.model,self.AtomicData_options,self.lead_R_tbtrans_stru)       
+        self.hamiltonian_get(self.allbonds_lead_R,self.hamil_block_lead_R,self.overlap_block_lead_R,self.H_lead_R)
+
+        if write_nc:
+            self.H_all.write(self.results_path+'structure.nc')
+            self.H_lead_L.write(self.results_path+'lead_L.nc')
+            self.H_lead_L.write(self.results_path+'lead_R.nc')
+        else:
+            print('Hamiltonian matrices have been generated, but not written to nc files(TBtrans input file).')
+
+
+
+    def read_rewrite_structure(self,structure_file:str,struct_options:dict,results_path:str):
+        '''The function `read_rewrite_structure` reads a structure file, extracts specific regions of the structure,
+        sorts the atoms in the structure, and outputs the sorted structures in XYZ and VASP file formats for later operations.
+        
+        Parameters
+        ----------
+        structure_file
+            The `structure_file` parameter is the path to the file containing the structure information of the
+        system you want to analyze. It can be in either VASP format (.vasp) or XYZ format (.xyz).
+        struct_options
+            The `struct_options` parameter is a dictionary that contains various options for the structure. 
+        result_path
+            The `result_path` parameter is the path where the output files will be saved. 
+        
+        Returns
+        -------
+            
+        - geom_all: the geometry of the entire structure,including the device and leads
+        - geom_lead_L: the geometry of the left lead
+        - geom_lead_R: the geometry of the right lead
+        - all_tbtrans_stru: the path to the sorted xyz file for the entire structure
+        - lead_L_tbtrans_stru: the path to the sorted xyz file for the left lead
+        - lead_R_tbtrans_stru: the path to the sorted xyz file for the right lead
+        
+        '''
+    
+    
+        lead_L_id=struct_options['lead_L']['id'].split('-')
+        lead_R_id=struct_options['lead_R']['id'].split('-')
+        # device_id=struct_options['device']['id'].split('-') 
+
+        lead_L_range=[i for i in range(int(lead_L_id[0]), int(lead_L_id[1]))]
+        lead_R_range=[i for i in range(int(lead_R_id[0]), int(lead_R_id[1]))]
+
+
+        # Structure input: read vasp file
+        # structure_vasp = sisl.io.carSileVASP(structure_file)
+        # geom_device = structure_vasp.read_geometry()
+        if structure_file.split('.')[-1]=='vasp':
+            structure_vasp = sisl.io.carSileVASP(structure_file)
+            geom_all = structure_vasp.read_geometry()
+            if geom_all.atoms.nspecie>1:
+                raise RuntimeError('ERROR! In transport calculation, VASP structure file is only valid for materials with one single element!')        
+        elif structure_file.split('.')[-1]=='xyz':
+            structure_xyz = sisl.io.xyzSile(structure_file)
+            geom_all = structure_xyz.read_geometry()
+        else:
+            raise RuntimeError('Structure file format is not supported. Only support vasp and xyz format')
+    # structure_xyz = sisl.io.xyzSile(structure_file)
+    # geom_device = structure_xyz.read_geometry()
+    #define lead geometry structure
+        geom_lead_L = geom_all.sub(lead_L_range) # subset of the geometry
+        geom_lead_R = geom_all.sub(lead_R_range)
+
+        #sort sturcture atoms according to z-direction: it's easier for later coding
+        #2,1,0 refers to sort by axis=0 firstly, then axis=1, last for axis=2
+        geom_lead_R = geom_lead_R.sort(axis=(2,1,0));geom_lead_L=geom_lead_L.sort(axis=(2,1,0))  
+        geom_all=geom_all.sort(axis=(2,1,0))
+    
+
+        lead_L_cor = geom_lead_L.axyz() #Return the atomic coordinates in the supercell of a given atom.
+        cell = np.array(geom_lead_L.lattice.cell)[:2]
+        Natom_PL = int(len(lead_L_cor)/2)
+        first_PL_leadL = lead_L_cor[Natom_PL:];second_PL_leadL =lead_L_cor[:Natom_PL]
+        R_vec = first_PL_leadL - second_PL_leadL
+        # assert np.abs(R_vec[0] - R_vec[-1]).sum() < 1e-5
+        assert np.abs(R_vec[0] - R_vec.mean(axis=0)).sum() < 1e-5
+        R_vec = R_vec.mean(axis=0) * 2
+        cell = np.concatenate([cell, R_vec.reshape(1,-1)])
+        # PL_leadL_zspace = first_PL_leadL[0][2]-second_PL_leadL[-1][2] # the distance between Principal layers
+        geom_lead_L.lattice.cell=cell
+        # assert geom_lead_L.lattice.cell[2,2]>0
+
+        #TODO: This version tbtrans_init only supports double lead case. Code for more leads will be added later.
+
+        lead_R_cor = geom_lead_R.axyz()
+        cell = np.array(geom_lead_R.lattice.cell)[:2]
+        Natom_PL = int(len(lead_R_cor)/2)
+        first_PL_leadR = lead_R_cor[:Natom_PL];second_PL_leadR = lead_R_cor[Natom_PL:]
+        R_vec = first_PL_leadR - second_PL_leadR
+        assert np.abs(R_vec[0] - R_vec.mean(axis=0)).sum() < 1e-5
+        R_vec = -1*R_vec.mean(axis=0) * 2
+        cell = np.concatenate([cell, R_vec.reshape(1,-1)])
+        # PL_leadR_zspace = second_PL_leadR[0][2]-first_PL_leadR[-1][2]
+        geom_lead_R.lattice.cell = cell
+
+        all_tbtrans_stru=results_path+'structure_tbtrans.xyz'
+        sorted_structure = sisl.io.xyzSile(all_tbtrans_stru,'w')
+        geom_all.write(sorted_structure)
+
+        lead_L_tbtrans_stru=results_path+'lead_L_tbtrans.xyz'
+        sorted_lead_L = sisl.io.xyzSile(lead_L_tbtrans_stru,'w')
+        geom_lead_L.write(sorted_lead_L)
+
+        lead_R_tbtrans_stru=results_path+'lead_R_tbtrans.xyz'
+        sorted_lead_R = sisl.io.xyzSile(lead_R_tbtrans_stru,'w')
+        geom_lead_R.write(sorted_lead_R)
+
+        # output sorted geometry into vasp Structure file: rewrite xyz files
+        ## writen as VASP for VESTA view
+        all_struct = read(all_tbtrans_stru)
+        all_vasp_struct = results_path+'structure_tbtrans.vasp'
+        write(all_vasp_struct,sort(all_struct),format='vasp')
+
+        return geom_all,geom_lead_L,geom_lead_R,all_tbtrans_stru,lead_L_tbtrans_stru,lead_R_tbtrans_stru
+
+
+
+    def orbitals_get(self,geom_all, geom_lead_L,geom_lead_R,basis_dict:dict):
+        '''The function `orbitals_get` takes in various inputs such as geometric devices, leads, deeptb model, and
+        configurations, and assigns orbitals number, orbital names, shell-electron numbers to the atoms in the given sisl geometries .
+
+            Here the geometry class is sisl.geometry, which is different from the structure class in dptb-negf.
+            We initialize sisl.geometry from structure files directly, therefore there is no orbital information in sisl.geometry.
+        
+        Parameters
+        ----------
+        geom_all
+            The `geom_all` parameter is the geometry of the whole structure. It contains information about the
+        atoms and their positions.
+        geom_lead_L
+            The `geom_lead_L` parameter represents the geometry of the left lead. It contains
+        information about the atoms and their positions in the lead.
+        geom_lead_R
+            The `geom_lead_R` parameter represents the geometry of the right lead. It contains 
+            information about the atoms in the lead, such as their positions and chemical symbols.
+        apiHrk
+            apiHrk has been loaded in the run.py file. It is used as an API for
+            performing certain operations or accessing certain functionalities when loading dptb model.
+        
+        '''        
+        n_species_lead_L = geom_lead_L.atoms.nspecie
+        n_species_lead_R = geom_lead_R.atoms.nspecie
+        n_species_all = geom_all.atoms.nspecie
+        n_species_list = [n_species_lead_L,n_species_lead_R,n_species_all]
+        geom_list = [geom_lead_L,geom_lead_R,geom_all]  
+
+
+        dict_element_orbital = basis_dict
+        dict_shell_electron = self.nel_atom
+
+        for n_species, geom_part in zip(n_species_list,geom_list):
+            # species_symbols = split_string(geom_part.atoms.formula())
+            ## get the chemical symbol of the part
+            # species_symbols = ''.join(char for char in geom_part.atoms.formula() if char.isalpha())
+            
+            uni_symbol_index = np.unique(geom_part.atoms.Z)
+            species_symbols=[atomic_num_dict_r[i] for i in uni_symbol_index]
+            
+            assert len(species_symbols)==n_species # number of chemical elements in this part
+
+            for i  in range(n_species): #determine the orbitals number for each species
+                element_orbital_list = dict_element_orbital[species_symbols[i]]
+                # Examples of elemet_orbital_list: ['3s', '3p', 'd*'] 
+                element_orbital_name = self._orbitals_name_get(element_orbital_list) 
+                # Examples of element_orbital_name: ['3s', '3py', '3pz', '3px', 'dxy*', 'dyz*', 'dz2*', 'dxz*', 'dx2-y2*']
+                
+                # shell_elec_num =  self._shell_electrons(species_symbols[i])
+                shell_elec_num =  dict_shell_electron[species_symbols[i]]
+                shell_elec_list = np.zeros(len(element_orbital_name))
+                shell_elec_list[:shell_elec_num]=1 #occupation number for each orbital, here we assume the system is in ground state
+
+                for atom_index in range(geom_part.na):
+                    if geom_part.atoms[atom_index].symbol == species_symbols[i]:
+                        geom_part.atoms[atom_index]._orbitals =[Orbital(-1, q0=q,tag=tag) for q,tag in zip(shell_elec_list,element_orbital_name)]
+                    # attribute sisl.Orbital object to each atom in sisl.geometry.atoms[x]._orbitals
+
+        geom_lead_L.atoms._update_orbitals()  #sisl use ._update_orbitals() to ensure the order of orbitals 
+        geom_lead_R.atoms._update_orbitals()
+        geom_all.atoms._update_orbitals()
+
+
+    def _orbitals_name_get(self,element_orbital_class:str):
+        '''The `_orbitals_name_get` function takes a list of element orbital classes and returns a list of
+        orbital names.
+        
+        Parameters
+        ----------
+        element_orbital_class
+            A list of strings representing the orbital classes of an element. Each string in the list
+        represents a different orbital class.
+        
+        Returns
+        -------
+            a list of orbital names.
+
+        Examples
+        --------
+        >>>element_orbital_name = self._orbitals_name_get(['2s', '2p'])
+        >>>element_orbital_name
+        ['2s', '2py', '2pz', '2px']
+        >>>element_orbital_name = self._orbitals_name_get(['3s', '3p', 'd*'])
+        >>>element_orbital_name
+        ['3s', '3py', '3pz', '3px', 'dxy*', 'dyz*', 'dz2*', 'dxz*', 'dx2-y2*']
+        '''
+        orbital_name_list=[] 
+        for orb_cla in element_orbital_class:  # ['3s', '3p', 'd*']          
+            orbital_name = re.findall(r'\d+|[a-zA-Z]+|\*',orb_cla)
+            orbital_name = sorted(orbital_name, key=lambda x: (x == '*',x.isnumeric(),x))# put s,p,d in the 1st position
+            
+            assert len(orbital_name)>1 #example 3d*:  orbital_name: ['d', '3', '*']
+            if orbital_name[0]=='s':
+                orbital_name_list += [orbital_name[1]+'s']
+            elif orbital_name[0]=='p':
+                if orbital_name[1].isnumeric(): # not polarized orbital
+                    orbital_name_list += [orbital_name[1]+'py',orbital_name[1]+'pz',orbital_name[1]+'px']
+                else:#polarized orbital
+                    orbital_name_list += ['py*','pz*','px*']
+            elif orbital_name[0]=='d':
+                if orbital_name[1].isnumeric(): # not polarized orbital
+                    orbital_name_list += [orbital_name[1]+'dxy',orbital_name[1]+'dyz',\
+                                            orbital_name[1]+'dz2',orbital_name[1]+'dxz',orbital_name[1]+'dx2-y2']
+                else:#polarized orbital
+                    orbital_name_list += ['dxy*','dyz*','dz2*','dxz*','dx2-y2*']
+            else:
+                raise RuntimeError("At this stage dptb-negf only supports s, p, d orbitals")
+        # print(orbital_name_list)
+        # raise RuntimeError('stop here')
+        return orbital_name_list      
+
+
+    def _load_model(self,model,AtomicData_options,structure_tbtrans_file:str):
+        '''The `load_dptb_model` function loads model and returns the Hamiltonian elements.
+        Parameters
+        ----------
+        checkfile : str
+            The `checkfile` parameter is the file path to the model checkpoint file. 
+        config : str
+            The `config` parameter is a string that represents the configuration file for the model. It
+        contains information such as the model architecture, hyperparameters, and other settings that are
+        necessary for loading and building the model.
+        structure_tbtrans_file : str
+            The `structure_tbtrans_file` parameter is the file path to the structure file in the TBtrans
+        format.
+        struct_option : dict
+            The `struct_option` parameter is a dictionary that contains various options for the structure. It
+        includes the following keys:
+        run_sk : bool
+            The `run_sk` parameter is a boolean flag that determines whether to run the model using the NNSK
+        (Neural Network Schrödinger-Kohn) method. If `run_sk` is set to `True`, the model will be run using
+        the NNSK method. If
+        use_correction : Optional[str]
+            The `use_correction` parameter is an optional parameter that specifies whether to use correction
+        terms in the model. It can be set to either `None` or a string value. If it is set to `None`, the
+        model will not use any correction terms. If it is set to a string value
+        
+        Returns
+        -------
+            The function `load_dptb_model` returns three variables: `allbonds`, `hamil_block`, and
+        `overlap_block`.
+        
+        '''
+        structase = read(structure_tbtrans_file)
+
+        data = AtomicData.from_ase(structase,**AtomicData_options)
+        data = AtomicData.to_AtomicDataDict(data)
+        data = model.idp(data)
+
+        data = model(data)
+        HR_dict = feature_to_block(data,model.idp) # get HR
+        allbonds = [] # a list contain bond info in the form like (type1,atom1_index,type2,atom2_index,displacement)
+        hamil_block = []
+        overlap_block = []
+
+        for key,value in HR_dict.items():
+            bond = key.split('_')
+            bond = torch.as_tensor([int(bond[i]) for i in range(len(bond))])
+            iatom,jatom = model.idp.untransform(data[AtomicDataDict.ATOM_TYPE_KEY][bond[0]-1]),\
+                            model.idp.untransform(data[AtomicDataDict.ATOM_TYPE_KEY][bond[1]-1])
+            allbonds.append(torch.cat([iatom,torch.tensor([bond[0]-1]),jatom,torch.tensor([bond[1]-1]),bond[2:]]))
+            hamil_block.append(value)
+        
+        allbonds = torch.stack(allbonds)
+        hamil_block = torch.stack(hamil_block)
+        overlap_block = torch.stack([torch.eye(hamil_block.shape[-1]) for i in range(hamil_block.shape[0])])
+        # TODO: check tbtrans support overlap matrix or not in TB-NEGF
+
+        return allbonds,hamil_block,overlap_block
+
+    def hamiltonian_get(self,allbonds:torch.tensor,hamil_block:torch.tensor,overlap_block:torch.tensor,Hamil_sisl):
+        '''The function `hamiltonian_get` takes in various parameters and calculates the Hamiltonian matrix
+        for a given set of bonds, storing the result in the `Hamil_sisl` matrix.
+        
+        Parameters
+        ----------
+        allbonds
+            A torch.tensor containing information about the bonds in the system. Each element of the list
+        corresponds to a bond and contains information such as the indices of the atoms involved in the
+        bond and the displacement vector between the atoms.
+        hamil_block
+            The `hamil_block` parameter is a block of the Hamiltonian matrix. It is a tensor that contains the
+        values of the Hamiltonian matrix elements for a specific bond in the system.
+        overlap_block
+            The `overlap_block` parameter is a block matrix representing the overlap between orbitals in the
+        Hamiltonian. It is used to calculate the Hamiltonian matrix elements.
+        Hamil_sisl
+            Hamil_sisl is sisl.hamiltonian that represents the Hamiltonian matrix. The first two
+        dimensions correspond to the orbital indices, and the third dimension represents the supercell
+        indices. The Hamiltonian matrix elements are stored in this array.
+            Inertially, Hamil_sisl is a scipy.sparse.csr_matrix.
+        energy_unit_option
+            The `energy_unit_option` parameter is a string that specifies the unit of energy for the
+        calculation. It can be either "Hartree" or "eV".
+        
+        '''   
+        x_max = abs(allbonds[:,-3].numpy()).max()
+        y_max = abs(allbonds[:,-2].numpy()).max()
+        z_max = abs(allbonds[:,-1].numpy()).max()
+        Hamil_sisl.set_nsc(a=2*abs(x_max)+1,b=2*abs(y_max)+1,c=2*abs(z_max)+1)
+        # set the number of super-cells in Hamiltonian object in sisl, which is based on allbonds results
+        
+
+        for i in range(len(allbonds)):
+            # if i%100==0:print('bond_index: ',i)
+            orb_first_a = Hamil_sisl.geometry.a2o(allbonds[i,1])
+            orb_last_a = Hamil_sisl.geometry.a2o(allbonds[i,1]+1)
+            orb_first_b = Hamil_sisl.geometry.a2o(allbonds[i,3])
+            orb_last_b = Hamil_sisl.geometry.a2o(allbonds[i,3]+1)
+
+
+            if allbonds[i][-3:].equal(torch.tensor([0,0,0])): #allbonds[i,1] is atom index
+                
+                for orb_a in range(orb_first_a,orb_last_a):
+                    for orb_b in range(orb_first_b,orb_last_b):
+                        Hamil_sisl[orb_a,orb_b]=hamil_block[i].detach().numpy()[orb_a-orb_first_a,orb_b-orb_first_b]*self.unit_constant
+                        # Hamil_sisl[orb_b,orb_a]=hamil_block[i].detach().numpy()[orb_b-orb_first_b,orb_a-orb_first_a]*unit_constant
+                        Hamil_sisl[orb_b,orb_a]=np.conjugate(Hamil_sisl[orb_a,orb_b])
+            else: 
+                x = allbonds[i,-3].numpy().tolist()
+                y = allbonds[i,-2].numpy().tolist()
+                z = allbonds[i,-1].numpy().tolist()
+
+                for orb_a in range(orb_first_a,orb_last_a):
+                    for orb_b in range(orb_first_b,orb_last_b):
+                        H_value = hamil_block[i].detach().numpy()[orb_a-orb_first_a,orb_b-orb_first_b]*self.unit_constant
+                        if H_value != 0:
+                            Hamil_sisl[orb_a,orb_b,(x,y,z)]=H_value
+                            Hamil_sisl[orb_b,orb_a,(-1*x,-1*y,-1*z)]=np.conjugate(Hamil_sisl[orb_a,orb_b,(x,y,z)])
+                        # Hamil_sisl[orb_b,orb_a,(-1*x,-1*y,-1*z)]=hamil_block[i].detach().numpy()[orb_b-orb_first_b,orb_a-orb_first_a]*unit_constant
+                
+                #TODO: At this stage, there is some problem using slice operation in sisl. I'm fixing it with the developer of sisl.
+                # I believe that the slice operation will be take soon.
+            
+        
diff --git a/dpnegf/tests/__init__.py b/dpnegf/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/dpnegf/tests/data/test_negf/test_negf_hamiltonian/run_input.json b/dpnegf/tests/data/test_negf/test_negf_hamiltonian/run_input.json
new file mode 100644
index 0000000..a2bb6a4
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_hamiltonian/run_input.json
@@ -0,0 +1,93 @@
+{
+    "task_options": {
+        "task": "negf",
+        "scf": false,
+        "block_tridiagonal": false,
+        "ele_T": 300,
+        "unit": "eV",
+        "scf_options": {
+            "mode": "PDIIS",
+            "mixing_period": 3,
+            "step_size": 0.05,
+            "n_history": 6,
+            "abs_err": 1e-06,
+            "rel_err": 0.0001,
+            "max_iter": 100
+        },
+        "stru_options": {
+            "kmesh": [
+                1,
+                1,
+                1
+            ],
+            "pbc": [
+                false,
+                false,
+                false
+            ],
+            "device": {
+                "id": "4-8",
+                "sort": true
+            },
+            "lead_L": {
+                "id": "0-4",
+                "voltage": 0.0
+            },
+            "lead_R": {
+                "id": "8-12",
+                "voltage": 0.0
+            }
+        },
+        "poisson_options": {
+            "solver": "fmm",
+            "err": 1e-05
+        },
+        "sgf_solver": "Sancho-Rubio",
+        "espacing": 0.1,
+        "emin": -2,
+        "emax": 2,
+        "e_fermi": -13.638587951660156,
+        "density_options": {
+            "method": "Ozaki",
+            "M_cut": 50,
+            "R": 1000000.0,
+            "n_gauss": 10
+        },
+        "eta_lead": 1e-5,
+        "eta_device": 0.0,
+        "out_dos": true,
+        "out_tc": true,
+        "out_ldos": true,
+        "out_current_nscf": true,
+        "out_density": false,
+        "out_potential": false,
+        "out_current": false,
+        "out_lcurrent": false
+    },
+    "AtomicData_options": {
+        "r_max":2.0
+        },
+    "common_options": {
+        "basis":{"C": [
+            "2s"]},
+        "device":"cpu",
+        "dtype": "float64",
+        "overlap":false
+    },
+    "model_options": {
+        "nnsk":{
+            "onsite":{
+                "method": "none"
+            },
+            "hopping":{
+                "method":"powerlaw",
+                "rs":1.6,
+                "w":0.3
+            },
+            "freeze":false,
+            "push":false,
+            "std":0.01,
+            "soc":{}
+        }
+    }
+}
\ No newline at end of file
diff --git a/dpnegf/tests/data/test_negf/test_negf_hamiltonian/test_negf_hamiltonian_init/.gitkeep b/dpnegf/tests/data/test_negf/test_negf_hamiltonian/test_negf_hamiltonian_init/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/chain.vasp b/dpnegf/tests/data/test_negf/test_negf_run/chain.vasp
new file mode 100644
index 0000000..81a1ab2
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/chain.vasp
@@ -0,0 +1,20 @@
+long_chain
+1.0
+        10.000000000        0.0000000000         0.0000000000
+        0.0000000000        10.0000000000         0.0000000000
+        0.0000000000         0.0000000000        19.2000000000
+    C
+   12
+Cartesian
+        0.000000000         0.000000000         3.200000000
+        0.000000000         0.000000000         4.800000000
+        0.000000000         0.000000000         0.000000000
+        0.000000000         0.000000000         1.600000000
+        0.000000000         0.000000000         6.400000000
+        0.000000000         0.000000000         8.000000000
+        0.000000000         0.000000000         9.600000000
+        0.000000000         0.000000000         11.20000000
+        0.000000000         0.000000000         12.80000000
+        0.000000000         0.000000000         14.40000000
+        0.000000000         0.000000000         16.00000000
+        0.000000000         0.000000000         17.60000000
\ No newline at end of file
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/graphene.xyz b/dpnegf/tests/data/test_negf/test_negf_run/graphene.xyz
new file mode 100644
index 0000000..092b6a2
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/graphene.xyz
@@ -0,0 +1,50 @@
+   48
+Lattice="20.00000000 0.00000000 0.00000000 0.00000000 4.92000008 0.00000000 0.00000000 0.00000000 25.56479931 " nsc="1 1 1" pbc="T T T" boundary_condition="PERIODIC PERIODIC  PERIODIC PERIODIC  PERIODIC PERIODIC"
+C   10.00000000  1.23000002  4.97094738
+C   10.00000000  3.69000006  4.97094738
+C   10.00000000  0.00000000  5.68105233
+C   10.00000000  2.46000004  5.68105233
+C   10.00000000  0.00000000  7.10134745
+C   10.00000000  2.46000004  7.10134745
+C   10.00000000  1.23000002  7.81145215
+C   10.00000000  3.69000006  7.81145215
+C   10.00000000  1.23000002  0.71014750
+C   10.00000000  3.69000006  0.71014750
+C   10.00000000  0.00000000  1.42025245
+C   10.00000000  2.46000004  1.42025245
+C   10.00000000  0.00000000  2.84054756
+C   10.00000000  2.46000004  2.84054756
+C   10.00000000  1.23000002  3.55065227
+C   10.00000000  3.69000006  3.55065227
+C   10.00000000  1.23000002  9.23174727
+C   10.00000000  3.69000006  9.23174727
+C   10.00000000  0.00000000  9.94185222
+C   10.00000000  2.46000004  9.94185222
+C   10.00000000  0.00000000  11.36214733
+C   10.00000000  2.46000004  11.36214733
+C   10.00000000  1.23000002  12.07225204
+C   10.00000000  3.69000006  12.07225204
+C   10.00000000  1.23000002  13.49254715
+C   10.00000000  3.69000006  13.49254715
+C   10.00000000  0.00000000  14.20265210
+C   10.00000000  2.46000004  14.20265210
+C   10.00000000  0.00000000  15.62294722
+C   10.00000000  2.46000004  15.62294722
+C   10.00000000  1.23000002  16.33305192
+C   10.00000000  3.69000006  16.33305192
+C   10.00000000  1.23000002  17.75334703
+C   10.00000000  3.69000006  17.75334703
+C   10.00000000  0.00000000  18.46345199
+C   10.00000000  2.46000004  18.46345199
+C   10.00000000  0.00000000  19.88374710
+C   10.00000000  2.46000004  19.88374710
+C   10.00000000  1.23000002  20.59385180
+C   10.00000000  3.69000006  20.59385180
+C   10.00000000  1.23000002  22.01414692
+C   10.00000000  3.69000006  22.01414692
+C   10.00000000  0.00000000  22.72425187
+C   10.00000000  2.46000004  22.72425187
+C   10.00000000  0.00000000  24.14454699
+C   10.00000000  2.46000004  24.14454699
+C   10.00000000  1.23000002  24.85465169
+C   10.00000000  3.69000006  24.85465169
\ No newline at end of file
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/negf_chain.json b/dpnegf/tests/data/test_negf/test_negf_run/negf_chain.json
new file mode 100644
index 0000000..ec34ee8
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/negf_chain.json
@@ -0,0 +1,78 @@
+{
+    "task_options": {
+        "task": "negf",
+        "scf": false,
+        "block_tridiagonal": false,
+        "ele_T": 300,
+        "unit": "Hartree",
+        "scf_options":{
+            "mode": "PDIIS",
+            "mixing_period": 3,
+            "step_size": 0.05,
+            "n_history": 6,
+            "abs_err": 1e-6,
+            "rel_err": 1e-4,
+            "max_iter": 100
+        },
+        "stru_options":{
+            "kmesh":[1,1,1],
+            "pbc":[false, false, false],
+            "device":{
+                "id":"4-8",
+                "sort": true
+            },
+            "lead_L":{
+                "id":"0-4",
+                "voltage":0.0
+            },
+            "lead_R":{
+                "id":"8-12",
+                "voltage":0.0
+            }
+        },
+        "poisson_options": {
+            "solver": "fmm",
+            "err": 1e-5
+        },
+        "sgf_solver": "Sancho-Rubio",
+        "espacing": 0.1,
+        "emin": -2,
+        "emax": 2,
+        "e_fermi": -13.638587951660156,
+        "density_options": {
+            "method": "Ozaki",
+            "M_cut": 50,
+            "R": 1e6
+        },
+        "eta_lead":1e-5,
+        "eta_device":0.0,
+        "out_dos": true,
+        "out_tc": true,
+        "out_ldos": true,
+        "out_current_nscf": true
+},
+
+"common_options": {
+    "onsitemode": "none",
+    "onsite_cutoff": 2.0,
+    "bond_cutoff": 2.0,
+    "env_cutoff": 2.0,
+    "atomtype": ["C"],
+    "proj_atom_neles": {"C": 1},
+    "proj_atom_anglr_m": {
+        "C": ["2s"]
+    }    
+},
+"model_options": {
+    "sknetwork": {
+        "sk_hop_nhidden": 1,
+        "sk_onsite_nhidden": 1
+    },
+    "skfunction": {
+        "sk_cutoff": 1.6,
+        "sk_decay_w": 0.3,
+        "skformula": "powerlaw"
+    }
+
+}
+}
\ No newline at end of file
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json b/dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json
new file mode 100644
index 0000000..d8140fb
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json
@@ -0,0 +1,58 @@
+{
+    "task_options": {
+        "task": "negf",
+        "scf": false,
+        "block_tridiagonal": false,
+        "ele_T": 300,
+        "unit": "eV",
+        "scf_options":{
+            "mode": "PDIIS",
+            "mixing_period": 3,
+            "step_size": 0.05,
+            "n_history": 6,
+            "abs_err": 1e-6,
+            "rel_err": 1e-4,
+            "max_iter": 100
+        },
+        "stru_options":{
+            "kmesh":[1,1,1],
+            "pbc":[false, false, false],
+            "device":{
+                "id":"4-8",
+                "sort": true
+            },
+            "lead_L":{
+                "id":"0-4",
+                "voltage":0.0
+            },
+            "lead_R":{
+                "id":"8-12",
+                "voltage":0.0
+            }
+        },
+        "poisson_options": {
+            "solver": "fmm",
+            "err": 1e-5
+        },
+        "sgf_solver": "Sancho-Rubio",
+        "espacing": 0.01,
+        "emin": -2,
+        "emax": 2,
+        "e_fermi": -13.638587951660156,
+        "density_options": {
+            "method": "Ozaki",
+            "M_cut": 50,
+            "R": 1e6
+        },
+        "eta_lead":1e-5,
+        "eta_device":0.0,
+        "out_dos": true,
+        "out_tc": true,
+        "out_ldos": true,
+        "out_current_nscf": true
+},    
+"AtomicData_options" :{
+    "r_max": 2.0
+},
+"structure":"./chain.vasp"
+}
\ No newline at end of file
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene.json b/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene.json
new file mode 100644
index 0000000..eba9ba4
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene.json
@@ -0,0 +1,80 @@
+{   
+	"task_options":
+        {
+            "task": "negf",
+            "scf": false,
+            "block_tridiagonal": false,
+            "ele_T": 500,
+            "unit": "Hartree",
+            "scf_options":{
+                "mode": "PDIIS",
+                "mixing_period": 3,
+                "step_size": 0.05,
+                "n_history": 6,
+                "abs_err": 1e-6,
+                "rel_err": 1e-4,
+                "max_iter": 100
+            },
+            "stru_options":{
+                "pbc":[false, true, false],
+                "kmesh":[1,3,1],
+                "gamma_center": true,
+                "time_reversal_symmetry": true,
+                "device":{
+                    "id":"16-32",
+                    "sort": true
+                },
+                "lead_L":{
+                    "id":"0-16",
+                    "voltage":0.0
+                },
+                "lead_R":{
+                    "id":"32-48",
+                    "voltage":0.0
+                }
+            },
+            "poisson_options": {
+                "solver": "fmm",
+                "err": 1e-5
+            },
+            "sgf_solver": "Sancho-Rubio",
+            "espacing": 0.2,
+            "emin": -5,
+            "emax": 5,
+            "e_fermi":  -13.638589859008789,
+            "density_options":{
+                "method": "Ozaki"
+            },
+            "eta_lead":1e-4,
+            "eta_device":1e-5,
+            "out_dos": false,
+            "out_tc": true,
+            "out_ldos": false,
+            "out_current_nscf": false,
+            "out_density": false,
+            "out_lcurrent": false
+    },
+    "common_options": {
+        "onsitemode": "none",
+        "onsite_cutoff": 2.0,
+        "bond_cutoff": 2.0,
+        "env_cutoff": 2.0,
+        "atomtype": ["C"],
+        "proj_atom_neles": {"C": 1},
+        "proj_atom_anglr_m": {
+            "C": ["2s"]
+        }    
+    },
+    "model_options": {
+        "sknetwork": {
+            "sk_hop_nhidden": 1,
+            "sk_onsite_nhidden": 1
+        },
+        "skfunction": {
+            "sk_cutoff": 1.6,
+            "sk_decay_w": 0.3,
+            "skformula": "powerlaw"
+        }
+    
+    }
+}
\ No newline at end of file
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene_new.json b/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene_new.json
new file mode 100644
index 0000000..269bdba
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene_new.json
@@ -0,0 +1,61 @@
+{   
+	"task_options":
+        {
+            "task": "negf",
+            "scf": false,
+            "block_tridiagonal": true,
+            "ele_T": 500,
+            "unit": "eV",
+            "scf_options":{
+                "mode": "PDIIS",
+                "mixing_period": 3,
+                "step_size": 0.05,
+                "n_history": 6,
+                "abs_err": 1e-6,
+                "rel_err": 1e-4,
+                "max_iter": 100
+            },
+            "stru_options":{
+                "pbc":[false, true, false],
+                "kmesh":[1,3,1],
+                "gamma_center": true,
+                "time_reversal_symmetry": true,
+                "device":{
+                    "id":"16-32",
+                    "sort": true
+                },
+                "lead_L":{
+                    "id":"0-16",
+                    "voltage":0.0
+                },
+                "lead_R":{
+                    "id":"32-48",
+                    "voltage":0.0
+                }
+            },
+            "poisson_options": {
+                "solver": "fmm",
+                "err": 1e-5
+            },
+            "sgf_solver": "Sancho-Rubio",
+            "espacing": 0.2,
+            "emin": -5,
+            "emax": 5,
+            "e_fermi":  -13.638589859008789,
+            "density_options":{
+                "method": "Ozaki"
+            },
+            "eta_lead":1e-4,
+            "eta_device":1e-5,
+            "out_dos": false,
+            "out_tc": true,
+            "out_ldos": false,
+            "out_current_nscf": false,
+            "out_density": false,
+            "out_lcurrent": false
+    },    
+    "AtomicData_options" :{
+        "r_max": 2.0
+    },
+    "structure":"./graphene.xyz"
+}
\ No newline at end of file
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C.json b/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C.json
new file mode 100644
index 0000000..90d11a6
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C.json
@@ -0,0 +1,9 @@
+{ "version":1,
+    "onsite": {},
+    "hopping": {
+        "C-C-2s-2s-0": [
+            0.04,
+            1.0
+        ]
+    }
+}
\ No newline at end of file
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_new.json b/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_new.json
new file mode 100644
index 0000000..76d56b4
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_new.json
@@ -0,0 +1,39 @@
+{ "version":2,
+"model_params": {
+    "onsite": {},
+    "hopping": {
+        "C-C-2s-2s-0": [
+            1.0884529828109601,
+            1.0
+        ]
+    }
+},
+"unit": "eV",
+"model_options": {
+    "nnsk": {
+        "onsite": {
+            "method": "none"
+        },
+        "hopping": {
+            "method": "powerlaw",
+            "rs": 1.6,
+            "w": 0.3
+        },
+        "soc": {},
+        "freeze": false,
+        "push": false,
+        "std": 0.01
+    }
+},
+"common_options": {
+    "basis": {
+        "C": [
+            "2s"
+        ]
+    },
+    "dtype": "float64",
+    "device": "cpu",
+    "overlap": false
+}
+}
+
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_newS.json b/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_newS.json
new file mode 100644
index 0000000..a536c01
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_newS.json
@@ -0,0 +1,45 @@
+{ "version":2,
+"model_params": {
+    "onsite": {},
+    "hopping": {
+        "C-C-2s-2s-0": [
+            0.4,
+            1.0
+        ]
+    },
+    "overlap": {
+        "C-C-2s-2s-0": [
+            0.05,
+            1.0
+        ]
+    }  
+},
+"unit": "eV",
+"model_options": {
+    "nnsk": {
+        "onsite": {
+            "method": "none"
+        },
+        "hopping": {
+            "method": "powerlaw",
+            "rs": 1.6,
+            "w": 0.3
+        },
+        "soc": {},
+        "freeze": false,
+        "push": false,
+        "std": 0.01
+    }
+},
+"common_options": {
+    "basis": {
+        "C": [
+            "2s"
+        ]
+    },
+    "dtype": "float64",
+    "device": "cpu",
+    "overlap": true
+}
+}
+
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene/show.ipynb b/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene/show.ipynb
new file mode 100644
index 0000000..483874d
--- /dev/null
+++ b/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene/show.ipynb
@@ -0,0 +1,661 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['k', 'wk', 'uni_grid', 'T_k', 'T_avg'])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "\n",
+    "negf_out = torch.load('results/negf.out.pth')\n",
+    "negf_out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h5py\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "HS_device_path = 'results/HS_device.h5'\n",
+    "HS_device = {}\n",
+    "with h5py.File(HS_device_path, \"r\") as f:\n",
+    "    for key in f.keys():\n",
+    "        if isinstance(f[key], h5py.Dataset): \n",
+    "            if  key == \"block_tridiagonal\":\n",
+    "                assert isinstance(f[key][()], (bool, np.bool_)), f\"Expected bool, but got {type(f[key])}\"\n",
+    "                # read Bool type: block_tridiagonal\n",
+    "                HS_device[key] = bool(f[key][()])\n",
+    "            elif key == \"kpoints\":\n",
+    "                assert isinstance(f[key][()], np.ndarray),f\"Expected np.ndarray, but got {type(f[key][()])}\"\n",
+    "                # read NumPy array: kpoints\n",
+    "                HS_device[key] = f[key][()]\n",
+    "            else:\n",
+    "                assert isinstance(f[key][()], np.ndarray),f\"Expected np.ndarray, but got {type(f[key][()])}\"\n",
+    "                # read NumPy array: HD, SD, Hall, Sall\n",
+    "                HS_device[key] = torch.tensor(f[key][()])\n",
+    "        else:  \n",
+    "            group = f[key]\n",
+    "            if key == \"subblocks\":\n",
+    "                HS_device[key] = group[key][()]  # read NumPy array\n",
+    "            else:\n",
+    "                # sub_keys format: {key}_k{idx_k}_b{idx_block}\n",
+    "                items = []\n",
+    "                sub_keys = sorted(group.keys())  # 确保按顺序读取\n",
+    "                current_idx_k = -1\n",
+    "                sublist = []\n",
+    "                for sub_key in sub_keys:\n",
+    "                    parts = sub_key.split(\"_k\")\n",
+    "                    if len(parts) < 2:\n",
+    "                        raise ValueError(f\"Unexpected dataset format: {sub_key}\")\n",
+    "                    idx_k, idx_block = map(int, parts[1].split(\"_b\"))\n",
+    "                    if idx_k != current_idx_k:\n",
+    "                        if sublist:\n",
+    "                            items.append(sublist)  # 先存入上一个 k 组\n",
+    "                        sublist = []  # 开始新的 k 组\n",
+    "                        current_idx_k = idx_k\n",
+    "                    # 读取 Dataset 并存入子列表\n",
+    "                    sublist.append(torch.tensor(group[sub_key][()]))\n",
+    "                if sublist:\n",
+    "                    items.append(sublist)  # 存入最后一组数据\n",
+    "                HS_device[key] = items"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['hd_k0_b0_real', 'hd_k0_b10_imag', 'hd_k0_b2_real']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = ['hd_k0_b0_real', 'hd_k0_b2_real','hd_k0_b10_imag']\n",
+    "sorted(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['hd_k0_b0_real', 'hd_k0_b2_real', 'hd_k0_b10_imag']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dptb.negf.negf_utils import natsorted\n",
+    "natsorted(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<KeysViewHDF5 ['hd_k0_b0', 'hd_k0_b1', 'hd_k0_b2', 'hd_k0_b3', 'hd_k0_b4', 'hd_k0_b5', 'hd_k0_b6', 'hd_k1_b0', 'hd_k1_b1', 'hd_k1_b2', 'hd_k1_b3', 'hd_k1_b4', 'hd_k1_b5', 'hd_k1_b6']>\n",
+      "['hd_k0_b0', 'hd_k0_b1', 'hd_k0_b2', 'hd_k0_b3', 'hd_k0_b4', 'hd_k0_b5', 'hd_k0_b6', 'hd_k1_b0', 'hd_k1_b1', 'hd_k1_b2', 'hd_k1_b3', 'hd_k1_b4', 'hd_k1_b5', 'hd_k1_b6']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['hd_k0_b0', 'hd_k0_b10', 'hd_k0_b2']"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HS_device_path = 'results/HS_device.h5'\n",
+    "HS_device = {}\n",
+    "with h5py.File(HS_device_path, \"r\") as f:\n",
+    "    key = \"hd\"\n",
+    "    group = f[key]\n",
+    "    print(group.keys())\n",
+    "    sub_keys = sorted(group.keys())\n",
+    "    print(sub_keys)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HDL\n",
+      "HL\n",
+      "HLL\n",
+      "SDL\n",
+      "SL\n",
+      "SLL\n",
+      "bloch_factor\n",
+      "kpoints\n",
+      "kpoints_bloch\n",
+      "useBloch\n"
+     ]
+    }
+   ],
+   "source": [
+    "HS_lead_path = 'results/HS_lead_L.h5'\n",
+    "HS_leads = {}\n",
+    "with h5py.File(HS_lead_path, \"r\") as f:\n",
+    "    for key in f.keys():\n",
+    "        print(key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "b'None'\n"
+     ]
+    }
+   ],
+   "source": [
+    "with h5py.File(HS_lead_path, \"r\") as f:\n",
+    "    print(f['bloch_factor'][()])\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HDL [[[0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.36699071+0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.36699071+0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
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+      "  [ 0.        +0.j          0.        +0.j\n",
+      "    0.        +0.j          0.        +0.j\n",
+      "    0.04587758+0.j          0.04587758+0.j\n",
+      "    1.        +0.j          0.        +0.j        ]\n",
+      "  [ 0.        +0.j          0.        +0.j\n",
+      "    0.        +0.j          0.        +0.j\n",
+      "    0.04587758+0.j          0.04587758+0.j\n",
+      "    0.        +0.j          1.        +0.j        ]]\n",
+      "\n",
+      " [[ 1.        +0.j          0.        +0.j\n",
+      "    0.04587757+0.j          0.04587757+0.j\n",
+      "    0.        +0.j          0.        +0.j\n",
+      "    0.        +0.j          0.        +0.j        ]\n",
+      "  [ 0.        +0.j          1.        +0.j\n",
+      "   -0.02293795-0.03973162j  0.04587757+0.j\n",
+      "    0.        +0.j          0.        +0.j\n",
+      "    0.        +0.j          0.        +0.j        ]\n",
+      "  [ 0.04587757+0.j         -0.02293795+0.03973162j\n",
+      "    1.        +0.j          0.        +0.j\n",
+      "    0.04587384+0.j          0.        +0.j\n",
+      "    0.        +0.j          0.        +0.j        ]\n",
+      "  [ 0.04587757+0.j          0.04587757+0.j\n",
+      "    0.        +0.j          1.        +0.j\n",
+      "    0.        +0.j          0.04587384+0.j\n",
+      "    0.        +0.j          0.        +0.j        ]\n",
+      "  [ 0.        +0.j          0.        +0.j\n",
+      "    0.04587384+0.j          0.        +0.j\n",
+      "    1.        +0.j          0.        +0.j\n",
+      "    0.04587758+0.j         -0.02293796+0.03973163j]\n",
+      "  [ 0.        +0.j          0.        +0.j\n",
+      "    0.        +0.j          0.04587384+0.j\n",
+      "    0.        +0.j          1.        +0.j\n",
+      "    0.04587758+0.j          0.04587758+0.j        ]\n",
+      "  [ 0.        +0.j          0.        +0.j\n",
+      "    0.        +0.j          0.        +0.j\n",
+      "    0.04587758+0.j          0.04587758+0.j\n",
+      "    1.        +0.j          0.        +0.j        ]\n",
+      "  [ 0.        +0.j          0.        +0.j\n",
+      "    0.        +0.j          0.        +0.j\n",
+      "   -0.02293796-0.03973163j  0.04587758+0.j\n",
+      "    0.        +0.j          1.        +0.j        ]]]\n",
+      "SLL [[[0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.04587384+0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.04587384+0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]]\n",
+      "\n",
+      " [[0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.04587384+0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.04587384+0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]\n",
+      "  [0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j\n",
+      "   0.        +0.j 0.        +0.j 0.        +0.j 0.        +0.j]]]\n",
+      "kpoints [[0.      0.      0.     ]\n",
+      " [0.      0.33333 0.     ]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_13043/3367532197.py:8: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+      "  elif dataset[()] == \"None\":\n"
+     ]
+    }
+   ],
+   "source": [
+    "HS_lead_path = 'results/HS_lead_L.h5'\n",
+    "HS_leads = {}\n",
+    "with h5py.File(HS_lead_path, \"r\") as f:\n",
+    "    for key in f.keys():\n",
+    "        dataset = f[key]  \n",
+    "        if dataset.dtype == np.bool_ or dataset.shape == ():  \n",
+    "            HS_leads[key] = bool(dataset[()])\n",
+    "        elif dataset[()] == \"None\":\n",
+    "            HS_leads[key] = None\n",
+    "        else:\n",
+    "            print(key,dataset[()])\n",
+    "            HS_leads[key] = torch.tensor(dataset[()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dptb.entrypoints.run import run\n",
+    "\n",
+    "\n",
+    "INPUT_file =  root_directory +\"/dptb/tests/data/test_negf/test_negf_run/negf_graphene_new.json\" \n",
+    "output =  root_directory +\"/dptb/tests/data/test_negf/test_negf_run/out_negf_graphene\"  \n",
+    "checkfile =  root_directory +'/dptb/tests/data/test_negf/test_negf_run/nnsk_C_new.json'\n",
+    "structure =  root_directory +\"/dptb/tests/data/test_negf/test_negf_run/graphene.xyz\" \n",
+    "\n",
+    "run(INPUT=INPUT_file,init_model=checkfile,output=output,run_sk=True,structure=structure,\\\n",
+    "log_level=5,log_path=output+\"/test.log\",use_correction=False)\n",
+    "\n",
+    "negf_results = torch.load(output+\"/results/negf.out.pth\")\n",
+    "\n",
+    "k_standard = np.array([[0. , 0. , 0.], [0. , 0.33333333, 0.]])\n",
+    "k = negf_results['k']\n",
+    "assert(abs(k-k_standard).max()<1e-5)  #compare with calculated kpoints\n",
+    "    \n",
+    "wk_standard = np.array([0.3333333333333333, 0.6666666666666666])\n",
+    "wk = np.array(negf_results['wk'])\n",
+    "assert abs(wk-wk_standard).max()<1e-5 #compare with calculated weight\n",
+    "\n",
+    "\n",
+    "T_k0 = negf_results['T_k'][str(negf_results['k'][0])]\n",
+    "T_k0_standard = [2.2307e-18, 7.0694e-18, 2.4631e-17, 9.6304e-17, 4.3490e-16, 2.3676e-15,\n",
+    "    1.6641e-14, 1.7068e-13, 3.3234e-12, 2.8054e-10, 9.9964e-01, 9.9985e-01,\n",
+    "    9.9989e-01, 9.9991e-01, 9.9991e-01, 9.9992e-01, 9.9992e-01, 9.9992e-01,\n",
+    "    9.9991e-01, 9.9987e-01, 4.0658e-08, 5.7304e-10, 8.4808e-11, 2.9762e-11,\n",
+    "    1.8432e-11, 1.8431e-11, 2.9762e-11, 8.4805e-11, 5.7300e-10, 4.0650e-08,\n",
+    "    9.9987e-01, 9.9991e-01, 9.9992e-01, 9.9992e-01, 9.9992e-01, 9.9991e-01,\n",
+    "    9.9991e-01, 9.9989e-01, 9.9985e-01, 9.9964e-01, 2.8058e-10, 3.3236e-12,\n",
+    "    1.7069e-13, 1.6642e-14, 2.3677e-15, 4.3491e-16, 9.6308e-17, 2.4632e-17,\n",
+    "    7.0696e-18, 2.2308e-18]\n",
+    "T_k0_standard = torch.tensor(T_k0_standard)\n",
+    "assert abs(T_k0-T_k0_standard).max()<1e-4\n",
+    "\n",
+    "T_k1 = negf_results['T_k'][str(negf_results['k'][1])]\n",
+    "T_k1_standard = [3.4867e-19, 1.0166e-18, 3.2013e-18, 1.1041e-17, 4.2506e-17, 1.8749e-16,\n",
+    "    9.8430e-16, 6.5273e-15, 6.0546e-14, 9.6364e-13, 4.5495e-11, 3.3900e-07,\n",
+    "    9.9983e-01, 9.9988e-01, 9.9990e-01, 1.9996e+00, 1.9998e+00, 1.9998e+00,\n",
+    "    1.9998e+00, 1.9998e+00, 1.9998e+00, 9.9992e-01, 9.9992e-01, 9.9992e-01,\n",
+    "    9.9992e-01, 9.9992e-01, 9.9992e-01, 9.9992e-01, 9.9992e-01, 1.9998e+00,\n",
+    "    1.9998e+00, 1.9998e+00, 1.9998e+00, 1.9998e+00, 1.9996e+00, 9.9990e-01,\n",
+    "    9.9988e-01, 9.9983e-01, 3.3921e-07, 4.5502e-11, 9.6372e-13, 6.0549e-14,\n",
+    "    6.5277e-15, 9.8436e-16, 1.8749e-16, 4.2507e-17, 1.1042e-17, 3.2014e-18,\n",
+    "    1.0167e-18, 3.4868e-19]\n",
+    "T_k1_standard = torch.tensor(T_k1_standard)\n",
+    "assert  abs(T_k1-T_k1_standard).max()<1e-4\n",
+    "\n",
+    "\n",
+    "T_avg = negf_results['T_avg']\n",
+    "T_avg_standard = [9.7602e-19, 3.0342e-18, 1.0345e-17, 3.9462e-17, 1.7330e-16, 9.1420e-16,\n",
+    "    6.2031e-15, 6.1245e-14, 1.1482e-12, 9.4156e-11, 3.3321e-01, 3.3328e-01,\n",
+    "    9.9985e-01, 9.9989e-01, 9.9990e-01, 1.6664e+00, 1.6665e+00, 1.6665e+00,\n",
+    "    1.6665e+00, 1.6665e+00, 1.3332e+00, 6.6661e-01, 6.6661e-01, 6.6661e-01,\n",
+    "    6.6661e-01, 6.6661e-01, 6.6661e-01, 6.6661e-01, 6.6661e-01, 1.3332e+00,\n",
+    "    1.6665e+00, 1.6665e+00, 1.6665e+00, 1.6665e+00, 1.6664e+00, 9.9990e-01,\n",
+    "    9.9989e-01, 9.9985e-01, 3.3328e-01, 3.3321e-01, 9.4171e-11, 1.1482e-12,\n",
+    "    6.1249e-14, 6.2035e-15, 9.1425e-16, 1.7331e-16, 3.9464e-17, 1.0345e-17,\n",
+    "    3.0343e-18, 9.7605e-19]\n",
+    "T_avg_standard = torch.tensor(T_avg_standard)\n",
+    "assert  abs(T_avg-T_avg_standard).max()<1e-4  #compare with calculated transmission at efermi"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deeptb-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/dpnegf/tests/test_negf_device_property.py b/dpnegf/tests/test_negf_device_property.py
new file mode 100644
index 0000000..459fba4
--- /dev/null
+++ b/dpnegf/tests/test_negf_device_property.py
@@ -0,0 +1,175 @@
+#test_negf_Device_set_leadLR
+from dpnegf.negf.device_property import DeviceProperty
+from dptb.nn.build import build_model
+import json
+from dpnegf.utils.make_kpoints import kmesh_sampling
+from dpnegf.utils.tools import j_must_have
+import numpy as np
+import torch
+from dpnegf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
+from dpnegf.negf.lead_property import LeadProperty
+from dpnegf.utils.constants import Boltzmann, eV2J
+import pytest
+
+
+@pytest.fixture(scope='session', autouse=True)
+def root_directory(request):
+    """
+    :return:
+    """
+    return str(request.config.rootdir)
+
+def test_negf_Device(root_directory):
+    model_ckpt=root_directory +'/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C.json'
+    results_path=root_directory +"/dpnegf/tests/data/test_negf"
+    input_path = root_directory +"/dpnegf/tests/data/test_negf/test_negf_hamiltonian/run_input.json"
+    structure=root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/chain.vasp"
+    # log_path=root_directory +"/dptb/tests/data/test_negf/test_negf_Device/test.log"
+    negf_json = json.load(open(input_path))
+    model = build_model(model_ckpt,model_options=negf_json['model_options'],common_options=negf_json['common_options'])
+
+
+    hamiltonian = NEGFHamiltonianInit(model=model,
+                                    AtomicData_options=negf_json['AtomicData_options'], 
+                                    structure=structure,
+                                    pbc_negf = negf_json['task_options']["stru_options"]['pbc'], 
+                                    stru_options = negf_json['task_options']['stru_options'],
+                                    unit = negf_json['task_options']['unit'], 
+                                    results_path=results_path,
+                                    torch_device = torch.device('cpu'),
+                                    block_tridiagonal=negf_json['task_options']['block_tridiagonal'])
+
+    # hamiltonian = NEGFHamiltonianInit(apiH=apiHrk, structase=structase, stru_options=task_options["stru_options"], results_path=results_path)
+    kpoints= kmesh_sampling(negf_json['task_options']["stru_options"]["kmesh"])
+    with torch.no_grad():
+        struct_device, struct_leads, _,_,_ = hamiltonian.initialize(kpoints=kpoints)
+
+    
+    deviceprop = DeviceProperty(hamiltonian, struct_device, results_path=results_path, efermi=negf_json['task_options']['e_fermi'])
+    deviceprop.set_leadLR(
+            lead_L=LeadProperty(
+            hamiltonian=hamiltonian, 
+            tab="lead_L", 
+            structure=struct_leads["lead_L"], 
+            results_path=results_path,
+            e_T=negf_json['task_options']['ele_T'],
+            efermi=negf_json['task_options']['e_fermi'], 
+            voltage=negf_json['task_options']["stru_options"]["lead_L"]["voltage"]
+        ),
+            lead_R=LeadProperty(
+            hamiltonian=hamiltonian, 
+            tab="lead_R", 
+            structure=struct_leads["lead_R"], 
+            results_path=results_path,
+            e_T=negf_json['task_options']['ele_T'],
+            efermi=negf_json['task_options']['e_fermi'], 
+            voltage=negf_json['task_options']["stru_options"]["lead_R"]["voltage"]
+        )
+    )
+    
+    # check device.Lead_L.structure
+    assert all(deviceprop.lead_L.structure.symbols=='C4')
+    assert deviceprop.lead_L.structure.pbc[0]==False
+    assert deviceprop.lead_L.structure.pbc[1]==False
+    assert deviceprop.lead_L.structure.pbc[2]==True
+    assert np.diag(np.array((deviceprop.lead_L.structure.cell-[10.0, 10.0, 6.4])<1e-4)).all()
+    assert deviceprop.lead_L.tab=="lead_L"
+    assert abs(deviceprop.mu+13.638587951660156)<1e-5
+    # check device.Lead_R.structure
+    assert all(deviceprop.lead_R.structure.symbols=='C4')
+    assert deviceprop.lead_R.structure.pbc[0]==False
+    assert deviceprop.lead_R.structure.pbc[1]==False
+    assert deviceprop.lead_R.structure.pbc[2]==True
+    assert np.diag(np.array((deviceprop.lead_R.structure.cell-[10.0, 10.0, 6.4])<1e-4)).all()
+    assert deviceprop.lead_R.tab=="lead_R"
+
+
+   # calculate Self energy and Green function
+    task_options = negf_json['task_options']
+    device = deviceprop
+
+    stru_options = j_must_have(task_options, "stru_options")
+    leads = stru_options.keys()
+    for ll in leads:
+        if ll.startswith("lead"): #calculate surface green function at E=0
+            getattr(device, ll).self_energy(
+                energy=torch.tensor([0]), 
+                kpoint=kpoints[0], 
+                eta_lead=task_options["eta_lead"],
+                method=task_options["sgf_solver"]
+                )
+
+        # check left and right leads' self-energy
+    lead_L_se_standard=torch.tensor([[-3.3171e-07-0.6096j]], dtype=torch.complex128)
+    lead_R_se_standard=torch.tensor([[-3.3171e-07-0.6096j]], dtype=torch.complex128)
+    print('device.lead_L.se:',device.lead_L.se)
+    print('device.lead_R.se:',device.lead_R.se)
+    assert  abs(device.lead_L.se-lead_L_se_standard).max()<1e-5
+    assert  abs(device.lead_R.se-lead_R_se_standard).max()<1e-5
+
+    device.cal_green_function(  energy=torch.tensor([0]),   #calculate device green function at E=0
+                            kpoint=kpoints[0], 
+                            eta_device=task_options["eta_device"], 
+                            block_tridiagonal=task_options["block_tridiagonal"]
+                            )
+
+    #check  green functions' results
+    assert list(device.greenfuncs.keys())==['g_trans','gr_lc', 'grd', 'grl', 'gru', 'gr_left', 'gnd', 'gnl',\
+                                        'gnu', 'gin_left', 'gpd', 'gpl', 'gpu', 'gip_left']
+    g_trans= torch.tensor([[ 1.0983e-11-8.2022e-01j, -8.2022e-01+4.4634e-07j,8.9264e-07+8.2022e-01j,  8.2022e-01-1.3390e-06j],
+            [-8.2022e-01+4.4634e-07j, -3.6607e-12-8.2022e-01j,-8.2021e-01+4.4631e-07j,  8.9264e-07+8.2022e-01j],
+            [ 8.9264e-07+8.2022e-01j, -8.2021e-01+4.4631e-07j,-3.6607e-12-8.2022e-01j, -8.2022e-01+4.4634e-07j],
+            [ 8.2022e-01-1.3390e-06j,  8.9264e-07+8.2022e-01j,-8.2022e-01+4.4634e-07j,  1.0983e-11-8.2022e-01j]],dtype=torch.complex128)
+    grd= [torch.tensor([[ 1.0983e-11-8.2022e-01j, -8.2022e-01+4.4634e-07j,8.9264e-07+8.2022e-01j,  8.2022e-01-1.3390e-06j],
+            [-8.2022e-01+4.4634e-07j, -3.6607e-12-8.2022e-01j,-8.2021e-01+4.4631e-07j,  8.9264e-07+8.2022e-01j],
+            [ 8.9264e-07+8.2022e-01j, -8.2021e-01+4.4631e-07j,-3.6607e-12-8.2022e-01j, -8.2022e-01+4.4634e-07j],
+            [ 8.2022e-01-1.3390e-06j,  8.9264e-07+8.2022e-01j,-8.2022e-01+4.4634e-07j,  1.0983e-11-8.2022e-01j]],dtype=torch.complex128)]
+
+    assert  abs(g_trans-device.greenfuncs['g_trans']).max()<1e-5
+    assert  abs(grd[0]-device.greenfuncs['grd'][0]).max()<1e-5
+    assert device.greenfuncs['grl'] == []
+    assert device.greenfuncs['gru'] == []
+
+    gr_left= [torch.tensor([[ 1.0983e-11-8.2022e-01j, -8.2022e-01+4.4634e-07j,8.9264e-07+8.2022e-01j,  8.2022e-01-1.3390e-06j],
+            [-8.2022e-01+4.4634e-07j, -3.6607e-12-8.2022e-01j,-8.2021e-01+4.4631e-07j,  8.9264e-07+8.2022e-01j],
+            [ 8.9264e-07+8.2022e-01j, -8.2021e-01+4.4631e-07j,-3.6607e-12-8.2022e-01j, -8.2022e-01+4.4634e-07j],
+            [ 8.2022e-01-1.3390e-06j,  8.9264e-07+8.2022e-01j,-8.2022e-01+4.4634e-07j,  1.0983e-11-8.2022e-01j]],dtype=torch.complex128)]
+
+    gnd = [torch.tensor([[ 8.2022e-01+0.0000e+00j, -4.4634e-07+2.2204e-16j,-8.2022e-01-3.1764e-22j,  1.3390e-06-5.5511e-17j],
+        [-4.4634e-07-2.7756e-16j,  8.2022e-01+2.6470e-23j, -4.4631e-07+2.7756e-16j, -8.2022e-01-2.3823e-22j],
+        [-8.2022e-01+2.9117e-22j, -4.4631e-07-2.2204e-16j, 8.2022e-01+7.9409e-23j, -4.4634e-07+1.1102e-16j],
+        [ 1.3390e-06+5.5511e-17j, -8.2022e-01+2.1176e-22j, -4.4634e-07-1.1102e-16j,  8.2022e-01+0.0000e+00j]],dtype=torch.complex128)]
+
+    assert  abs(gr_left[0]-device.greenfuncs['gr_left'][0]).max()<1e-5
+    assert  abs(gnd[0]-device.greenfuncs['gnd'][0]).max()<1e-5
+    assert device.greenfuncs['gnl'] == []
+    assert device.greenfuncs['gnu'] == []
+
+    gin_left=[torch.tensor([[ 8.2022e-01+0.0000e+00j, -4.4634e-07+2.2204e-16j, -8.2022e-01-3.1764e-22j,  1.3390e-06-5.5511e-17j],
+        [-4.4634e-07-2.7756e-16j,  8.2022e-01+2.6470e-23j, -4.4631e-07+2.7756e-16j, -8.2022e-01-2.3823e-22j],
+        [-8.2022e-01+2.9117e-22j, -4.4631e-07-2.2204e-16j, 8.2022e-01+7.9409e-23j, -4.4634e-07+1.1102e-16j],
+        [ 1.3390e-06+5.5511e-17j, -8.2022e-01+2.1176e-22j,-4.4634e-07-1.1102e-16j,  8.2022e-01+0.0000e+00j]],dtype=torch.complex128)]
+    assert  abs(gin_left[0]-device.greenfuncs['gin_left'][0]).max()<1e-5
+
+    assert device.greenfuncs['gpd']== None
+    assert device.greenfuncs['gpl']== None
+    assert device.greenfuncs['gpu']== None
+    assert device.greenfuncs['gip_left']== None
+
+    Tc=device._cal_tc_() #transmission
+    assert abs(Tc-1)<1e-5
+
+    dos = device._cal_dos_()
+    dos_standard = torch.tensor(2.0887, dtype=torch.float64)
+    assert abs(dos-dos_standard)<1e-4
+
+    ldos = device._cal_ldos_()
+    torch.set_printoptions(precision=8)
+    print('ldos:',ldos)
+    ldos_standard = torch.tensor([0.2611, 0.2611, 0.2611, 0.2611], dtype=torch.float64)*2
+    
+    assert abs(ldos_standard-ldos).max()<1e-4
+
+
+
+
diff --git a/dpnegf/tests/test_negf_kmesh_sampling.py b/dpnegf/tests/test_negf_kmesh_sampling.py
new file mode 100644
index 0000000..b4b416f
--- /dev/null
+++ b/dpnegf/tests/test_negf_kmesh_sampling.py
@@ -0,0 +1,156 @@
+
+import pytest
+import numpy as np
+from dpnegf.utils.make_kpoints import kmesh_sampling_negf
+
+
+@pytest.fixture(scope='session', autouse=True)
+def root_directory(request):
+    """
+    :return:
+    """
+    return str(request.config.rootdir)
+
+
+def test_negf_ksampling(root_directory):
+
+    #-------- 1D ksampling-----------
+
+    ## even meshgrid
+    meshgrid = [1,4,1]
+    ### Gamma center
+    is_gamma_center = True
+    is_time_reversal = True
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[0.  , 0.  , 0.  ],[0.  , 0.25, 0.  ],[0.  , 0.5 , 0.  ]])).max()<1e-5 
+    assert abs(wk - np.array([0.25, 0.5 , 0.25])).max()<1e-5 
+
+    is_time_reversal = False
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[0., 0., 0.],[0., 0.25, 0.],[0., 0.5, 0.],[0., 0.75, 0.]])).max()<1e-5 
+    assert abs(wk - np.array([0.25, 0.25 ,0.25, 0.25])).max()<1e-5
+
+    ### Monkhorst-Packing sampling
+    is_gamma_center = False
+    is_time_reversal = True
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    print(kp)
+    assert abs(kp-np.array([[0.  , 0.125  , 0.  ],[0.  , 0.375, 0.  ]])).max()<1e-5 
+    assert abs(wk-np.array([0.5, 0.5])).max()<1e-5
+
+    is_time_reversal = False
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp-np.array([[ 0., -0.375,  0.],[ 0., -0.125, 0.],[ 0.,0.125,0.],[ 0.,0.375, 0.]])).max()<1e-5 
+    assert abs(wk - np.array([0.25, 0.25, 0.25, 0.25])).max()<1e-5
+
+    ## odd meshgrid
+    meshgrid = [1,5,1]
+    ### Gamma center
+    is_gamma_center = True
+    is_time_reversal = True
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[0., 0., 0.],[0., 0.2, 0. ],[0., 0.4 , 0.]])).max()<1e-5 
+    assert abs(wk - np.array([0.2, 0.4, 0.4])).max()<1e-5
+
+    is_time_reversal = False
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[0., 0., 0.],[0., 0.2, 0. ],[0., 0.4 , 0.],[0., 0.6 , 0.],[0., 0.8 , 0.]])).max()<1e-5
+    assert abs(wk - np.array([0.2, 0.2 ,0.2, 0.2, 0.2])).max()<1e-5
+
+    ### Monkhorst-Packing sampling
+    is_gamma_center = False
+    is_time_reversal = True
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp-np.array([[0.  , 0.0  ,0 ],[0.  , 0.2  ,0 ],[0., 0.4, 0 ]])).max()<1e-5 
+    assert abs(wk-np.array([0.2, 0.4, 0.4])).max()<1e-5
+
+    is_time_reversal = False
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp-np.array([[ 0., -0.4,  0.],[ 0., -0.2, 0.],[ 0.,0.0,0.],[ 0.,0.2, 0.],[ 0.,0.4,0.]])).max()<1e-5 
+    assert abs(wk - np.array([0.2, 0.2, 0.2, 0.2, 0.2])).max()<1e-5
+
+    #-------- 1D ksampling-----------
+
+
+
+
+    #-------- 2D ksampling-----------
+    ## even meshgrid
+    meshgrid = [2,2,1]
+    ### Gamma center
+    is_gamma_center = True
+    is_time_reversal = True
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[0.  , 0.  , 0.  ],[0.  , 0.5, 0.  ],[0.5  , 0. , 0.  ],[0.5  , 0.5 , 0.  ]])).max()<1e-5
+    assert abs(wk - np.array([0.25, 0.25 , 0.25, 0.25])).max()<1e-5
+
+    is_time_reversal = False
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[0.  , 0.  , 0.  ],[0.  , 0.5, 0.  ],[0.5  , 0. , 0.  ],[0.5  , 0.5 , 0.  ]])).max()<1e-5
+    assert abs(wk - np.array([0.25, 0.25 , 0.25, 0.25])).max()<1e-5
+
+    ### Monkhorst-Packing sampling
+    is_gamma_center = False
+    is_time_reversal = True
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    print(kp)
+    assert abs(kp - np.array([[0.25, -0.25,  0.  ],[0.25,  0.25,  0.  ]])).max()<1e-5
+    assert abs(wk - np.array([0.5,0.5])).max()<1e-5
+
+    is_time_reversal = False
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[ -0.25, -0.25,  0.  ],[ -0.25,  0.25,  0.  ],[ 0.25, -0.25,  0.  ],[ 0.25,0.25, 0.  ]])).max()<1e-5
+    assert abs(wk - np.array([0.25, 0.25, 0.25, 0.25])).max()<1e-5
+
+    ## odd meshgrid
+    meshgrid = [3,3,1]
+    ### Gamma center
+    is_gamma_center = True
+    is_time_reversal = True
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[0.        , 0.        , 0.        ],
+       [0.        , 0.33333333, 0.        ],
+       [0.33333333, 0.        , 0.        ],
+       [0.33333333, 0.33333333, 0.        ],
+       [0.33333333, 0.66666667, 0.        ]])).max()<1e-5
+    assert abs(wk - np.array([0.11111111, 0.22222222, 0.22222222, 0.22222222, 0.22222222])).max()<1e-5
+
+    is_time_reversal = False
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[0.        , 0.        , 0.        ],
+       [0.        , 0.33333333, 0.        ],
+       [0.        , 0.66666667, 0.        ],
+       [0.33333333, 0.        , 0.        ],
+       [0.33333333, 0.33333333, 0.        ],
+       [0.33333333, 0.66666667, 0.        ],
+       [0.66666667, 0.        , 0.        ],
+       [0.66666667, 0.33333333, 0.        ],
+       [0.66666667, 0.66666667, 0.        ]])).max()<1e-5
+    assert abs(wk - np.array([0.11111111, 0.11111111, 0.11111111, 0.11111111, 0.11111111,
+       0.11111111, 0.11111111, 0.11111111, 0.11111111])).max()<1e-5
+
+    ### Monkhorst-Packing sampling
+    is_gamma_center = False
+    is_time_reversal = True
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    print(kp)
+    assert abs(kp - np.array([[ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.33333333,  0.        ],
+       [ 0.33333333, -0.33333333,  0.        ],
+       [ 0.33333333,  0.        ,  0.        ],
+       [ 0.33333333,  0.33333333,  0.        ]])).max()<1e-5
+    assert abs(wk - np.array([0.11111111, 0.22222222, 0.22222222, 0.22222222, 0.22222222])).max()<1e-5
+
+    is_time_reversal = False
+    kp , wk = kmesh_sampling_negf(meshgrid, is_gamma_center, is_time_reversal)
+    assert abs(kp - np.array([[-0.33333333, -0.33333333,  0.        ],
+       [-0.33333333,  0.        ,  0.        ],
+       [-0.33333333,  0.33333333,  0.        ],
+       [ 0.        , -0.33333333,  0.        ],
+       [ 0.        ,  0.        ,  0.        ],
+       [ 0.        ,  0.33333333,  0.        ],
+       [ 0.33333333, -0.33333333,  0.        ],
+       [ 0.33333333,  0.        ,  0.        ],
+       [ 0.33333333,  0.33333333,  0.        ]])).max()<1e-5
+    assert abs(wk - np.array([0.11111111, 0.11111111, 0.11111111, 0.11111111, 0.11111111,
+       0.11111111, 0.11111111, 0.11111111, 0.11111111])).max()<1e-5
\ No newline at end of file
diff --git a/dpnegf/tests/test_negf_negf_hamiltonian_init.py b/dpnegf/tests/test_negf_negf_hamiltonian_init.py
new file mode 100644
index 0000000..e5269e1
--- /dev/null
+++ b/dpnegf/tests/test_negf_negf_hamiltonian_init.py
@@ -0,0 +1,277 @@
+# Hamiltonian
+from dpnegf.utils.make_kpoints import kmesh_sampling
+from dpnegf.negf.device_property import DeviceProperty
+from dpnegf.negf.lead_property import LeadProperty
+from dptb.nn.build import build_model
+import json
+
+
+import numpy as np
+import torch
+import pytest
+from dpnegf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
+
+
+@pytest.fixture(scope='session', autouse=True)
+def root_directory(request):
+    """
+    :return:
+    """
+    return str(request.config.rootdir)
+
+
+def test_negf_Hamiltonian(root_directory):
+
+    model_ckpt=root_directory +'/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C.json'
+    results_path=root_directory +"/dpnegf/tests/data/test_negf/test_negf_hamiltonian/test_negf_hamiltonian_init"
+    input_path = root_directory +"/dpnegf/tests/data/test_negf/test_negf_hamiltonian/run_input.json"
+    structure=root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/chain.vasp"
+    # log_path=root_directory +"/dptb/tests/data/test_negf/test_negf_Device/test.log"
+    negf_json = json.load(open(input_path))
+    model = build_model(model_ckpt,model_options=negf_json['model_options'],common_options=negf_json['common_options'])
+
+
+    hamiltonian = NEGFHamiltonianInit(model=model,
+                                    AtomicData_options=negf_json['AtomicData_options'], 
+                                    structure=structure,
+                                    pbc_negf = negf_json['task_options']["stru_options"]['pbc'], 
+                                    stru_options = negf_json['task_options']['stru_options'],
+                                    unit = negf_json['task_options']['unit'], 
+                                    results_path=results_path,
+                                    torch_device = torch.device('cpu'),
+                                    block_tridiagonal=negf_json['task_options']['block_tridiagonal'])
+
+    # hamiltonian = NEGFHamiltonianInit(apiH=apiHrk, structase=structase, stru_options=task_options["stru_options"], results_path=results_path)
+    kpoints= kmesh_sampling(negf_json['task_options']["stru_options"]["kmesh"])
+    with torch.no_grad():
+        struct_device, struct_leads, _,_,_ = hamiltonian.initialize(kpoints=kpoints)
+
+    
+    deviceprop = DeviceProperty(hamiltonian, struct_device, results_path=results_path, efermi=negf_json['task_options']['e_fermi'])
+    deviceprop.set_leadLR(
+            lead_L=LeadProperty(
+            hamiltonian=hamiltonian, 
+            tab="lead_L", 
+            structure=struct_leads["lead_L"], 
+            results_path=results_path,
+            e_T=negf_json['task_options']['ele_T'],
+            efermi=negf_json['task_options']['e_fermi'], 
+            voltage=negf_json['task_options']["stru_options"]["lead_L"]["voltage"]
+        ),
+            lead_R=LeadProperty(
+            hamiltonian=hamiltonian, 
+            tab="lead_R", 
+            structure=struct_leads["lead_R"], 
+            results_path=results_path,
+            e_T=negf_json['task_options']['ele_T'],
+            efermi=negf_json['task_options']['e_fermi'], 
+            voltage=negf_json['task_options']["stru_options"]["lead_R"]["voltage"]
+        )
+    )
+
+    leads = ["lead_L", "lead_R"]
+    e=0  
+    for ll in leads:
+        getattr(deviceprop, ll).self_energy(
+            energy=e, 
+            kpoint=kpoints[0], 
+            eta_lead=negf_json['task_options']["eta_lead"],
+            method=negf_json['task_options']["sgf_solver"],
+            save=False
+            )
+    print("lead_L self energy:",deviceprop.lead_L.se)
+    print("lead_R self energy:",deviceprop.lead_R.se)
+
+    lead_L_se_standard = torch.tensor([[1.8103e-08-0.6096j]], dtype=torch.complex128)
+    assert abs(deviceprop.lead_L.se-lead_L_se_standard).max()<1e-5
+    lead_R_se_standard = torch.tensor([[1.8103e-08-0.6096j]], dtype=torch.complex128)
+    assert abs(deviceprop.lead_R.se-lead_R_se_standard).max()<1e-5
+
+    #check device's Hamiltonian
+    assert all(struct_device.symbols=="C4")
+    assert all(struct_device.pbc)==False
+    assert np.diag(np.array(struct_device.cell==[10.0, 10.0, 19.2])).all()
+    
+    #check lead_L's Hamiltonian
+ 
+    assert all(struct_leads["lead_L"].symbols=="C4")
+    assert struct_leads["lead_L"].pbc[0]==False
+    assert struct_leads["lead_L"].pbc[1]==False
+    assert struct_leads["lead_L"].pbc[2]==True
+    assert np.diag(np.array(struct_leads["lead_L"].cell==[10.0, 10.0, -6.4])).all()
+    
+    #check lead_R's Hamiltonian
+ 
+    assert all(struct_leads["lead_R"].symbols=="C4")
+    assert struct_leads["lead_R"].pbc[0]==False
+    assert struct_leads["lead_R"].pbc[1]==False
+    assert struct_leads["lead_R"].pbc[2]==True
+    assert np.diag(np.array(struct_leads["lead_R"].cell==[10.0, 10.0, 6.4])).all()
+
+
+    #check hs_device
+    h_device = hamiltonian.get_hs_device(kpoint=np.array([0,0,0]),V=0,block_tridiagonal=False)[0][0]
+    print(hamiltonian.get_hs_device(kpoint=np.array([0,0,0]),V=0,block_tridiagonal=False)[0])
+    h_device_standard = torch.tensor([[-13.6386+0.j,   0.6096+0.j,   0.0000+0.j,   0.0000+0.j],
+        [  0.6096+0.j, -13.6386+0.j,   0.6096+0.j,   0.0000+0.j],
+        [  0.0000+0.j,   0.6096+0.j, -13.6386+0.j,   0.6096+0.j],
+        [  0.0000+0.j,   0.0000+0.j,   0.6096+0.j, -13.6386+0.j]],dtype=torch.complex128)
+    assert abs(h_device-h_device_standard).max()<1e-4
+
+    s_device = hamiltonian.get_hs_device(kpoint=np.array([0,0,0]),V=0,block_tridiagonal=False)[1][0]
+    print(hamiltonian.get_hs_device(kpoint=np.array([0,0,0]),V=0,block_tridiagonal=False)[1][0])
+    s_standard = torch.eye(4)
+    assert abs(s_device-s_standard).max()<1e-5
+
+
+
+    #check hs_lead
+    hl_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[0][0]
+    hl_lead_standard = torch.tensor([-13.6386+0.j,   0.6096+0.j], dtype=torch.complex128)    
+    assert abs(hl_lead-hl_lead_standard).max()<1e-4
+
+    hll_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[1][0]
+    hll_lead_standard = torch.tensor([0.0000+0.j, 0.6096+0.j], dtype=torch.complex128)
+    print(hll_lead)    
+    assert abs(hll_lead-hll_lead_standard).max()<1e-4
+
+    hDL_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[2]
+    hDL_lead_standard = torch.tensor([[0.0000+0.j, 0.6096+0.j],
+        [0.0000+0.j, 0.0000+0.j],
+        [0.0000+0.j, 0.0000+0.j],
+        [0.0000+0.j, 0.0000+0.j]], dtype=torch.complex128)   
+    assert abs(hDL_lead-hDL_lead_standard).max()<1e-5
+
+    sl_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[3]
+    sl_lead_standard = torch.eye(2)   
+    assert abs(sl_lead-sl_lead_standard).max()<1e-5
+
+    sll_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[4]
+    sll_lead_standard = torch.zeros(2)    
+    assert abs(sll_lead-sll_lead_standard).max()<1e-5
+
+    sDL_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[5]
+    sDL_lead_standard = torch.zeros([4,2])    
+    assert abs(sDL_lead-sDL_lead_standard).max()<1e-5   
+
+
+    # check device norbs
+    na = len(hamiltonian.device_norbs)
+    device_norbs_standard=[1,1,1,1]
+    assert na == 4
+    assert hamiltonian.device_norbs==device_norbs_standard
+
+   
+
+
+
+
+# def _test_negf_Hamiltonian(root_directory):
+
+#     model_ckpt=root_directory +'/dptb/tests/data/test_negf/test_negf_run/nnsk_C.json'
+#     jdata = root_directory +"/dptb/tests/data/test_negf/test_negf_hamiltonian/run_input.json"
+#     structure=root_directory +"/dptb/tests/data/test_negf/test_negf_run/chain.vasp"
+#     log_path=root_directory +"/dptb/tests/data/test_negf/test_negf_hamiltonian/test.log"
+
+#     apihost = NNSKHost(checkpoint=model_ckpt, config=jdata)
+#     apihost.register_plugin(InitSKModel())
+#     apihost.build()
+#     apiHrk = NN2HRK(apihost=apihost, mode='nnsk')
+#     jdata = j_loader(jdata)
+#     task_options = j_must_have(jdata, "task_options")
+
+#     run_opt = {
+#             "run_sk": True,
+#             "init_model":model_ckpt,
+#             "results_path":root_directory +"/dptb/tests/data/test_negf/test_negf_hamiltonian/",
+#             "structure":structure,
+#             "log_path": log_path,
+#             "log_level": 5,
+#             "use_correction":False
+#         }
+
+
+#     structase=read(run_opt['structure'])
+#     results_path=run_opt.get('results_path')
+#     kpoints=np.array([[0,0,0]])
+
+#     hamiltonian = NEGFHamiltonianInit(apiH=apiHrk, structase=structase, stru_options=task_options["stru_options"], results_path=results_path)
+#     with torch.no_grad():
+#         struct_device, struct_leads = hamiltonian.initialize(kpoints=kpoints)
+    
+#     #check device's Hamiltonian
+#     assert all(struct_device.symbols=="C4")
+#     assert all(struct_device.pbc)==False
+#     assert np.diag(np.array(struct_device.cell==[10.0, 10.0, 19.2])).all()
+    
+#     #check lead_L's Hamiltonian
+ 
+#     assert all(struct_leads["lead_L"].symbols=="C4")
+#     assert struct_leads["lead_L"].pbc[0]==False
+#     assert struct_leads["lead_L"].pbc[1]==False
+#     assert struct_leads["lead_L"].pbc[2]==True
+#     assert np.diag(np.array(struct_leads["lead_L"].cell==[10.0, 10.0, -6.4])).all()
+    
+#     #check lead_R's Hamiltonian
+ 
+#     assert all(struct_leads["lead_R"].symbols=="C4")
+#     assert struct_leads["lead_R"].pbc[0]==False
+#     assert struct_leads["lead_R"].pbc[1]==False
+#     assert struct_leads["lead_R"].pbc[2]==True
+#     assert np.diag(np.array(struct_leads["lead_R"].cell==[10.0, 10.0, 6.4])).all()
+
+
+#     #check hs_device
+#     h_device = hamiltonian.get_hs_device(kpoint=np.array([0,0,0]),V=0,block_tridiagonal=False)[0][0]
+#     print(hamiltonian.get_hs_device(kpoint=np.array([0,0,0]),V=0,block_tridiagonal=False)[0])
+#     h_device_standard = torch.tensor([[-13.6386+0.j,   0.6096+0.j,   0.0000+0.j,   0.0000+0.j],
+#         [  0.6096+0.j, -13.6386+0.j,   0.6096+0.j,   0.0000+0.j],
+#         [  0.0000+0.j,   0.6096+0.j, -13.6386+0.j,   0.6096+0.j],
+#         [  0.0000+0.j,   0.0000+0.j,   0.6096+0.j, -13.6386+0.j]],dtype=torch.complex128)
+#     assert abs(h_device-h_device_standard).max()<1e-4
+
+#     s_device = hamiltonian.get_hs_device(kpoint=np.array([0,0,0]),V=0,block_tridiagonal=False)[1][0]
+#     print(hamiltonian.get_hs_device(kpoint=np.array([0,0,0]),V=0,block_tridiagonal=False)[1][0])
+#     s_standard = torch.eye(4)
+#     assert abs(s_device-s_standard).max()<1e-5
+
+
+
+#     #check hs_lead
+#     hl_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[0][0]
+#     hl_lead_standard = torch.tensor([-13.6386+0.j,   0.6096+0.j], dtype=torch.complex128)    
+#     assert abs(hl_lead-hl_lead_standard).max()<1e-4
+
+#     hll_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[1][0]
+#     hll_lead_standard = torch.tensor([0.0000+0.j, 0.6096+0.j], dtype=torch.complex128)
+#     print(hll_lead)    
+#     assert abs(hll_lead-hll_lead_standard).max()<1e-4
+
+#     hDL_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[2]
+#     hDL_lead_standard = torch.tensor([[0.0000+0.j, 0.6096+0.j],
+#         [0.0000+0.j, 0.0000+0.j],
+#         [0.0000+0.j, 0.0000+0.j],
+#         [0.0000+0.j, 0.0000+0.j]], dtype=torch.complex128)   
+#     assert abs(hDL_lead-hDL_lead_standard).max()<1e-5
+
+#     sl_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[3]
+#     sl_lead_standard = torch.eye(2)   
+#     assert abs(sl_lead-sl_lead_standard).max()<1e-5
+
+#     sll_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[4]
+#     sll_lead_standard = torch.zeros(2)    
+#     assert abs(sll_lead-sll_lead_standard).max()<1e-5
+
+#     sDL_lead = hamiltonian.get_hs_lead(kpoint=np.array([0,0,0]),tab="lead_L",v=0)[5]
+#     sDL_lead_standard = torch.zeros([4,2])    
+#     assert abs(sDL_lead-sDL_lead_standard).max()<1e-5   
+
+
+#     # check device norbs
+#     na = len(hamiltonian.device_norbs)
+#     device_norbs_standard=[1,1,1,1]
+#     assert na == 4
+#     assert hamiltonian.device_norbs==device_norbs_standard
+
+   
+
diff --git a/dpnegf/tests/test_negf_run.py b/dpnegf/tests/test_negf_run.py
new file mode 100644
index 0000000..4f98c7b
--- /dev/null
+++ b/dpnegf/tests/test_negf_run.py
@@ -0,0 +1,170 @@
+from dpnegf.entrypoints.run import run
+import pytest
+import torch
+import numpy as np
+import os
+
+
+@pytest.fixture(scope='session', autouse=True)
+def root_directory(request):
+    """
+    :return:
+    """
+    return str(request.config.rootdir)
+
+# NEGF calculaion in 1D carbon chain with zero-bias transmission 1 G0
+
+def test_negf_run_chain(root_directory):
+    INPUT_file =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json" 
+    output =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/out_negf_chain"  
+    checkfile =  root_directory +'/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_new.json'
+    structure =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/chain.vasp" 
+
+    run(INPUT=INPUT_file,init_model=checkfile,structure=structure,output=output,\
+    log_level=5,log_path=output+"/output.log")
+
+    
+    negf_out_path = output+"/results/negf.out.pth"
+    assert os.path.exists(negf_out_path), "NEGF calculation output file not found"
+    negf_results = torch.load(negf_out_path)
+    trans = negf_results['T_avg']
+    assert(abs(trans[int(len(trans)/2)]-1)<1e-5)  #compare with calculated transmission at efermi
+
+
+    if os.path.exists(output+"/results"):
+        os.system("rm -r "+output+"/results")
+
+
+
+def test_negf_run_orth(root_directory):
+    INPUT_file =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene_new.json" 
+    output =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene"  
+    checkfile =  root_directory +'/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_new.json'
+    structure =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/graphene.xyz" 
+
+    run(INPUT=INPUT_file,init_model=checkfile,output=output,run_sk=True,structure=structure,\
+    log_level=5,log_path=output+"/test.log",use_correction=False)
+
+
+    negf_out_path = output+"/results/negf.out.pth"
+    assert os.path.exists(negf_out_path), "NEGF calculation output file not found"
+    negf_results = torch.load(negf_out_path)
+    
+    k_standard = np.array([[0. , 0. , 0.], [0. , 0.33333333, 0.]])
+    k = negf_results['k']
+    assert(abs(k-k_standard).max()<1e-5)  #compare with calculated kpoints
+      
+    wk_standard = np.array([0.3333333333333333, 0.6666666666666666])
+    wk = np.array(negf_results['wk'])
+    assert abs(wk-wk_standard).max()<1e-5 #compare with calculated weight
+
+
+    T_k0 = negf_results['T_k'][str(negf_results['k'][0])]
+    T_k0_standard = [2.2307e-18, 7.0694e-18, 2.4631e-17, 9.6304e-17, 4.3490e-16, 2.3676e-15,
+        1.6641e-14, 1.7068e-13, 3.3234e-12, 2.8054e-10, 9.9964e-01, 9.9985e-01,
+        9.9989e-01, 9.9991e-01, 9.9991e-01, 9.9992e-01, 9.9992e-01, 9.9992e-01,
+        9.9991e-01, 9.9987e-01, 4.0658e-08, 5.7304e-10, 8.4808e-11, 2.9762e-11,
+        1.8432e-11, 1.8431e-11, 2.9762e-11, 8.4805e-11, 5.7300e-10, 4.0650e-08,
+        9.9987e-01, 9.9991e-01, 9.9992e-01, 9.9992e-01, 9.9992e-01, 9.9991e-01,
+        9.9991e-01, 9.9989e-01, 9.9985e-01, 9.9964e-01, 2.8058e-10, 3.3236e-12,
+        1.7069e-13, 1.6642e-14, 2.3677e-15, 4.3491e-16, 9.6308e-17, 2.4632e-17,
+        7.0696e-18, 2.2308e-18]
+    T_k0_standard = torch.tensor(T_k0_standard)
+    assert abs(T_k0-T_k0_standard).max()<1e-4
+
+    T_k1 = negf_results['T_k'][str(negf_results['k'][1])]
+    T_k1_standard = [3.4867e-19, 1.0166e-18, 3.2013e-18, 1.1041e-17, 4.2506e-17, 1.8749e-16,
+        9.8430e-16, 6.5273e-15, 6.0546e-14, 9.6364e-13, 4.5495e-11, 3.3900e-07,
+        9.9983e-01, 9.9988e-01, 9.9990e-01, 1.9996e+00, 1.9998e+00, 1.9998e+00,
+        1.9998e+00, 1.9998e+00, 1.9998e+00, 9.9992e-01, 9.9992e-01, 9.9992e-01,
+        9.9992e-01, 9.9992e-01, 9.9992e-01, 9.9992e-01, 9.9992e-01, 1.9998e+00,
+        1.9998e+00, 1.9998e+00, 1.9998e+00, 1.9998e+00, 1.9996e+00, 9.9990e-01,
+        9.9988e-01, 9.9983e-01, 3.3921e-07, 4.5502e-11, 9.6372e-13, 6.0549e-14,
+        6.5277e-15, 9.8436e-16, 1.8749e-16, 4.2507e-17, 1.1042e-17, 3.2014e-18,
+        1.0167e-18, 3.4868e-19]
+    T_k1_standard = torch.tensor(T_k1_standard)
+    assert  abs(T_k1-T_k1_standard).max()<1e-4
+
+
+    T_avg = negf_results['T_avg']
+    T_avg_standard = [9.7602e-19, 3.0342e-18, 1.0345e-17, 3.9462e-17, 1.7330e-16, 9.1420e-16,
+        6.2031e-15, 6.1245e-14, 1.1482e-12, 9.4156e-11, 3.3321e-01, 3.3328e-01,
+        9.9985e-01, 9.9989e-01, 9.9990e-01, 1.6664e+00, 1.6665e+00, 1.6665e+00,
+        1.6665e+00, 1.6665e+00, 1.3332e+00, 6.6661e-01, 6.6661e-01, 6.6661e-01,
+        6.6661e-01, 6.6661e-01, 6.6661e-01, 6.6661e-01, 6.6661e-01, 1.3332e+00,
+        1.6665e+00, 1.6665e+00, 1.6665e+00, 1.6665e+00, 1.6664e+00, 9.9990e-01,
+        9.9989e-01, 9.9985e-01, 3.3328e-01, 3.3321e-01, 9.4171e-11, 1.1482e-12,
+        6.1249e-14, 6.2035e-15, 9.1425e-16, 1.7331e-16, 3.9464e-17, 1.0345e-17,
+        3.0343e-18, 9.7605e-19]
+    T_avg_standard = torch.tensor(T_avg_standard)
+    assert  abs(T_avg-T_avg_standard).max()<1e-4  #compare with calculated transmission at efermi
+
+    if os.path.exists(output+"/results"):
+        os.system("rm -r "+output+"/results")
+
+
+
+def test_negf_run_S(root_directory):
+    INPUT_file =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene_new.json" 
+    output =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene"  
+    checkfile =  root_directory +'/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_newS.json'
+    structure =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/graphene.xyz" 
+
+    run(INPUT=INPUT_file,init_model=checkfile,output=output,run_sk=True,structure=structure,\
+    log_level=5,log_path=output+"/test.log",use_correction=False)
+
+    negf_out_path = output+"/results/negf.out.pth"
+    assert os.path.exists(negf_out_path), "NEGF calculation output file not found"
+    negf_results = torch.load(negf_out_path)
+
+    k_standard = np.array([[0. , 0. , 0.], [0. , 0.33333333, 0.]])
+    k = negf_results['k']
+    assert(abs(k-k_standard).max()<1e-5)  #compare with calculated kpoints
+      
+    wk_standard = np.array([0.3333333333333333, 0.6666666666666666])
+    wk = np.array(negf_results['wk'])
+    assert abs(wk-wk_standard).max()<1e-5 #compare with calculated weight
+
+
+    T_k0 = negf_results['T_k'][str(negf_results['k'][0])]
+    T_k0_standard = [5.3533e-16, 1.7583e-15, 6.6429e-15, 3.0157e-14, 1.7671e-13, 1.5268e-12,
+        2.6431e-11, 2.9100e-09, 9.9979e-01, 9.9988e-01, 9.9991e-01, 9.9992e-01,
+        9.9993e-01, 9.9993e-01, 9.9993e-01, 9.9993e-01, 9.9993e-01, 9.9993e-01,
+        9.9991e-01, 9.9986e-01, 1.1692e-08, 3.8933e-10, 7.0993e-11, 2.7496e-11,
+        1.8139e-11, 1.9213e-11, 3.3478e-11, 1.0907e-10, 1.0088e-09, 6.0637e-07,
+        9.9988e-01, 9.9990e-01, 9.9991e-01, 9.9990e-01, 9.9990e-01, 9.9988e-01,
+        9.9985e-01, 9.9969e-01, 3.1857e-10, 1.5012e-12, 4.5775e-14, 2.9746e-15,
+        2.9804e-16, 3.9799e-17, 6.5382e-18, 1.2576e-18, 2.7403e-19, 6.6083e-20,
+        1.7337e-20, 4.8842e-21]
+    T_k0_standard = torch.tensor(T_k0_standard)
+    assert abs(T_k0-T_k0_standard).max()<1e-4
+
+    T_k1 = negf_results['T_k'][str(negf_results['k'][1])]
+    T_k1_standard = [4.9559e-17, 1.3964e-16, 4.3406e-16, 1.5227e-15, 6.2301e-15, 3.1271e-14,
+        2.0969e-13, 2.2174e-12, 5.6095e-11, 2.8496e-08, 9.9983e-01, 9.9989e-01,
+        9.9991e-01, 9.9992e-01, 1.9996e+00, 1.9998e+00, 1.9998e+00, 1.9998e+00,
+        1.9998e+00, 1.9998e+00, 1.9998e+00, 9.9992e-01, 9.9992e-01, 9.9992e-01,
+        9.9992e-01, 9.9992e-01, 9.9992e-01, 9.9991e-01, 1.9995e+00, 1.9998e+00,
+        1.9998e+00, 1.9998e+00, 1.9997e+00, 1.9996e+00, 9.9988e-01, 9.9984e-01,
+        9.9968e-01, 2.5744e-10, 1.2387e-12, 3.7395e-14, 2.3958e-15, 2.3651e-16,
+        3.1124e-17, 5.0410e-18, 9.5653e-19, 2.0574e-19, 4.9005e-20, 1.2706e-20,
+        3.5398e-21, 1.0487e-21]
+    T_k1_standard = torch.tensor(T_k1_standard)
+    assert  abs(T_k1-T_k1_standard).max()<1e-4
+
+
+    T_avg = negf_results['T_avg']
+    T_avg_standard = [2.1148e-16, 6.7919e-16, 2.5037e-15, 1.1067e-14, 6.3055e-14, 5.2978e-13,
+        8.9502e-12, 9.7148e-10, 3.3326e-01, 3.3329e-01, 9.9985e-01, 9.9990e-01,
+        9.9991e-01, 9.9992e-01, 1.6664e+00, 1.6665e+00, 1.6665e+00, 1.6665e+00,
+        1.6665e+00, 1.6665e+00, 1.3332e+00, 6.6661e-01, 6.6661e-01, 6.6661e-01,
+        6.6661e-01, 6.6661e-01, 6.6661e-01, 6.6661e-01, 1.3330e+00, 1.3332e+00,
+        1.6665e+00, 1.6665e+00, 1.6665e+00, 1.6664e+00, 9.9988e-01, 9.9985e-01,
+        9.9974e-01, 3.3323e-01, 1.0702e-10, 5.2534e-13, 1.6856e-14, 1.1492e-15,
+        1.2010e-16, 1.6627e-17, 2.8171e-18, 5.5635e-19, 1.2401e-19, 3.0499e-20,
+        8.1390e-21, 2.3272e-21]
+    T_avg_standard = torch.tensor(T_avg_standard)
+    assert  abs(T_avg-T_avg_standard).max()<1e-4  #compare with calculated transmission at efermi
+
+    # if os.path.exists(output+"/results"):
+    #     os.system("rm -r "+output+"/results")
\ No newline at end of file
diff --git a/dpnegf/utils/__init__.py b/dpnegf/utils/__init__.py
new file mode 100644
index 0000000..ba418a0
--- /dev/null
+++ b/dpnegf/utils/__init__.py
@@ -0,0 +1 @@
+from .loggers import set_log_handles
diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
new file mode 100644
index 0000000..a6f32a9
--- /dev/null
+++ b/dpnegf/utils/argcheck.py
@@ -0,0 +1,1874 @@
+from typing import List, Callable, Dict, Any, Union
+from dargs import dargs, Argument, Variant, ArgumentEncoder
+import logging
+from numbers import Number
+
+
+log = logging.getLogger(__name__)
+
+nnsk_model_config_checklist = ['unit','skfunction-skformula']
+nnsk_model_config_updatelist = ['sknetwork-sk_hop_nhidden', 'sknetwork-sk_onsite_nhidden', 'sknetwork-sk_soc_nhidden']
+dptb_model_config_checklist = ['dptb-if_batch_normalized', 'dptb-hopping_net_type', 'dptb-soc_net_type', 'dptb-env_net_type', 'dptb-onsite_net_type', 'dptb-hopping_net_activation', 'dptb-soc_net_activation', 'dptb-env_net_activation', 'dptb-onsite_net_activation', 
+                        'dptb-hopping_net_neuron', 'dptb-env_net_neuron', 'dptb-soc_net_neuron', 'dptb-onsite_net_neuron', 'dptb-axis_neuron', 'skfunction-skformula', 'sknetwork-sk_onsite_nhidden', 
+                        'sknetwork-sk_hop_nhidden']
+
+
+def gen_doc_train(*, make_anchor=True, make_link=True, **kwargs):
+    if make_link:
+        make_anchor = True
+    co = common_options()
+    tr = train_options()
+    da = data_options()
+    mo = model_options()
+    ptr = []
+    ptr.append(co.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs))
+    ptr.append(tr.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs))
+    ptr.append(da.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs))
+    ptr.append(mo.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs))
+
+    key_words = []
+    for ii in "\n\n".join(ptr).split("\n"):
+        if "argument path" in ii:
+            key_words.append(ii.split(":")[1].replace("`", "").strip())
+    # ptr.insert(0, make_index(key_words))
+
+    return "\n\n".join(ptr)
+
+
+def gen_doc_run(*, make_anchor=True, make_link=True, **kwargs):
+    if make_link:
+        make_anchor = True
+    rop = run_options()
+
+    ptr = []
+    ptr.append(rop.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs))
+
+    key_words = []
+    for ii in "\n\n".join(ptr).split("\n"):
+        if "argument path" in ii:
+            key_words.append(ii.split(":")[1].replace("`", "").strip())
+    # ptr.insert(0, make_index(key_words))
+
+    return "\n\n".join(ptr)
+
+
+def gen_doc_setinfo(*, make_anchor=True, make_link=True, **kwargs):
+    if make_link:
+        make_anchor = True
+    sio = set_info_options()
+    ptr = []
+    ptr.append(sio.gen_doc(make_anchor=make_anchor, make_link=make_link, **kwargs))
+
+    key_words = []
+    for ii in "\n\n".join(ptr).split("\n"):
+        if "argument path" in ii:
+            key_words.append(ii.split(":")[1].replace("`", "").strip())
+    # ptr.insert(0, make_index(key_words))
+
+    return "\n\n".join(ptr)
+
+
+def common_options():
+    doc_device = "The device to run the calculation, choose among `cpu` and `cuda[:int]`, Default: `cpu`"
+    doc_dtype = """The digital number's precison, choose among: 
+                    Default: `float32`
+                        - `float32`: indicating torch.float32
+                        - `float64`: indicating torch.float64
+                """
+    
+    doc_seed = "The random seed used to initialize the parameters and determine the shuffling order of datasets. Default: `3982377700`"
+    doc_basis = "The atomic orbitals used to construct the basis. e.p. {'A':['2s','2p','s*'],'B':'[3s','3p']}"
+    doc_overlap = "Whether to calculate the overlap matrix. Default: False"
+
+    args = [
+        Argument("basis", dict, optional=False, doc=doc_basis),
+        Argument("overlap", bool, optional=True, default=False, doc=doc_overlap),
+        Argument("device", str, optional = True, default="cpu", doc = doc_device),
+        Argument("dtype", str, optional = True, default="float32", doc = doc_dtype),
+        Argument("seed", int, optional=True, default=3982377700, doc=doc_seed),
+    ]
+
+    doc_common_options = ""
+
+    return Argument("common_options", dict, optional=False, sub_fields=args, sub_variants=[], doc=doc_common_options)
+
+
+def train_options():
+    doc_num_epoch = "Total number of training epochs. It is worth noted, if the model is reloaded with `-r` or `--restart` option, epoch which have been trained will counted from the time that the checkpoint is saved."
+    doc_save_freq = "Frequency, or every how many iteration to saved the current model into checkpoints, The name of checkpoint is formulated as `latest|best_dptb|nnsk_b<bond_cutoff>_c<sk_cutoff>_w<sk_decay_w>`. Default: `10`"
+    doc_validation_freq = "Frequency or every how many iteration to do model validation on validation datasets. Default: `10`"
+    doc_display_freq = "Frequency, or every how many iteration to display the training log to screem. Default: `1`"
+    doc_use_tensorboard = "Set true to use tensorboard. It will record iteration error once every `25` iterations, epoch error once per epoch. " \
+                          "There are tree types of error will be recorded. `train_loss_iter` is iteration loss, `train_loss_last` is the error of the last iteration in an epoch, `train_loss_mean` is the mean error of all iterations in an epoch." \
+                          "Learning rates are tracked as well. A folder named `tensorboard_logs` will be created in the working directory. Use `tensorboard --logdir=tensorboard_logs` to view the logs." \
+                          "Default: `False`"
+    update_lr_per_step_flag = "Set true to update learning rate per-step. By default, it's false."
+
+    doc_optimizer = "\
+        The optimizer setting for selecting the gradient optimizer of model training. Optimizer supported includes `Adam`, `SGD` and `LBFGS` \n\n\
+        For more information about these optmization algorithm, we refer to:\n\n\
+        - `Adam`: [Adam: A Method for Stochastic Optimization.](https://arxiv.org/abs/1412.6980)\n\n\
+        - `SGD`: [Stochastic Gradient Descent.](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html)\n\n\
+        - `LBFGS`: [On the limited memory BFGS method for large scale optimization.](http://users.iems.northwestern.edu/~nocedal/PDFfiles/limited-memory.pdf) \n\n\
+    "
+    doc_lr_scheduler = "The learning rate scheduler tools settings, the lr scheduler is used to scales down the learning rate during the training process. Proper setting can make the training more stable and efficient. The supported lr schedular includes: `Exponential Decaying (exp)`, `Linear multiplication (linear)`, `Reduce on pleatau (rop)`, `Cyclic learning rate (cyclic)`. See more documentation on Pytorch. "
+    doc_batch_size = "The batch size used in training, Default: 1"
+    doc_ref_batch_size = "The batch size used in reference data, Default: 1"
+    doc_val_batch_size = "The batch size used in validation data, Default: 1"
+    doc_max_ckpt = "The maximum number of saved checkpoints, Default: 4"
+    
+    args = [
+        Argument("num_epoch", int, optional=False, doc=doc_num_epoch),
+        Argument("batch_size", int, optional=True, default=1, doc=doc_batch_size),
+        Argument("ref_batch_size", int, optional=True, default=1, doc=doc_ref_batch_size),
+        Argument("val_batch_size", int, optional=True, default=1, doc=doc_val_batch_size),
+        Argument("optimizer", dict, sub_fields=[], optional=True, default={}, sub_variants=[optimizer()], doc = doc_optimizer),
+        Argument("lr_scheduler", dict, sub_fields=[], optional=True, default={}, sub_variants=[lr_scheduler()], doc = doc_lr_scheduler),
+        Argument("save_freq", int, optional=True, default=10, doc=doc_save_freq),
+        Argument("validation_freq", int, optional=True, default=10, doc=doc_validation_freq),
+        Argument("display_freq", int, optional=True, default=1, doc=doc_display_freq),
+        Argument("use_tensorboard", bool, optional=True, default=False, doc=doc_use_tensorboard),
+        Argument("update_lr_per_step_flag", bool, optional=True, default=False, doc=update_lr_per_step_flag),
+        Argument("max_ckpt", int, optional=True, default=4, doc=doc_max_ckpt),
+        loss_options()
+    ]
+
+    doc_train_options = "Options that defines the training behaviour of DeePTB."
+
+    return Argument("train_options", dict, sub_fields=args, sub_variants=[], optional=True, doc=doc_train_options)
+
+def test_options():
+    doc_display_freq = "Frequency, or every how many iteration to display the training log to screem. Default: `1`"
+    doc_batch_size = "The batch size used in testing, Default: 1"
+    
+    args = [
+        Argument("batch_size", int, optional=True, default=1, doc=doc_batch_size),
+        Argument("display_freq", int, optional=True, default=1, doc=doc_display_freq),
+        loss_options()
+    ]
+
+    doc_test_options = "Options that defines the testing behaviour of DeePTB."
+
+    return Argument("test_options", dict, sub_fields=args, sub_variants=[], optional=False, doc=doc_test_options)
+
+
+def Adam():
+    doc_lr = "learning rate. Default: 1e-3"
+    doc_betas = "coefficients used for computing running averages of gradient and its square Default: (0.9, 0.999)"
+    doc_eps = "term added to the denominator to improve numerical stability, Default: 1e-8"
+    doc_weight_decay = "weight decay (L2 penalty), Default: 0"
+    doc_amsgrad = "whether to use the AMSGrad variant of this algorithm from the paper On the [Convergence of Adam and Beyond](https://openreview.net/forum?id=ryQu7f-RZ) ,Default: False"
+
+    return [
+        Argument("lr", float, optional=True, default=1e-3, doc=doc_lr),
+        Argument("betas", list, optional=True, default=[0.9, 0.999], doc=doc_betas),
+        Argument("eps", float, optional=True, default=1e-8, doc=doc_eps),
+        Argument("weight_decay", float, optional=True, default=0, doc=doc_weight_decay),
+        Argument("amsgrad", bool, optional=True, default=False, doc=doc_amsgrad)
+    ]
+
+def SGD():
+    doc_lr = "learning rate. Default: 1e-3"
+    doc_weight_decay = "weight decay (L2 penalty), Default: 0"
+    doc_momentum = "momentum factor Default: 0"
+    doc_dampening = "dampening for momentum, Default: 0"
+    doc_nesterov = "enables Nesterov momentum, Default: False"
+
+    return [
+        Argument("lr", float, optional=True, default=1e-3, doc=doc_lr),
+        Argument("momentum", float, optional=True, default=0., doc=doc_momentum),
+        Argument("weight_decay", float, optional=True, default=0., doc=doc_weight_decay),
+        Argument("dampening", float, optional=True, default=0., doc=doc_dampening),
+        Argument("nesterov", bool, optional=True, default=False, doc=doc_nesterov)
+    ]
+
+
+def RMSprop():
+    doc_lr = "learning rate. Default: 1e-2"
+    doc_alpha = "smoothing constant, Default: 0.99"
+    doc_eps = "term added to the denominator to improve numerical stability, Default: 1e-8"
+    doc_weight_decay = "weight decay (L2 penalty), Default: 0"
+    doc_momentum = "momentum factor, Default: 0"
+    doc_centered = "if True, compute the centered RMSProp, the gradient is normalized by an estimation of its variance, Default: False"
+
+    return [
+        Argument("lr", float, optional=True, default=1e-2, doc=doc_lr),
+        Argument("alpha", float, optional=True, default=0.99, doc=doc_alpha),
+        Argument("eps", float, optional=True, default=1e-8, doc=doc_eps),
+        Argument("weight_decay", float, optional=True, default=0, doc=doc_weight_decay),
+        Argument("momentum", float, optional=True, default=0, doc=doc_momentum),
+        Argument("centered", bool, optional=True, default=False, doc=doc_centered)
+    ]
+
+
+def LBFGS():
+    doc_lr = "learning rate. Default: 1"
+    doc_max_iter = "maximal number of iterations per optimization step. Default: 20"
+    doc_max_eval = "maximal number of function evaluations per optimization step. Default: None -> max_iter*1.25"
+    # doc_tolerance_grad = "termination tolerance on first order optimality (default: 1e-7)."
+    # doc_line_search_fn = "either 'strong_wolfe' or None (default: None)."
+    # doc_history_size = "update history size. Default: 100"
+    # doc_tolerance_change = "termination tolerance on function value/parameter changes (default: 1e-9)."
+
+    return [
+        Argument("lr", float, optional=True, default=1, doc=doc_lr),
+        Argument("max_iter", int, optional=True, default=20, doc=doc_max_iter),
+        Argument("max_eval", int, optional=True, default=None, doc=doc_max_eval)
+    ]
+
+def optimizer():
+    doc_type = "select type of optimizer, support type includes: `Adam`, `SGD` and `LBFGS`. Default: `Adam`"
+
+    return Variant("type", [
+            Argument("Adam", dict, Adam()),
+            Argument("SGD", dict, SGD()),
+            Argument("RMSprop", dict, RMSprop()),
+            Argument("LBFGS", dict, LBFGS()),
+        ],optional=True, default_tag="Adam", doc=doc_type)
+
+def ExponentialLR():
+    doc_gamma = "Multiplicative factor of learning rate decay."
+
+    return [
+        Argument("gamma", float, optional=True, default=0.999, doc=doc_gamma)
+    ]
+
+def LinearLR():
+    doc_start_factor = "The number we multiply learning rate in the first epoch. \
+        The multiplication factor changes towards end_factor in the following epochs. Default: 1./3."
+    doc_end_factor = "The number we multiply learning rate in the first epoch. \
+    The multiplication factor changes towards end_factor in the following epochs. Default: 1./3."
+    doc_total_iters = "The number of iterations that multiplicative factor reaches to 1. Default: 5."
+
+    return [
+        Argument("start_factor", float, optional=True, default=0.3333333, doc=doc_start_factor),
+        Argument("end_factor", float, optional=True, default=0.3333333, doc=doc_end_factor),
+        Argument("total_iters", int, optional=True, default=5, doc=doc_total_iters)
+    ]
+
+def ReduceOnPlateau():
+    doc_mode = "One of min, max. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; \
+        in max mode it will be reduced when the quantity monitored has stopped increasing. Default: 'min'."
+    doc_factor = "Factor by which the learning rate will be reduced. new_lr = lr * factor. Default: 0.1."
+    doc_patience = "Number of epochs with no improvement after which learning rate will be reduced. For example, \
+        if patience = 2, then we will ignore the first 2 epochs with no improvement, \
+        and will only decrease the LR after the 3rd epoch if the loss still hasn't improved then. Default: 10."
+    doc_threshold = "Threshold for measuring the new optimum, to only focus on significant changes. Default: 1e-4."
+    doc_threshold_mode = "One of rel, abs. In rel mode, dynamic_threshold = best * ( 1 + threshold ) in 'max' mode or \
+        best * ( 1 - threshold ) in min mode. In abs mode, \
+        dynamic_threshold = best + threshold in max mode or best - threshold in min mode. Default: 'rel'."
+    doc_cooldown = "Number of epochs to wait before resuming normal operation after lr has been reduced. Default: 0."
+    doc_min_lr = "A scalar or a list of scalars. \
+        A lower bound on the learning rate of all param groups or each group respectively. Default: 0."
+    doc_eps = "Minimal decay applied to lr. \
+        If the difference between new and old lr is smaller than eps, the update is ignored. Default: 1e-8."
+
+    return [
+        Argument("mode", str, optional=True, default="min", doc=doc_mode),
+        Argument("factor", float, optional=True, default=0.1, doc=doc_factor),
+        Argument("patience", int, optional=True, default=10, doc=doc_patience),
+        Argument("threshold", float, optional=True, default=1e-4, doc=doc_threshold),
+        Argument("threshold_mode", str, optional=True, default="rel", doc=doc_threshold_mode),
+        Argument("cooldown", int, optional=True, default=0, doc=doc_cooldown),
+        Argument("min_lr", [float, list], optional=True, default=0, doc=doc_min_lr),
+        Argument("eps", float, optional=True, default=1e-8, doc=doc_eps),
+    ]
+
+def CyclicLR():
+    doc_base_lr = "Initial learning rate which is the lower boundary in the cycle for each parameter group."
+    doc_max_lr = "Upper learning rate boundaries in the cycle for each parameter group. Functionally, it defines the cycle amplitude (max_lr - base_lr). The lr at any cycle is the sum of base_lr and some scaling of the amplitude; therefore max_lr may not actually be reached depending on scaling function."
+    doc_step_size_up = "Number of training iterations in the increasing half of a cycle. Default: 2000"
+    doc_step_size_down = "Number of training iterations in the decreasing half of a cycle. If step_size_down is None, it is set to step_size_up. Default: None"
+    doc_mode = "One of {triangular, triangular2, exp_range}. Values correspond to policies detailed above. If scale_fn is not None, this argument is ignored. Default: 'triangular'"
+    doc_gamma = "Constant in 'exp_range' scaling function: gamma**(cycle iterations) Default: 1.0"
+    doc_scale_fn = "Custom scaling policy defined by a single argument lambda function, where 0 <= scale_fn(x) <= 1 for all x >= 0. If specified, then 'mode' is ignored. Default: None"
+    doc_scale_mode = "{'cycle', 'iterations'}. Defines whether scale_fn is evaluated on cycle number or cycle iterations (training iterations since start of cycle). Default: 'cycle'"
+    doc_cycle_momentum = "If True, momentum is cycled inversely to learning rate between 'base_momentum' and 'max_momentum'. Default: True"
+    doc_base_momentum = "Lower momentum boundaries in the cycle for each parameter group. Note that momentum is cycled inversely to learning rate; at the start of a cycle, momentum is 'max_momentum' and learning rate is 'base_lr'. Default: 0.8"
+    doc_max_momentum = "Upper momentum boundaries in the cycle for each parameter group. Functionally, it defines the cycle amplitude (max_momentum - base_momentum). The momentum at any cycle is the difference of max_momentum and some scaling of the amplitude; therefore base_momentum may not actually be reached depending on scaling function. Note that momentum is cycled inversely to learning rate; at the start of a cycle, momentum is 'max_momentum' and learning rate is 'base_lr'. Default: 0.9"
+    doc_last_epoch = "The index of the last batch. This parameter is used when resuming a training job. Since step() should be invoked after each batch instead of after each epoch, this number represents the total number of batches computed, not the total number of epochs computed. When last_epoch=-1, the schedule is started from the beginning. Default: -1"
+    doc_verbose = "If True, prints a message to stdout for each update. Default: False."
+
+    return [
+        Argument("base_lr", [float, list], optional=False, doc=doc_base_lr),
+        Argument("max_lr", [float, list], optional=False, doc=doc_max_lr),
+        Argument("step_size_up", int, optional=True, default=10, doc=doc_step_size_up),
+        Argument("step_size_down", int, optional=True, default=40, doc=doc_step_size_down),
+        Argument("mode", str, optional=True, default="exp_range", doc=doc_mode),
+        Argument("gamma", float, optional=True, default=1.0, doc=doc_gamma),
+        Argument("scale_fn", object, optional=True, default=None, doc=doc_scale_fn),
+        Argument("scale_mode", str, optional=True, default="cycle", doc=doc_scale_mode),
+        Argument("cycle_momentum", bool, optional=True, default=False, doc=doc_cycle_momentum),
+        Argument("base_momentum", [float, list], optional=True, default=0.8, doc=doc_base_momentum),
+        Argument("max_momentum", [float, list], optional=True, default=0.9, doc=doc_max_momentum),
+        Argument("last_epoch", int, optional=True, default=-1, doc=doc_last_epoch),
+        Argument("verbose", [bool, str], optional=True, default="deprecated", doc=doc_verbose)
+    ]
+
+
+def CosineAnnealingLR():
+    doc_T_max = "Maximum number of iterations. Default: 100."
+    doc_eta_min = "Minimum learning rate. Default: 0."
+
+    return [
+        Argument("T_max", int, optional=True, default=100, doc=doc_T_max),
+        Argument("eta_min", float, optional=True, default=0, doc=doc_eta_min),
+    ]
+
+def lr_scheduler():
+    doc_type = "select type of lr_scheduler, support type includes `exp`, `linear`"
+
+    return Variant("type", [
+            Argument("exp", dict, ExponentialLR()),
+            Argument("linear", dict, LinearLR()),
+            Argument("rop", dict, ReduceOnPlateau(), doc="rop: reduce on plateau"),
+            Argument("cos", dict, CosineAnnealingLR(), doc="cos: cosine annealing"),
+            Argument("cyclic", dict, CyclicLR(), doc="Cyclic learning rate")
+        ],optional=True, default_tag="exp", doc=doc_type)
+
+
+def train_data_sub():
+    doc_root = "This is where the dataset stores data files."
+    doc_prefix = "The prefix of the folders under root, which will be loaded in dataset."
+    doc_ham = "Choose whether the Hamiltonian blocks (and overlap blocks, if provided) are loaded when building dataset."
+    doc_eig = "Choose whether the eigenvalues and k-points are loaded when building dataset."
+    doc_vlp = "Choose whether the overlap blocks are loaded when building dataset."
+    doc_DM = "Choose whether the density matrix is loaded when building dataset."
+    doc_separator = "the sepatator used to separate the prefix and suffix in the dataset directory. Default: '.'"
+    
+    args = [
+        Argument("type", str, optional=True, default="DefaultDataset", doc="The type of dataset."),
+        Argument("root", str, optional=False, doc=doc_root),
+        Argument("prefix", str, optional=True, default=None, doc=doc_prefix),
+        Argument("separator", str, optional=True, default='.', doc=doc_separator),
+        Argument("get_Hamiltonian", bool, optional=True, default=False, doc=doc_ham),
+        Argument("get_overlap", bool, optional=True, default=False, doc=doc_vlp),
+        Argument("get_DM", bool, optional=True, default=False, doc=doc_DM),
+        Argument("get_eigenvalues", bool, optional=True, default=False, doc=doc_eig)
+    ]
+
+    doc_train = "The dataset settings for training."
+
+    return Argument("train", dict, optional=False, sub_fields=args, sub_variants=[], doc=doc_train)
+
+def validation_data_sub():
+    doc_root = "This is where the dataset stores data files."
+    doc_prefix = "The prefix of the folders under root, which will be loaded in dataset."
+    doc_ham = "Choose whether the Hamiltonian blocks (and overlap blocks, if provided) are loaded when building dataset."
+    doc_eig = "Choose whether the eigenvalues and k-points are loaded when building dataset."
+    doc_vlp = "Choose whether the overlap blocks are loaded when building dataset."
+    doc_DM = "Choose whether the density matrix is loaded when building dataset."
+    doc_separator = "the sepatator used to separate the prefix and suffix in the dataset directory. Default: '.'"
+
+    args = [
+        Argument("type", str, optional=True, default="DefaultDataset", doc="The type of dataset."),
+        Argument("root", str, optional=False, doc=doc_root),
+        Argument("prefix", str, optional=True, default=None, doc=doc_prefix),
+        Argument("separator", str, optional=True, default='.', doc=doc_separator),
+        Argument("get_Hamiltonian", bool, optional=True, default=False, doc=doc_ham),
+        Argument("get_overlap", bool, optional=True, default=False, doc=doc_vlp),
+        Argument("get_DM", bool, optional=True, default=False, doc=doc_DM),
+        Argument("get_eigenvalues", bool, optional=True, default=False, doc=doc_eig)
+    ]
+
+    doc_validation = "The dataset settings for validation."
+
+    return Argument("validation", dict, optional=True, sub_fields=args, sub_variants=[], doc=doc_validation)
+
+def reference_data_sub():
+    doc_root = "This is where the dataset stores data files."
+    doc_prefix = "The prefix of the folders under root, which will be loaded in dataset."
+    doc_ham = "Choose whether the Hamiltonian blocks (and overlap blocks, if provided) are loaded when building dataset."
+    doc_eig = "Choose whether the eigenvalues and k-points are loaded when building dataset."
+    doc_vlp = "Choose whether the overlap blocks are loaded when building dataset."
+    doc_DM = "Choose whether the density matrix is loaded when building dataset."
+    doc_separator = "the sepatator used to separate the prefix and suffix in the dataset directory. Default: '.'"
+
+    args = [
+        Argument("type", str, optional=True, default="DefaultDataset", doc="The type of dataset."),
+        Argument("root", str, optional=False, doc=doc_root),
+        Argument("prefix", str, optional=True, default=None, doc=doc_prefix),
+        Argument("separator", str, optional=True, default='.', doc=doc_separator),
+        Argument("get_Hamiltonian", bool, optional=True, default=False, doc=doc_ham),
+        Argument("get_overlap", bool, optional=True, default=False, doc=doc_vlp),
+        Argument("get_DM", bool, optional=True, default=False, doc=doc_DM),
+        Argument("get_eigenvalues", bool, optional=True, default=False, doc=doc_eig)
+    ]
+
+    doc_reference = "The dataset settings for reference."
+    
+    return Argument("reference", dict, optional=True, sub_fields=args, sub_variants=[], doc=doc_reference)
+
+def test_data_sub():
+    doc_root = "This is where the dataset stores data files."
+    doc_prefix = "The prefix of the folders under root, which will be loaded in dataset."
+    doc_ham = "Choose whether the Hamiltonian blocks (and overlap blocks, if provided) are loaded when building dataset."
+    doc_eig = "Choose whether the eigenvalues and k-points are loaded when building dataset."
+    doc_vlp = "Choose whether the overlap blocks are loaded when building dataset."
+    doc_DM = "Choose whether the density matrix is loaded when building dataset."
+    doc_separator = "the sepatator used to separate the prefix and suffix in the dataset directory. Default: '.'"
+
+    args = [
+        Argument("type", str, optional=True, default="DefaultDataset", doc="The type of dataset."),
+        Argument("root", str, optional=False, doc=doc_root),
+        Argument("prefix", str, optional=True, default=None, doc=doc_prefix),
+        Argument("get_Hamiltonian", bool, optional=True, default=False, doc=doc_ham),
+        Argument("get_eigenvalues", bool, optional=True, default=False, doc=doc_eig),
+        Argument("get_overlap", bool, optional=True, default=False, doc=doc_vlp),
+        Argument("get_DM", bool, optional=True, default=False, doc=doc_DM),
+        Argument("separator", str, optional=True, default='.', doc=doc_separator)
+    ]
+
+    doc_test = "The dataset settings for testing."
+    
+    return Argument("test", dict, optional=False, sub_fields=args, default={}, sub_variants=[], doc=doc_test)
+
+
+def data_options():
+    args = [
+            Argument("r_max", [float,int], optional=True, default=5.0, doc="r_max"),
+            Argument("oer_max", [float,int], optional=True, default=5.0, doc="oer_max"),
+            Argument("er_max", [float,int], optional=True, default=5.0, doc="er_max"),
+            train_data_sub(),
+            validation_data_sub(),
+            reference_data_sub()
+            ]
+
+    doc_data_options = "The options for dataset settings in training."
+
+    return Argument("data_options", dict, sub_fields=args, sub_variants=[], optional=False, doc=doc_data_options)
+
+def test_data_options():
+
+    args = [
+        test_data_sub()
+    ]
+
+    doc_test_data_options = "The options for dataset settings in testing"
+
+    return Argument("data_options", dict, sub_fields=args, sub_variants=[], optional=False, doc=doc_test_data_options)
+
+
+def embedding():
+    doc_method = "The parameters to define the embedding model."
+
+    return Variant("method", [
+            Argument("se2", dict, se2()),
+            Argument("baseline", dict, baseline()),
+            Argument("deeph-e3", dict, deephe3()),
+            Argument("e3baseline_5", dict, e3baselinev5()),
+            Argument("e3baseline_6", dict, e3baselinev5()),
+            Argument("slem", dict, slem()),
+            Argument("lem", dict, slem()),
+            Argument("e3baseline_nonlocal", dict, e3baselinev5()),
+        ],optional=True, default_tag="se2", doc=doc_method)
+
+def se2():
+
+    doc_rs = "The soft cutoff where the smooth function starts."
+    doc_rc = "The hard cutoff where the smooth function value ~0.0"
+    doc_n_axis = "the out axis shape of the deepmd-se2 descriptor."
+    doc_radial_net = "network to build the descriptors."
+
+    doc_neurons = "the size of nn for descriptor"
+    doc_activation = "activation"
+    doc_if_batch_normalized = "whether to turn on the batch normalization."
+
+    radial_net = [
+        Argument("neurons", list, optional=False, doc=doc_neurons),
+        Argument("activation", str, optional=True, default="tanh", doc=doc_activation),
+        Argument("if_batch_normalized", bool, optional=True, default=False, doc=doc_if_batch_normalized),
+    ]
+
+    return [
+        Argument("rs", [float, int], optional=False, doc=doc_rs),
+        Argument("rc", [float, int], optional=False, doc=doc_rc),
+        Argument("radial_net", dict, sub_fields=radial_net, optional=False, doc=doc_radial_net),
+        Argument("n_axis", [int, None], optional=True, default=None, doc=doc_n_axis),
+    ]
+
+
+def baseline():
+
+    doc_rs = ""
+    doc_rc = ""
+    doc_n_axis = ""
+    doc_radial_embedding = ""
+
+    doc_neurons = ""
+    doc_activation = ""
+    doc_if_batch_normalized = ""
+
+    radial_embedding = [
+        Argument("neurons", list, optional=False, doc=doc_neurons),
+        Argument("activation", str, optional=True, default="tanh", doc=doc_activation),
+        Argument("if_batch_normalized", bool, optional=True, default=False, doc=doc_if_batch_normalized),
+    ]
+
+    return [
+        Argument("p", [float, int], optional=False, doc=doc_rs),
+        Argument("rc", [float, int], optional=False, doc=doc_rc),
+        Argument("n_basis", int, optional=False, doc=doc_rc),
+        Argument("n_radial", int, optional=False, doc=doc_rc),
+        Argument("n_sqrt_radial", int, optional=False, doc=doc_rc),
+        Argument("n_layer", int, optional=False, doc=doc_rc),
+        Argument("radial_net", dict, sub_fields=radial_embedding, optional=False, doc=doc_radial_embedding),
+        Argument("hidden_net", dict, sub_fields=radial_embedding, optional=False, doc=doc_radial_embedding),
+        Argument("n_axis", [int, None], optional=True, default=None, doc=doc_n_axis),
+    ]
+
+def deephe3():
+    doc_irreps_embed = ""
+    doc_irreps_mid = ""
+    doc_lmax = ""
+    doc_n_basis = ""
+    doc_rc = ""
+    doc_n_layer = ""
+
+    return [
+            Argument("irreps_embed", str, optional=True, default="64x0e", doc=doc_irreps_embed),
+            Argument("irreps_mid", str, optional=True, default="64x0e+32x1o+16x2e+8x3o+8x4e+4x5o", doc=doc_irreps_mid),
+            Argument("lmax", int, optional=True, default=3, doc=doc_lmax),
+            Argument("n_basis", int, optional=True, default=128, doc=doc_n_basis),
+            Argument("rc", float, optional=False, doc=doc_rc),
+            Argument("n_layer", int, optional=True, default=3, doc=doc_n_layer),
+        ]
+
+def e3baseline():
+    doc_irreps_hidden = ""
+    doc_lmax = ""
+    doc_avg_num_neighbors = ""
+    doc_n_radial_basis = ""
+    doc_r_max = ""
+    doc_n_layers = ""
+    doc_env_embed_multiplicity = ""
+    doc_linear_after_env_embed = ""
+    doc_latent_resnet_update_ratios_learnable = ""
+    doc_latent_kwargs = ""
+
+    return [
+            Argument("irreps_hidden", str, optional=True, default="64x0e+32x1o+16x2e+8x3o+8x4e+4x5o", doc=doc_irreps_hidden),
+            Argument("lmax", int, optional=True, default=3, doc=doc_lmax),
+            Argument("avg_num_neighbors", [int, float], optional=True, default=50, doc=doc_avg_num_neighbors),
+            Argument("r_max", [float, int, dict], optional=False, doc=doc_r_max),
+            Argument("n_layers", int, optional=True, default=3, doc=doc_n_layers),
+            Argument("n_radial_basis", int, optional=True, default=3, doc=doc_n_radial_basis),
+            Argument("PolynomialCutoff_p", int, optional=True, default=6, doc="The order of polynomial cutoff function. Default: 6"),
+            Argument(
+                "latent_kwargs", dict,
+                optional={
+                "mlp_latent_dimensions": [128, 128, 256],
+                "mlp_nonlinearity": "silu",
+                "mlp_initialization": "uniform"
+            }, 
+            default=None, 
+            doc=doc_latent_kwargs
+            ),
+            Argument("env_embed_multiplicity", int, optional=True, default=1, doc=doc_env_embed_multiplicity),
+            Argument("linear_after_env_embed", bool, optional=True, default=False, doc=doc_linear_after_env_embed),
+            Argument("latent_resnet_update_ratios_learnable", bool, optional=True, default=False, doc=doc_latent_resnet_update_ratios_learnable)
+        ]
+
+def e3baselinev5():
+    doc_irreps_hidden = ""
+    doc_lmax = ""
+    doc_avg_num_neighbors = ""
+    doc_n_radial_basis = ""
+    doc_r_max = ""
+    doc_n_layers = ""
+    doc_env_embed_multiplicity = ""
+
+    return [
+            Argument("irreps_hidden", str, optional=False, doc=doc_irreps_hidden),
+            Argument("lmax", int, optional=False, doc=doc_lmax),
+            Argument("avg_num_neighbors", [int, float], optional=False, doc=doc_avg_num_neighbors),
+            Argument("r_max", [float, int, dict], optional=False, doc=doc_r_max),
+            Argument("n_layers", int, optional=False, doc=doc_n_layers),
+            Argument("n_radial_basis", int, optional=True, default=10, doc=doc_n_radial_basis),
+            Argument("PolynomialCutoff_p", int, optional=True, default=6, doc="The order of polynomial cutoff function. Default: 6"),
+            Argument("cutoff_type", str, optional=True, default="polynomial", doc="The type of cutoff function. Default: polynomial"),
+            Argument("env_embed_multiplicity", int, optional=True, default=1, doc=doc_env_embed_multiplicity),
+            Argument("tp_radial_emb", bool, optional=True, default=False, doc="Whether to use tensor product radial embedding."),
+            Argument("tp_radial_channels", list, optional=True, default=[128, 128], doc="The number of channels in tensor product radial embedding."),
+            Argument("latent_channels", list, optional=True, default=[128, 128], doc="The number of channels in latent embedding."),
+            Argument("latent_dim", int, optional=True, default=256, doc="The dimension of latent embedding."),
+            Argument("res_update", bool, optional=True, default=True, doc="Whether to use residual update."),
+            Argument("res_update_ratios", float, optional=True, default=0.5, doc="The ratios of residual update, should in (0,1)."),
+            Argument("res_update_ratios_learnable", bool, optional=True, default=False, doc="Whether to make the ratios of residual update learnable."),
+        ]
+
+def slem():
+    doc_irreps_hidden = ""
+    doc_avg_num_neighbors = ""
+    doc_n_radial_basis = ""
+    doc_r_max = ""
+    doc_n_layers = ""
+    doc_env_embed_multiplicity = ""
+
+    return [
+            Argument("irreps_hidden", str, optional=False, doc=doc_irreps_hidden),
+            Argument("avg_num_neighbors", [int, float], optional=False, doc=doc_avg_num_neighbors),
+            Argument("r_max", [float, int, dict], optional=False, doc=doc_r_max),
+            Argument("n_layers", int, optional=False, doc=doc_n_layers),
+            
+            Argument("n_radial_basis", int, optional=True, default=10, doc=doc_n_radial_basis),
+            Argument("PolynomialCutoff_p", int, optional=True, default=6, doc="The order of polynomial cutoff function. Default: 6"),
+            Argument("cutoff_type", str, optional=True, default="polynomial", doc="The type of cutoff function. Default: polynomial"),
+            Argument("env_embed_multiplicity", int, optional=True, default=10, doc=doc_env_embed_multiplicity),
+            Argument("tp_radial_emb", bool, optional=True, default=False, doc="Whether to use tensor product radial embedding."),
+            Argument("tp_radial_channels", list, optional=True, default=[32], doc="The number of channels in tensor product radial embedding."),
+            Argument("latent_channels", list, optional=True, default=[32], doc="The number of channels in latent embedding."),
+            Argument("latent_dim", int, optional=True, default=64, doc="The dimension of latent embedding."),
+            Argument("res_update", bool, optional=True, default=True, doc="Whether to use residual update."),
+            Argument("res_update_ratios", float, optional=True, default=0.5, doc="The ratios of residual update, should in (0,1)."),
+            Argument("res_update_ratios_learnable", bool, optional=True, default=False, doc="Whether to make the ratios of residual update learnable."),
+        ]
+
+
+def prediction():
+    doc_method = "The options to indicate the prediction model. Can be sktb or e3tb."
+    doc_nn = "neural network options for prediction model."
+
+    return Variant("method", [
+            Argument("sktb", dict, sktb_prediction(), doc=doc_nn),
+            Argument("e3tb", dict, e3tb_prediction(), doc=doc_nn),
+        ], optional=False, doc=doc_method)
+
+def sktb_prediction():
+    doc_neurons = "neurons in the neural network."
+    doc_activation = "activation function."
+    doc_if_batch_normalized = "if to turn on batch normalization"
+
+    nn = [
+        Argument("neurons", list, optional=False, doc=doc_neurons),
+        Argument("activation", str, optional=True, default="tanh", doc=doc_activation),
+        Argument("if_batch_normalized", bool, optional=True, default=False, doc=doc_if_batch_normalized),
+    ]
+
+    return nn
+
+
+def e3tb_prediction():
+    doc_scales_trainable = "whether to scale the trianing target."
+    doc_shifts_trainable = "whether to shift the training target."
+    doc_neurons = "neurons in the neural network."
+    doc_activation = "activation function."
+    doc_if_batch_normalized = "if to turn on batch normalization"
+
+    nn = [
+        Argument("scales_trainable", bool, optional=True, default=False, doc=doc_scales_trainable),
+        Argument("shifts_trainable", bool, optional=True, default=False, doc=doc_shifts_trainable),
+        Argument("neurons", list, optional=True, default=None, doc=doc_neurons),
+        Argument("activation", str, optional=True, default="tanh", doc=doc_activation),
+        Argument("if_batch_normalized", bool, optional=True, default=False, doc=doc_if_batch_normalized),
+    ]
+
+    return nn
+
+
+
+def model_options():
+
+    doc_model_options = "The parameters to define the `nnsk`,`mix` and `dptb` model."
+    doc_embedding = "The parameters to define the embedding model."
+    doc_prediction = "The parameters to define the prediction model"
+
+    return Argument("model_options", dict, sub_fields=[
+        Argument("embedding", dict, optional=True, sub_fields=[], sub_variants=[embedding()], doc=doc_embedding),
+        Argument("prediction", dict, optional=True, sub_fields=[], sub_variants=[prediction()], doc=doc_prediction),
+        nnsk(),
+        dftbsk(),
+        ], sub_variants=[], optional=True, doc=doc_model_options)
+
+def dftbsk():
+    doc_dftbsk = "The parameters to define the dftb sk model."
+
+    return Argument("dftbsk", dict, sub_fields=[
+                Argument("skdata", str, optional=False, doc="The path to the skfile or sk database."),
+                Argument("r_max", float, optional=False, doc="the cutoff values to use sk files."),
+                ], sub_variants=[], optional=True, doc=doc_dftbsk)
+
+def nnsk():
+    doc_nnsk = "The parameters to define the nnsk model."
+    doc_onsite = "The onsite options to define the onsite of nnsk model."
+    doc_hopping = "The hopping options to define the hopping of nnsk model."
+    doc_soc = """The soc options to define the soc of nnsk model,
+                Default: {} # empty dict\n
+                - {'method':'none'} : use database soc value. 
+                - {'method':uniform} : set lambda_il; assign a soc lambda value for each orbital -l on each atomtype i; l=0,1,2 for s p d."""
+    doc_freeze = """The parameters to define the freeze of nnsk model can be bool and string and list.\n
+                    Default: False\n
+                     - True: freeze all the nnsk parameters\n
+                     - False: train all the nnsk parameters\n 
+                     - 'hopping','onsite','overlap' and 'soc' to freeze the corresponding parameters.
+                     - list of the strings e.g. ['overlap','soc'] to freeze both overlap and soc parameters."""
+    doc_std = "The std value to initialize the nnsk parameters. Default: 0.01"
+
+    # overlap = Argument("overlap", bool, optional=True, default=False, doc="The parameters to define the overlap correction of nnsk model.")
+
+    return Argument("nnsk", dict, sub_fields=[
+            Argument("onsite", dict, optional=False, sub_fields=[], sub_variants=[onsite()], doc=doc_onsite), 
+            Argument("hopping", dict, optional=False, sub_fields=[], sub_variants=[hopping()], doc=doc_hopping),
+            Argument("soc", dict, optional=True, default={}, doc=doc_soc),
+            Argument("freeze", [bool,str,list], optional=True, default=False, doc=doc_freeze),
+            Argument("std", float, optional=True, default=0.01, doc=doc_std),
+            push(),
+        ], sub_variants=[], optional=True, doc=doc_nnsk)
+
+def push():
+    doc_rs_thr = "The step size for cutoff value for smooth function in the nnsk anlytical formula."
+    doc_rc_thr = "The step size for cutoff value for smooth function in the nnsk anlytical formula."
+    doc_w_thr = "The step size for decay factor w."
+    doc_ovp_thr = "The step size for overlap reduction"
+    doc_period = "the interval of iterations to modify the rs w values."
+
+    return Argument("push", [bool,dict], sub_fields=[
+        Argument("rs_thr", [int,float], optional=True, default=0., doc=doc_rs_thr),
+        Argument("rc_thr", [int,float], optional=True, default=0., doc=doc_rc_thr),
+        Argument("w_thr",  [int,float], optional=True,  default=0., doc=doc_w_thr),
+        Argument("ovp_thr", [int,float], optional=True, default=0., doc=doc_ovp_thr),
+        Argument("period", int, optional=True, default=100, doc=doc_period),
+    ], sub_variants=[], optional=True, default=False, doc="The parameters to define the push the soft cutoff of nnsk model.")
+
+def onsite():
+    doc_method = """The onsite correction mode, the onsite energy is expressed as the energy of isolated atoms plus the model correction, the correction mode are:
+                    Default: `none`: use the database onsite energy value.
+                    - `strain`: The strain mode correct the onsite matrix densly by $$H_{i,i}^{lm,l^\prime m^\prime} = \epsilon_l^0 \delta_{ll^\prime}\delta_{mm^\prime} + \sum_p \sum_{\zeta} \Big[ \mathcal{U}_{\zeta}(\hat{\br}_{ip}) \ \epsilon_{ll^\prime \zeta} \Big]_{mm^\prime}$$ which is also parameterized as a set of Slater-Koster like integrals.\n\n\
+                    - `uniform`: The correction is a energy shift respect of orbital of each atom. Which is formally written as: 
+                                $$H_{i,i}^{lm,l^\prime m^\prime} = (\epsilon_l^0+\epsilon_l^\prime) \delta_{ll^\prime}\delta_{mm^\prime}$$ Where $\epsilon_l^0$ is the isolated energy level from the DeePTB onsite database, and $\epsilon_l^\prime$ is the parameters to fit.
+                    - `NRL`: use the NRL-TB formula.
+                """
+
+    doc_rs = "The smooth cutoff `fc` for strain model. rs is where fc = 0.5"
+    doc_w = "The decay factor of `fc` for strain and nrl model."
+    doc_rc = "The smooth cutoff of `fc` for nrl model, rc is where fc ~ 0.0"
+    doc_lda = "The lambda type encoding value in nrl model. now only support elementary substance"
+
+    strain = [
+        Argument("rs", float, optional=True, default=6.0, doc=doc_rs),
+        Argument("w", float, optional=True, default=0.1, doc=doc_w),
+    ]
+
+    NRL = [
+        Argument("rs", float, optional=True, default=6.0, doc=doc_rc),
+        Argument("w", float, optional=True, default=0.1, doc=doc_w),
+        Argument("lda", float, optional=True, default=1.0, doc=doc_lda)
+    ]
+
+    return Variant("method", [
+                    Argument("strain", dict, strain),
+                    Argument("uniform", dict, []),
+                    Argument("NRL", dict, NRL),
+                    Argument("none", dict, []),
+                ],optional=False, doc=doc_method)
+
+def hopping():
+    doc_method = """The hopping formula. 
+                    -  `powerlaw`: the powerlaw formula for bond length dependence for sk integrals.
+                    -  `varTang96`: a variational formula based on Tang96 formula.
+                    -  `NRL0`: the old version of NRL formula for overlap, we set overlap and hopping share same options.
+                    -  `NRL1`: the new version of NRL formula for overlap. 
+                    """
+    doc_rs_soft = "The cut-off for smooth function fc for powerlaw and varTang96, fc(rs)=0.5"
+    doc_w = " The decay w in fc"
+    doc_rs_hard = "The cut-off for smooth function fc, fc(rs) = 0."
+
+    powerlaw = [
+        Argument("rs", float, optional=True, default=6.0, doc=doc_rs_soft),
+        Argument("w", float, optional=True, default=0.1, doc=doc_w),
+    ]
+    varTang96 = [
+        Argument("rs", float, optional=True, default=6.0, doc=doc_rs_soft),
+        Argument("w", float, optional=True, default=0.1, doc=doc_w),
+    ]
+    common_params = [
+        Argument("rs", float, optional=True, default=6.0, doc=doc_rs_hard),
+        Argument("w", float, optional=True, default=0.1, doc=doc_w),
+    ]
+
+    formulas = [
+        'poly1pow',
+        'poly2pow',
+        'poly3pow',
+        'poly4pow',
+        'poly2exp',
+        'poly3exp',
+        'poly4exp',
+        'NRL0',
+        "NRL1"]
+
+    args = [
+        Argument("powerlaw", dict, powerlaw),
+        Argument("varTang96", dict, varTang96),
+        Argument("custom", dict, [])
+    ]
+
+    for ii in formulas:
+        args.append(Argument(ii, dict, common_params))
+    
+    return Variant("method", args,optional=False, doc=doc_method)
+
+
+def loss_options():
+    doc_method = """The loss function type, defined by a string like `<fitting target>_<loss type>`, Default: `eigs_l2dsf`. supported loss functions includes:\n\n\
+                    - `eigvals`: The mse loss predicted and labeled eigenvalues and Delta eigenvalues between different k.
+                    - `hamil`: 
+                    - `hamil_abs`:
+                    - `hamil_blas`:
+                """
+    doc_train = "Loss options for training."
+    doc_validation = "Loss options for validation."
+    doc_reference = "Loss options for reference data in training."
+
+    hamil = [
+        Argument("onsite_shift", bool, optional=True, default=False, doc="Whether to use onsite shift in loss function. Default: False"),
+    ]
+
+    wt = [
+        Argument("onsite_weight", [int, float, dict], optional=True, default=1., doc="Whether to use onsite shift in loss function. Default: False"),
+        Argument("hopping_weight", [int, float, dict], optional=True, default=1., doc="Whether to use onsite shift in loss function. Default: False"),
+    ]
+
+    eigvals = [
+        Argument("diff_on", bool, optional=True, default=False, doc="Whether to use random differences in loss function. Default: False"),
+        Argument("eout_weight", float, optional=True, default=0.01, doc="The weight of eigenvalue out of range. Default: 0.01"),
+        Argument("diff_weight", float, optional=True, default=0.01, doc="The weight of eigenvalue difference. Default: 0.01"),
+        Argument("diff_valence", [dict,None], optional=True, default=None, doc="set the difference of the number of valence electrons in DFT and TB. eg {'A':6,'B':7}, Default: None, which means no difference"),
+        Argument("spin_deg", int, optional=True, default=2, doc="The spin degeneracy of band structure. Default: 2"),
+    ]
+
+    eig_ham = [
+        Argument("coeff_ham", float, optional=True, default=1., doc="The coefficient of the hamiltonian penalty. Default: 1"),
+        Argument("coeff_ovp", float, optional=True, default=1., doc="The coefficient of the hamiltonian penalty. Default: 1"),
+    ]
+
+    skints = [
+        Argument("skdata", str, optional=False, doc="The path to the skfile or sk database."),
+    ]
+
+    loss_args = Variant("method", [
+        # Argument("hamil", dict, sub_fields=hamil),
+        Argument("eigvals", dict, sub_fields=eigvals),
+        Argument("skints", dict, sub_fields=skints),
+        Argument("hamil_abs", dict, sub_fields=hamil),
+        Argument("hamil_blas", dict, sub_fields=hamil),
+        Argument("hamil_wt", dict, sub_fields=hamil+wt),
+        Argument("eig_ham", dict, sub_fields=hamil+eigvals+eig_ham),
+    ], optional=False, doc=doc_method)
+
+    
+
+    args = [
+        Argument("train", dict, optional=False, sub_fields=[], sub_variants=[loss_args], doc=doc_train),
+        Argument("validation", dict, optional=True, sub_fields=[], sub_variants=[loss_args], doc=doc_validation),
+        Argument("reference", dict, optional=True, sub_fields=[], sub_variants=[loss_args], doc=doc_reference),
+    ]
+
+    doc_loss_options = ""
+    return Argument("loss_options", dict, sub_fields=args, sub_variants=[], optional=False, doc=doc_loss_options)
+
+
+def normalize(data):
+
+    co = common_options()
+    tr = train_options()
+    da = data_options()
+    mo = model_options()
+
+    base = Argument("base", dict, [co, tr, da, mo])
+    data = base.normalize_value(data)
+    # data = base.normalize_value(data, trim_pattern="_*")
+    base.check_value(data, strict=True)
+    
+    # add check loss and use wannier:
+    
+    # if data['data_options']['use_wannier']:
+    #     if not data['loss_options']['losstype'] .startswith("block"):
+    #         log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n')
+    
+    # if data['loss_options']['losstype'] .startswith("block"):
+    #     if not data['data_options']['use_wannier']:
+    #         log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n")
+    #         raise ValueError
+
+    return data
+
+# def normalize_restart(data):
+
+#     co = common_options()
+#     da = data_options()
+
+#     base = Argument("base", dict, [co, da])
+#     data = base.normalize_value(data)
+#     # data = base.normalize_value(data, trim_pattern="_*")
+#     base.check_value(data, strict=True)
+    
+#     # add check loss and use wannier:
+    
+#     # if data['data_options']['use_wannier']:
+#     #     if not data['loss_options']['losstype'] .startswith("block"):
+#     #         log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n')
+    
+#     # if data['loss_options']['losstype'] .startswith("block"):
+#     #     if not data['data_options']['use_wannier']:
+#     #         log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n")
+#     #         raise ValueError
+
+#     return data
+
+# def normalize_init_model(data):
+
+#     co = common_options()
+#     da = data_options()
+#     tr = train_options()
+
+#     base = Argument("base", dict, [co, da, tr])
+#     data = base.normalize_value(data)
+#     # data = base.normalize_value(data, trim_pattern="_*")
+#     base.check_value(data, strict=True)
+    
+#     # add check loss and use wannier:
+    
+#     # if data['data_options']['use_wannier']:
+#     #     if not data['loss_options']['losstype'] .startswith("block"):
+#     #         log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n')
+    
+#     # if data['loss_options']['losstype'] .startswith("block"):
+#     #     if not data['data_options']['use_wannier']:
+#     #         log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n")
+#     #         raise ValueError
+
+#     return data
+
+def normalize_test(data):
+
+    co = common_options()
+    da = test_data_options()
+    to = test_options()
+    
+    base = Argument("base", dict, [co, da, to, lo])
+    data = base.normalize_value(data)
+    # data = base.normalize_value(data, trim_pattern="_*")
+    base.check_value(data, strict=True)
+
+    return data
+
+
+
+
+def tbtrans_negf():
+    doc_scf = ""
+    doc_block_tridiagonal = ""
+    doc_ele_T = ""
+    doc_unit = ""
+    doc_scf_options = ""
+    doc_stru_options = ""
+    doc_poisson_options = ""
+    doc_sgf_solver = ""
+    doc_espacing = ""
+    doc_emin = ""
+    doc_emax = ""
+    doc_e_fermi = ""
+    doc_eta_lead = ""
+    doc_eta_device = ""
+    doc_out_dos = ""
+    doc_out_tc = ""
+    doc_out_current = ""
+    doc_out_current_nscf = ""
+    doc_out_ldos = ""
+    doc_out_density = ""
+    doc_out_lcurrent = ""
+    doc_density_options = ""
+    doc_out_potential = ""
+
+    return [
+        Argument("scf", bool, optional=True, default=False, doc=doc_scf),
+        Argument("block_tridiagonal", bool, optional=True, default=False, doc=doc_block_tridiagonal),
+        Argument("ele_T", [float, int], optional=False, doc=doc_ele_T),
+        Argument("unit", str, optional=True, default="Hartree", doc=doc_unit),
+        Argument("scf_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[scf_options()], doc=doc_scf_options),
+        Argument("stru_options", dict, optional=False, sub_fields=stru_options(), doc=doc_stru_options),
+        Argument("poisson_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[poisson_options()], doc=doc_poisson_options),
+        Argument("sgf_solver", str, optional=True, default="Sancho-Rubio", doc=doc_sgf_solver),
+        Argument("espacing", [int, float], optional=False, doc=doc_espacing),
+        Argument("emin", [int, float], optional=False, doc=doc_emin),
+        Argument("emax", [int, float], optional=False, doc=doc_emax),
+        Argument("e_fermi", [int, float], optional=False, doc=doc_e_fermi),
+        Argument("density_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[density_options()], doc=doc_density_options),
+        Argument("eta_lead", [int, float], optional=True, default=1e-5, doc=doc_eta_lead),
+        Argument("eta_device", [int, float], optional=True, default=0., doc=doc_eta_device),
+        Argument("out_dos", bool, optional=True, default=False, doc=doc_out_dos),
+        Argument("out_tc", bool, optional=True, default=False, doc=doc_out_tc),
+        Argument("out_density", bool, optional=True, default=False, doc=doc_out_density),
+        Argument("out_potential", bool, optional=True, default=False, doc=doc_out_potential),
+        Argument("out_current", bool, optional=True, default=False, doc=doc_out_current),
+        Argument("out_current_nscf", bool, optional=True, default=False, doc=doc_out_current_nscf),
+        Argument("out_ldos", bool, optional=True, default=False, doc=doc_out_ldos),
+        Argument("out_lcurrent", bool, optional=True, default=False, doc=doc_out_lcurrent)
+    ]
+
+
+
+
+
+def negf():
+    doc_scf = ""
+    doc_block_tridiagonal = ""
+    doc_ele_T = ""
+    doc_unit = ""
+    doc_scf_options = ""
+    doc_stru_options = ""
+    doc_poisson_options = ""
+    doc_sgf_solver = ""
+    doc_espacing = ""
+    doc_emin = ""
+    doc_emax = ""
+    doc_e_fermi = ""
+    doc_eta_lead = ""
+    doc_eta_device = ""
+    doc_out_dos = ""
+    doc_out_tc = ""
+    doc_out_current = ""
+    doc_out_current_nscf = ""
+    doc_out_ldos = ""
+    doc_out_density = ""
+    doc_out_lcurrent = ""
+    doc_density_options = ""
+    doc_out_potential = ""
+
+    return [
+        Argument("scf", bool, optional=True, default=False, doc=doc_scf),
+        Argument("block_tridiagonal", bool, optional=True, default=False, doc=doc_block_tridiagonal),
+        Argument("ele_T", [float, int], optional=False, doc=doc_ele_T),
+        Argument("unit", str, optional=True, default="Hartree", doc=doc_unit),
+        Argument("use_saved_HS", bool, optional=True, default=False, doc="Whether to use saved Hamiltonian and overlap matrix"),
+        Argument("saved_HS_path", str, optional=True, default=None, doc="The path to the saved Hamiltonian and overlap matrix"),
+        Argument("scf_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[scf_options()], doc=doc_scf_options),
+        Argument("stru_options", dict, optional=False, sub_fields=stru_options(), doc=doc_stru_options),
+        Argument("poisson_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[poisson_options()], doc=doc_poisson_options),
+        Argument("sgf_solver", str, optional=True, default="Sancho-Rubio", doc=doc_sgf_solver),
+        Argument("self_energy_save", bool, optional=True, default=False, doc="whether to save the self energy"),
+        Argument("self_energy_save_path", str, optional=True, default=None, doc="the path to save the self energy"),
+        Argument("se_info_display", bool, optional=True, default=False, doc="whether to display the self energy information"),
+        Argument("espacing", [int, float], optional=False, doc=doc_espacing),
+        Argument("emin", [int, float], optional=False, doc=doc_emin),
+        Argument("emax", [int, float], optional=False, doc=doc_emax),
+        Argument("e_fermi", [int, float], optional=False, doc=doc_e_fermi),
+        Argument("density_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[density_options()], doc=doc_density_options),
+        Argument("eta_lead", [int, float], optional=True, default=1e-5, doc=doc_eta_lead),
+        Argument("eta_device", [int, float], optional=True, default=0., doc=doc_eta_device),
+        Argument("out_dos", bool, optional=True, default=False, doc=doc_out_dos),
+        Argument("out_tc", bool, optional=True, default=False, doc=doc_out_tc),
+        Argument("out_density", bool, optional=True, default=False, doc=doc_out_density),
+        Argument("out_potential", bool, optional=True, default=False, doc=doc_out_potential),
+        Argument("out_current", bool, optional=True, default=False, doc=doc_out_current),
+        Argument("out_current_nscf", bool, optional=True, default=False, doc=doc_out_current_nscf),
+        Argument("out_ldos", bool, optional=True, default=False, doc=doc_out_ldos),
+        Argument("out_lcurrent", bool, optional=True, default=False, doc=doc_out_lcurrent)
+    ]
+
+def stru_options():
+    doc_kmesh = ""
+    doc_pbc = ""
+    doc_device = ""
+    doc_lead_L = ""
+    doc_lead_R = ""
+    doc_gamma_center=""
+    doc_time_reversal_symmetry=""
+    return [
+        Argument("device", dict, optional=False, sub_fields=device(), doc=doc_device),
+        Argument("lead_L", dict, optional=False, sub_fields=lead(), doc=doc_lead_L),
+        Argument("lead_R", dict, optional=False, sub_fields=lead(), doc=doc_lead_R),
+        Argument("kmesh", list, optional=True, default=[1,1,1], doc=doc_kmesh),
+        Argument("pbc", list, optional=True, default=[False, False, False], doc=doc_pbc),
+        Argument("gamma_center", list, optional=True, default=True, doc=doc_gamma_center),
+        Argument("time_reversal_symmetry", list, optional=True, default=True, doc=doc_time_reversal_symmetry)
+    ]
+
+def device():
+    doc_id=""
+    doc_sort=""
+
+    return [
+        Argument("id", str, optional=False, doc=doc_id),
+        Argument("sort", bool, optional=True, default=True, doc=doc_sort)
+    ]
+
+def lead():
+    doc_id=""
+    doc_voltage=""
+    doc_useBloch=""
+    doc_bloch_factor=""
+    return [
+        Argument("id", str, optional=False, doc=doc_id),
+        Argument("voltage", [int, float], optional=False, doc=doc_voltage),
+        Argument("useBloch", bool, optional=True, default=False, doc=doc_useBloch),
+        Argument("bloch_factor", list, optional=True, default=[1,1,1], doc=doc_bloch_factor)
+    ]
+
+def scf_options():
+    doc_mode = ""
+    doc_PDIIS = ""
+
+    return Variant("mode", [
+        Argument("PDIIS", dict, PDIIS(), doc=doc_PDIIS)
+        ], optional=True, default_tag="PDIIS", doc=doc_mode)
+
+def PDIIS():
+    doc_mixing_period = ""
+    doc_step_size = ""
+    doc_n_history = ""
+    doc_abs_err = ""
+    doc_rel_err = ""
+    doc_max_iter = ""
+
+    return [
+        Argument("mixing_period", int, optional=True, default=3, doc=doc_mixing_period),
+        Argument("step_size", [int, float], optional=True, default=0.05, doc=doc_step_size),
+        Argument("n_history", int, optional=True, default=6, doc=doc_n_history),
+        Argument("abs_err", [int, float], optional=True, default=1e-6, doc=doc_abs_err),
+        Argument("rel_err", [int, float], optional=True, default=1e-4, doc=doc_rel_err),
+        Argument("max_iter", int, optional=True, default=100, doc=doc_max_iter)
+    ]
+
+def poisson_options():
+    doc_solver = ""
+    doc_fmm = ""
+    doc_pyamg= ""
+    doc_scipy= ""
+    return Variant("solver", [
+        Argument("fmm", dict, fmm(), doc=doc_fmm),
+        Argument("pyamg", dict, pyamg(), doc=doc_pyamg),
+        Argument("scipy", dict, scipy(), doc=doc_scipy)
+    ], optional=True, default_tag="fmm", doc=doc_solver)
+
+def density_options():
+    doc_method = ""
+    doc_Ozaki = ""
+    doc_Fiori = ""
+    return Variant("method", [
+        Argument("Ozaki", dict, Ozaki(), doc=doc_Ozaki),
+        Argument("Fiori", dict, Fiori(), doc=doc_Fiori)
+    ], optional=True, default_tag="Ozaki", doc=doc_method)
+
+def Ozaki():
+    doc_M_cut = ""
+    doc_R = ""
+    doc_n_gauss = ""
+    return [
+        Argument("R", [int, float], optional=True, default=1e6, doc=doc_R),
+        Argument("M_cut", int, optional=True, default=30, doc=doc_M_cut),
+        Argument("n_gauss", int, optional=True, default=10, doc=doc_n_gauss),
+    ]
+
+def Fiori():
+    doc_n_gauss = ""
+    doc_integrate_way=""
+    return [
+        Argument("integrate_way", int, optional=True, default='direct', doc=doc_integrate_way),
+        Argument("n_gauss", int, optional=True, default=100, doc=doc_n_gauss)
+    ]
+
+def fmm():
+    doc_err = ""
+
+    return [
+        Argument("err", [int, float], optional=True, default=1e-5, doc=doc_err)
+    ]
+
+def pyamg():
+    doc_err = ""
+    doc_tolerance=""
+    doc_grid=""
+    doc_gate=""
+    doc_dielectric=""
+    doc_doped=""
+    doc_max_iter=""
+    doc_mix_rate=""
+    doc_poisson_dtype="The dtype of the poisson solver"
+    return [
+        Argument("err", [int, float], optional=True, default=1e-5, doc=doc_err),
+        Argument("tolerance", [int, float], optional=True, default=1e-7, doc=doc_tolerance),
+        Argument("max_iter", int, optional=True, default=100, doc=doc_max_iter),
+        Argument("mix_rate", int, optional=True, default=0.25, doc=doc_mix_rate),
+        Argument("poisson_dtype", str, optional=True, default='float64', doc=doc_poisson_dtype),
+        Argument("grid", dict, optional=False, sub_fields=grid(), doc=doc_grid),
+        Argument("gate_top", dict, optional=False, sub_fields=gate(), doc=doc_gate),
+        Argument("gate_bottom", dict, optional=False, sub_fields=gate(), doc=doc_gate),
+        Argument("dielectric_region", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("doped_region", dict, optional=False, sub_fields=doped(), doc=doc_doped)
+    ]
+
+def scipy():
+    doc_err = ""
+    doc_tolerance=""
+    doc_grid=""
+    doc_gate=""
+    doc_dielectric=""
+    doc_doped=""
+    doc_max_iter=""
+    doc_mix_rate=""
+    doc_poisson_dtype="The dtype of the poisson solver"
+    return [
+        Argument("err", [int, float], optional=True, default=1e-5, doc=doc_err),
+        Argument("tolerance", [int, float], optional=True, default=1e-7, doc=doc_tolerance),
+        Argument("max_iter", int, optional=True, default=100, doc=doc_max_iter),
+        Argument("mix_rate", int, optional=True, default=0.25, doc=doc_mix_rate),
+        Argument("poisson_dtype", str, optional=True, default='float64', doc=doc_poisson_dtype),
+        Argument("grid", dict, optional=True, sub_fields=grid(), doc=doc_grid),
+        Argument("gate_top", dict, optional=True, sub_fields=gate(), doc=doc_gate),
+        Argument("gate_bottom", dict, optional=True, sub_fields=gate(), doc=doc_gate),
+        Argument("dielectric_region", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("doped_region1", dict, optional=True, sub_fields=doped(), doc=doc_doped),
+        Argument("doped_region2", dict, optional=True, sub_fields=doped(), doc=doc_doped)
+    ]
+
+def grid():
+    doc_xrange=""
+    doc_yrange=""
+    doc_zrange=""
+    return [
+        Argument("x_range", str, optional=False, doc=doc_xrange),
+        Argument("y_range", str, optional=False, doc=doc_yrange),
+        Argument("z_range", str, optional=False, doc=doc_zrange),
+    ]
+
+def gate():
+    doc_xrange=""
+    doc_yrange=""
+    doc_zrange=""
+    doc_voltage=""
+    return [
+        Argument("x_range", str, optional=False, doc=doc_xrange),
+        Argument("y_range", str, optional=False, doc=doc_yrange),
+        Argument("z_range", str, optional=False, doc=doc_zrange),
+        Argument("voltage", [int, float], optional=False, doc=doc_voltage)
+    ]
+
+def dielectric():
+    doc_xrange=""
+    doc_yrange=""
+    doc_zrange=""
+    doc_permittivity=""
+    return [
+        Argument("x_range", str, optional=False, doc=doc_xrange),
+        Argument("y_range", str, optional=False, doc=doc_yrange),
+        Argument("z_range", str, optional=False, doc=doc_zrange),
+        Argument("relative permittivity", [int, float], optional=False, doc=doc_permittivity)
+    ]
+
+def doped():
+    doc_xrange=""
+    doc_yrange=""
+    doc_zrange=""
+    doc_charge=""
+    return [
+        Argument("x_range", str, optional=False, doc=doc_xrange),
+        Argument("y_range", str, optional=False, doc=doc_yrange),
+        Argument("z_range", str, optional=False, doc=doc_zrange),
+        Argument("charge", [int, float], optional=False, doc=doc_charge)
+    ]
+
+def run_options():
+    doc_task = "the task to run, includes: band, dos, pdos, FS2D, FS3D, ifermi"
+    doc_structure = "the structure to run the task"
+    doc_gui = "To use the GUI or not"
+    doc_device = "The device to run the calculation, choose among `cpu` and `cuda[:int]`, Default: None. default None means to use the device seeting in the model ckpt file."
+    doc_dtype = """The digital number's precison, choose among: 
+                    Default: None,
+                        - `float32`: indicating torch.float32
+                        - `float64`: indicating torch.float64
+                    default None means to use the device seeting in the model ckpt file.
+                """
+    doc_pbc = """The periodic boundary condition, choose among: 
+                    Default: True,
+                        - True: indicating the structure is periodic
+                        - False: indicating the structure is not periodic
+                        - list of bool: indicating the structure is periodic in x,y,z direction respectively.
+                """
+ 
+    args = [
+        Argument("task_options", dict, sub_fields=[], optional=True, sub_variants=[task_options()], doc = doc_task),
+        Argument("structure", [str,None], optional=True, default=None, doc = doc_structure),
+        Argument("pbc", [None, bool, list], optional=True, doc=doc_pbc, default=None),
+        Argument("use_gui", bool, optional=True, default=False, doc = doc_gui),
+        Argument("device", [str,None], optional = True, default=None, doc = doc_device),
+        Argument("dtype", [str,None], optional = True, default=None, doc = doc_dtype),
+        AtomicData_options_sub()
+    ]
+
+    return Argument("run_op", dict, args)
+
+def normalize_run(data):
+
+    run_op = run_options()
+    data = run_op.normalize_value(data)
+    run_op.check_value(data, strict=True)
+    
+    return data
+
+def task_options():
+    doc_task = '''The string define the task DeePTB conduct, includes: 
+                    - `band`: for band structure plotting. 
+                    - `dos`: for density of states plotting.
+                    - `pdos`: for projected density of states plotting.
+                    - `FS2D`: for 2D fermi-surface plotting.
+                    - `FS3D`: for 3D fermi-surface plotting.
+                    - `write_sk`: for transcript the nnsk model to standard sk parameter table
+                    - `ifermi`: for fermi surface plotting.
+                    - `negf`: for non-equilibrium green function calculation.
+                    - `tbtrans_negf`: for non-equilibrium green function calculation with tbtrans.
+                '''
+    write_block = []
+
+    return Variant("task", [
+            Argument("band", dict, band()),
+            Argument("dos", dict, dos()),
+            Argument("pdos", dict, pdos()),
+            Argument("FS2D", dict, FS2D()),
+            Argument("FS3D", dict, FS3D()),
+            Argument("write_sk", dict, write_sk()),
+            Argument("ifermi", dict, ifermi()),
+            Argument("negf", dict, negf()),
+            Argument("tbtrans_negf", dict, tbtrans_negf()),
+            Argument("write_block", dict, write_block),
+        ],optional=False, doc=doc_task)
+
+def band():
+    doc_kline_type ="""The different type to build kpath line mode.
+                    - "abacus" : the abacus format 
+                    - "vasp" : the vasp format
+                    - "ase" : the ase format
+                    """
+    doc_kpath = "for abacus, this is list, for vasp it is a string to specifc the kpath."
+    doc_klabels = "the labels for high symmetry kpoint"
+    doc_emin="the min energy to show the band plot"
+    doc_emax="the max energy to show the band plot"
+    doc_E_fermi = "the fermi level used to plot band"
+    doc_ref_band = "the reference band structure to be ploted together with dptb bands."
+    doc_nel_atom = "the valence electron number of each type of atom."
+    
+    return [
+        Argument("kline_type", str, optional=False, doc=doc_kline_type),
+        Argument("kpath", [str,list], optional=False, doc=doc_kpath),
+        Argument("klabels", list, optional=True, default=[''], doc=doc_klabels),
+        Argument("E_fermi", [float, int, None], optional=True, doc=doc_E_fermi, default=None),
+        Argument("emin", [float, int, None], optional=True, doc=doc_emin, default=None),
+        Argument("emax", [float, int, None], optional=True, doc=doc_emax, default=None),
+        Argument("nkpoints", int, optional=True, doc=doc_emax, default=0),
+        Argument("ref_band", [str, None], optional=True, default=None, doc=doc_ref_band),
+        Argument("nel_atom", [dict,None], optional=True, default=None, doc=doc_nel_atom)
+    ]
+
+
+def dos():
+    doc_mesh_grid = ""
+    doc_gamma_center = ""
+    doc_sigma = ""
+    doc_npoints = ""
+    doc_width = ""
+    doc_E_fermi=""
+
+    return [
+        Argument("mesh_grid", list, optional=False, doc=doc_mesh_grid),
+        Argument("sigma", float, optional=False, doc=doc_sigma),
+        Argument("npoints", int, optional=False, doc=doc_npoints),
+        Argument("width", list, optional=False, doc=doc_width),
+        Argument("E_fermi", [float, int, None], optional=True, doc=doc_E_fermi, default=None),
+        Argument("gamma_center", bool, optional=True, default=False, doc=doc_gamma_center)
+    ]
+
+def pdos():
+    doc_mesh_grid = ""
+    doc_gamma_center = ""
+    doc_sigma = ""
+    doc_npoints = ""
+    doc_width = ""
+    doc_E_fermi=""
+    doc_atom_index = ""
+    doc_orbital_index = ""
+
+    return [
+        Argument("mesh_grid", list, optional=False, doc=doc_mesh_grid),
+        Argument("sigma", float, optional=False, doc=doc_sigma),
+        Argument("npoints", int, optional=False, doc=doc_npoints),
+        Argument("width", list, optional=False, doc=doc_width),
+        Argument("E_fermi", [float, int, None], optional=True, doc=doc_E_fermi, default=None),
+        Argument("atom_index", list, optional=False, doc=doc_atom_index),
+        Argument("orbital_index", list, optional=False, doc=doc_orbital_index),
+        Argument("gamma_center", bool, optional=True, default=False, doc=doc_gamma_center)
+    ]
+
+def FS2D():
+    doc_mesh_grid = ""
+    doc_E0 = ""
+    doc_sigma = ""
+    doc_intpfactor = ""
+
+    return [
+        Argument("mesh_grid", list, optional=False, doc=doc_mesh_grid),
+        Argument("sigma", float, optional=False, doc=doc_sigma),
+        Argument("E0", int, optional=False, doc=doc_E0),
+        Argument("intpfactor", int, optional=False, doc=doc_intpfactor)
+    ]
+
+def FS3D():
+    doc_mesh_grid = ""
+    doc_E0 = ""
+    doc_sigma = ""
+    doc_intpfactor = ""
+
+    return [
+        Argument("mesh_grid", list, optional=False, doc=doc_mesh_grid),
+        Argument("sigma", float, optional=False, doc=doc_sigma),
+        Argument("E0", int, optional=False, doc=doc_E0),
+        Argument("intpfactor", int, optional=False, doc=doc_intpfactor)
+    ]
+
+
+def ifermi():
+    doc_fermi = ""
+    doc_prop = ""
+    doc_mesh_grid = ""
+    doc_mu = ""
+    doc_sigma = ""
+    doc_intpfactor = ""
+    doc_wigner_seitz = ""
+    doc_nworkers = ""
+    doc_plot_type = "plot_type: Method used for plotting. Valid options are: matplotlib, plotly, mayavi, crystal_toolkit."
+    doc_use_gui=""
+    doc_plot_fs_bands = ""
+    doc_fs_plane = ""
+    doc_fs_distanc= ""
+    doc_color_properties ="""color_properties: Whether to use the properties to color the Fermi surface.
+                If the properties is a vector then the norm of the properties will be
+                used. Note, this will only take effect if the Fermi surface has
+                properties. If set to True, the viridis colormap will be used.
+                Alternative colormaps can be selected by setting ``color_properties``
+                to a matplotlib colormap name. This setting will override the ``colors``
+                option. For vector properties, the arrows are colored according to the
+                norm of the properties by default. If used in combination with the
+                ``projection_axis`` option, the color will be determined by the dot
+                product of the properties with the projection axis."""
+    doc_fs_plot_options=""
+    doc_projection_axis = """projection_axis: Projection axis that can be used to calculate the color of
+                vector properties. If None, the norm of the properties will be used,
+                otherwise the color will be determined by the dot product of the
+                properties with the projection axis. Only has an effect when used with
+                the ``vector_properties`` option."""
+
+    doc_velocity = ""
+    doc_colormap = ""
+    doc_prop_plane = ""
+    doc_prop_distance=""
+    doc_prop_plot_options=""
+    doc_hide_surface = """hide_surface: Whether to hide the Fermi surface. Only recommended in combination with the ``vector_properties`` option."""
+    doc_hide_labels ="""hide_labels: Whether to show the high-symmetry k-point labels."""
+    doc_hide_cell = """hide_cell: Whether to show the reciprocal cell boundary."""
+    doc_vector_spacing="""vector_spacing: The rough spacing between arrows. Uses a custom algorithm
+                for resampling the Fermi surface to ensure that arrows are not too close
+                together. Only has an effect when used with the ``vector_properties``
+                option."""
+    doc_azimuth="azimuth: The azimuth of the viewpoint in degrees. i.e. the angle subtended by the position vector on a sphere projected on to the x-y plane."
+    doc_elevation="The zenith angle of the viewpoint in degrees, i.e. the angle subtended by the position vector and the z-axis."
+    doc_colors ="""The color specification for the iso-surfaces. Valid options are:
+                - A single color to use for all Fermi surfaces, specified as a tuple of
+                  rgb values from 0 to 1. E.g., red would be ``(1, 0, 0)``.
+                - A list of colors, specified as above.
+                - A dictionary of ``{Spin.up: color1, Spin.down: color2}``, where the
+                  colors are specified as above.
+                - A string specifying which matplotlib colormap to use. See
+                  https://matplotlib.org/tutorials/colors/colormaps.html for more
+                  information.
+                - ``None``, in which case the default colors will be used.
+                """
+
+    """Defaults."""
+
+    AZIMUTH = 45.0
+    ELEVATION = 35.0
+    VECTOR_SPACING = 0.2
+    COLORMAP = "viridis"
+    SYMPREC = 1e-3
+    KTOL = 1e-5
+    SCALE = 4
+
+ 
+    plot_options=[
+        Argument("colors", [str,dict,list,None], optional=True, default=None, doc=doc_colors),
+        Argument("projection_axis", [list,None], optional=True, default=None, doc=doc_projection_axis),
+        Argument("hide_surface", bool, optional=True, default=False, doc=doc_hide_surface),
+        Argument("hide_labels", bool, optional=True, default=False, doc=doc_hide_labels),
+        Argument("hide_cell", bool, optional=True, default=False, doc=doc_hide_cell),
+        Argument("vector_spacing",float, optional=True, default=VECTOR_SPACING, doc=doc_vector_spacing),
+        Argument("azimuth", float, optional=True, default=AZIMUTH, doc=doc_azimuth),
+        Argument("elevation", float, optional=True, default=ELEVATION, doc=doc_elevation),
+    ]
+
+
+    plot_options_fs=[
+        Argument("projection_axis", [list,None], optional=True, default=None, doc=doc_projection_axis)
+    ]
+    args_fermi = [
+        Argument("mesh_grid", list, optional = False, default=[2,2,2], doc = doc_mesh_grid),
+        Argument("mu", [float,int], optional = False, default=0.0, doc = doc_mu),
+        Argument("sigma", float, optional = True, default=0.1, doc = doc_sigma),
+        Argument("intpfactor", int, optional = False, default=1, doc = doc_intpfactor),
+        Argument("wigner_seitz", bool, optional = True, default=True, doc = doc_wigner_seitz),
+        Argument("nworkers", int, optional = True, default=-1, doc = doc_nworkers),
+        Argument("plot_type", str, optional = True, default="plotly", doc = doc_plot_type),
+        Argument("use_gui", bool, optional = True, default=False, doc = doc_use_gui),
+        Argument("plot_fs_bands", bool, optional = True, default = False, doc = doc_plot_fs_bands),
+        Argument("fs_plane", list, optional = True, default=[0,0,1], doc = doc_fs_plane),
+        Argument("fs_distance", [int,float], optional = True, default=0, doc = doc_fs_distanc),
+        Argument("plot_options", dict, optional=True, sub_fields=plot_options, sub_variants=[], default={}, doc=doc_fs_plot_options)
+    ]
+
+
+    args_prop = [
+        Argument("velocity", bool, optional = True, default=False, doc = doc_velocity),
+        Argument("color_properties", [str,bool], optional = True, default=False, doc = doc_color_properties),
+        Argument("colormap", str, optional = True,default="viridis",doc = doc_colormap),
+        Argument("prop_plane", list, optional = True, default=[0,0,1],doc = doc_prop_plane),
+        Argument("prop_distance", [int,float], optional = True, default=0, doc = doc_prop_distance),
+        Argument("plot_options", dict, optional = True, sub_fields=plot_options, sub_variants=[], default={}, doc = doc_prop_plot_options)
+    ]
+
+    fermiarg = Argument("fermisurface", dict, optional=False, sub_fields=args_fermi, sub_variants=[], default={}, doc=doc_fermi)
+    prop = Argument("property", dict, optional=True, sub_fields=args_prop, sub_variants=[], default={}, doc=doc_prop)
+
+    return [fermiarg, prop]
+
+def write_sk():
+    doc_thr = ""
+    doc_format = ""
+
+    return [
+        Argument("format", str, optional=True, default="sktable",  doc=doc_format),
+        Argument("thr", float, optional=True, default=1e-3, doc=doc_thr)
+    ]
+
+
+def host_normalize(data):
+
+    co = common_options()
+    mo = model_options()
+
+    base = Argument("base", dict, [co, mo])
+    data = base.normalize_value(data)
+    # data = base.normalize_value(data, trim_pattern="_*")
+    base.check_value(data, strict=False)
+
+    return data
+
+
+def normalize_bandinfo(data):
+    doc_band_min = ""
+    doc_band_max = ""
+    doc_emin = ""
+    doc_emax = ""
+    doc_gap_penalty = ""
+    doc_fermi_band = ""
+    doc_loss_gap_eta = ""
+    doc_eout_weight=""
+    doc_weight = ""
+    doc_wannier_proj = ""
+    doc_orb_wan = ""
+
+    args = [
+        Argument("band_min", int, optional=True, doc=doc_band_min, default=0),
+        Argument("band_max", [int, None], optional=True, doc=doc_band_max, default=None),
+        Argument("emin", [float, None], optional=True, doc=doc_emin,default=None),
+        Argument("emax", [float, None], optional=True, doc=doc_emax,default=None),
+        Argument("gap_penalty", bool, optional=True, doc=doc_gap_penalty, default=False),
+        Argument("fermi_band", int, optional=True, doc=doc_fermi_band,default=0),
+        Argument("loss_gap_eta", float, optional=True, doc=doc_loss_gap_eta, default=0.01),
+        Argument("eout_weight", float, optional=True, doc=doc_eout_weight, default=0.00),
+        Argument("weight", [int, float, list], optional=True, doc=doc_weight, default=1.),
+        Argument("wannier_proj",dict, optional=True, doc=doc_wannier_proj, default={}),
+        Argument("orb_wan",[dict, None], optional=True, doc=doc_orb_wan, default=None)
+    ]
+    bandinfo = Argument("bandinfo", dict, sub_fields=args)
+    data = bandinfo.normalize_value(data)
+    bandinfo.check_value(data, strict=True)
+
+    return data
+
+def bandinfo_sub():
+    doc_band_min = """the minum band index for the training band window with respected to the correctly selected DFT bands.
+                   `important`: before setting this tag you should make sure you have already  exclude all the irrelevant in your training data.
+                                This logic for band_min and max is based on the simple fact the total number TB bands > the bands you care.   
+                   """
+    doc_band_max = "The maxmum band index for training band window"
+    doc_emin = "the minmum energy window, 0 meand the min value of the band at index band_min"
+    doc_emax = "the max energy window, emax value is respect to the min value of the band at index band_min"
+    
+    args = [
+        Argument("band_min", int, optional=True, doc=doc_band_min, default=0),
+        Argument("band_max", [int, None], optional=True, doc=doc_band_max, default=None),
+        Argument("emin", [float, None], optional=True, doc=doc_emin,default=None),
+        Argument("emax", [float, None], optional=True, doc=doc_emax,default=None),
+    ]
+
+    return Argument("bandinfo", dict, optional=True, sub_fields=args, sub_variants=[], doc="")
+
+def AtomicData_options_sub():
+    doc_r_max = "the cutoff value for bond considering in TB model."
+    doc_er_max = "The cutoff value for environment for each site for env correction model. should set for nnsk+env correction model."
+    doc_oer_max = "The cutoff value for onsite environment for nnsk model, for now only need to set in strain and NRL mode."
+    doc_pbc = "The periodic condition for the structure, can bool or list of bool to specific x,y,z direction."
+    
+    args = [
+        Argument("r_max", [float, int, dict], optional=False, doc=doc_r_max, default=4.0),
+        Argument("er_max", [float, int, dict], optional=True, doc=doc_er_max, default=None),
+        Argument("oer_max", [float, int, dict], optional=True, doc=doc_oer_max,default=None)
+    ]
+
+    return Argument("AtomicData_options", dict, optional=True, sub_fields=args, sub_variants=[], doc="", default=None)
+
+def set_info_options():
+    doc_nframes = "Number of frames in this trajectory."
+    doc_natoms = "Number of atoms in each frame."
+    doc_pos_type = "Type of atomic position input. Can be frac / cart / ase."
+    doc_pbc = "The periodic condition for the structure, can bool or list of bool to specific x,y,z direction."
+
+    args = [
+        Argument("nframes", int, optional=False, doc=doc_nframes),
+        Argument("natoms", int, optional=True, default=-1, doc=doc_natoms),
+        Argument("pos_type", str, optional=False, doc=doc_pos_type),
+        Argument("pbc", [bool, list], optional=False, doc=doc_pbc),
+        bandinfo_sub()
+    ]
+
+    return Argument("setinfo", dict, sub_fields=args)
+
+def lmdbset_info_options():
+    doc_r_max = "the cutoff value for bond considering in TB model."
+
+    args = [
+        Argument("r_max", [float, int, dict], optional=False, doc=doc_r_max, default=4.0)
+    ]
+    return Argument("setinfo", dict, sub_fields=args)
+
+def normalize_setinfo(data):
+
+    setinfo = set_info_options()
+    data = setinfo.normalize_value(data)
+    setinfo.check_value(data, strict=True)
+
+    return data
+
+def normalize_lmdbsetinfo(data):
+
+    setinfo = lmdbset_info_options()
+    data = setinfo.normalize_value(data)
+    setinfo.check_value(data, strict=True)
+
+    return data
+
+
+def format_cuts(rcut: Union[Dict[str, Number], Number], decay_w: Number, nbuffer: int) -> Union[Dict[str, Number], Number]:
+    if not isinstance(decay_w, Number) or decay_w <= 0:
+        raise ValueError("decay_w should be a positive number")
+
+    buffer_addition = decay_w * nbuffer
+
+    if isinstance(rcut, dict):
+        return {key: value + buffer_addition for key, value in rcut.items()}
+    elif isinstance(rcut, Number):
+        return rcut + buffer_addition
+    else:
+        raise TypeError("rcut should be a dict or a number")
+
+def get_cutoffs_from_model_options(model_options):
+    """
+    Extract cutoff values from the provided model options.
+
+    This function retrieves the cutoff values `r_max`, `er_max`, and `oer_max` from the `model_options` 
+    dictionary. It handles different model types such as `embedding`, `nnsk`, and `dftbsk`, ensuring 
+    that the appropriate cutoff values are provided and valid.
+
+    Parameters:
+    model_options (dict): A dictionary containing model configuration options. It may include keys 
+                          like `embedding`, `nnsk`, and `dftbsk` with their respective cutoff values.
+
+    Returns:
+    tuple: A tuple containing the cutoff values (`r_max`, `er_max`, `oer_max`).
+
+    Raises:
+    ValueError: If neither `r_max` nor `rc` is provided in `model_options` for embedding.
+    AssertionError: If `r_max` is provided outside the `nnsk` or `dftbsk` context when those models are used.
+
+    Logs:
+    Error messages if required cutoff values are missing or incorrectly provided.
+    """
+    r_max, er_max, oer_max = None, None, None
+    if model_options.get("embedding",None) is not None:
+        # switch according to the embedding method
+        embedding = model_options.get("embedding")
+        if embedding["method"] == "se2":
+            er_max = embedding["rc"]
+        elif embedding["method"] in ["slem", "lem"]:
+            r_max = embedding["r_max"]
+        else:
+            log.error("The method of embedding have not been defined in get cutoff functions")
+            raise NotImplementedError("The method of embedding have not been defined in get cutoff functions")
+        
+    if model_options.get("nnsk", None) is not None:
+        assert r_max is None, "r_max should not be provided in outside the nnsk for training nnsk model."
+        if model_options["nnsk"]["hopping"].get("rs",None) is not None:
+            # 其他方法在模型公式中，已经包含了 +5w 的范围，所以这里为了保险额外加上3w 的范围; 
+            # 对于两个特例，powerlaw 和 varTang96 的情况，为了和旧版存档的兼容, 模型公式的本身并没有 +5w 的范围，所以这里额外加上8w 的范围。
+            if model_options["nnsk"]["hopping"]['method'] in ["powerlaw","varTang96"]:
+                # r_max = model_options["nnsk"]["hopping"]["rs"] + 8 * model_options["nnsk"]["hopping"]["w"]
+                r_max = format_cuts(model_options["nnsk"]["hopping"]["rs"], model_options["nnsk"]["hopping"]["w"], 8)
+            else:
+                # r_max = model_options["nnsk"]["hopping"]["rs"] + 3 * model_options["nnsk"]["hopping"]["w"]
+                r_max = format_cuts(model_options["nnsk"]["hopping"]["rs"], model_options["nnsk"]["hopping"]["w"], 3)
+
+        if model_options["nnsk"]["onsite"].get("rs",None) is not None:
+            if  model_options["nnsk"]["onsite"]['method'] == "strain" and model_options["nnsk"]["hopping"]['method'] in ["powerlaw","varTang96"]:
+                # oer_max = model_options["nnsk"]["onsite"]["rs"] + 8 * model_options["nnsk"]["onsite"]["w"]
+                oer_max = format_cuts(model_options["nnsk"]["onsite"]["rs"], model_options["nnsk"]["onsite"]["w"], 8)
+            else:
+                # oer_max = model_options["nnsk"]["onsite"]["rs"] + 3 * model_options["nnsk"]["onsite"]["w"]
+                oer_max = format_cuts(model_options["nnsk"]["onsite"]["rs"], model_options["nnsk"]["onsite"]["w"], 3)
+    
+    elif model_options.get("dftbsk", None) is not None:
+        assert r_max is None, "r_max should not be provided in outside the dftbsk for training dftbsk model."
+        r_max = model_options["dftbsk"].get("r_max")
+    
+    else:
+        # not nnsk not dftbsk, must be only env or E3. the embedding should be provided.
+        assert model_options.get("embedding",None) is not None   
+
+    return r_max, er_max, oer_max
+def collect_cutoffs(jdata):
+    """
+    Collect cutoff values from the provided JSON data.
+
+    This function extracts the cutoff values `r_max`, `er_max`, and `oer_max` from the `model_options` 
+    in the provided JSON data. If the `nnsk` push model is used, it ensures that the necessary 
+    cutoff values are provided in `data_options` and overrides the values from `model_options` 
+    accordingly.
+
+    Parameters:
+    jdata (dict): A dictionary containing model and data options. It must include `model_options` 
+                  and optionally `data_options` if `nnsk` push model is used.
+
+    Returns:
+    dict: A dictionary containing the cutoff options with keys `r_max`, `er_max`, and `oer_max`.
+
+    Raises:
+    AssertionError: If required keys are missing in `jdata` or if `r_max` is not provided when 
+                    using the `nnsk` push model.
+
+    Logs:
+    Various informational messages about the cutoff values and their sources.
+    """ 
+
+    model_options = jdata["model_options"]
+    r_max, er_max, oer_max = get_cutoffs_from_model_options(model_options)
+
+    if model_options.get("nnsk", None) is not None:
+        if model_options["nnsk"]["push"]:
+            assert jdata.get("data_options",None) is not None, "data_options should be provided in jdata for nnsk push"
+            assert jdata['data_options'].get("r_max") is not None, "r_max should be provided in data_options for nnsk push"
+            log.info('YOU ARE USING NNSK PUSH MODEL, r_max will be used from data_options. Be careful! check the value in data options and model options. r_max or rs/rc !')
+            r_max = jdata['data_options']['r_max']
+
+            if model_options["nnsk"]["onsite"]["method"] in ["strain", "NRL"]:
+                assert jdata['data_options'].get("oer_max") is not None, "oer_max should be provided in data_options for nnsk push with strain onsite mode"
+                log.info('YOU ARE USING NNSK PUSH MODEL with `strain` onsite mode, oer_max will be used from data_options. Be careful! check the value in data options and model options. rs/rc !')
+                oer_max = jdata['data_options']['oer_max']
+
+            if jdata['data_options'].get("er_max") is not None:
+                log.info("IN PUSH mode, the env correction should not be used. the er_max will not take effect.")     
+        else:
+            if  jdata['data_options'].get("r_max") is not None:
+                log.info("When not nnsk/push. the cutoffs will take from the model options: r_max  rs and rc values. this seting in data_options will be ignored.")
+
+    assert r_max is not None
+    cutoff_options = ({"r_max": r_max, "er_max": er_max, "oer_max": oer_max})
+    
+    log.info("-"*66)
+    log.info('     {:<55}    '.format("Cutoff options:"))
+    log.info('     {:<55}    '.format(" "*30))
+    log.info('     {:<16} : {:<36}    '.format("r_max", f"{r_max}"))
+    log.info('     {:<16} : {:<36}    '.format("er_max", f"{er_max}"))
+    log.info('     {:<16} : {:<36}    '.format("oer_max", f"{oer_max}"))
+    log.info("-"*66)
+
+    return cutoff_options
+
+
+def normalize(data):
+
+    co = common_options()
+    tr = train_options()
+    da = data_options()
+    mo = model_options()
+
+    base = Argument("base", dict, [co, tr, da, mo])
+    data = base.normalize_value(data)
+    # data = base.normalize_value(data, trim_pattern="_*")
+    base.check_value(data, strict=True)
+    
+    # add check loss and use wannier:
+    
+    # if data['data_options']['use_wannier']:
+    #     if not data['loss_options']['losstype'] .startswith("block"):
+    #         log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n')
+    
+    # if data['loss_options']['losstype'] .startswith("block"):
+    #     if not data['data_options']['use_wannier']:
+    #         log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n")
+    #         raise ValueError
+
+    return data
+
+def normalize_skf2nnsk(data):
+    common_ops = [
+        Argument("basis", [dict,str], optional=False, default='auto', doc="The basis set for the model, can be a dict or a string, default is 'auto'."),
+        Argument("skdata",str, optional=False, doc="The path to the skf file."),
+        Argument("device",str, optional=True, default='cpu', doc="The device to run the calculation, choose among `cpu` and `cuda[:int]`, Default: 'cpu'."),
+        Argument("dtype",str, optional=True, default='float32', doc="The digital number's precison, choose among: 'float32', 'float64', Default: 'float32'."),
+        Argument("seed", int, optional=True, default=3982377700, doc="The random seed used to initialize the parameters and determine the shuffling order of datasets. Default: `3982377700`")
+    ]
+
+    model_ops = [
+        Argument('method',str, optional=False, default='poly2pow', doc="The method for the hopping term, default is 'powerlaw'."),
+        Argument('rs',[float,None,int], optional=True, default=None, doc="The rs value for the hopping term."),
+        Argument('w', [float,int], optional=True, default=0.2, doc="The w value for the hopping term."),
+        Argument('atomic_radius',[str,dict], optional=True, default='cov', doc="The atomic radius for the hopping term, default is 'cov'.")
+    ]
+    
+    doc_lr_scheduler = "The learning rate scheduler tools settings, the lr scheduler is used to scales down the learning rate during the training process. Proper setting can make the training more stable and efficient. The supported lr schedular includes: `Exponential Decaying (exp)`, `Linear multiplication (linear)`"
+    doc_optimizer = "\
+        The optimizer setting for selecting the gradient optimizer of model training. Optimizer supported includes `Adam`, `SGD` and `LBFGS` \n\n\
+        For more information about these optmization algorithm, we refer to:\n\n\
+        - `Adam`: [Adam: A Method for Stochastic Optimization.](https://arxiv.org/abs/1412.6980)\n\n\
+        - `SGD`: [Stochastic Gradient Descent.](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html)\n\n\
+        - `LBFGS`: [On the limited memory BFGS method for large scale optimization.](http://users.iems.northwestern.edu/~nocedal/PDFfiles/limited-memory.pdf) \n\n\
+    "
+    
+    train_ops = [
+        Argument('nstep', int, optional=False, doc="The number of steps for the training."),
+        Argument('nsample', int, optional=True, default=256, doc="The number of steps for the training."),
+        Argument('max_elmt_batch', int, optional=True, default=4, doc="The max number of elements in a batch."),
+        Argument('dis_freq', int, optional=True, default=1, doc="The frequency of the display."),
+        Argument('save_freq', int, optional=True, default=1, doc="The frequency of the save."),
+        Argument("optimizer", dict, sub_fields=[], optional=True, default={}, sub_variants=[optimizer()], doc = doc_optimizer),
+        Argument("lr_scheduler", dict, sub_fields=[], optional=True, default={}, sub_variants=[lr_scheduler()], doc = doc_lr_scheduler)
+    ]
+    co = Argument("common_options", dict, optional=False, sub_fields=common_ops, sub_variants=[], doc='The common options.')
+    mo = Argument("model_options", dict, optional=False, sub_fields=model_ops, sub_variants=[], doc='The model options.')
+    tr =  Argument("train_options", dict, sub_fields=train_ops, sub_variants=[], optional=False, doc='The training options.')
+
+    base = Argument("base", dict, [co, mo, tr])
+    data = base.normalize_value(data)
+    # data = base.normalize_value(data, trim_pattern="_*")
+    base.check_value(data, strict=True)
+
+    return data
+    
diff --git a/dptb_negf/utils/constants.py b/dpnegf/utils/constants.py
similarity index 100%
rename from dptb_negf/utils/constants.py
rename to dpnegf/utils/constants.py
diff --git a/dpnegf/utils/loggers.py b/dpnegf/utils/loggers.py
new file mode 100644
index 0000000..06b1178
--- /dev/null
+++ b/dpnegf/utils/loggers.py
@@ -0,0 +1,114 @@
+"""Logger initialization for package."""
+
+import logging
+import os
+from typing import TYPE_CHECKING, Optional
+
+if TYPE_CHECKING:
+    from pathlib import Path
+logging.getLogger(__name__)
+
+__all__ = ["set_log_handles"]
+
+# logger formater
+FFORMATTER = logging.Formatter(
+    "[%(asctime)s] %(app_name)s %(levelname)-7s %(name)-45s %(message)s"
+)
+CFORMATTER = logging.Formatter(
+#    "%(app_name)s %(levelname)-7s |-> %(name)-45s %(message)s"
+    "%(app_name)s %(levelname)-7s %(message)s"
+)
+
+class _AppFilter(logging.Filter):
+    """Add field `app_name` to log messages."""
+
+    def filter(self, record):
+        record.app_name = "DPNEGF"
+        return True
+
+
+def set_log_handles(
+    level: int,
+    log_path: Optional["Path"] = None
+):
+    """Set desired level for package loggers and add file handlers.
+
+    Parameters
+    ----------
+    level: int
+        logging level
+    log_path: Optional[str]
+        path to log file, if None logs will be send only to console. If the parent
+        directory does not exist it will be automatically created, by default None
+    mpi_log : Optional[str], optional
+        mpi log type. Has three options. `master` will output logs to file and console
+        only from rank==0. `collect` will write messages from all ranks to one file
+        opened under rank==0 and to console. `workers` will open one log file for each
+        worker designated by its rank, console behaviour is the same as for `collect`.
+        If this argument is specified, package 'mpi4py' must be already installed.
+        by default None
+
+    Raises
+    ------
+    RuntimeError
+        If the argument `mpi_log` is specified, package `mpi4py` is not installed.
+
+    References
+    ----------
+    https://groups.google.com/g/mpi4py/c/SaNzc8bdj6U
+    https://stackoverflow.com/questions/35869137/avoid-tensorflow-print-on-standard-error
+    https://stackoverflow.com/questions/56085015/suppress-openmp-debug-messages-when-running-tensorflow-on-cpu
+
+    Notes
+    -----
+    Logging levels:
+
+    +---------+--------------+----------------+----------------+----------------+
+    |         | our notation | python logging | tensorflow cpp | OpenMP         |
+    +=========+==============+================+================+================+
+    | debug   | 10           | 10             | 0              | 1/on/true/yes  |
+    +---------+--------------+----------------+----------------+----------------+
+    | info    | 20           | 20             | 1              | 0/off/false/no |
+    +---------+--------------+----------------+----------------+----------------+
+    | warning | 30           | 30             | 2              | 0/off/false/no |
+    +---------+--------------+----------------+----------------+----------------+
+    | error   | 40           | 40             | 3              | 0/off/false/no |
+    +---------+--------------+----------------+----------------+----------------+
+
+    """
+    # silence logging for OpenMP when running on CPU if level is any other than debug
+    if level <= 10:
+        os.environ["KMP_WARNINGS"] = "FALSE"
+
+    # set TF cpp internal logging level
+    os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(int((level / 10) - 1))
+
+    # get root logger
+    root_log = logging.getLogger()
+
+    # remove all old handlers
+    root_log.setLevel(level)
+    for hdlr in root_log.handlers[:]:
+        root_log.removeHandler(hdlr)
+
+    # * add console handler ************************************************************
+    ch = logging.StreamHandler()
+    ch.setFormatter(CFORMATTER)
+
+    ch.setLevel(level)
+    ch.addFilter(_AppFilter())
+    root_log.addHandler(ch)
+
+    # * add file handler ***************************************************************
+    if log_path:
+
+        # create directory
+        log_path.parent.mkdir(exist_ok=True, parents=True)
+
+        fh = logging.FileHandler(log_path, mode="w")
+        fh.setFormatter(FFORMATTER)
+
+        if fh:
+            fh.setLevel(level)
+            fh.addFilter(_AppFilter())
+            root_log.addHandler(fh)
diff --git a/dpnegf/utils/make_kpoints.py b/dpnegf/utils/make_kpoints.py
new file mode 100644
index 0000000..e969f93
--- /dev/null
+++ b/dpnegf/utils/make_kpoints.py
@@ -0,0 +1,394 @@
+import numpy as np
+import ase
+import logging
+log = logging.getLogger(__name__)
+
+
+def rot_revlatt_2D(rev_latt,index=[0,1]): # 0, x; 1,y, 2,z
+    """ Transform the coordinate system of reciprocal lattice vectors. 
+    The new coordinate system is defined by the two reciprocal lattice vectors with index [0,1] in the original coordinate system. 
+    The new x-axis is along the reciprocal lattice vector with index 0, and the new z-axis is perpendicular to the new x-axis and the reciprocal lattice vector with index 1.
+    The new y-axis is perpendicular to the new x-axis and the new z-axis. 
+    The new coordinate system is right-handed. 
+    The new reciprocal lattice vectors are returned as a 3x3 matrix. 
+    The transformation matrix is also returned. The new reciprocal lattice vectors are obtained by new_rev_latt = rev_latt @ newcorr.I
+
+
+    Parameters
+    ----------
+    rev_latt : numpy.matrix
+        The reciprocal lattice vectors in the original coordinate system. A 3x3 matrix.
+    index : list. [i1, i2]
+        A list of 2 integers, the index of the two reciprocal lattice vectors to be used to define the new coordinate system. 
+        The index of the reciprocal lattice vector is 0, 1, or 2, corresponding to the x, y, and z direction, respectively.
+
+    Returns
+    -------
+    rev_latt_new : numpy.matrix
+        The reciprocal lattice vectors in the new coordinate system. A 3x3 matrix.
+    newcorr : numpy.matrix
+        The transformation matrix. The new reciprocal lattice vectors are obtained by new_rev_latt = rev_latt @ newcorr.I
+    """
+
+    if isinstance(rev_latt, np.matrix):
+        if rev_latt.shape != (3,3):
+            log.error("Error! rev_latt must be a 3x3 matrix!")
+            raise ValueError
+    else:
+        log.error("Error! rev_latt must be a 3x3 matrix!")
+        raise ValueError
+    
+    index_left  = [0,1,2]
+    for i in index:
+        index_left.remove(i)
+
+    vec1 = np.array(rev_latt[index[0]]).reshape(-1)
+    vec2 = np.array(rev_latt[index[1]]).reshape(-1)
+    vec3 = np.array(rev_latt[index_left[0]]).reshape(-1)
+
+    avec1 = vec1/np.linalg.norm(vec1)
+    avec3 = np.cross(avec1,vec2)/np.linalg.norm(np.cross(avec1,vec2))
+    avec2 = np.cross(avec3,avec1)
+    if np.dot(np.cross(avec1,avec2),avec3) < 0:
+        avec3 = -avec3
+    newcorr = np.zeros((3,3))    
+    newcorr[index[0]] = avec1
+    newcorr[index[1]] = avec2
+    newcorr[index_left[0]] = avec3
+    newcorr = np.mat(newcorr)
+
+    rev_latt_new = rev_latt @ newcorr.I
+
+    return rev_latt_new, newcorr
+
+
+def kmesh_fs(meshgrid=[1,1,1]):
+    """ Generate k-points on mesh for fermi surface calculation. The k-points are centered at Gamma point. and with endpoints [0,1]. 
+
+    Parameters
+    ----------
+    meshgrid : list. [N1, N2, N3]
+        A list of 3 integers, the number of k-points in each direction.
+    
+    """
+
+    Nx, Ny, Nz = meshgrid
+    lx, ly, lz = np.linspace(0, 1, Nx), np.linspace(0, 1, Ny), np.linspace(0, 1, Nz)
+    xx, yy, zz = np.meshgrid(lx, ly, lz, indexing='ij')
+    kgrids  = np.array([xx.reshape(-1), yy.reshape(-1), zz.reshape(-1)]).T
+
+    return (lx,ly,lz), kgrids
+
+
+def monkhorst_pack(meshgrid=[1,1,1]):
+    """ Generate k-points using Monkhorst-Pack method based on given meshgrid.
+    
+    Parameters
+    ----------
+    meshgrid : list. [N1, N2, N3]
+        A list of 3 integers, the number of k-points in each direction.
+    
+    Returns
+    -------
+    kpoints : numpy.ndarray
+        A numpy array of k-points.
+    
+    This function is modified from ASE.
+    """
+    if len(meshgrid) != 3  or not (np.array(meshgrid,dtype=int) > 0).all():
+        log.error("Error! meshgrid must be a list of 3 positive integers!")
+        raise ValueError
+
+    kpoints = np.indices(meshgrid).transpose((1, 2, 3, 0)).reshape((-1, 3))
+    kpoints = (kpoints + 0.5) / meshgrid - 0.5
+    return kpoints
+
+def gamma_center(meshgrid=[1,1,1]):
+    """ Generates a gamma centered k-point mesh based on the given meshgrid.
+
+    Parameters
+    ----------
+    meshgrid : list. [N1, N2, N3]
+        A list of 3 integers, the number of k-points in each direction.
+
+    Returns
+    -------
+    kpoints : numpy.ndarray
+        A numpy array of k-points.
+    """
+    if len(meshgrid) != 3  or not (np.array(meshgrid,dtype=int) > 0).all():
+        log.error("Error! meshgrid must be a list of 3 positive integers!")
+        raise ValueError
+
+    kpoints = np.indices(meshgrid).transpose((1, 2, 3, 0)).reshape((-1, 3))
+    kpoints = (kpoints) / meshgrid
+    return kpoints
+
+
+
+
+def kmesh_sampling(meshgrid=[1,1,1], is_gamma_center=True):
+    """ Generate k-points using Monkhorst-Pack method based on given meshgrid. The k-points are centered at Gamma point by default.
+     
+    """
+
+    kpoints = np.indices(meshgrid).transpose((1, 2, 3, 0)).reshape((-1, 3))
+
+    if is_gamma_center:
+        kpoints = gamma_center(meshgrid)
+    else:
+        kpoints = monkhorst_pack(meshgrid)
+    return kpoints
+
+
+def kmesh_sampling_negf(meshgrid=[1,1,1], is_gamma_center=True, is_time_reversal=True):
+    """ Generate k-points for NEGF based on given meshgrid. Through time symmetry reduction, the number of k-points is reduced.
+     
+    """
+
+    if is_time_reversal:
+        kpoints,wk = time_symmetry_reduce(meshgrid, is_gamma_center=is_gamma_center)
+            
+    else:
+        kpoints = kmesh_sampling(meshgrid, is_gamma_center=is_gamma_center)
+        wk = np.ones(len(kpoints))/len(kpoints)
+    
+    return kpoints,wk
+
+
+def time_symmetry_reduce(meshgrid=[1,1,1], is_gamma_center=True):
+    '''Reduce the number of k-points in a meshgrid by applying symmetry operations.
+
+    For gamma centered meshgrid, k-points range from 0 to 1 in each dimension initially. 
+    For non-gamma centered meshgrid, k-points range from -0.5 to 0.5 in each dimension initially.
+
+    With time symmetry reduction, the number of k-points is reduced and limited to [0,0.5] in x-direction.
+    
+    Parameters
+    ----------
+    meshgrid
+        The `meshgrid` parameter specifies the number of k-points in each direction. 
+    is_gamma_center
+        The parameter "is_gamma_center" is a boolean value that determines whether the k-point mesh must be
+    centered around the gamma point (0, 0, 0) or not. 
+    
+    Returns
+    -------
+        the reduced k-points and their corresponding weights.
+    
+    '''
+
+    k_points = kmesh_sampling(meshgrid, is_gamma_center=is_gamma_center)
+    k_points_with_tr = []
+    kweight = []
+
+    
+    if is_gamma_center:
+        k_points[k_points>0.5] = k_points[k_points>0.5] - 1
+
+    k_points = np.round(k_points, decimals=5)
+
+    for kp in k_points:
+        if (-kp).tolist() not in k_points_with_tr:
+            k_points_with_tr.append(kp.tolist())
+            kweight.append(1)
+        else:
+            kweight[k_points_with_tr.index((-kp).tolist())] += 1
+
+    k_points_with_tr = np.array(k_points_with_tr)
+
+    # make the reduced kpoints in [0,0.5] in x-direction
+    if is_gamma_center:
+        k_points_with_tr[k_points_with_tr < 0] += 1 
+    else: # MP sampling
+        k_points_with_tr =  -1 * k_points_with_tr # due to time revesal symmetry
+
+    # sort the k-points
+    k_sort_indx = np.lexsort((k_points_with_tr[:, 2], k_points_with_tr[:, 1], k_points_with_tr[:, 0]))
+    k_points_with_tr = k_points_with_tr[k_sort_indx]
+    kweight = np.array(kweight)/len(k_points) # normalize the weight to one
+    kweight = kweight[k_sort_indx]
+    assert abs(kweight.sum() - 1.0) < 1e-5, "The sum of weight is not 1.0"
+
+    return k_points_with_tr, kweight
+
+
+
+
+def kgrid_spacing(structase,kspacing:float,sampling='MP'):
+    """Generate k-points based on the given k-spacing and sampling method.
+    
+    Parameters
+    ----------
+    structase : ase.Atoms
+        The structure in ASE format.
+    kspacing : float
+        The k-spacing.
+    sampling : str
+        The sampling method. 'MP' for Monkhorst-Pack method, 'Gamma' for gamma centered method.
+
+    Returns
+    -------
+    kpoints : numpy.ndarray
+        A numpy array of k-points.
+    """
+    
+    assert isinstance(structase,ase.Atoms)
+    rev_latt = 2*np.pi*np.mat(structase.cell).I
+    meshgrid = np.ceil(np.dot(rev_latt.T, np.array([1/kspacing, 1/kspacing, 1/kspacing]))).astype(int)
+
+    if sampling == 'MP':
+        kpoints = monkhorst_pack(meshgrid)
+    elif sampling == 'Gamma':
+        kpoints = gamma_center(meshgrid)
+    else:
+        log.error("Error! sampling must be either 'MP' or 'Gamma'!, by default it is MP using the monkhorst_pack method.")
+        raise ValueError
+
+    return kpoints
+
+
+def abacus_kpath(structase, kpath):
+    '''> The function `abacus_kpath` takes in a structure and a list of high symmetry points. It returns a list of k-points, a list of x-values, and a list of high symmetry k-points.
+    
+    Parameters
+    ----------
+    structase : ase.Atoms
+        The structure in ASE format.
+    kpath : list
+        A list of high symmetry points. Each high symmetry point is a list of 4 elements: [kx, ky, kz, nk], where nk is the number of k-points to be used in the path between this high symmetry point and the next one.
+
+    Returns
+    -------
+    kpath_list : list
+        A list of k-points.
+    kdist_list : list
+        A list of x-values.
+    high_sym_kpoints : list
+        A list of high symmetry k-points.
+    '''
+    kpath = np.asarray(kpath)
+    assert kpath.shape[-1] == 4
+    assert  len(kpath.shape) == 2
+    kpoints = kpath[:,0:3]
+    num_kp = kpath[:,3].astype(int)
+    assert num_kp[-1] == 1
+
+    kpath_list = []
+    for i in range(len(kpoints)-1):
+        tmp = np.linspace(kpoints[i],kpoints[i+1],num_kp[i]+1)[0:num_kp[i]]
+        kpath_list.append(tmp)
+    
+    kpath_list.append(kpoints[-1:])
+    kpath_list = np.concatenate(kpath_list,axis=0)
+
+    #rev_latt = 2*np.pi*np.mat(ase_struct.cell).I
+    # rev_latt =(np.matrix(structase.cell).I.T)
+    rev_latt =  np.linalg.inv(np.array(structase.cell).T)
+    kdiff = kpoints[1:] - kpoints[:-1]
+    # kdiff_cart = np.asarray(kdiff * rev_latt)
+    kdiff_cart = np.dot(kdiff, rev_latt)
+    kdist  = np.linalg.norm(kdiff_cart,axis=1)
+    
+    kdist_list = []
+    high_sym_kpoints = []
+    for i in range(len(kdist)):
+        if num_kp[i]==1:
+            kdist[i]=0
+    for i in range(len(kdist)):
+        tmp = np.linspace(np.sum(kdist[:i]), np.sum(kdist[:i+1]),num_kp[i]+1)[0:num_kp[i]]
+        high_sym_kpoints.append(tmp[0])
+        if i==0:
+            kdist_list = tmp.copy()
+        else:
+            kdist_list = np.concatenate([kdist_list,tmp],axis=0)
+    kdist_list = np.append(kdist_list,[np.sum(kdist)])
+    high_sym_kpoints.append(np.sum(kdist))
+    high_sym_kpoints = np.asarray(high_sym_kpoints)
+
+    return kpath_list, kdist_list, high_sym_kpoints
+
+
+
+def ase_kpath(structase, pathstr:str, total_nkpoints:int):
+    '''> The function `ase_kpath` takes in a structure, a string of high symmetry points, and the total
+    number of k-points to be used in the band structure calculation. It returns a list of k-points, a
+    list of x-values, a list of high symmetry k-points, and a list of labels
+    
+    Parameters
+    ----------
+    structase
+        the ase structure object
+    pathstr : str
+        a string that defines the path in reciprocal space.
+    total_nkpoints : int
+        the total number of k-points along the path
+    '''
+    
+    kpath = structase.cell.bandpath(pathstr, npoints=total_nkpoints)
+    xlist, high_sym_kpoints, labels = kpath.get_linear_kpoint_axis()
+    klist = kpath.kpts
+    return klist, xlist, high_sym_kpoints, labels
+
+def vasp_kpath(structase, pathstr:str, high_sym_kpoints_dict:dict, number_in_line:int):
+    """The function `vasp_kpath` takes in a structure, a string of high symmetry points, a dictionary of high symmetry points, and the number of k-points in each line. 
+    It returns a list of k-points, a list of x-values, a list of high symmetry k-points, and a list of labels.
+
+    Parameters:
+    -----------
+    structase: ase structure object
+    pathstr: str
+        a string that defines the path in reciprocal space.
+    high_sym_kpoints: dict
+        a dictionary of high symmetry points
+    number_in_line: int
+        the number of k-points in each line
+
+    Returns:
+    --------
+    klist: np.array, float, shape [N,3]
+        a list of k-points
+    xlist: np.array, float, shape [N]
+        a list of x-values
+    xlist_label: list[float]
+        a list of high symmetry k-points
+    klabels: list[str]
+    """
+
+    kpath = []
+    klist = []
+
+    for i  in range(len(pathstr)):
+        kline = (pathstr[i].split('-'))
+        kpath.append([high_sym_kpoints_dict[kline[0]],high_sym_kpoints_dict[kline[1]]])
+        kline_list = np.linspace(high_sym_kpoints_dict[kline[0]], high_sym_kpoints_dict[kline[1]], number_in_line)
+        klist.append(kline_list)
+        if i == 0:  
+            klabels = [(pathstr[i].split('-')[0])]
+        else:
+            if pathstr[i].split('-')[0] == pathstr[i-1].split('-')[1]:
+                klabels.append(pathstr[i].split('-')[0])
+            else:
+                klabels.append(pathstr[i-1].split('-')[1] + '|' + pathstr[i].split('-')[0])
+            if i == len(pathstr)-1:
+                klabels.append(pathstr[i].split('-')[1])
+
+    kpath = np.asarray(kpath)
+    klist = np.concatenate(klist)
+
+
+    rev_latt = np.mat(structase.cell).I.T
+    #rev_latt = 2*np.pi*np.mat(ase_struct.cell).I
+    kdiff = kpath[:,1] - kpath[:,0]
+    kdiff_cart = np.asarray(kdiff * rev_latt)
+    kdist  = np.linalg.norm(kdiff_cart,axis=1)
+
+    xlist_label = [0] 
+    for i in range(len(kdist)):
+        if i == 0:
+            xlist = np.linspace(0,kdist[i],number_in_line)
+        else:
+            xlist = np.concatenate([xlist, xlist[-1] + np.linspace(0,kdist[i],number_in_line)])
+        xlist_label.append(xlist[-1])
+    
+    return klist, xlist, xlist_label, klabels
\ No newline at end of file
diff --git a/dptb_negf/utils/tools.py b/dpnegf/utils/tools.py
similarity index 100%
rename from dptb_negf/utils/tools.py
rename to dpnegf/utils/tools.py
diff --git a/dptb_negf/negf/__init__.py b/dptb_negf/negf/__init__.py
deleted file mode 100644
index 2979dcd..0000000
--- a/dptb_negf/negf/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from dptb.negf import *
\ No newline at end of file
diff --git a/examples/atomic_chain/input_files/chain.vasp b/examples/atomic_chain/input_files/chain.vasp
new file mode 100644
index 0000000..81a1ab2
--- /dev/null
+++ b/examples/atomic_chain/input_files/chain.vasp
@@ -0,0 +1,20 @@
+long_chain
+1.0
+        10.000000000        0.0000000000         0.0000000000
+        0.0000000000        10.0000000000         0.0000000000
+        0.0000000000         0.0000000000        19.2000000000
+    C
+   12
+Cartesian
+        0.000000000         0.000000000         3.200000000
+        0.000000000         0.000000000         4.800000000
+        0.000000000         0.000000000         0.000000000
+        0.000000000         0.000000000         1.600000000
+        0.000000000         0.000000000         6.400000000
+        0.000000000         0.000000000         8.000000000
+        0.000000000         0.000000000         9.600000000
+        0.000000000         0.000000000         11.20000000
+        0.000000000         0.000000000         12.80000000
+        0.000000000         0.000000000         14.40000000
+        0.000000000         0.000000000         16.00000000
+        0.000000000         0.000000000         17.60000000
\ No newline at end of file
diff --git a/examples/atomic_chain/input_files/negf_chain_new.json b/examples/atomic_chain/input_files/negf_chain_new.json
new file mode 100644
index 0000000..a69668f
--- /dev/null
+++ b/examples/atomic_chain/input_files/negf_chain_new.json
@@ -0,0 +1,61 @@
+{
+    "task_options": {
+        "task": "negf",
+        "scf": false,
+        "block_tridiagonal": false,
+        "ele_T": 300,
+        "unit": "eV",
+        "scf_options":{
+            "mode": "PDIIS",
+            "mixing_period": 3,
+            "step_size": 0.05,
+            "n_history": 6,
+            "abs_err": 1e-6,
+            "rel_err": 1e-4,
+            "max_iter": 100
+        },
+        "stru_options":{
+            "gamma_center": true,
+            "time_reversal_symmetry": true,
+            "kmesh":[1,1,1],
+            "pbc":[false, false, false],
+            "device":{
+                "id":"4-8",
+                "sort": true
+            },
+            "lead_L":{
+                "id":"0-4",
+                "voltage":0.0,
+                "useBloch": false
+            },
+            "lead_R":{
+                "id":"8-12",
+                "voltage":0.0,
+                "useBloch": false
+            }
+        },
+        "poisson_options": {
+            "solver": "fmm",
+            "err": 1e-5
+        },
+        "sgf_solver": "Sancho-Rubio",
+        "espacing": 0.01,
+        "emin": -2,
+        "emax": 2,
+        "e_fermi": -13.638587951660156,
+        "density_options": {
+            "method": "Fiori",
+            "integrate_way": "direct"
+        },
+        "eta_lead":1e-5,
+        "eta_device":0.0,
+        "out_dos": true,
+        "out_tc": true,
+        "out_ldos": true,
+        "out_current_nscf": true
+},    
+"AtomicData_options" :{
+    "r_max": 2.0
+},
+"structure":"./chain.vasp"
+}
diff --git a/examples/atomic_chain/input_files/nnsk_C_new.json b/examples/atomic_chain/input_files/nnsk_C_new.json
new file mode 100644
index 0000000..65f5e13
--- /dev/null
+++ b/examples/atomic_chain/input_files/nnsk_C_new.json
@@ -0,0 +1,39 @@
+{ "version":2,
+"model_params": {
+    "onsite": {},
+    "hopping": {
+        "C-C-2s-2s-0": [
+            1.0884529828109601,
+            1.0
+        ]
+    }
+},
+"unit": "eV",
+"model_options": {
+    "nnsk": {
+        "onsite": {
+            "method": "none"
+        },
+        "hopping": {
+            "method": "powerlaw",
+            "rs": 1.6,
+            "w": 0.3
+        },
+        "soc": {},
+        "freeze": false,
+        "push": false,
+        "std": 0.01
+    }
+},
+"common_options": {
+    "basis": {
+        "C": [
+            "2s"
+        ]
+    },
+    "dtype": "float32",
+    "device": "cpu",
+    "overlap": false
+}
+}
+
diff --git a/examples/atomic_chain/run.ipynb b/examples/atomic_chain/run.ipynb
new file mode 100644
index 0000000..51b317a
--- /dev/null
+++ b/examples/atomic_chain/run.ipynb
@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8c5c64a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "\n",
+    "from dpnegf.runner.NEGF import NEGF\n",
+    "from dptb.nn.build import build_model\n",
+    "import json\n",
+    "\n",
+    "from dpnegf.utils.loggers import set_log_handles\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0f64a6b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n"
+     ]
+    }
+   ],
+   "source": [
+    "INPUT_file =  \"./input_files/negf_chain_new.json\" \n",
+    "model =  \"./input_files/nnsk_C_new.json\"\n",
+    "structure =  \"./input_files/chain.vasp\" \n",
+    "output = \"output\"  \n",
+    "\n",
+    "if os.path.exists(output):\n",
+    "    os.system('rm -rf %s' % output)\n",
+    "\n",
+    "\n",
+    "negf_json = json.load(open(INPUT_file))\n",
+    "model_json = json.load(open(model))\n",
+    "\n",
+    "log_path = output+'/log'\n",
+    "log_level = logging.INFO\n",
+    "set_log_handles(log_level, Path(log_path) if log_path else None)\n",
+    "\n",
+    "model = build_model(model,model_options= model_json['model_options'],\n",
+    "                    common_options=model_json['common_options'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "830d67a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    ------ k-point for NEGF -----\n",
+      "DPNEGF INFO    Gamma Center: True\n",
+      "DPNEGF INFO    Time Reversal: True\n",
+      "DPNEGF INFO    k-points Num: 1\n",
+      "DPNEGF INFO    k-points: [[0 0 0]]\n",
+      "DPNEGF INFO    k-points weights: [1.]\n",
+      "DPNEGF INFO    --------------------------------\n",
+      "DPNEGF INFO    The AtomicData_options is {'r_max': 2.0}\n",
+      "/opt/mamba/envs/deeptb-dev/lib/python3.10/site-packages/torch/nested/__init__.py:58: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)\n",
+      "DPNEGF INFO    The coupling width of lead_L is 1.\n",
+      "DPNEGF INFO    The coupling width of lead_R is 1.\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -2.000\n",
+      "DPNEGF INFO    computing green's function at e = -1.599\n",
+      "DPNEGF INFO    computing green's function at e = -1.198\n",
+      "DPNEGF INFO    computing green's function at e = -0.797\n",
+      "DPNEGF INFO    computing green's function at e = -0.396\n",
+      "DPNEGF INFO    computing green's function at e = 0.005\n",
+      "DPNEGF INFO    computing green's function at e = 0.406\n",
+      "DPNEGF INFO    computing green's function at e = 0.807\n",
+      "DPNEGF INFO    computing green's function at e = 1.208\n",
+      "DPNEGF INFO    computing green's function at e = 1.609\n"
+     ]
+    }
+   ],
+   "source": [
+    "negf = NEGF(\n",
+    "    model=model,\n",
+    "    AtomicData_options=negf_json['AtomicData_options'],\n",
+    "    structure=structure,\n",
+    "    results_path=output,  \n",
+    "    **negf_json['task_options']\n",
+    ")\n",
+    "   \n",
+    "negf.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "db275dee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "negf_out = torch.load('./output/negf.out.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7a29b9ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negf_out.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8eb092f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['DOS'][str(negf_out['k'][0])])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('DOS')\n",
+    "plt.title('DOS vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "4a2fe762",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('Transmission')\n",
+    "plt.title('Transmission vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "deeptb-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index 8fff307..f36accf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,13 +1,14 @@
 [tool.poetry]
-name = "dptb-negf"
+name = "dpnegf"
 version = "0.1.0"
 license = "LGPL-3.0"
 description = "A nonequilibrium Green’s function (NEGF) module based on dptb."
 authors = ["J. Zou <jijiezou@stu.pku.edu.cn>","Q. Gu <guqq@pku.edu.cn>", "Z. Zhanghao <zhouyinzhanghao@gmail.com>"]
 readme = "README.md"
-repository = "https://github.com/DeePTB-Lab/DeePTB-negf"
+repository = "https://github.com/DeePTB-Lab/dpnegf"
 
 [tool.poetry.dependencies]
+dptb = ">=2.1.0"
 python = ">=3.9, <=3.12.9"
 pytest = ">=7.2.0"
 pytest-order = "1.2.0"
@@ -70,7 +71,7 @@ build-backend = "poetry_dynamic_versioning.backend"
 [tool.poetry-dynamic-versioning]
 enable = true
 vcs = "git"
-strict = true
+strict = false
 format-jinja = """
     {%- if distance == 0 -%}
         {{ serialize_pep440(base, stage, revision) }}

From ce752b9271cc4d64dcc72d5d7a8871ce5ffa31d0 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 19 May 2025 16:37:15 +0800
Subject: [PATCH 002/152] =?UTF-8?q?refactor:=20=E7=A7=BB=E9=99=A4=E6=9C=AA?=
 =?UTF-8?q?=E4=BD=BF=E7=94=A8=E7=9A=84=E5=AF=BC=E5=85=A5=E4=BB=A5=E6=B8=85?=
 =?UTF-8?q?=E7=90=86=E4=BB=A3=E7=A0=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/negf_hamiltonian_init.py | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 5fe0369..89190b2 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -1,18 +1,8 @@
 from typing import List
 import torch
-from dpnegf.negf.areshkin_pole_sum import pole_maker
-from dpnegf.negf.recursive_green_cal import recursive_gf
-from dpnegf.negf.surface_green import selfEnergy
-from dpnegf.negf.negf_utils import quad, gauss_xw,update_kmap,leggauss
-from dpnegf.negf.ozaki_res_cal import ozaki_residues
-from dpnegf.negf.areshkin_pole_sum import pole_maker
 from ase.io import read,write
-from dpnegf.negf.poisson import Density2Potential, getImg
-from dpnegf.negf.scf_method import SCFMethod
 import logging
 import os
-import torch.optim as optim
-from dpnegf.utils.tools import j_must_have
 import numpy as np
 
 import ase

From 3a7db816b93190420e768527121281659caa9048 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 19 May 2025 17:11:46 +0800
Subject: [PATCH 003/152] clone argcheck from dptb

---
 dpnegf/utils/argcheck.py | 110 +++++++++++++++++++++------------------
 1 file changed, 58 insertions(+), 52 deletions(-)

diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index a6f32a9..bb8e693 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -8,8 +8,8 @@
 
 nnsk_model_config_checklist = ['unit','skfunction-skformula']
 nnsk_model_config_updatelist = ['sknetwork-sk_hop_nhidden', 'sknetwork-sk_onsite_nhidden', 'sknetwork-sk_soc_nhidden']
-dptb_model_config_checklist = ['dptb-if_batch_normalized', 'dptb-hopping_net_type', 'dptb-soc_net_type', 'dptb-env_net_type', 'dptb-onsite_net_type', 'dptb-hopping_net_activation', 'dptb-soc_net_activation', 'dptb-env_net_activation', 'dptb-onsite_net_activation', 
-                        'dptb-hopping_net_neuron', 'dptb-env_net_neuron', 'dptb-soc_net_neuron', 'dptb-onsite_net_neuron', 'dptb-axis_neuron', 'skfunction-skformula', 'sknetwork-sk_onsite_nhidden', 
+dptb_model_config_checklist = ['dptb-if_batch_normalized', 'dptb-hopping_net_type', 'dptb-soc_net_type', 'dptb-env_net_type', 'dptb-onsite_net_type', 'dptb-hopping_net_activation', 'dptb-soc_net_activation', 'dptb-env_net_activation', 'dptb-onsite_net_activation',
+                        'dptb-hopping_net_neuron', 'dptb-env_net_neuron', 'dptb-soc_net_neuron', 'dptb-onsite_net_neuron', 'dptb-axis_neuron', 'skfunction-skformula', 'sknetwork-sk_onsite_nhidden',
                         'sknetwork-sk_hop_nhidden']
 
 
@@ -75,7 +75,7 @@ def common_options():
                         - `float32`: indicating torch.float32
                         - `float64`: indicating torch.float64
                 """
-    
+
     doc_seed = "The random seed used to initialize the parameters and determine the shuffling order of datasets. Default: `3982377700`"
     doc_basis = "The atomic orbitals used to construct the basis. e.p. {'A':['2s','2p','s*'],'B':'[3s','3p']}"
     doc_overlap = "Whether to calculate the overlap matrix. Default: False"
@@ -116,7 +116,7 @@ def train_options():
     doc_ref_batch_size = "The batch size used in reference data, Default: 1"
     doc_val_batch_size = "The batch size used in validation data, Default: 1"
     doc_max_ckpt = "The maximum number of saved checkpoints, Default: 4"
-    
+
     args = [
         Argument("num_epoch", int, optional=False, doc=doc_num_epoch),
         Argument("batch_size", int, optional=True, default=1, doc=doc_batch_size),
@@ -140,7 +140,7 @@ def train_options():
 def test_options():
     doc_display_freq = "Frequency, or every how many iteration to display the training log to screem. Default: `1`"
     doc_batch_size = "The batch size used in testing, Default: 1"
-    
+
     args = [
         Argument("batch_size", int, optional=True, default=1, doc=doc_batch_size),
         Argument("display_freq", int, optional=True, default=1, doc=doc_display_freq),
@@ -335,7 +335,7 @@ def train_data_sub():
     doc_vlp = "Choose whether the overlap blocks are loaded when building dataset."
     doc_DM = "Choose whether the density matrix is loaded when building dataset."
     doc_separator = "the sepatator used to separate the prefix and suffix in the dataset directory. Default: '.'"
-    
+
     args = [
         Argument("type", str, optional=True, default="DefaultDataset", doc="The type of dataset."),
         Argument("root", str, optional=False, doc=doc_root),
@@ -396,7 +396,7 @@ def reference_data_sub():
     ]
 
     doc_reference = "The dataset settings for reference."
-    
+
     return Argument("reference", dict, optional=True, sub_fields=args, sub_variants=[], doc=doc_reference)
 
 def test_data_sub():
@@ -420,7 +420,7 @@ def test_data_sub():
     ]
 
     doc_test = "The dataset settings for testing."
-    
+
     return Argument("test", dict, optional=False, sub_fields=args, default={}, sub_variants=[], doc=doc_test)
 
 
@@ -560,8 +560,8 @@ def e3baseline():
                 "mlp_latent_dimensions": [128, 128, 256],
                 "mlp_nonlinearity": "silu",
                 "mlp_initialization": "uniform"
-            }, 
-            default=None, 
+            },
+            default=None,
             doc=doc_latent_kwargs
             ),
             Argument("env_embed_multiplicity", int, optional=True, default=1, doc=doc_env_embed_multiplicity),
@@ -610,7 +610,7 @@ def slem():
             Argument("avg_num_neighbors", [int, float], optional=False, doc=doc_avg_num_neighbors),
             Argument("r_max", [float, int, dict], optional=False, doc=doc_r_max),
             Argument("n_layers", int, optional=False, doc=doc_n_layers),
-            
+
             Argument("n_radial_basis", int, optional=True, default=10, doc=doc_n_radial_basis),
             Argument("PolynomialCutoff_p", int, optional=True, default=6, doc="The order of polynomial cutoff function. Default: 6"),
             Argument("cutoff_type", str, optional=True, default="polynomial", doc="The type of cutoff function. Default: polynomial"),
@@ -707,7 +707,7 @@ def nnsk():
     # overlap = Argument("overlap", bool, optional=True, default=False, doc="The parameters to define the overlap correction of nnsk model.")
 
     return Argument("nnsk", dict, sub_fields=[
-            Argument("onsite", dict, optional=False, sub_fields=[], sub_variants=[onsite()], doc=doc_onsite), 
+            Argument("onsite", dict, optional=False, sub_fields=[], sub_variants=[onsite()], doc=doc_onsite),
             Argument("hopping", dict, optional=False, sub_fields=[], sub_variants=[hopping()], doc=doc_hopping),
             Argument("soc", dict, optional=True, default={}, doc=doc_soc),
             Argument("freeze", [bool,str,list], optional=True, default=False, doc=doc_freeze),
@@ -731,7 +731,7 @@ def push():
     ], sub_variants=[], optional=True, default=False, doc="The parameters to define the push the soft cutoff of nnsk model.")
 
 def onsite():
-    doc_method = """The onsite correction mode, the onsite energy is expressed as the energy of isolated atoms plus the model correction, the correction mode are:
+    doc_method = r"""The onsite correction mode, the onsite energy is expressed as the energy of isolated atoms plus the model correction, the correction mode are:
                     Default: `none`: use the database onsite energy value.
                     - `strain`: The strain mode correct the onsite matrix densly by $$H_{i,i}^{lm,l^\prime m^\prime} = \epsilon_l^0 \delta_{ll^\prime}\delta_{mm^\prime} + \sum_p \sum_{\zeta} \Big[ \mathcal{U}_{\zeta}(\hat{\br}_{ip}) \ \epsilon_{ll^\prime \zeta} \Big]_{mm^\prime}$$ which is also parameterized as a set of Slater-Koster like integrals.\n\n\
                     - `uniform`: The correction is a energy shift respect of orbital of each atom. Which is formally written as: 
@@ -805,7 +805,7 @@ def hopping():
 
     for ii in formulas:
         args.append(Argument(ii, dict, common_params))
-    
+
     return Variant("method", args,optional=False, doc=doc_method)
 
 
@@ -856,7 +856,7 @@ def loss_options():
         Argument("eig_ham", dict, sub_fields=hamil+eigvals+eig_ham),
     ], optional=False, doc=doc_method)
 
-    
+
 
     args = [
         Argument("train", dict, optional=False, sub_fields=[], sub_variants=[loss_args], doc=doc_train),
@@ -879,13 +879,13 @@ def normalize(data):
     data = base.normalize_value(data)
     # data = base.normalize_value(data, trim_pattern="_*")
     base.check_value(data, strict=True)
-    
+
     # add check loss and use wannier:
-    
+
     # if data['data_options']['use_wannier']:
     #     if not data['loss_options']['losstype'] .startswith("block"):
     #         log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n')
-    
+
     # if data['loss_options']['losstype'] .startswith("block"):
     #     if not data['data_options']['use_wannier']:
     #         log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n")
@@ -902,13 +902,13 @@ def normalize(data):
 #     data = base.normalize_value(data)
 #     # data = base.normalize_value(data, trim_pattern="_*")
 #     base.check_value(data, strict=True)
-    
+
 #     # add check loss and use wannier:
-    
+
 #     # if data['data_options']['use_wannier']:
 #     #     if not data['loss_options']['losstype'] .startswith("block"):
 #     #         log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n')
-    
+
 #     # if data['loss_options']['losstype'] .startswith("block"):
 #     #     if not data['data_options']['use_wannier']:
 #     #         log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n")
@@ -926,13 +926,13 @@ def normalize(data):
 #     data = base.normalize_value(data)
 #     # data = base.normalize_value(data, trim_pattern="_*")
 #     base.check_value(data, strict=True)
-    
+
 #     # add check loss and use wannier:
-    
+
 #     # if data['data_options']['use_wannier']:
 #     #     if not data['loss_options']['losstype'] .startswith("block"):
 #     #         log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n')
-    
+
 #     # if data['loss_options']['losstype'] .startswith("block"):
 #     #     if not data['data_options']['use_wannier']:
 #     #         log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n")
@@ -945,7 +945,7 @@ def normalize_test(data):
     co = common_options()
     da = test_data_options()
     to = test_options()
-    
+
     base = Argument("base", dict, [co, da, to, lo])
     data = base.normalize_value(data)
     # data = base.normalize_value(data, trim_pattern="_*")
@@ -1193,8 +1193,8 @@ def pyamg():
         Argument("mix_rate", int, optional=True, default=0.25, doc=doc_mix_rate),
         Argument("poisson_dtype", str, optional=True, default='float64', doc=doc_poisson_dtype),
         Argument("grid", dict, optional=False, sub_fields=grid(), doc=doc_grid),
-        Argument("gate_top", dict, optional=False, sub_fields=gate(), doc=doc_gate),
-        Argument("gate_bottom", dict, optional=False, sub_fields=gate(), doc=doc_gate),
+        Argument("gate_top", dict, optional=False, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("gate_bottom", dict, optional=False, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("dielectric_region", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region", dict, optional=False, sub_fields=doped(), doc=doc_doped)
     ]
@@ -1215,9 +1215,12 @@ def scipy():
         Argument("max_iter", int, optional=True, default=100, doc=doc_max_iter),
         Argument("mix_rate", int, optional=True, default=0.25, doc=doc_mix_rate),
         Argument("poisson_dtype", str, optional=True, default='float64', doc=doc_poisson_dtype),
+        Argument("with_Dirichlet_leads", bool, optional=True, default=False, doc="Whether to use Dirichlet boundary condition for leads"),
         Argument("grid", dict, optional=True, sub_fields=grid(), doc=doc_grid),
-        Argument("gate_top", dict, optional=True, sub_fields=gate(), doc=doc_gate),
-        Argument("gate_bottom", dict, optional=True, sub_fields=gate(), doc=doc_gate),
+        Argument("gate_top", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("gate_bottom", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("lead_L", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("lead_R", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("dielectric_region", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region1", dict, optional=True, sub_fields=doped(), doc=doc_doped),
         Argument("doped_region2", dict, optional=True, sub_fields=doped(), doc=doc_doped)
@@ -1233,7 +1236,7 @@ def grid():
         Argument("z_range", str, optional=False, doc=doc_zrange),
     ]
 
-def gate():
+def Dirichlet_BC():
     doc_xrange=""
     doc_yrange=""
     doc_zrange=""
@@ -1242,7 +1245,7 @@ def gate():
         Argument("x_range", str, optional=False, doc=doc_xrange),
         Argument("y_range", str, optional=False, doc=doc_yrange),
         Argument("z_range", str, optional=False, doc=doc_zrange),
-        Argument("voltage", [int, float], optional=False, doc=doc_voltage)
+        Argument("voltage", [int, float], optional=True, doc=doc_voltage, default=None)
     ]
 
 def dielectric():
@@ -1286,7 +1289,7 @@ def run_options():
                         - False: indicating the structure is not periodic
                         - list of bool: indicating the structure is periodic in x,y,z direction respectively.
                 """
- 
+
     args = [
         Argument("task_options", dict, sub_fields=[], optional=True, sub_variants=[task_options()], doc = doc_task),
         Argument("structure", [str,None], optional=True, default=None, doc = doc_structure),
@@ -1304,7 +1307,7 @@ def normalize_run(data):
     run_op = run_options()
     data = run_op.normalize_value(data)
     run_op.check_value(data, strict=True)
-    
+
     return data
 
 def task_options():
@@ -1340,17 +1343,20 @@ def band():
                     - "vasp" : the vasp format
                     - "ase" : the ase format
                     """
-    doc_kpath = "for abacus, this is list, for vasp it is a string to specifc the kpath."
+    doc_kpath = "for abacus, this is list of list of float, for vasp it is a list[str] to specify the kpath."
     doc_klabels = "the labels for high symmetry kpoint"
     doc_emin="the min energy to show the band plot"
     doc_emax="the max energy to show the band plot"
     doc_E_fermi = "the fermi level used to plot band"
     doc_ref_band = "the reference band structure to be ploted together with dptb bands."
     doc_nel_atom = "the valence electron number of each type of atom."
-    
+    doc_high_sym_kpoints = "the high symmetry kpoints dict, e.g. {'G':[0,0,0],'K':[0.5,0.5,0]}, only used for kline_type is vasp"
+    doc_num_in_line = "the number of kpoints in each line path, only used for kline_type is vasp."
     return [
         Argument("kline_type", str, optional=False, doc=doc_kline_type),
         Argument("kpath", [str,list], optional=False, doc=doc_kpath),
+        Argument("high_sym_kpoints",dict,optional=True,default={},doc=doc_high_sym_kpoints),
+        Argument("number_in_line", int, optional=True, default=None, doc=doc_num_in_line),
         Argument("klabels", list, optional=True, default=[''], doc=doc_klabels),
         Argument("E_fermi", [float, int, None], optional=True, doc=doc_E_fermi, default=None),
         Argument("emin", [float, int, None], optional=True, doc=doc_emin, default=None),
@@ -1493,7 +1499,7 @@ def ifermi():
     KTOL = 1e-5
     SCALE = 4
 
- 
+
     plot_options=[
         Argument("colors", [str,dict,list,None], optional=True, default=None, doc=doc_colors),
         Argument("projection_axis", [list,None], optional=True, default=None, doc=doc_projection_axis),
@@ -1602,7 +1608,7 @@ def bandinfo_sub():
     doc_band_max = "The maxmum band index for training band window"
     doc_emin = "the minmum energy window, 0 meand the min value of the band at index band_min"
     doc_emax = "the max energy window, emax value is respect to the min value of the band at index band_min"
-    
+
     args = [
         Argument("band_min", int, optional=True, doc=doc_band_min, default=0),
         Argument("band_max", [int, None], optional=True, doc=doc_band_max, default=None),
@@ -1617,7 +1623,7 @@ def AtomicData_options_sub():
     doc_er_max = "The cutoff value for environment for each site for env correction model. should set for nnsk+env correction model."
     doc_oer_max = "The cutoff value for onsite environment for nnsk model, for now only need to set in strain and NRL mode."
     doc_pbc = "The periodic condition for the structure, can bool or list of bool to specific x,y,z direction."
-    
+
     args = [
         Argument("r_max", [float, int, dict], optional=False, doc=doc_r_max, default=4.0),
         Argument("er_max", [float, int, dict], optional=True, doc=doc_er_max, default=None),
@@ -1713,7 +1719,7 @@ def get_cutoffs_from_model_options(model_options):
         else:
             log.error("The method of embedding have not been defined in get cutoff functions")
             raise NotImplementedError("The method of embedding have not been defined in get cutoff functions")
-        
+
     if model_options.get("nnsk", None) is not None:
         assert r_max is None, "r_max should not be provided in outside the nnsk for training nnsk model."
         if model_options["nnsk"]["hopping"].get("rs",None) is not None:
@@ -1733,14 +1739,14 @@ def get_cutoffs_from_model_options(model_options):
             else:
                 # oer_max = model_options["nnsk"]["onsite"]["rs"] + 3 * model_options["nnsk"]["onsite"]["w"]
                 oer_max = format_cuts(model_options["nnsk"]["onsite"]["rs"], model_options["nnsk"]["onsite"]["w"], 3)
-    
+
     elif model_options.get("dftbsk", None) is not None:
-        assert r_max is None, "r_max should not be provided in outside the dftbsk for training dftbsk model."
+        assert r_max is None, "r_max should not be provided other than the dftbsk param section for training dftbsk model."
         r_max = model_options["dftbsk"].get("r_max")
-    
+
     else:
         # not nnsk not dftbsk, must be only env or E3. the embedding should be provided.
-        assert model_options.get("embedding",None) is not None   
+        assert model_options.get("embedding",None) is not None
 
     return r_max, er_max, oer_max
 def collect_cutoffs(jdata):
@@ -1765,7 +1771,7 @@ def collect_cutoffs(jdata):
 
     Logs:
     Various informational messages about the cutoff values and their sources.
-    """ 
+    """
 
     model_options = jdata["model_options"]
     r_max, er_max, oer_max = get_cutoffs_from_model_options(model_options)
@@ -1783,14 +1789,14 @@ def collect_cutoffs(jdata):
                 oer_max = jdata['data_options']['oer_max']
 
             if jdata['data_options'].get("er_max") is not None:
-                log.info("IN PUSH mode, the env correction should not be used. the er_max will not take effect.")     
+                log.info("IN PUSH mode, the env correction should not be used. the er_max will not take effect.")
         else:
             if  jdata['data_options'].get("r_max") is not None:
                 log.info("When not nnsk/push. the cutoffs will take from the model options: r_max  rs and rc values. this seting in data_options will be ignored.")
 
     assert r_max is not None
     cutoff_options = ({"r_max": r_max, "er_max": er_max, "oer_max": oer_max})
-    
+
     log.info("-"*66)
     log.info('     {:<55}    '.format("Cutoff options:"))
     log.info('     {:<55}    '.format(" "*30))
@@ -1813,13 +1819,13 @@ def normalize(data):
     data = base.normalize_value(data)
     # data = base.normalize_value(data, trim_pattern="_*")
     base.check_value(data, strict=True)
-    
+
     # add check loss and use wannier:
-    
+
     # if data['data_options']['use_wannier']:
     #     if not data['loss_options']['losstype'] .startswith("block"):
     #         log.info(msg='\n Warning! set data_options use_wannier true, but the loss type is not block_l2! The the wannier TB will not be used when training!\n')
-    
+
     # if data['loss_options']['losstype'] .startswith("block"):
     #     if not data['data_options']['use_wannier']:
     #         log.error(msg="\n ERROR! for block loss type, must set data_options:use_wannier True\n")
@@ -1842,7 +1848,7 @@ def normalize_skf2nnsk(data):
         Argument('w', [float,int], optional=True, default=0.2, doc="The w value for the hopping term."),
         Argument('atomic_radius',[str,dict], optional=True, default='cov', doc="The atomic radius for the hopping term, default is 'cov'.")
     ]
-    
+
     doc_lr_scheduler = "The learning rate scheduler tools settings, the lr scheduler is used to scales down the learning rate during the training process. Proper setting can make the training more stable and efficient. The supported lr schedular includes: `Exponential Decaying (exp)`, `Linear multiplication (linear)`"
     doc_optimizer = "\
         The optimizer setting for selecting the gradient optimizer of model training. Optimizer supported includes `Adam`, `SGD` and `LBFGS` \n\n\
@@ -1851,7 +1857,7 @@ def normalize_skf2nnsk(data):
         - `SGD`: [Stochastic Gradient Descent.](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html)\n\n\
         - `LBFGS`: [On the limited memory BFGS method for large scale optimization.](http://users.iems.northwestern.edu/~nocedal/PDFfiles/limited-memory.pdf) \n\n\
     "
-    
+
     train_ops = [
         Argument('nstep', int, optional=False, doc="The number of steps for the training."),
         Argument('nsample', int, optional=True, default=256, doc="The number of steps for the training."),
@@ -1871,4 +1877,4 @@ def normalize_skf2nnsk(data):
     base.check_value(data, strict=True)
 
     return data
-    
+

From b10dfb76b61c86a464b63cf25ac5a54f9c9d2e41 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 19 May 2025 17:13:27 +0800
Subject: [PATCH 004/152] =?UTF-8?q?refactor:=20=E6=9B=B4=E6=96=B0=E5=8C=96?=
 =?UTF-8?q?=E5=AD=A6=E5=8A=BF=E5=8F=98=E9=87=8F=EF=BC=8C=E4=BF=9D=E8=AF=81?=
 =?UTF-8?q?=E7=94=B5=E5=8E=8B=E4=B8=80=E8=87=B4=E6=80=A7=E5=92=8C=E4=BB=A3?=
 =?UTF-8?q?=E7=A0=81=E6=B8=85=E6=99=B0=E5=BA=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/density.py       |  4 ++--
 dpnegf/negf/lead_property.py |  8 ++++----
 dpnegf/runner/NEGF.py        | 19 ++++++++-----------
 3 files changed, 14 insertions(+), 17 deletions(-)

diff --git a/dpnegf/negf/density.py b/dpnegf/negf/density.py
index 15c4462..909805f 100644
--- a/dpnegf/negf/density.py
+++ b/dpnegf/negf/density.py
@@ -300,8 +300,8 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
             
             A_Ld = [1j*(deviceprop.grd[i]-deviceprop.grd[i].conj().T)-A_Rd[i] for i in range(len(A_Rd))]
             # the chemical potential in fermi_dirac is always set as lead_L.mu, representing the source and drain fermi level
-            gnd = [A_Ld[i]*deviceprop.lead_L.fermi_dirac(e+deviceprop.lead_L.efermi) \
-                    +A_Rd[i]*deviceprop.lead_R.fermi_dirac(e+deviceprop.lead_R.efermi) for i in range(len(A_Ld))]
+            gnd = [A_Ld[i]*deviceprop.lead_L.fermi_dirac(e+deviceprop.lead_L.mu) \
+                    +A_Rd[i]*deviceprop.lead_R.fermi_dirac(e+deviceprop.lead_R.mu) for i in range(len(A_Ld))]
             gpd = [A_Ld[i] + A_Rd[i] - gnd[i] for i in range(len(A_Ld))]
                 
 
diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 1786842..1618f97 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -165,7 +165,7 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                 sLL=self.SLLk,
                 hDL=HDL_reduced,
                 sDL=SDL_reduced,             #TODO: check chemiPot settiing is correct or not
-                chemiPot=self.efermi, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
+                chemiPot=self.mu, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
                 etaLead=eta_lead, 
                 method=method
             )
@@ -192,7 +192,7 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                     hLL=self.HLLk,
                     sL=self.SLk,
                     sLL=self.SLLk,            #TODO: check chemiPot settiing is correct or not
-                    chemiPot=self.efermi, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
+                    chemiPot=self.mu, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
                     etaLead=eta_lead, 
                     method=method
                 )
@@ -216,9 +216,9 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk) 
             # HDL_reduced, SDL_reduced = self.HDL, self.SDL
             if not isinstance(energy, torch.Tensor):
-                eeshifted = torch.scalar_tensor(energy, dtype=torch.complex128) + self.efermi
+                eeshifted = torch.scalar_tensor(energy, dtype=torch.complex128) + self.mu
             else:
-                eeshifted = energy + self.efermi
+                eeshifted = energy + self.mu
             # self.se = (eeshifted*self.SDL-self.HDL) @ sgf_k[:b,:b] @ (eeshifted*self.SDL.conj().T-self.HDL.conj().T)
             self.se = (eeshifted*SDL_reduced-HDL_reduced) @ sgf_k[:b,:b] @ (eeshifted*SDL_reduced.conj().T-HDL_reduced.conj().T)
 
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 66e657d..87f650d 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -1,21 +1,12 @@
-from typing import List
 import torch
-from dpnegf.negf.recursive_green_cal import recursive_gf
-from dpnegf.negf.surface_green import selfEnergy
 from dpnegf.negf.negf_utils import quad, gauss_xw,leggauss,update_kmap
 from dpnegf.negf.ozaki_res_cal import ozaki_residues
 from dpnegf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
 from dpnegf.negf.density import Ozaki,Fiori
-from dpnegf.negf.areshkin_pole_sum import pole_maker
 from dpnegf.negf.device_property import DeviceProperty
 from dpnegf.negf.lead_property import LeadProperty
-from ase.io import read
 import ase
-from dpnegf.negf.poisson import Density2Potential, getImg
-from dpnegf.negf.scf_method import SCFMethod
 from dpnegf.utils.constants import Boltzmann, eV2J
-import os
-from dpnegf.utils.tools import j_must_have
 import numpy as np
 from dpnegf.utils.make_kpoints import kmesh_sampling_negf
 import logging
@@ -210,8 +201,14 @@ def __init__(self,
         # self.LDOS_integral = {}  # for electron density integral
         self.free_charge = {} # net charge: hole - electron
         # Dirichlet region for Poisson equation
-        self.Dirichlet_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("gate") or i.startswith("electrode")]
-        
+        if self.scf:
+            for lead in ["lead_L", "lead_R"]:
+                if "voltage" in self.poisson_options[lead] and self.poisson_options[lead]["voltage"]:
+                    assert self.stru_options[lead]["voltage"] == self.poisson_options[lead]["voltage"], f"{lead} voltage should be consistent"
+                else:
+                    self.poisson_options[lead]["voltage"] = self.stru_options[lead]["voltage"]
+        self.Dirichlet_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("gate")\
+                                    or i.startswith("lead")]
         self.dielectric_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("dielectric")]
         self.doped_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("doped")]
 

From fdf8d355c1dae606b1721d11160c3f73e3e0e5c5 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 19 May 2025 22:19:43 +0800
Subject: [PATCH 005/152] feature: add Ef calculation in NEGF.py

---
 dpnegf/runner/NEGF.py     | 41 +++++++++++++++++++++++++++++++++++----
 dpnegf/utils/argcheck.py  |  8 +++++---
 dpnegf/utils/constants.py | 12 +++++++++++-
 3 files changed, 53 insertions(+), 8 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 87f650d..cfcb84e 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -1,7 +1,9 @@
 import torch
 from dpnegf.negf.negf_utils import quad, gauss_xw,leggauss,update_kmap
+from dpnegf.utils.constants import valence_electron
 from dpnegf.negf.ozaki_res_cal import ozaki_residues
 from dpnegf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
+from dptb.postprocess.elec_struc_cal import ElecStruCal
 from dpnegf.negf.density import Ozaki,Fiori
 from dpnegf.negf.device_property import DeviceProperty
 from dpnegf.negf.lead_property import LeadProperty
@@ -61,6 +63,10 @@ def __init__(self,
         self.eta_lead = eta_lead; self.eta_device = eta_device
         self.emin = emin; self.emax = emax; self.espacing = espacing
         self.stru_options = stru_options
+        if e_fermi is None:
+            for lead in ["lead_L", "lead_R"]:
+                assert self.stru_options[lead]["kmesh_lead_Ef"] is not None, f"{lead} kmesh_lead_Ef should be set if e_fermi is None"
+
 
         self.use_saved_HS = use_saved_HS
         self.saved_HS_path = saved_HS_path
@@ -124,9 +130,36 @@ def __init__(self,
                 self.negf_hamiltonian.initialize(kpoints=self.kpoints,block_tridiagnal=self.block_tridiagonal,\
                                                  useBloch=self.useBloch,bloch_factor=self.bloch_factor,\
                                                  use_saved_HS=self.use_saved_HS, saved_HS_path=self.saved_HS_path)
-        # self.subblocks = subblocks # for not block_tridiagonal case, subblocks is [HD.shape[1]]
+        e_fermi = {}
+        if not self.e_fermi:        
+            elec_cal = ElecStruCal(model=model,device=self.torch_device)
+            if nel_atom is None:
+                log.warning(msg="nel_atom is not set, using the default value")
+                nel_atom = {}
+                for lead in ["lead_L", "lead_R"]:
+                    unique_elements = struct_leads[lead].get_chemical_symbols()
+                    for ele in unique_elements:
+                        if ele not in valence_electron:
+                            raise ValueError(f"Element {ele} is not in the valence electron dictionary")
+                        else:
+                            nel_atom[ele] = valence_electron[ele]
+            for lead in ["lead_L", "lead_R"]:
+                _, e_fermi[lead]  = elec_cal.get_fermi_level(data=struct_leads[lead], 
+                                nel_atom = nel_atom,
+                                meshgrid=self.stru_options[lead]["kmesh_lead_Ef"],
+                                AtomicData_options=AtomicData_options,
+                                smearing_method=self.stru_options["e_fermi_smearing"],
+                                temp=100.0)
+        else:
+            e_fermi["lead_L"] = self.e_fermi
+            e_fermi["lead_R"] = self.e_fermi
+        
+        self.e_fermi = e_fermi
+        log.info(msg="Fermi level for lead_L: {0}".format(e_fermi["lead_L"]))
+        log.info(msg="Fermi level for lead_R: {0}".format(e_fermi["lead_R"]))
 
-        self.deviceprop = DeviceProperty(self.negf_hamiltonian, struct_device, results_path=self.results_path, efermi=self.e_fermi)
+        self.deviceprop = DeviceProperty(self.negf_hamiltonian, struct_device, results_path=self.results_path, \
+                                         efermi=self.e_fermi)
         self.deviceprop.set_leadLR(
                 lead_L=LeadProperty(
                 hamiltonian=self.negf_hamiltonian, 
@@ -134,7 +167,7 @@ def __init__(self,
                 structure=struct_leads["lead_L"], 
                 results_path=self.results_path,
                 e_T=self.ele_T,
-                efermi=self.e_fermi, 
+                efermi=self.e_fermi["lead_L"], 
                 voltage=self.stru_options["lead_L"]["voltage"],
                 useBloch=self.useBloch,
                 bloch_factor=self.bloch_factor,
@@ -148,7 +181,7 @@ def __init__(self,
                 structure=struct_leads["lead_R"], 
                 results_path=self.results_path, 
                 e_T=self.ele_T,
-                efermi=self.e_fermi, 
+                efermi=self.e_fermi["lead_R"], 
                 voltage=self.stru_options["lead_R"]["voltage"],
                 useBloch=self.useBloch,
                 bloch_factor=self.bloch_factor,
diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index bb8e693..ce59a6d 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1053,7 +1053,7 @@ def negf():
         Argument("espacing", [int, float], optional=False, doc=doc_espacing),
         Argument("emin", [int, float], optional=False, doc=doc_emin),
         Argument("emax", [int, float], optional=False, doc=doc_emax),
-        Argument("e_fermi", [int, float], optional=False, doc=doc_e_fermi),
+        Argument("e_fermi", [int, float], optional=True, default=None ,doc=doc_e_fermi),
         Argument("density_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[density_options()], doc=doc_density_options),
         Argument("eta_lead", [int, float], optional=True, default=1e-5, doc=doc_eta_lead),
         Argument("eta_device", [int, float], optional=True, default=0., doc=doc_eta_device),
@@ -1082,7 +1082,8 @@ def stru_options():
         Argument("kmesh", list, optional=True, default=[1,1,1], doc=doc_kmesh),
         Argument("pbc", list, optional=True, default=[False, False, False], doc=doc_pbc),
         Argument("gamma_center", list, optional=True, default=True, doc=doc_gamma_center),
-        Argument("time_reversal_symmetry", list, optional=True, default=True, doc=doc_time_reversal_symmetry)
+        Argument("time_reversal_symmetry", list, optional=True, default=True, doc=doc_time_reversal_symmetry),
+        Argument("e_fermi_smearing", str, optional=True, default="FD", doc="The smearing method for Fermi level. Default: FD"),
     ]
 
 def device():
@@ -1103,7 +1104,8 @@ def lead():
         Argument("id", str, optional=False, doc=doc_id),
         Argument("voltage", [int, float], optional=False, doc=doc_voltage),
         Argument("useBloch", bool, optional=True, default=False, doc=doc_useBloch),
-        Argument("bloch_factor", list, optional=True, default=[1,1,1], doc=doc_bloch_factor)
+        Argument("bloch_factor", list, optional=True, default=[1,1,1], doc=doc_bloch_factor),
+        Argument("kmesh_lead_Ef", list, optional=True, doc="The kmesh for lead Fermi level calculation."),
     ]
 
 def scf_options():
diff --git a/dpnegf/utils/constants.py b/dpnegf/utils/constants.py
index a4eeb9b..7d89be0 100644
--- a/dpnegf/utils/constants.py
+++ b/dpnegf/utils/constants.py
@@ -102,4 +102,14 @@
 dtype_dict = {"float32": torch.float32, "float64": torch.float64}
 # k = Boltzmann # k is the Boltzmann constant in old NEGF module
 Coulomb = 6.24150974e18 # in the unit of eV*Angstrom
-eV2J = 1.6021766208e-19 # in the unit of J
\ No newline at end of file
+eV2J = 1.6021766208e-19 # in the unit of J
+
+valence_electron = {
+        'H': 1, 'He': 2,
+        'Li': 1, 'Be': 2, 'B': 3, 'C': 4, 'N': 5, 'O': 6, 'F': 7, 'Ne': 8,
+        'Na': 1, 'Mg': 2, 'Al': 3, 'Si': 4, 'P': 5, 'S': 6, 'Cl': 7, 'Ar': 8,
+        'K': 1, 'Ca': 2,
+        'Sc': 3, 'Ti': 4, 'V': 5, 'Cr': 6, 'Mn': 7, 'Fe': 8,
+        'Co': 9, 'Ni': 10, 'Cu': 11, 'Zn': 12,
+        'Ga': 3, 'Ge': 4, 'As': 5, 'Se': 6, 'Br': 7, 'Kr': 8,
+    }
\ No newline at end of file

From 6a659fd979a8e783624c80e9e0b4c53a8c8a89be Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 20 May 2025 16:49:30 +0800
Subject: [PATCH 006/152] feature: add Fermi level calculation for Lead_L/R in
 NEGF.py

---
 dpnegf/negf/lead_property.py |   2 +-
 dpnegf/negf/negf_utils.py    |  11 +++-
 dpnegf/runner/NEGF.py        | 100 +++++++++++++++++++++++------------
 dpnegf/utils/argcheck.py     |   5 +-
 4 files changed, 81 insertions(+), 37 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 1618f97..d6999ff 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -165,7 +165,7 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                 sLL=self.SLLk,
                 hDL=HDL_reduced,
                 sDL=SDL_reduced,             #TODO: check chemiPot settiing is correct or not
-                chemiPot=self.mu, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
+                chemiPot=self.mu,
                 etaLead=eta_lead, 
                 method=method
             )
diff --git a/dpnegf/negf/negf_utils.py b/dpnegf/negf/negf_utils.py
index 4d68cf9..0c98970 100644
--- a/dpnegf/negf/negf_utils.py
+++ b/dpnegf/negf/negf_utils.py
@@ -745,4 +745,13 @@ def write_vesta_lcurrent(positions, vesta_file, lcurrent, current, outpath):
     data = data[:replace_start]+text+data[replace_end:]
 
     with open(outpath, "w") as f:
-        f.write(data)
\ No newline at end of file
+        f.write(data)
+
+def is_fully_covered(lead_region, doped_region):
+    '''Check if the lead region is fully covered by the doped region.'''
+    for dim in ['x_range', 'y_range', 'z_range']:
+        lead_start, lead_end = map(float, lead_region.get(dim, None).split(':'))
+        dope_start, dope_end = map(float, doped_region.get(dim, None).split(':'))
+        if lead_start < dope_start or lead_end > dope_end:
+            return False
+    return True
\ No newline at end of file
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index cfcb84e..59485de 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -7,6 +7,7 @@
 from dpnegf.negf.density import Ozaki,Fiori
 from dpnegf.negf.device_property import DeviceProperty
 from dpnegf.negf.lead_property import LeadProperty
+from dpnegf.negf.negf_utils import is_fully_covered
 import ase
 from dpnegf.utils.constants import Boltzmann, eV2J
 import numpy as np
@@ -34,7 +35,7 @@ def __init__(self,
                 model: torch.nn.Module,
                 AtomicData_options: dict, 
                 structure: Union[AtomicData, ase.Atoms, str],
-                ele_T: float,e_fermi: float,
+                ele_T: float,
                 emin: float, emax: float, espacing: float,
                 density_options: dict,
                 unit: str,
@@ -42,6 +43,7 @@ def __init__(self,
                 stru_options: dict,eta_lead: float,eta_device: float,
                 block_tridiagonal: bool,
                 sgf_solver: str,
+                e_fermi: float=None,
                 use_saved_HS: bool=False, saved_HS_path: str=None,
                 self_energy_save: bool=False, self_energy_save_path: str=None, se_info_display: bool=False,
                 out_tc: bool=False,out_dos: bool=False,out_density: bool=False,out_potential: bool=False,
@@ -65,7 +67,7 @@ def __init__(self,
         self.stru_options = stru_options
         if e_fermi is None:
             for lead in ["lead_L", "lead_R"]:
-                assert self.stru_options[lead]["kmesh_lead_Ef"] is not None, f"{lead} kmesh_lead_Ef should be set if e_fermi is None"
+                assert "kmesh_lead_Ef" in self.stru_options[lead], f"{lead} must have 'kmesh_lead_Ef' set in stru_options if e_fermi is None"
 
 
         self.use_saved_HS = use_saved_HS
@@ -130,34 +132,51 @@ def __init__(self,
                 self.negf_hamiltonian.initialize(kpoints=self.kpoints,block_tridiagnal=self.block_tridiagonal,\
                                                  useBloch=self.useBloch,bloch_factor=self.bloch_factor,\
                                                  use_saved_HS=self.use_saved_HS, saved_HS_path=self.saved_HS_path)
+
+        ## Poisson equation settings
+        self.poisson_options = poisson_options
+        # self.LDOS_integral = {}  # for electron density integral
+        self.free_charge = {} # net charge: hole - electron
+        # Dirichlet region for Poisson equation
+        if self.scf:
+            for lead_tag in ["lead_L", "lead_R"]:
+                if "voltage" in self.poisson_options[lead_tag] and self.poisson_options[lead_tag]["voltage"]:
+                    assert self.stru_options[lead_tag]["voltage"] == self.poisson_options[lead_tag]["voltage"], f"{lead_tag} voltage should be consistent"
+                else:
+                    self.poisson_options[lead_tag]["voltage"] = self.stru_options[lead_tag]["voltage"]
+        self.Dirichlet_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("gate")\
+                                    or i.startswith("lead")]
+        self.dielectric_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("dielectric")]
+        self.doped_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("doped")]
+
+
+
+        # calculate Fermi level
+        log.info(msg="-------------Fermi level calculation-------------")
         e_fermi = {}
         if not self.e_fermi:        
             elec_cal = ElecStruCal(model=model,device=self.torch_device)
-            if nel_atom is None:
-                log.warning(msg="nel_atom is not set, using the default value")
-                nel_atom = {}
-                for lead in ["lead_L", "lead_R"]:
-                    unique_elements = struct_leads[lead].get_chemical_symbols()
-                    for ele in unique_elements:
-                        if ele not in valence_electron:
-                            raise ValueError(f"Element {ele} is not in the valence electron dictionary")
-                        else:
-                            nel_atom[ele] = valence_electron[ele]
-            for lead in ["lead_L", "lead_R"]:
-                _, e_fermi[lead]  = elec_cal.get_fermi_level(data=struct_leads[lead], 
-                                nel_atom = nel_atom,
-                                meshgrid=self.stru_options[lead]["kmesh_lead_Ef"],
+            nel_atom_lead = self.get_nel_atom_lead(struct_leads, self.poisson_options, self.doped_region)
+            log.info(msg="Number of electrons in lead_L: {0}".format(nel_atom_lead["lead_L"]))
+            log.info(msg="Number of electrons in lead_R: {0}".format(nel_atom_lead["lead_R"]))
+            for lead_tag in ["lead_L", "lead_R"]:
+                _, e_fermi[lead_tag]  = elec_cal.get_fermi_level(data=struct_leads[lead_tag], 
+                                nel_atom = nel_atom_lead[lead_tag],
+                                meshgrid=self.stru_options[lead_tag]["kmesh_lead_Ef"],
                                 AtomicData_options=AtomicData_options,
-                                smearing_method=self.stru_options["e_fermi_smearing"],
+                                smearing_method=self.stru_options.get("e_fermi_smearing", "FD"),
                                 temp=100.0)
         else:
             e_fermi["lead_L"] = self.e_fermi
             e_fermi["lead_R"] = self.e_fermi
+            log.warning(msg="Fermi level is set to {0} from input file".format(self.e_fermi))
         
         self.e_fermi = e_fermi
         log.info(msg="Fermi level for lead_L: {0}".format(e_fermi["lead_L"]))
         log.info(msg="Fermi level for lead_R: {0}".format(e_fermi["lead_R"]))
+        log.info(msg="=================================================\n")
 
+        # initialize deviceprop and leadprop
         self.deviceprop = DeviceProperty(self.negf_hamiltonian, struct_device, results_path=self.results_path, \
                                          efermi=self.e_fermi)
         self.deviceprop.set_leadLR(
@@ -195,7 +214,10 @@ def __init__(self,
         # self.density_options = j_must_have(self.jdata, "density_options")
         self.density_options = density_options
         if self.density_options["method"] == "Ozaki":
-            self.density = Ozaki(R=self.density_options["R"], M_cut=self.density_options["M_cut"], n_gauss=self.density_options["n_gauss"])
+            self.density = Ozaki(R=self.density_options["R"], 
+                                 M_cut=self.density_options["M_cut"], 
+                                 n_gauss=self.density_options["n_gauss"])
+            
         elif self.density_options["method"] == "Fiori":
             if self.density_options["integrate_way"] == "gauss":
                 assert self.density_options["n_gauss"] is not None, "n_gauss should be set for Fiori method using gauss integration"
@@ -229,21 +251,6 @@ def __init__(self,
         self.generate_energy_grid()
         self.out = {}
 
-        ## Poisson equation settings
-        self.poisson_options = poisson_options
-        # self.LDOS_integral = {}  # for electron density integral
-        self.free_charge = {} # net charge: hole - electron
-        # Dirichlet region for Poisson equation
-        if self.scf:
-            for lead in ["lead_L", "lead_R"]:
-                if "voltage" in self.poisson_options[lead] and self.poisson_options[lead]["voltage"]:
-                    assert self.stru_options[lead]["voltage"] == self.poisson_options[lead]["voltage"], f"{lead} voltage should be consistent"
-                else:
-                    self.poisson_options[lead]["voltage"] = self.stru_options[lead]["voltage"]
-        self.Dirichlet_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("gate")\
-                                    or i.startswith("lead")]
-        self.dielectric_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("dielectric")]
-        self.doped_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("doped")]
 
 
 
@@ -658,7 +665,32 @@ def get_grid(self,grid_info,structase):
 
         # grid = Grid(xg,yg,zg,xa,ya,za)
         grid = Grid(xg,yg,za,xa,ya,za) #TODO: change back to zg
-        return grid       
+        return grid     
+
+    def get_nel_atom_lead(self, struct_leads, poisson_options, doped_region):
+        nel_atom = self.stru_options.get("nel_atom", None)
+        if nel_atom is None:
+            log.warning(msg="nel_atom is None, using valence electron number by default")
+        nel_atom_lead = {}
+        for lead_tag in ["lead_L", "lead_R"]:
+            nel_atom_lead[lead_tag] = {}
+            unique_elements = struct_leads[lead_tag].get_chemical_symbols()
+            for ele in unique_elements:
+                if nel_atom is None:
+                    if ele not in valence_electron:
+                        raise ValueError(f"Element {ele} is not in the valence electron dictionary")
+                    nel_atom_lead[lead_tag][ele] = valence_electron[ele]
+                else:
+                    if ele not in nel_atom:
+                        raise ValueError(f"Element {ele} is not in the nel_atom dictionary")
+                    nel_atom_lead[lead_tag][ele] = nel_atom[ele]
+            # subtract dope charge if the lead is fully covered by a doped region
+            lead_region = poisson_options[lead_tag]
+            for doped in doped_region:
+                if is_fully_covered(lead_region, doped):
+                    for key in nel_atom_lead[lead_tag].keys():
+                        nel_atom_lead[lead_tag][key] -= float(doped["charge"])
+        return nel_atom_lead  
     
     def fermi_dirac(self, x) -> torch.Tensor:
         return 1 / (1 + torch.exp(x / self.kBT))
diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index ce59a6d..791e535 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1075,6 +1075,8 @@ def stru_options():
     doc_lead_R = ""
     doc_gamma_center=""
     doc_time_reversal_symmetry=""
+    doc_e_fermi_smearing="The smearing method for Fermi level."
+    doc_nel_atom = "The number of electrons in each element."
     return [
         Argument("device", dict, optional=False, sub_fields=device(), doc=doc_device),
         Argument("lead_L", dict, optional=False, sub_fields=lead(), doc=doc_lead_L),
@@ -1083,7 +1085,8 @@ def stru_options():
         Argument("pbc", list, optional=True, default=[False, False, False], doc=doc_pbc),
         Argument("gamma_center", list, optional=True, default=True, doc=doc_gamma_center),
         Argument("time_reversal_symmetry", list, optional=True, default=True, doc=doc_time_reversal_symmetry),
-        Argument("e_fermi_smearing", str, optional=True, default="FD", doc="The smearing method for Fermi level. Default: FD"),
+        Argument("e_fermi_smearing", str, optional=True, default="FD", doc=doc_e_fermi_smearing),
+        Argument("nel_atom", [dict,None], optional=True, default=None, doc=doc_nel_atom)
     ]
 
 def device():

From f8bfced71ccf2431f101ca87e62fb7eb4e98d3b2 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 20 May 2025 21:00:09 +0800
Subject: [PATCH 007/152] =?UTF-8?q?feature:=20=E6=9B=B4=E6=96=B0=E7=94=B5?=
 =?UTF-8?q?=E6=9E=81HR=E4=BB=A5=E8=80=83=E8=99=91=E6=8E=BA=E6=9D=82?=
 =?UTF-8?q?=E5=BC=95=E8=B5=B7=E7=9A=84=E7=94=B5=E5=8E=8B=E5=81=8F=E7=A7=BB?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/negf_hamiltonian_init.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 89190b2..9e87d79 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -384,6 +384,14 @@ def Hamiltonian_initialized(self,kpoints:List[List[float]],useBloch:bool,bloch_f
                     hL[ik][torch.abs(hL[ik])<h_lead_threshold] = 0
                     hLL[ik][torch.abs(hLL[ik])<h_lead_threshold] = 0
 
+                # update the lead HR if the leads are doped
+                bias_shift = -1*self.stru_options[kk]["voltage"] #eV
+                for ik in range(HK.shape[0]):
+                    hL[ik] = hL[ik] + bias_shift * sL[ik]
+                    hLL[ik] = hLL[ik] + bias_shift * sLL[ik]
+                    HDL[ik] = HDL[ik] + bias_shift * SDL[ik]
+
+
                 HS_leads.update({
                     "HL":hL.cdouble()*self.h_factor, 
                     "SL":sL.cdouble(), 

From 96484fe22a48143b40d4cf176923670e8c4d6442 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 21 May 2025 16:18:09 +0800
Subject: [PATCH 008/152] =?UTF-8?q?refactor:=20=E6=9B=B4=E6=96=B0Fiori?=
 =?UTF-8?q?=E7=B1=BB=E4=B8=AD=E7=9A=84=E6=96=AD=E8=A8=80=E5=92=8C=E9=94=99?=
 =?UTF-8?q?=E8=AF=AF=E6=B6=88=E6=81=AF=E4=BB=A5=E5=A2=9E=E5=BC=BA=E5=8F=AF?=
 =?UTF-8?q?=E8=AF=BB=E6=80=A7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/density.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/dpnegf/negf/density.py b/dpnegf/negf/density.py
index 909805f..f08765e 100644
--- a/dpnegf/negf/density.py
+++ b/dpnegf/negf/density.py
@@ -244,7 +244,7 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
                                 device_atom_norbs,potential_at_atom,with_Dirichlet_leads,free_charge,
                                 eta_lead=1e-5, eta_device=1e-5):
         if integrate_way == "gauss":
-            assert self.n_gauss is not None, "n_gauss must be set in the Fiori class"
+            assert self.n_gauss is not None, "n_gauss must be set in the Fiori class with gauss integration"
             if self.xs is None:
                 self.xs, self.wlg = gauss_xw(xl=torch.scalar_tensor(e_grid[0]), xu=torch.scalar_tensor(e_grid[-1]), n=self.n_gauss)
                 # self.xs = self.xs.numpy();self.wlg = self.wlg.numpy()
@@ -260,7 +260,7 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
             integrate_range = e_grid
             pre_factor = dE * torch.ones(len(e_grid))
         else:
-            raise ValueError("integrate_way only supports gauss and direct in this version")
+            raise ValueError("integrate_way only supports 'gauss' and 'direct' in this version")
 
 
 
@@ -278,7 +278,7 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
                 deviceprop.lead_L.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=True)
                 deviceprop.lead_R.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=True)
             
-            
+             
             deviceprop.cal_green_function(energy=e, kpoint=kpoint, block_tridiagonal=block_tridiagonal,\
                                           eta_device=eta_device,Vbias = Vbias)
 

From 9796c5e12ed7dd83559682a45c97771849d849c0 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 21 May 2025 17:24:11 +0800
Subject: [PATCH 009/152] refactor: rescale energy reference point as E_ref in
 Green Func cal and self energy cal

---
 dpnegf/negf/density.py                    |  4 +-
 dpnegf/negf/device_property.py            | 45 +++++++++++++++--------
 dpnegf/negf/lead_property.py              | 16 ++++----
 dpnegf/negf/negf_hamiltonian_init.py      | 11 +++---
 dpnegf/negf/recursive_green_cal.py        |  4 +-
 dpnegf/negf/surface_green.py              |  7 ++--
 dpnegf/runner/NEGF.py                     | 42 +++++++++++++--------
 dpnegf/tests/test_negf_device_property.py |  2 +-
 8 files changed, 77 insertions(+), 54 deletions(-)

diff --git a/dpnegf/negf/density.py b/dpnegf/negf/density.py
index f08765e..afbdde8 100644
--- a/dpnegf/negf/density.py
+++ b/dpnegf/negf/density.py
@@ -300,8 +300,8 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
             
             A_Ld = [1j*(deviceprop.grd[i]-deviceprop.grd[i].conj().T)-A_Rd[i] for i in range(len(A_Rd))]
             # the chemical potential in fermi_dirac is always set as lead_L.mu, representing the source and drain fermi level
-            gnd = [A_Ld[i]*deviceprop.lead_L.fermi_dirac(e+deviceprop.lead_L.mu) \
-                    +A_Rd[i]*deviceprop.lead_R.fermi_dirac(e+deviceprop.lead_R.mu) for i in range(len(A_Ld))]
+            gnd = [A_Ld[i]*deviceprop.lead_L.fermi_dirac(e+deviceprop.lead_L.chemiPot_lead) \
+                    +A_Rd[i]*deviceprop.lead_R.fermi_dirac(e+deviceprop.lead_R.chemiPot_lead) for i in range(len(A_Ld))]
             gpd = [A_Ld[i] + A_Rd[i] - gnd[i] for i in range(len(A_Ld))]
                 
 
diff --git a/dpnegf/negf/device_property.py b/dpnegf/negf/device_property.py
index a6414e5..3ae5da4 100644
--- a/dpnegf/negf/device_property.py
+++ b/dpnegf/negf/device_property.py
@@ -76,7 +76,8 @@ class DeviceProperty(object):
             calculate density matrix     
         
     '''    
-    def __init__(self, hamiltonian, structure, results_path, e_T=300, efermi=0.) -> None:
+    def __init__(self, hamiltonian, structure, results_path, e_T=300, 
+                 efermi: dict=None, chemiPot: dict=None, E_ref: float=None ) -> None:
         self.greenfuncs = 0
         self.hamiltonian = hamiltonian
         self.structure = structure # ase Atoms
@@ -86,7 +87,13 @@ def __init__(self, hamiltonian, structure, results_path, e_T=300, efermi=0.) ->
         self.kBT = Boltzmann * e_T / eV2J
         self.e_T = e_T
         self.efermi = efermi
-        self.mu = self.efermi
+        self.chemiPot = chemiPot
+        if E_ref is None:
+            self.E_ref = efermi
+            log.info(f"Using efermi as E_ref in DeviceProperty: {self.E_ref}")
+        else:
+            self.E_ref = E_ref
+
         self.kpoint = None  # kpoint for cal_green_function
         self.newK_flag = None # whether the kpoint is new or not in cal_green_function
         self.newV_flag = None # whether the voltage is new or not in cal_green_function
@@ -158,8 +165,8 @@ def cal_green_function(self, energy, kpoint, eta_device=0., block_tridiagonal=Tr
         if Vbias is None:
             if os.path.exists(os.path.join(self.results_path, "POTENTIAL.pth")):
                 self.V = torch.load(os.path.join(self.results_path, "POTENTIAL.pth"))
-            elif abs(self.mu - self.efermi) > 1e-7:
-                self.V = torch.tensor(self.efermi - self.mu)
+            # elif abs([self.chemiPot[lead] - self.efermi[lead] for lead in ["lead_L","lead_R"]]).max() > 1e-7:
+            #     self.V = torch.tensor(self.efermi - self.chemiPot)
             else:
                 self.V = torch.tensor(0.)
         else:
@@ -187,10 +194,9 @@ def cal_green_function(self, energy, kpoint, eta_device=0., block_tridiagonal=Tr
         
         seL = self.lead_L.se
         seR = self.lead_R.se
-        # seinL = -i \Sigma_L^< = \Gamma_L f_L
         # Fluctuation-Dissipation theorem
-        seinL = 1j*(seL-seL.conj().T) * self.lead_L.fermi_dirac(energy+self.mu).reshape(-1)
-        seinR = 1j*(seR-seR.conj().T) * self.lead_R.fermi_dirac(energy+self.mu).reshape(-1)
+        seinL = 1j*(seL-seL.conj().T) * self.lead_L.fermi_dirac(energy+self.E_ref).reshape(-1)
+        seinR = 1j*(seR-seR.conj().T) * self.lead_R.fermi_dirac(energy+self.E_ref).reshape(-1)
         s01, s02 = s_in[0].shape # The shape of the first H block
         se01, se02 = seL.shape # The shape of the left self-energy
         s11, s12 = s_in[-1].shape
@@ -217,7 +223,7 @@ def cal_green_function(self, energy, kpoint, eta_device=0., block_tridiagonal=Tr
         ans = recursive_gf(energy, hl=self.hl, hd=self.hd, hu=self.hu,
                             sd=self.sd, su=self.su, sl=self.sl, 
                             left_se=seL, right_se=seR, seP=None, s_in=s_in,
-                            s_out=None, eta=eta_device, chemiPot=self.mu)
+                            s_out=None, eta=eta_device, E_ref = self.E_ref)
         s_in[0][:idx0,:idy0] = s_in[0][:idx0,:idy0] - seinL[:idx0,:idy0]
         s_in[-1][-idx1:,-idy1:] = s_in[-1][-idx1:,-idy1:] - seinR[-idx1:,-idy1:]
             # green shape [[g_trans, grd, grl,...],[g_trans, ...]]
@@ -236,7 +242,9 @@ def _cal_current_(self, espacing):
         '''calculate the current based on the voltage difference 
 
         At this stage, this method only supports the calculation of the current in the 
-        non-self-consistent field (nscf) calculation. So this function is not used.
+        non-self-consistent field (nscf) calculation. 
+        
+        So this function is not used.
         
         Parameters
         ----------
@@ -258,8 +266,10 @@ def fcn(e):
         cc = leggauss(fcn=self._cal_tc_)
         
         int_grid, int_weight = gauss_xw(xl=xl, xu=xu, n=int((xu-xl)/espacing))
+        
 
-        self.__CURRENT__ = simpson((self.lead_L.fermi_dirac(self.ee+self.mu) - self.lead_R.fermi_dirac(self.ee+self.mu)) * self.tc, self.ee)
+        self.__CURRENT__ = simpson(y=(self.lead_L.fermi_dirac(self.ee+self.E_ref) 
+                                    - self.lead_R.fermi_dirac(self.ee+self.E_ref)) * self.tc, x=self.ee)
 
     def _cal_current_nscf_(self, energy_grid, tc):
         '''calculates the non self consistent field (nscf) current.
@@ -279,6 +289,8 @@ def _cal_current_nscf_(self, energy_grid, tc):
             calculated current
 
         '''
+        assert abs(self.lead_L.efermi-self.lead_R.efermi)<5e-4, "The Fermi energy of the left and right leads should be equal in nscf calculation."
+        efermi = self.lead_L.efermi
         f = lambda x,mu: 1 / (1 + torch.exp((x - mu) / self.kBT))
 
         emin = energy_grid.min()
@@ -292,16 +304,17 @@ def _cal_current_nscf_(self, energy_grid, tc):
         cc = []
 
         for dv in vv * 0.5:
-            I = simpson(y=(f(energy_grid+self.mu, self.lead_L.efermi-vm+dv) - f(energy_grid+self.mu, self.lead_R.efermi-vm-dv)) * tc, x=energy_grid)
+            I = simpson(y=(f(energy_grid+efermi, efermi-vm+dv) 
+                           -f(energy_grid+efermi, efermi-vm-dv)) * tc, x=energy_grid)
             cc.append(I)
 
         return vv, cc
     
-    def fermi_dirac(self, x) -> torch.Tensor:
-        '''
-        calculates the Fermi-Dirac distribution function for a given energy.
-        '''
-        return 1 / (1 + torch.exp((x - self.mu) / self.kBT))
+    # def fermi_dirac(self, x) -> torch.Tensor:
+    #     '''
+    #     calculates the Fermi-Dirac distribution function for a given energy.
+    #     '''
+    #     return 1 / (1 + torch.exp((x - self.chemiPot) / self.kBT))
 
 
     def _cal_tc_(self):
diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index d6999ff..d5652a0 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -78,7 +78,7 @@ def __init__(self, tab, hamiltonian, structure, results_path, voltage,\
         self.kBT = Boltzmann * e_T / eV2J
         self.e_T = e_T
         self.efermi = efermi
-        self.mu = self.efermi - self.voltage
+        self.chemiPot_lead = self.efermi - self.voltage # unit: eV
         self.kpoint = None
         self.voltage_old = None
         
@@ -158,14 +158,14 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk)
             
             self.se, _ = selfEnergy(
-                ee=energy,
+                ee=energy + self.efermi,
                 hL=self.HLk,
                 hLL=self.HLLk,
                 sL=self.SLk,
                 sLL=self.SLLk,
                 hDL=HDL_reduced,
                 sDL=SDL_reduced,             #TODO: check chemiPot settiing is correct or not
-                chemiPot=self.mu,
+                voltage=self.voltage,
                 etaLead=eta_lead, 
                 method=method
             )
@@ -187,12 +187,12 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                     = self.hamiltonian.get_hs_lead(k_bloch, tab=self.tab, v=self.voltage)
                 
                 _, sgf = selfEnergy(
-                    ee=energy,
+                    ee=energy + self.efermi,
                     hL=self.HLk,
                     hLL=self.HLLk,
                     sL=self.SLk,
                     sLL=self.SLLk,            #TODO: check chemiPot settiing is correct or not
-                    chemiPot=self.mu, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
+                    voltage=self.voltage, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
                     etaLead=eta_lead, 
                     method=method
                 )
@@ -216,9 +216,9 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk) 
             # HDL_reduced, SDL_reduced = self.HDL, self.SDL
             if not isinstance(energy, torch.Tensor):
-                eeshifted = torch.scalar_tensor(energy, dtype=torch.complex128) + self.mu
+                eeshifted = torch.scalar_tensor(energy, dtype=torch.complex128) + self.efermi + self.voltage
             else:
-                eeshifted = energy + self.mu
+                eeshifted = energy + self.efermi + self.voltage
             # self.se = (eeshifted*self.SDL-self.HDL) @ sgf_k[:b,:b] @ (eeshifted*self.SDL.conj().T-self.HDL.conj().T)
             self.se = (eeshifted*SDL_reduced-HDL_reduced) @ sgf_k[:b,:b] @ (eeshifted*SDL_reduced.conj().T-HDL_reduced.conj().T)
 
@@ -292,7 +292,7 @@ def sigmaLR2Gamma(self, se):
         return 1j * (se - se.conj().T)
     
     def fermi_dirac(self, x) -> torch.Tensor:
-        return 1 / (1 + torch.exp((x - self.mu)/ self.kBT))
+        return 1 / (1 + torch.exp((x - self.chemiPot_lead)/ self.kBT))
     
     @property
     def gamma(self):
diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 9e87d79..d66cdab 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -385,12 +385,11 @@ def Hamiltonian_initialized(self,kpoints:List[List[float]],useBloch:bool,bloch_f
                     hLL[ik][torch.abs(hLL[ik])<h_lead_threshold] = 0
 
                 # update the lead HR if the leads are doped
-                bias_shift = -1*self.stru_options[kk]["voltage"] #eV
-                for ik in range(HK.shape[0]):
-                    hL[ik] = hL[ik] + bias_shift * sL[ik]
-                    hLL[ik] = hLL[ik] + bias_shift * sLL[ik]
-                    HDL[ik] = HDL[ik] + bias_shift * SDL[ik]
-
+                # bias_shift = -1*self.stru_options[kk]["voltage"] #eV
+                # for ik in range(HK.shape[0]):
+                #     hL[ik] = hL[ik] + bias_shift * sL[ik]
+                #     hLL[ik] = hLL[ik] + bias_shift * sLL[ik]
+                #     HDL[ik] = HDL[ik] + bias_shift * SDL[ik]
 
                 HS_leads.update({
                     "HL":hL.cdouble()*self.h_factor, 
diff --git a/dpnegf/negf/recursive_green_cal.py b/dpnegf/negf/recursive_green_cal.py
index bce7e2b..445c4d2 100644
--- a/dpnegf/negf/recursive_green_cal.py
+++ b/dpnegf/negf/recursive_green_cal.py
@@ -197,7 +197,7 @@ def recursive_gf_cal(energy, mat_l_list, mat_d_list, mat_u_list, sd, su, sl, s_i
                gpd, gpl, gpu, gip_left
 
 
-def recursive_gf(energy, hl, hd, hu, sd, su, sl, left_se, right_se, seP=None, chemiPot=0.0, s_in=0, s_out=0,
+def recursive_gf(energy, hl, hd, hu, sd, su, sl, left_se, right_se, seP=None, E_ref=0.0, s_in=0, s_out=0,
                  eta=1e-5):
     
     """The recursive Green's function algorithm is taken from
@@ -253,7 +253,7 @@ def recursive_gf(energy, hl, hd, hu, sd, su, sl, left_se, right_se, seP=None, ch
         Left-conencted blocks of the retarded Green's function
     """
 
-    shift_energy = energy + chemiPot
+    shift_energy = energy + E_ref
     # shift_energy = torch.scalar_tensor(shift_energy, dtype=torch.complex128)
 
     # if isinstance(hd,torch.Tensor): # hd, hl, hu are torch.nested.nested_tensor
diff --git a/dpnegf/negf/surface_green.py b/dpnegf/negf/surface_green.py
index 6c48e3a..7b10ccd 100644
--- a/dpnegf/negf/surface_green.py
+++ b/dpnegf/negf/surface_green.py
@@ -140,7 +140,8 @@ def sgfn(gs, *params):
 
             return torch.mean(out, dim=0)
 
-def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=False, chemiPot=0.0, dtype=torch.complex128, device='cpu', method='Lopez-Sancho'):
+def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=False, 
+               voltage=0.0, dtype=torch.complex128, device='cpu', method='Lopez-Sancho'):
     '''calculates the self-energy and surface Green's function for a given  Hamiltonian and overlap matrix.
     
     Parameters
@@ -185,9 +186,9 @@ def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=Fals
     #     eeshifted = ee - voltage
 
     if not isinstance(ee, torch.Tensor):
-        eeshifted = torch.scalar_tensor(ee, dtype=dtype) + chemiPot
+        eeshifted = torch.scalar_tensor(ee, dtype=dtype) + voltage
     else:
-        eeshifted = ee + chemiPot
+        eeshifted = ee + voltage
         
 
     if hDL == None:
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 59485de..d83ce36 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -65,6 +65,7 @@ def __init__(self,
         self.eta_lead = eta_lead; self.eta_device = eta_device
         self.emin = emin; self.emax = emax; self.espacing = espacing
         self.stru_options = stru_options
+        self.poisson_options = poisson_options
         if e_fermi is None:
             for lead in ["lead_L", "lead_R"]:
                 assert "kmesh_lead_Ef" in self.stru_options[lead], f"{lead} must have 'kmesh_lead_Ef' set in stru_options if e_fermi is None"
@@ -116,7 +117,16 @@ def __init__(self,
         self.unit = unit
         self.scf = scf
         self.block_tridiagonal = block_tridiagonal
-        # computing the hamiltonian  #需要改写NEGFHamiltonianInit   
+        for lead_tag in ["lead_L", "lead_R"]:
+            if self.scf:
+                if "voltage" in self.poisson_options[lead_tag] and self.poisson_options[lead_tag]["voltage"]:
+                    assert self.stru_options[lead_tag]["voltage"] == self.poisson_options[lead_tag]["voltage"], f"{lead_tag} voltage should be consistent"
+                else:
+                    self.poisson_options[lead_tag]["voltage"] = self.stru_options[lead_tag]["voltage"]
+            else:
+                assert self.stru_options[lead_tag]["voltage"] == 0, f"{lead_tag} voltage should be 0 in non-scf calculation"
+
+        # computing the hamiltonian
         self.negf_hamiltonian = NEGFHamiltonianInit(model=model,
                                                     AtomicData_options=AtomicData_options, 
                                                     structure=structure,
@@ -134,16 +144,8 @@ def __init__(self,
                                                  use_saved_HS=self.use_saved_HS, saved_HS_path=self.saved_HS_path)
 
         ## Poisson equation settings
-        self.poisson_options = poisson_options
-        # self.LDOS_integral = {}  # for electron density integral
         self.free_charge = {} # net charge: hole - electron
         # Dirichlet region for Poisson equation
-        if self.scf:
-            for lead_tag in ["lead_L", "lead_R"]:
-                if "voltage" in self.poisson_options[lead_tag] and self.poisson_options[lead_tag]["voltage"]:
-                    assert self.stru_options[lead_tag]["voltage"] == self.poisson_options[lead_tag]["voltage"], f"{lead_tag} voltage should be consistent"
-                else:
-                    self.poisson_options[lead_tag]["voltage"] = self.stru_options[lead_tag]["voltage"]
         self.Dirichlet_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("gate")\
                                     or i.startswith("lead")]
         self.dielectric_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("dielectric")]
@@ -169,16 +171,25 @@ def __init__(self,
         else:
             e_fermi["lead_L"] = self.e_fermi
             e_fermi["lead_R"] = self.e_fermi
-            log.warning(msg="Fermi level is set to {0} from input file".format(self.e_fermi))
+            log.info(msg="Fermi level is set to {0} from input file".format(self.e_fermi))
         
         self.e_fermi = e_fermi
+        chemiPot = {lead: e_fermi[lead] - self.stru_options[lead]["voltage"]  for lead in ["lead_L", "lead_R"]}
+        # set the chemical potential for the leads
+        E_ref = 0.5 * (chemiPot["lead_L"] + chemiPot["lead_R"]) # Energy reference point
+
         log.info(msg="Fermi level for lead_L: {0}".format(e_fermi["lead_L"]))
         log.info(msg="Fermi level for lead_R: {0}".format(e_fermi["lead_R"]))
+        if abs(e_fermi["lead_L"]-e_fermi["lead_R"]) > 5e-4:
+            raise ValueError("This is a heterogeneous system, which is not supported in this version.")
+        # In this version, dpnegf does not support the heterogeneous case, where the Fermi level is different in the leads
+        # because left-lead and right-lead Fermi level are calculated separately, which may be erroneous due to different vaccum level
+
         log.info(msg="=================================================\n")
 
         # initialize deviceprop and leadprop
-        self.deviceprop = DeviceProperty(self.negf_hamiltonian, struct_device, results_path=self.results_path, \
-                                         efermi=self.e_fermi)
+        self.deviceprop = DeviceProperty(self.negf_hamiltonian, struct_device, results_path=self.results_path,
+                                         efermi=self.e_fermi, chemiPot=chemiPot, E_ref=E_ref)
         self.deviceprop.set_leadLR(
                 lead_L=LeadProperty(
                 hamiltonian=self.negf_hamiltonian, 
@@ -211,7 +222,6 @@ def __init__(self,
         )
 
         # initialize density class
-        # self.density_options = j_must_have(self.jdata, "density_options")
         self.density_options = density_options
         if self.density_options["method"] == "Ozaki":
             self.density = Ozaki(R=self.density_options["R"], 
@@ -288,7 +298,7 @@ def generate_energy_grid(self):
 
         if cal_pole and  self.density_options["method"] == "Ozaki":
             self.poles, self.residues = ozaki_residues(M_cut=self.density_options["M_cut"])
-            self.poles = 1j* self.poles * self.kBT + self.deviceprop.lead_L.mu - self.deviceprop.mu
+            self.poles = 1j* self.poles * self.kBT + self.deviceprop.lead_L.chemiPot - self.deviceprop.chemiPot
 
         if cal_int_grid:
             xl = torch.tensor(min(v_list)-8*self.kBT)
@@ -453,8 +463,8 @@ def negf_compute(self,scf_require=False,Vbias=None):
                     else:
                         # TODO: consider the case with heterogeneous Dirichlet leads
                         # In this case, the Dirichlet conditions in leads and gate are set as electrochemical potential(Fermi level + voltage)
-                        assert getattr(self.deviceprop, "lead_L").voltage == self.stru_options["lead_L"]["voltage"]
-                        assert getattr(self.deviceprop, "lead_R").voltage == self.stru_options["lead_R"]["voltage"]
+                        for lead_tag in ["lead_L", "lead_R"]:
+                            assert getattr(self.deviceprop, lead_tag).voltage == self.stru_options[lead_tag]["voltage"]
 
                     if self.negf_hamiltonian.subblocks is None:
                             self.negf_hamiltonian.subblocks = self.negf_hamiltonian.get_hs_device(only_subblocks=True)
diff --git a/dpnegf/tests/test_negf_device_property.py b/dpnegf/tests/test_negf_device_property.py
index 459fba4..c89d248 100644
--- a/dpnegf/tests/test_negf_device_property.py
+++ b/dpnegf/tests/test_negf_device_property.py
@@ -74,7 +74,7 @@ def test_negf_Device(root_directory):
     assert deviceprop.lead_L.structure.pbc[2]==True
     assert np.diag(np.array((deviceprop.lead_L.structure.cell-[10.0, 10.0, 6.4])<1e-4)).all()
     assert deviceprop.lead_L.tab=="lead_L"
-    assert abs(deviceprop.mu+13.638587951660156)<1e-5
+    assert abs(deviceprop.E_ref+13.638587951660156)<1e-5
     # check device.Lead_R.structure
     assert all(deviceprop.lead_R.structure.symbols=='C4')
     assert deviceprop.lead_R.structure.pbc[0]==False

From 097d052effc03b849a00f6a14711fcd52f17bceb Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 22 May 2025 22:10:03 +0800
Subject: [PATCH 010/152] =?UTF-8?q?feature:=20=E6=B7=BB=E5=8A=A0ElecStruCa?=
 =?UTF-8?q?l=E7=B1=BB=E4=BB=A5=E8=AE=A1=E7=AE=97=E7=94=B5=E5=AD=90?=
 =?UTF-8?q?=E7=BB=93=E6=9E=84=E5=B1=9E=E6=80=A7=E5=92=8C=E8=B4=B9=E7=B1=B3?=
 =?UTF-8?q?=E8=83=BD=E7=BA=A7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/utils/elec_struc_cal.py | 365 +++++++++++++++++++++++++++++++++
 1 file changed, 365 insertions(+)
 create mode 100644 dpnegf/utils/elec_struc_cal.py

diff --git a/dpnegf/utils/elec_struc_cal.py b/dpnegf/utils/elec_struc_cal.py
new file mode 100644
index 0000000..56da164
--- /dev/null
+++ b/dpnegf/utils/elec_struc_cal.py
@@ -0,0 +1,365 @@
+import numpy as np
+from ase.io import read
+import ase
+import numpy as np
+from typing import Union
+import torch
+import logging
+log = logging.getLogger(__name__)
+from dptb.data import AtomicData, AtomicDataDict
+from dptb.nn.energy import Eigenvalues
+from dpnegf.utils.argcheck import get_cutoffs_from_model_options
+from copy import deepcopy
+from dpnegf.utils.constants import Boltzmann,eV2J
+
+# This class `ElecStruCal`  is designed to calculate electronic structure properties such as
+# eigenvalues and Fermi energy based on provided input data and model. 
+# It serve as a basic post-processing class to load data and provide Fermi energy.
+
+class ElecStruCal(object):
+    def __init__ (
+            self, 
+            model: torch.nn.Module,
+            device: Union[str, torch.device]=None
+            ):
+        '''It initializes ElecStruCal object with a neural network model, optional results path, GUI
+        usage flag, and device information, and sets up eigenvalues  based on model properties.
+        
+        Parameters
+        ----------
+        model : torch.nn.Module
+            The `model` parameter is expected to be an instance of `torch.nn.Module` that you want to load.
+        device : Union[str, torch.device]
+            The `device` parameter in the `__init__` function is used to specify the device on which the model
+        will be loaded and run. It can be either a string representing the device (e.g., 'cpu' or 'cuda') or
+        a torch.device object.
+        
+        '''
+        if  device is None:
+            device = model.device
+        if isinstance(device, str):
+            device = torch.device(device)
+        self.device = device
+        self.model = model
+        self.model.eval()
+        self.overlap = hasattr(model, 'overlap')
+
+        if not self.model.transform:
+            log.error('The model.transform is not True, please check the model.')
+            raise RuntimeError('The model.transform is not True, please check the model.')
+        
+        if self.overlap:
+            self.eigv = Eigenvalues(
+                idp=model.idp,
+                device=self.device,
+                s_edge_field=AtomicDataDict.EDGE_OVERLAP_KEY,
+                s_node_field=AtomicDataDict.NODE_OVERLAP_KEY,
+                s_out_field=AtomicDataDict.OVERLAP_KEY,
+                dtype=model.dtype,
+            )
+        else:
+            self.eigv = Eigenvalues(
+                idp=model.idp,
+                device=self.device,
+                dtype=model.dtype,
+            )
+        r_max, er_max, oer_max  = get_cutoffs_from_model_options(model.model_options)
+        self.cutoffs = {'r_max': r_max, 'er_max': er_max, 'oer_max': oer_max}
+    def get_data(self,data: Union[AtomicData, ase.Atoms, str],pbc:Union[bool,list]=None, device: Union[str, torch.device]=None, AtomicData_options:dict=None):
+        '''The function `get_data` takes input data in the form of a string, ase.Atoms object, or AtomicData
+        object, processes it accordingly, and returns the AtomicData class.
+        
+        Parameters
+        ----------
+        data : Union[AtomicData, ase.Atoms, str]
+            The `data` parameter in the `get_data` function can be one of the following types: 
+        string, ase.Atoms object, or AtomicData object.
+        AtomicData_options : dict
+            The `AtomicData_options` parameter is a dictionary that contains options or configurations for
+        creating an `AtomicData` object from an `ase.Atoms` object.
+        device : Union[str, torch.device]
+            The `device` parameter in the `get_data` function is used to specify the device on which the data
+        should be processed. If no device is provided, it defaults to `self.device`.
+        
+        Returns
+        -------
+            the loaded AtomicData object.
+        
+        '''
+        atomic_options = deepcopy(self.cutoffs)
+        if pbc is not None:
+            # 这一句要结合后面AtomicData.from_ase(structase, **atomic_options) 看。在from_ase中
+            # pbc = kwargs.pop("pbc", atoms.pbc), 所以当默认 调用get_dat 传入 pbc = None 时，
+            # atomic_options 中并没有 pbc 这个key，所以在from_ase中，pbc = atoms.pbc 默认采用atoms的pbc
+            # 当传入pbc 非None时，atomic_options中会有pbc这个key，所以from_ase中的pbc 将不会采用atoms的pbc。 
+            # 逻辑线埋的比较深，需要注意。
+            atomic_options.update({'pbc': pbc})
+
+        if AtomicData_options is not None:
+            if AtomicData_options.get('r_max', None) is not None:
+                if atomic_options['r_max'] != AtomicData_options.get('r_max'):
+                    atomic_options['r_max'] = AtomicData_options.get('r_max')
+                    log.warning(f'Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: {AtomicData_options.get("r_max")}')
+                    log.warning(f'This is very dangerous, please make sure you know what you are doing.')
+            if AtomicData_options.get('er_max', None) is not None:
+                if atomic_options['er_max'] != AtomicData_options.get('er_max'):
+                    atomic_options['er_max'] = AtomicData_options.get('er_max')
+                    log.warning(f'Overwrite the er_max setting in the model with the er_max setting in the AtomicData_options: {AtomicData_options.get("er_max")}')
+                    log.warning(f'This is very dangerous, please make sure you know what you are doing.')
+            if AtomicData_options.get('oer_max', None) is not None:
+                if atomic_options['oer_max'] != AtomicData_options.get('oer_max'):
+                    atomic_options['oer_max'] = AtomicData_options.get('oer_max')
+                    log.warning(f'Overwrite the oer_max setting in the model with the oer_max setting in the AtomicData_options: {AtomicData_options.get("oer_max")}')
+                    log.warning(f'This is very dangerous, please make sure you know what you are doing.')
+        
+        else:
+            if atomic_options['r_max'] is None:
+                log.error('The r_max is not provided in model_options, please provide it in AtomicData_options.')
+                raise RuntimeError('The r_max is not provided in model_options, please provide it in AtomicData_options.')
+            
+        if isinstance(data, str):
+            structase = read(data)
+            data = AtomicData.from_ase(structase, **atomic_options)
+        elif isinstance(data, ase.Atoms):
+            structase = data
+            data = AtomicData.from_ase(structase, **atomic_options)
+        elif isinstance(data, AtomicData):
+            # structase = data.to("cpu").to_ase()
+            log.info('The data is already an instance of AtomicData. Then the data is used directly.')
+            data = data
+        else:
+            raise ValueError('data should be either a string, ase.Atoms, or AtomicData')
+        
+        if device is None:
+            device = self.device
+        data = AtomicData.to_AtomicDataDict(data.to(device))
+        data = self.model.idp(data)
+
+        return data
+
+
+    def get_eigs(self, data: Union[AtomicData, ase.Atoms, str], klist: np.ndarray, pbc:Union[bool,list]=None, AtomicData_options:dict=None):
+        '''This function calculates eigenvalues for Hk at specified k-points.
+        
+        Parameters
+        ----------
+        data : Union[AtomicData, ase.Atoms, str]
+            The `data` parameter in the `get_eigs` function can be of type `AtomicData`, `ase.Atoms`, or `str`.
+        klist : np.ndarray
+            The `klist` parameter in the `get_eigs` function is expected to be a numpy array containing a list
+        of k-points. These k-points are used to calculate the eigenvalues of the system.
+        AtomicData_options : dict
+            The `AtomicData_options` parameter is a dictionary that contains options for configuring the
+        `AtomicData` object.
+        
+        Returns
+        -------
+            The function `get_eigs` returns the loaded data and the energy eigenvalues as a numpy array.
+        
+        '''
+            
+        data  = self.get_data(data=data, pbc=pbc, device=self.device,AtomicData_options=AtomicData_options)
+        # set the kpoint of the AtomicData
+        data[AtomicDataDict.KPOINT_KEY] = \
+            torch.nested.as_nested_tensor([torch.as_tensor(klist, dtype=self.model.dtype, device=self.device)])
+        # get the eigenvalues
+        data = self.model(data)
+        if self.overlap == True:
+            assert data.get(AtomicDataDict.EDGE_OVERLAP_KEY) is not None
+        data = self.eigv(data)
+
+        return data, data[AtomicDataDict.ENERGY_EIGENVALUE_KEY][0].detach().cpu().numpy()
+
+    def get_fermi_level(self, data: Union[AtomicData, ase.Atoms, str], nel_atom: dict, \
+                        meshgrid: list = None, klist: np.ndarray=None, pbc:Union[bool,list]=None,AtomicData_options:dict=None,
+                        q_tol:float=1e-10,smearing_method:str='FD',temp:float=300,vbias:float=None):
+        '''This function calculates the Fermi level based on provided data with iteration method, electron counts per atom, and
+        optional parameters like specific k-points and eigenvalues.
+        
+        Parameters
+        ----------
+        data : Union[AtomicData, ase.Atoms, str]
+            The `data` parameter in the `get_fermi_level` method can accept an instance of `AtomicData`,
+        `ase.Atoms`, or a string.
+        nel_atom : dict
+            The `nel_atom` parameter is a dictionary that contains the number of valence electrons for each
+        atom type in your system. It is used to calculate the Fermi level based on the total number of
+        valence electrons specified for each atom type.
+        kmesh : list
+            The `kmesh` parameter is used to specify the k-point mesh for sampling in the Brillouin zone. It is
+        a list that defines the mesh grid for k-point sampling. If `klist` is not provided, the k-points
+        will be generated based on this mesh.
+        klist : np.ndarray
+            The `klist` parameter is a numpy array that contains a list of k-points in the Brillouin zone. It
+        is used in the calculation of the Fermi level in the provided function `get_fermi_level`. 
+        Note that if `klist` and kmesh are both provided, the `klist` parameter will be used to calculate the Fermi level.
+        AtomicData_options : dict
+            The `AtomicData_options` parameter in the `get_fermi_level` method is a dictionary that allows you
+        to pass additional options or settings related to Atomicdata processing.
+        eigenvalues : np.ndarray
+            The `eigenvalues` parameter in the `get_fermi_level` method is an optional parameter that allows
+        you to provide pre-calculated eigenvalues for the system. If `eigenvalues` is provided, the method
+        will use these provided eigenvalues directly. Otherwise, the eigenvalues will be calculated from the model 
+        on the specified k-points (from kmesh or klist).
+        q_tol: float
+            The `q_tol` parameter in the `get_fermi_level` function represents the tolerance level for the
+        calculated charge compared to the total number of electrons.
+        smearing_method : str
+            The `smearing_method` parameter in the `get_fermi_level` function is used to specify the method of
+        smearing to be used in the calculation of the Fermi energy. The default method is 'FD' (Fermi-Dirac).
+        Other possible methods include 'Gaussian'.
+        temp : float
+            The `temp` parameter in the `get_fermi_level` function represents the temperature for smearing in the
+        calculation of the Fermi energy.
+        
+        Returns
+        -------
+            The function `get_fermi_level` returns two values: `data` and `E_fermi`.
+        
+        '''
+
+
+        assert meshgrid is not None or klist is not None, 'kmesh or klist should be provided.'
+        assert isinstance(nel_atom, dict)
+        
+        # klist would be used if provided, otherwise kmesh would be used to generate klist
+        if klist is None:
+            from dptb.utils.make_kpoints import kmesh_sampling_negf
+            klist,wk = kmesh_sampling_negf(meshgrid=meshgrid, is_gamma_center=True, is_time_reversal=True)
+            log.info(f'KPOINTS  kmesh sampling: {klist.shape[0]} kpoints')
+        else:
+            wk = np.ones(klist.shape[0])/klist.shape[0]
+            log.info(f'KPOINTS  klist: {klist.shape[0]} kpoints')
+
+        # eigenvalues would be used if provided, otherwise the eigenvalues would be calculated from the model on the specified k-points
+        if not AtomicDataDict.ENERGY_EIGENVALUE_KEY in data:
+            data, eigs = self.get_eigs(data=data, klist=klist, pbc=pbc, AtomicData_options=AtomicData_options) 
+            log.info('Getting eigenvalues from the model.')
+        else:
+            log.info('The eigenvalues are already in data. will use them.')
+            eigs = data[AtomicDataDict.ENERGY_EIGENVALUE_KEY][0].detach().cpu().numpy()
+        
+        
+        if nel_atom is not None:
+            atomtype_list = data[AtomicDataDict.ATOM_TYPE_KEY].flatten().tolist()
+            atomtype_symbols = np.asarray(self.model.idp.type_names)[atomtype_list].tolist()
+            total_nel = np.array([nel_atom[s] for s in atomtype_symbols]).sum()
+            if hasattr(self.model,'soc_param'):
+                spindeg = 1
+            else:
+                spindeg = 2
+            E_fermi = self.cal_E_fermi(eigs, total_nel, spindeg, wk, 
+                                       q_tol= q_tol, smearing_method = smearing_method,temp=temp)
+            log.info(f'Estimated E_fermi: {E_fermi} based on the valence electrons setting nel_atom : {nel_atom} .')
+        else:
+            E_fermi = None
+            raise RuntimeError('nel_atom should be provided to calculate Fermi energy.')
+        
+        return data, E_fermi
+
+
+    
+    @classmethod
+    def cal_E_fermi(cls,eigenvalues: np.ndarray, total_electrons: int, spindeg: int=2,wk: np.ndarray=None,
+                    q_tol:float=1e-10,smearing_method:str='FD',temp:float=300):
+        '''This  function calculates the Fermi energy using iteration algorithm.
+
+            In this version, the function calculates the Fermi energy in the case of spin-degeneracy. 
+        The smearing method here is to ensure the convergence of the Fermi energy calculation especially in metal systems.
+        The detailed description of the smearing methods can be found in dos Santos, F. J. and N. Marzari (2023). "Fermi energy 
+        determination for advanced smearing techniques." Physical Review B 107(19): 195122.
+	       
+        Parameters
+        ----------
+        eigenvalues : np.ndarray
+            The `eigenvalues` parameter is expected to be a NumPy array containing the eigenvalues of the system. 
+        total_electrons : int
+            The `total_electrons` parameter represents the total number of electrons in the system. It is used
+        in the calculation of the Fermi energy.
+        spindeg : int, optional
+            The `spindeg` parameter in the `cal_E_fermi` method represents the spin degeneracy factor, which is
+        typically equal to 2 for systems with spin-degeneracy.
+        wk : np.ndarray
+            The `wk` parameter in the `cal_E_fermi` function represents the weights assigned to each kpoints
+        in the calculation. If `wk` is not provided by the user, the function assigns equal weight to each
+        kpoint for the calculation of the Fermi energy.
+        q_tol: float
+            The `q_tol` parameter in the `cal_E_fermi` function represents the tolerance level for the
+        calculated charge compared to the total number of electrons.
+        smearing_method : str
+            The `smearing_method` parameter in the `cal_E_fermi` function is used to specify the method of
+        smearing to be used in the calculation of the Fermi energy. The default method is 'FD' (Fermi-Dirac).
+        Other possible methods include 'Gaussian'.
+        temp : float
+            The `temp` parameter in the `cal_E_fermi` function represents the temperature for smearing in the
+        calculation of the Fermi energy.
+
+        Returns
+        -------
+            The Fermi energy `Ef`
+        
+        '''        
+        
+        nextafter = np.nextafter
+        total_electrons = total_electrons / spindeg # This version is for the case of spin-degeneracy
+        log.info('Calculating Fermi energy in the case of spin-degeneracy.')
+            
+        
+        # calculate boundaries
+        min_Ef, max_Ef = eigenvalues.min(), eigenvalues.max()
+        kT = Boltzmann/eV2J * temp
+        drange = kT*np.sqrt(-np.log(q_tol*1e-2))
+        min_Ef = min_Ef - drange
+        max_Ef = max_Ef + drange
+
+        Ef = (min_Ef + max_Ef) * 0.5
+
+        if wk is None:
+            wk = np.ones(eigenvalues.shape[0]) / eigenvalues.shape[0]
+            log.info('wk is not provided, using equal weight for kpoints.')
+
+        icounter = 0
+        while nextafter(min_Ef, max_Ef) < max_Ef:
+        # while icounter <= 150:
+            icounter += 1
+            # Calculate guessed charge
+            wk = wk.reshape(-1,1)
+            if smearing_method == 'FD':
+                q_cal = (wk * cls.fermi_dirac_smearing(eigenvalues,kT=kT, mu=Ef)).sum()
+            elif smearing_method == 'Gaussian':
+                q_cal = (wk * cls.Gaussian_smearing(eigenvalues,sigma = kT, mu=Ef)).sum()
+            else:
+                raise ValueError(f'Unknown smearing method: {smearing_method}')
+
+            if abs(q_cal - total_electrons) < q_tol:
+                log.info(f'Fermi energy converged after {icounter} iterations.')
+                log.info(f'q_cal: {q_cal*spindeg}, total_electrons: {total_electrons*spindeg}, diff q: {abs(q_cal - total_electrons)*spindeg}')
+                return Ef
+
+            if q_cal >= total_electrons:
+                max_Ef = Ef
+            else:
+                min_Ef = Ef
+            Ef = (min_Ef + max_Ef) * 0.5
+
+        log.warning(f'Fermi level bisection did not converge under tolerance {q_tol} after {icounter} iterations.')
+        log.info(f'q_cal: {q_cal*spindeg}, total_electrons: {total_electrons*spindeg}, diff q: {abs(q_cal - total_electrons)*spindeg}')
+        return Ef
+    
+    @classmethod
+    def fermi_dirac_smearing(cls, E, kT=0.025852, mu=0.0):
+        x = (E - mu) / kT
+        mask_min = x < -40.0 # 40 results e16 precision
+        mask_max = x > 40.0
+        mask_in_limit = ~(mask_min | mask_max)
+        out = np.zeros_like(x)
+        out[mask_min] = 1.0
+        out[mask_max] = 0.0
+        out[mask_in_limit] = 1.0 / (np.expm1(x[mask_in_limit]) + 2.0)
+        return out
+        
+    @classmethod
+    def Gaussian_smearing(cls, E, sigma=0.025852, mu=0.0):
+        from scipy.special import erfc
+        x = (mu - E) / sigma
+        return 0.5 * erfc(-1*x)
\ No newline at end of file

From 3cbfdf8540aace0b1e466283dfcfa4e98baeafe3 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 23 May 2025 09:49:58 +0800
Subject: [PATCH 011/152] =?UTF-8?q?feature:=20=E6=9B=B4=E6=96=B0get=5Fferm?=
 =?UTF-8?q?i=5Flevel=E6=96=B9=E6=B3=95=E4=BB=A5=E6=94=AF=E6=8C=81=E7=94=B5?=
 =?UTF-8?q?=E5=8E=8B=E5=81=8F=E7=BD=AE=E5=8F=82=E6=95=B0Vbias?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/utils/elec_struc_cal.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/dpnegf/utils/elec_struc_cal.py b/dpnegf/utils/elec_struc_cal.py
index 56da164..fdfdd5c 100644
--- a/dpnegf/utils/elec_struc_cal.py
+++ b/dpnegf/utils/elec_struc_cal.py
@@ -172,7 +172,7 @@ def get_eigs(self, data: Union[AtomicData, ase.Atoms, str], klist: np.ndarray, p
 
     def get_fermi_level(self, data: Union[AtomicData, ase.Atoms, str], nel_atom: dict, \
                         meshgrid: list = None, klist: np.ndarray=None, pbc:Union[bool,list]=None,AtomicData_options:dict=None,
-                        q_tol:float=1e-10,smearing_method:str='FD',temp:float=300,vbias:float=None):
+                        q_tol:float=1e-10,smearing_method:str='FD',temp:float=300,Vbias:float=None):
         '''This function calculates the Fermi level based on provided data with iteration method, electron counts per atom, and
         optional parameters like specific k-points and eigenvalues.
         
@@ -238,8 +238,11 @@ def get_fermi_level(self, data: Union[AtomicData, ase.Atoms, str], nel_atom: dic
         else:
             log.info('The eigenvalues are already in data. will use them.')
             eigs = data[AtomicDataDict.ENERGY_EIGENVALUE_KEY][0].detach().cpu().numpy()
-        
-        
+
+        if Vbias is not None:
+            log.info(f'Adding vbias to the eigenvalues.')
+            eigs = eigs - Vbias # unit: eV
+            
         if nel_atom is not None:
             atomtype_list = data[AtomicDataDict.ATOM_TYPE_KEY].flatten().tolist()
             atomtype_symbols = np.asarray(self.model.idp.type_names)[atomtype_list].tolist()

From 9b23a2bcef07c9b7d8e08ac50d6ed027d2e81aa7 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 23 May 2025 16:39:03 +0800
Subject: [PATCH 012/152] =?UTF-8?q?=E5=88=A0=E9=99=A4=E7=94=B5=E5=8E=8B?=
 =?UTF-8?q?=E5=81=8F=E7=A7=BB=E7=9A=84=E7=9B=B8=E5=85=B3=E4=BB=A3=E7=A0=81?=
 =?UTF-8?q?=E6=B3=A8=E9=87=8A?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/negf_hamiltonian_init.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index d66cdab..ff2f256 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -384,12 +384,6 @@ def Hamiltonian_initialized(self,kpoints:List[List[float]],useBloch:bool,bloch_f
                     hL[ik][torch.abs(hL[ik])<h_lead_threshold] = 0
                     hLL[ik][torch.abs(hLL[ik])<h_lead_threshold] = 0
 
-                # update the lead HR if the leads are doped
-                # bias_shift = -1*self.stru_options[kk]["voltage"] #eV
-                # for ik in range(HK.shape[0]):
-                #     hL[ik] = hL[ik] + bias_shift * sL[ik]
-                #     hLL[ik] = hLL[ik] + bias_shift * sLL[ik]
-                #     HDL[ik] = HDL[ik] + bias_shift * SDL[ik]
 
                 HS_leads.update({
                     "HL":hL.cdouble()*self.h_factor, 

From eed2136ef39023db95b9ec44badf63eac3800ca4 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 23 May 2025 16:39:43 +0800
Subject: [PATCH 013/152] =?UTF-8?q?refactor:=20=E6=9B=B4=E6=96=B0=E8=83=BD?=
 =?UTF-8?q?=E9=87=8F=E5=8F=82=E8=80=83=E7=82=B9E=5Fref=EF=BC=8C=E5=A2=9E?=
 =?UTF-8?q?=E5=BC=BA=E7=94=B5=E5=8E=8B=E5=81=8F=E7=BD=AE=E6=94=AF=E6=8C=81?=
 =?UTF-8?q?=EF=BC=8C=E5=90=88=E7=90=86=E5=8C=96Poisson=E8=87=AA=E6=B4=BD?=
 =?UTF-8?q?=E6=B1=82=E8=A7=A3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/device_property.py |  3 ++-
 dpnegf/negf/lead_property.py   | 22 +++++++++++++---------
 dpnegf/negf/surface_green.py   |  6 +++---
 dpnegf/runner/NEGF.py          | 34 ++++++++++++++++++++--------------
 4 files changed, 38 insertions(+), 27 deletions(-)

diff --git a/dpnegf/negf/device_property.py b/dpnegf/negf/device_property.py
index 3ae5da4..68ccc20 100644
--- a/dpnegf/negf/device_property.py
+++ b/dpnegf/negf/device_property.py
@@ -289,7 +289,8 @@ def _cal_current_nscf_(self, energy_grid, tc):
             calculated current
 
         '''
-        assert abs(self.lead_L.efermi-self.lead_R.efermi)<5e-4, "The Fermi energy of the left and right leads should be equal in nscf calculation."
+        if abs(self.lead_L.efermi-self.lead_R.efermi)<5e-4:
+            log.warning(msg="The Fermi energy of the left and right leads should be equal in nscf current calculation.")
         efermi = self.lead_L.efermi
         f = lambda x,mu: 1 / (1 + torch.exp((x - mu) / self.kBT))
 
diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index d5652a0..a013457 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -66,10 +66,10 @@ class LeadProperty(object):
         calculate the Gamma function from the self energy.
 
     '''
-    def __init__(self, tab, hamiltonian, structure, results_path, voltage,\
+    def __init__(self, tab, hamiltonian, structure, results_path, voltage, \
                  structure_leads_fold:ase.Atoms=None,bloch_sorted_indice:torch.Tensor=None, useBloch: bool=False, \
                     bloch_factor: List[int]=[1,1,1],bloch_R_list:List=None,\
-                    e_T=300, efermi=0.0) -> None:
+                    e_T=300, efermi:float=0.0, E_ref:float=None) -> None:
         self.hamiltonian = hamiltonian
         self.structure = structure
         self.tab = tab
@@ -78,7 +78,11 @@ def __init__(self, tab, hamiltonian, structure, results_path, voltage,\
         self.kBT = Boltzmann * e_T / eV2J
         self.e_T = e_T
         self.efermi = efermi
-        self.chemiPot_lead = self.efermi - self.voltage # unit: eV
+        if E_ref is None:
+            self.E_ref = efermi
+        else:
+            self.E_ref = E_ref
+        self.chemiPot_lead = self.efermi # unit: eV
         self.kpoint = None
         self.voltage_old = None
         
@@ -158,14 +162,14 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk)
             
             self.se, _ = selfEnergy(
-                ee=energy + self.efermi,
+                ee=energy,
                 hL=self.HLk,
                 hLL=self.HLLk,
                 sL=self.SLk,
                 sLL=self.SLLk,
                 hDL=HDL_reduced,
                 sDL=SDL_reduced,             #TODO: check chemiPot settiing is correct or not
-                voltage=self.voltage,
+                E_ref=self.E_ref,
                 etaLead=eta_lead, 
                 method=method
             )
@@ -187,12 +191,12 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                     = self.hamiltonian.get_hs_lead(k_bloch, tab=self.tab, v=self.voltage)
                 
                 _, sgf = selfEnergy(
-                    ee=energy + self.efermi,
+                    ee=energy,
                     hL=self.HLk,
                     hLL=self.HLLk,
                     sL=self.SLk,
                     sLL=self.SLLk,            #TODO: check chemiPot settiing is correct or not
-                    voltage=self.voltage, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
+                    E_ref=self.E_ref, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
                     etaLead=eta_lead, 
                     method=method
                 )
@@ -216,9 +220,9 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk) 
             # HDL_reduced, SDL_reduced = self.HDL, self.SDL
             if not isinstance(energy, torch.Tensor):
-                eeshifted = torch.scalar_tensor(energy, dtype=torch.complex128) + self.efermi + self.voltage
+                eeshifted = torch.scalar_tensor(energy, dtype=torch.complex128) + self.E_ref
             else:
-                eeshifted = energy + self.efermi + self.voltage
+                eeshifted = energy + self.E_ref
             # self.se = (eeshifted*self.SDL-self.HDL) @ sgf_k[:b,:b] @ (eeshifted*self.SDL.conj().T-self.HDL.conj().T)
             self.se = (eeshifted*SDL_reduced-HDL_reduced) @ sgf_k[:b,:b] @ (eeshifted*SDL_reduced.conj().T-HDL_reduced.conj().T)
 
diff --git a/dpnegf/negf/surface_green.py b/dpnegf/negf/surface_green.py
index 7b10ccd..c339c79 100644
--- a/dpnegf/negf/surface_green.py
+++ b/dpnegf/negf/surface_green.py
@@ -141,7 +141,7 @@ def sgfn(gs, *params):
             return torch.mean(out, dim=0)
 
 def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=False, 
-               voltage=0.0, dtype=torch.complex128, device='cpu', method='Lopez-Sancho'):
+               E_ref=0.0, dtype=torch.complex128, device='cpu', method='Lopez-Sancho'):
     '''calculates the self-energy and surface Green's function for a given  Hamiltonian and overlap matrix.
     
     Parameters
@@ -186,9 +186,9 @@ def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=Fals
     #     eeshifted = ee - voltage
 
     if not isinstance(ee, torch.Tensor):
-        eeshifted = torch.scalar_tensor(ee, dtype=dtype) + voltage
+        eeshifted = torch.scalar_tensor(ee, dtype=dtype) + E_ref
     else:
-        eeshifted = ee + voltage
+        eeshifted = ee + E_ref
         
 
     if hDL == None:
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index d83ce36..eab9df6 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -3,7 +3,7 @@
 from dpnegf.utils.constants import valence_electron
 from dpnegf.negf.ozaki_res_cal import ozaki_residues
 from dpnegf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
-from dptb.postprocess.elec_struc_cal import ElecStruCal
+from dpnegf.utils.elec_struc_cal import ElecStruCal
 from dpnegf.negf.density import Ozaki,Fiori
 from dpnegf.negf.device_property import DeviceProperty
 from dpnegf.negf.lead_property import LeadProperty
@@ -162,31 +162,35 @@ def __init__(self,
             log.info(msg="Number of electrons in lead_L: {0}".format(nel_atom_lead["lead_L"]))
             log.info(msg="Number of electrons in lead_R: {0}".format(nel_atom_lead["lead_R"]))
             for lead_tag in ["lead_L", "lead_R"]:
+                # in non-zero bias case, the voltage effect is included in the Fermi level calculation
+                # Therefore in this case the e_fermi is electrochemical potential
                 _, e_fermi[lead_tag]  = elec_cal.get_fermi_level(data=struct_leads[lead_tag], 
                                 nel_atom = nel_atom_lead[lead_tag],
                                 meshgrid=self.stru_options[lead_tag]["kmesh_lead_Ef"],
                                 AtomicData_options=AtomicData_options,
                                 smearing_method=self.stru_options.get("e_fermi_smearing", "FD"),
-                                temp=100.0)
+                                temp=100.0,Vbias=self.stru_options[lead_tag]["voltage"])
         else:
             e_fermi["lead_L"] = self.e_fermi
             e_fermi["lead_R"] = self.e_fermi
             log.info(msg="Fermi level is set to {0} from input file".format(self.e_fermi))
+            log.warning(msg="This should be zero-bias system with homogeneous leads.")
         
         self.e_fermi = e_fermi
-        chemiPot = {lead: e_fermi[lead] - self.stru_options[lead]["voltage"]  for lead in ["lead_L", "lead_R"]}
-        # set the chemical potential for the leads
-        E_ref = 0.5 * (chemiPot["lead_L"] + chemiPot["lead_R"]) # Energy reference point
-
-        log.info(msg="Fermi level for lead_L: {0}".format(e_fermi["lead_L"]))
-        log.info(msg="Fermi level for lead_R: {0}".format(e_fermi["lead_R"]))
-        if abs(e_fermi["lead_L"]-e_fermi["lead_R"]) > 5e-4:
-            raise ValueError("This is a heterogeneous system, which is not supported in this version.")
-        # In this version, dpnegf does not support the heterogeneous case, where the Fermi level is different in the leads
-        # because left-lead and right-lead Fermi level are calculated separately, which may be erroneous due to different vaccum level
-
+        chemiPot = {lead: e_fermi[lead] for lead in ["lead_L", "lead_R"]}
+        if abs(self.e_fermi["lead_L"]-self.e_fermi["lead_R"]) > 5e-4: # non-zero bias
+            assert abs(self.stru_options["lead_L"]["voltage"]-self.stru_options["lead_R"]["voltage"]) > 5e-4, "This is a heterogeneous system, which is not supported in this version."
+            E_ref = 0.5 * (chemiPot["lead_L"] + chemiPot["lead_R"]) 
+            log.info(msg="Electrochemical potential for lead_L: {0}".format(chemiPot["lead_L"]))
+            log.info(msg="Electrochemical potential for lead_R: {0}".format(chemiPot["lead_R"]))
+            # In this version, dpnegf does not support the heterogeneous case, where the Fermi level is different in the leads
+            # because left-lead and right-lead Fermi level are calculated separately, which may be erroneous due to different vaccum level
+        else: # zero bias
+            E_ref = self.e_fermi["lead_L"]
+            log.info(msg="Fermi level for lead_L: {0}".format(e_fermi["lead_L"]))
+            log.info(msg="Fermi level for lead_R: {0}".format(e_fermi["lead_R"]))
         log.info(msg="=================================================\n")
-
+        # E_ref = self.e_fermi["lead_L"]
         # initialize deviceprop and leadprop
         self.deviceprop = DeviceProperty(self.negf_hamiltonian, struct_device, results_path=self.results_path,
                                          efermi=self.e_fermi, chemiPot=chemiPot, E_ref=E_ref)
@@ -199,6 +203,7 @@ def __init__(self,
                 e_T=self.ele_T,
                 efermi=self.e_fermi["lead_L"], 
                 voltage=self.stru_options["lead_L"]["voltage"],
+                E_ref=E_ref,
                 useBloch=self.useBloch,
                 bloch_factor=self.bloch_factor,
                 structure_leads_fold=structure_leads_fold["lead_L"],
@@ -213,6 +218,7 @@ def __init__(self,
                 e_T=self.ele_T,
                 efermi=self.e_fermi["lead_R"], 
                 voltage=self.stru_options["lead_R"]["voltage"],
+                E_ref=E_ref,
                 useBloch=self.useBloch,
                 bloch_factor=self.bloch_factor,
                 structure_leads_fold=structure_leads_fold["lead_R"],

From c42926baf90b6473c502cfab2fd6851535505550 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 23 May 2025 20:19:43 +0800
Subject: [PATCH 014/152] =?UTF-8?q?refactor:=20=E7=BB=9F=E4=B8=80e=5Ffermi?=
 =?UTF-8?q?=E5=92=8CchemiPot=E5=8F=98=E9=87=8F=E5=90=8D=EF=BC=8C=E7=AC=A6?=
 =?UTF-8?q?=E5=90=88=E5=85=B6=E7=89=A9=E7=90=86=E6=84=8F=E4=B9=89?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/device_property.py |  2 +-
 dpnegf/negf/lead_property.py   |  2 +-
 dpnegf/runner/NEGF.py          | 27 ++++++++++++++++-----------
 3 files changed, 18 insertions(+), 13 deletions(-)

diff --git a/dpnegf/negf/device_property.py b/dpnegf/negf/device_property.py
index 68ccc20..8b94bc7 100644
--- a/dpnegf/negf/device_property.py
+++ b/dpnegf/negf/device_property.py
@@ -86,7 +86,7 @@ def __init__(self, hamiltonian, structure, results_path, e_T=300,
         self.device = "cpu"
         self.kBT = Boltzmann * e_T / eV2J
         self.e_T = e_T
-        self.efermi = efermi
+        # self.efermi = efermi
         self.chemiPot = chemiPot
         if E_ref is None:
             self.E_ref = efermi
diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index a013457..842633f 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -82,7 +82,7 @@ def __init__(self, tab, hamiltonian, structure, results_path, voltage, \
             self.E_ref = efermi
         else:
             self.E_ref = E_ref
-        self.chemiPot_lead = self.efermi # unit: eV
+        self.chemiPot_lead = efermi - voltage
         self.kpoint = None
         self.voltage_old = None
         
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index eab9df6..d049122 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -153,9 +153,9 @@ def __init__(self,
 
 
 
-        # calculate Fermi level
+        # calculate Fermi level and electrochemical potential
         log.info(msg="-------------Fermi level calculation-------------")
-        e_fermi = {}
+        e_fermi = {}; chemiPot = {}
         if not self.e_fermi:        
             elec_cal = ElecStruCal(model=model,device=self.torch_device)
             nel_atom_lead = self.get_nel_atom_lead(struct_leads, self.poisson_options, self.doped_region)
@@ -169,28 +169,33 @@ def __init__(self,
                                 meshgrid=self.stru_options[lead_tag]["kmesh_lead_Ef"],
                                 AtomicData_options=AtomicData_options,
                                 smearing_method=self.stru_options.get("e_fermi_smearing", "FD"),
-                                temp=100.0,Vbias=self.stru_options[lead_tag]["voltage"])
+                                temp=100.0)
         else:
             e_fermi["lead_L"] = self.e_fermi
             e_fermi["lead_R"] = self.e_fermi
             log.info(msg="Fermi level is set to {0} from input file".format(self.e_fermi))
             log.warning(msg="This should be zero-bias system with homogeneous leads.")
         
+        
+        for lead_tag in ["lead_L", "lead_R"]:
+            chemiPot[lead_tag] = e_fermi[lead_tag] - self.stru_options[lead_tag]["voltage"]
+        
         self.e_fermi = e_fermi
-        chemiPot = {lead: e_fermi[lead] for lead in ["lead_L", "lead_R"]}
-        if abs(self.e_fermi["lead_L"]-self.e_fermi["lead_R"]) > 5e-4: # non-zero bias
+        self.chemiPot = chemiPot
+            
+        if abs(self.chemiPot["lead_L"]-self.chemiPot["lead_R"]) > 5e-4: # non-zero bias
             assert abs(self.stru_options["lead_L"]["voltage"]-self.stru_options["lead_R"]["voltage"]) > 5e-4, "This is a heterogeneous system, which is not supported in this version."
-            E_ref = 0.5 * (chemiPot["lead_L"] + chemiPot["lead_R"]) 
-            log.info(msg="Electrochemical potential for lead_L: {0}".format(chemiPot["lead_L"]))
-            log.info(msg="Electrochemical potential for lead_R: {0}".format(chemiPot["lead_R"]))
+            E_ref = 0.5 * (self.chemiPot["lead_L"] + self.chemiPot["lead_R"]) 
+            log.info(msg="Electrochemical potential for lead_L: {0}".format(self.chemiPot["lead_L"]))
+            log.info(msg="Electrochemical potential for lead_R: {0}".format(self.chemiPot["lead_R"]))
             # In this version, dpnegf does not support the heterogeneous case, where the Fermi level is different in the leads
             # because left-lead and right-lead Fermi level are calculated separately, which may be erroneous due to different vaccum level
         else: # zero bias
             E_ref = self.e_fermi["lead_L"]
-            log.info(msg="Fermi level for lead_L: {0}".format(e_fermi["lead_L"]))
-            log.info(msg="Fermi level for lead_R: {0}".format(e_fermi["lead_R"]))
+            log.info(msg="Fermi level for lead_L: {0}".format(self.e_fermi["lead_L"]))
+            log.info(msg="Fermi level for lead_R: {0}".format(self.e_fermi["lead_R"]))
         log.info(msg="=================================================\n")
-        # E_ref = self.e_fermi["lead_L"]
+
         # initialize deviceprop and leadprop
         self.deviceprop = DeviceProperty(self.negf_hamiltonian, struct_device, results_path=self.results_path,
                                          efermi=self.e_fermi, chemiPot=chemiPot, E_ref=E_ref)

From 40952fe9fdceff8bc14bea775c805d824e4a62c4 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 23 May 2025 20:20:02 +0800
Subject: [PATCH 015/152] =?UTF-8?q?refactor:=20=E7=A7=BB=E9=99=A4get=5Ffer?=
 =?UTF-8?q?mi=5Flevel=E6=96=B9=E6=B3=95=E4=B8=AD=E7=9A=84Vbias=E5=8F=82?=
 =?UTF-8?q?=E6=95=B0=E5=8F=8A=E7=9B=B8=E5=85=B3=E9=80=BB=E8=BE=91=EF=BC=8C?=
 =?UTF-8?q?=E7=AE=80=E5=8C=96=E5=87=BD=E6=95=B0=E6=8E=A5=E5=8F=A3?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/utils/elec_struc_cal.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/dpnegf/utils/elec_struc_cal.py b/dpnegf/utils/elec_struc_cal.py
index fdfdd5c..2518851 100644
--- a/dpnegf/utils/elec_struc_cal.py
+++ b/dpnegf/utils/elec_struc_cal.py
@@ -172,7 +172,7 @@ def get_eigs(self, data: Union[AtomicData, ase.Atoms, str], klist: np.ndarray, p
 
     def get_fermi_level(self, data: Union[AtomicData, ase.Atoms, str], nel_atom: dict, \
                         meshgrid: list = None, klist: np.ndarray=None, pbc:Union[bool,list]=None,AtomicData_options:dict=None,
-                        q_tol:float=1e-10,smearing_method:str='FD',temp:float=300,Vbias:float=None):
+                        q_tol:float=1e-10,smearing_method:str='FD',temp:float=300):
         '''This function calculates the Fermi level based on provided data with iteration method, electron counts per atom, and
         optional parameters like specific k-points and eigenvalues.
         
@@ -239,9 +239,6 @@ def get_fermi_level(self, data: Union[AtomicData, ase.Atoms, str], nel_atom: dic
             log.info('The eigenvalues are already in data. will use them.')
             eigs = data[AtomicDataDict.ENERGY_EIGENVALUE_KEY][0].detach().cpu().numpy()
 
-        if Vbias is not None:
-            log.info(f'Adding vbias to the eigenvalues.')
-            eigs = eigs - Vbias # unit: eV
             
         if nel_atom is not None:
             atomtype_list = data[AtomicDataDict.ATOM_TYPE_KEY].flatten().tolist()

From a4764f715a41c2dea44a35abeba9948b6d63f2f3 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sat, 24 May 2025 15:22:55 +0800
Subject: [PATCH 016/152] =?UTF-8?q?refactor:=20=E6=9B=B4=E6=96=B0=E6=B3=A8?=
 =?UTF-8?q?=E9=87=8A=E4=BB=A5=E6=8F=90=E9=AB=98=E4=BB=A3=E7=A0=81=E5=8F=AF?=
 =?UTF-8?q?=E8=AF=BB=E6=80=A7=EF=BC=8C=E4=BF=AE=E6=AD=A3=E9=94=99=E8=AF=AF?=
 =?UTF-8?q?=E4=BF=A1=E6=81=AF=E6=8F=90=E7=A4=BA?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/lead_property.py         |  2 +-
 dpnegf/negf/negf_hamiltonian_init.py |  4 ++--
 dpnegf/runner/NEGF.py                | 26 ++++++++++++++------------
 3 files changed, 17 insertions(+), 15 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 842633f..3053589 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -196,7 +196,7 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                     hLL=self.HLLk,
                     sL=self.SLk,
                     sLL=self.SLLk,            #TODO: check chemiPot settiing is correct or not
-                    E_ref=self.E_ref, # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
+                    E_ref=self.E_ref,  # temmporarily change to self.efermi for the case in which applying lead bias to corresponding to Nanotcad
                     etaLead=eta_lead, 
                     method=method
                 )
diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index ff2f256..f31274f 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -86,7 +86,7 @@ def __init__(self,
             raise ValueError('structure must be ase.Atoms or str')
         
         # check the structure cell is larger than the range of device and leads
-        # In DeePTB-NEGF, the whole structure should be completely included in the cell
+        # In DPNEGF, the whole structure should be completely included in the cell
         # for correct prediction of Hamiltonian and overlap matrix.
         # TODO: Add support for non-ortho cell
         xrange,yrange,zrange = self.structase.positions[:,0].max()-self.structase.positions[:,0].min(),\
@@ -132,7 +132,7 @@ def __init__(self,
         elif self.unit == "Ry":
             self.h_factor = 13.605662285137
         else:
-            log.error("The unit name is not correct !")
+            log.error(msg="The unit is not supported, please use Hartree, eV or Ry.")
             raise ValueError
 
         # obtain atom_norbs
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index d049122..ee65e09 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -118,9 +118,9 @@ def __init__(self,
         self.scf = scf
         self.block_tridiagonal = block_tridiagonal
         for lead_tag in ["lead_L", "lead_R"]:
-            if self.scf:
-                if "voltage" in self.poisson_options[lead_tag] and self.poisson_options[lead_tag]["voltage"]:
-                    assert self.stru_options[lead_tag]["voltage"] == self.poisson_options[lead_tag]["voltage"], f"{lead_tag} voltage should be consistent"
+            if self.scf and lead_tag in self.poisson_options:
+                if "voltage" in self.poisson_options[lead_tag] and "voltage" in self.stru_options[lead_tag]:
+                    assert self.stru_options[lead_tag]["voltage"]==self.poisson_options[lead_tag]["voltage"], f"{lead_tag} voltage should be consistent"
                 else:
                     self.poisson_options[lead_tag]["voltage"] = self.stru_options[lead_tag]["voltage"]
             else:
@@ -143,9 +143,11 @@ def __init__(self,
                                                  useBloch=self.useBloch,bloch_factor=self.bloch_factor,\
                                                  use_saved_HS=self.use_saved_HS, saved_HS_path=self.saved_HS_path)
 
-        ## Poisson equation settings
         self.free_charge = {} # net charge: hole - electron
-        # Dirichlet region for Poisson equation
+        #  Regions for Poisson equation
+        ## Dirichlet region: gate and leads
+        ## Dielectric region: dielectrics
+        ## Doped region: doped atomic sites, usually in leads region
         self.Dirichlet_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("gate")\
                                     or i.startswith("lead")]
         self.dielectric_region = [self.poisson_options[i] for i in self.poisson_options if i.startswith("dielectric")]
@@ -153,17 +155,15 @@ def __init__(self,
 
 
 
-        # calculate Fermi level and electrochemical potential
         log.info(msg="-------------Fermi level calculation-------------")
         e_fermi = {}; chemiPot = {}
+        # calculate Fermi level
         if not self.e_fermi:        
             elec_cal = ElecStruCal(model=model,device=self.torch_device)
             nel_atom_lead = self.get_nel_atom_lead(struct_leads, self.poisson_options, self.doped_region)
             log.info(msg="Number of electrons in lead_L: {0}".format(nel_atom_lead["lead_L"]))
             log.info(msg="Number of electrons in lead_R: {0}".format(nel_atom_lead["lead_R"]))
             for lead_tag in ["lead_L", "lead_R"]:
-                # in non-zero bias case, the voltage effect is included in the Fermi level calculation
-                # Therefore in this case the e_fermi is electrochemical potential
                 _, e_fermi[lead_tag]  = elec_cal.get_fermi_level(data=struct_leads[lead_tag], 
                                 nel_atom = nel_atom_lead[lead_tag],
                                 meshgrid=self.stru_options[lead_tag]["kmesh_lead_Ef"],
@@ -176,22 +176,24 @@ def __init__(self,
             log.info(msg="Fermi level is set to {0} from input file".format(self.e_fermi))
             log.warning(msg="This should be zero-bias system with homogeneous leads.")
         
-        
+        # calculate electrochemical potential
         for lead_tag in ["lead_L", "lead_R"]:
             chemiPot[lead_tag] = e_fermi[lead_tag] - self.stru_options[lead_tag]["voltage"]
         
         self.e_fermi = e_fermi
         self.chemiPot = chemiPot
-            
-        if abs(self.chemiPot["lead_L"]-self.chemiPot["lead_R"]) > 5e-4: # non-zero bias
+        log.info(msg="-------------------------------------------------\n")
+        if abs(self.chemiPot["lead_L"]-self.chemiPot["lead_R"]) > 5e-4: # non-zero bias case
             assert abs(self.stru_options["lead_L"]["voltage"]-self.stru_options["lead_R"]["voltage"]) > 5e-4, "This is a heterogeneous system, which is not supported in this version."
             E_ref = 0.5 * (self.chemiPot["lead_L"] + self.chemiPot["lead_R"]) 
+            log.info(msg="Non-zero bias case detected.")
             log.info(msg="Electrochemical potential for lead_L: {0}".format(self.chemiPot["lead_L"]))
             log.info(msg="Electrochemical potential for lead_R: {0}".format(self.chemiPot["lead_R"]))
             # In this version, dpnegf does not support the heterogeneous case, where the Fermi level is different in the leads
             # because left-lead and right-lead Fermi level are calculated separately, which may be erroneous due to different vaccum level
-        else: # zero bias
+        else: # zero bias case
             E_ref = self.e_fermi["lead_L"]
+            log.info(msg="Zero bias case detected.")
             log.info(msg="Fermi level for lead_L: {0}".format(self.e_fermi["lead_L"]))
             log.info(msg="Fermi level for lead_R: {0}".format(self.e_fermi["lead_R"]))
         log.info(msg="=================================================\n")

From 92bf10f6b06bc88ff17304c6b9973ca38900e740 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sat, 24 May 2025 20:59:59 +0800
Subject: [PATCH 017/152] =?UTF-8?q?refactor:=20=E6=9B=B4=E6=96=B0get=5Fnel?=
 =?UTF-8?q?=5Fatom=5Flead=E6=96=B9=E6=B3=95=EF=BC=8C=E5=8F=8A=E9=83=A8?=
 =?UTF-8?q?=E5=88=86=E6=B3=A8=E9=87=8A?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/density.py   |  4 ++--
 dpnegf/runner/NEGF.py    | 40 ++++++++++++++++++++--------------------
 dpnegf/utils/argcheck.py |  5 ++++-
 3 files changed, 26 insertions(+), 23 deletions(-)

diff --git a/dpnegf/negf/density.py b/dpnegf/negf/density.py
index afbdde8..f1e2361 100644
--- a/dpnegf/negf/density.py
+++ b/dpnegf/negf/density.py
@@ -269,8 +269,8 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
                 # Follow the NanoTCAD ViDES code:
                 #   Because the Dirichlet leads are not included, self-energy of the leads would be updated in each iteration
                 #   to ensure the Neuamnn boundary condition in the leads.
-                deviceprop.lead_L.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead)
-                deviceprop.lead_R.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead)
+                deviceprop.lead_L.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=False)
+                deviceprop.lead_R.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=False)
 
             else:
                 # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index ee65e09..4d14b72 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -118,11 +118,9 @@ def __init__(self,
         self.scf = scf
         self.block_tridiagonal = block_tridiagonal
         for lead_tag in ["lead_L", "lead_R"]:
-            if self.scf and lead_tag in self.poisson_options:
-                if "voltage" in self.poisson_options[lead_tag] and "voltage" in self.stru_options[lead_tag]:
+            if self.scf:
+                if "voltage" in self.poisson_options.get(lead_tag, {}) and "voltage" in self.stru_options[lead_tag]:
                     assert self.stru_options[lead_tag]["voltage"]==self.poisson_options[lead_tag]["voltage"], f"{lead_tag} voltage should be consistent"
-                else:
-                    self.poisson_options[lead_tag]["voltage"] = self.stru_options[lead_tag]["voltage"]
             else:
                 assert self.stru_options[lead_tag]["voltage"] == 0, f"{lead_tag} voltage should be 0 in non-scf calculation"
 
@@ -160,10 +158,14 @@ def __init__(self,
         # calculate Fermi level
         if not self.e_fermi:        
             elec_cal = ElecStruCal(model=model,device=self.torch_device)
-            nel_atom_lead = self.get_nel_atom_lead(struct_leads, self.poisson_options, self.doped_region)
+            nel_atom_lead = self.get_nel_atom_lead(
+                                struct_leads, 
+                                charge={lead_tag: self.stru_options[lead_tag].get("charge", 0) for lead_tag in ["lead_L", "lead_R"]}
+                                )
             log.info(msg="Number of electrons in lead_L: {0}".format(nel_atom_lead["lead_L"]))
             log.info(msg="Number of electrons in lead_R: {0}".format(nel_atom_lead["lead_R"]))
             for lead_tag in ["lead_L", "lead_R"]:
+                log.info(msg="-----Calculating Fermi level for {0}-----".format(lead_tag))
                 _, e_fermi[lead_tag]  = elec_cal.get_fermi_level(data=struct_leads[lead_tag], 
                                 nel_atom = nel_atom_lead[lead_tag],
                                 meshgrid=self.stru_options[lead_tag]["kmesh_lead_Ef"],
@@ -182,7 +184,7 @@ def __init__(self,
         
         self.e_fermi = e_fermi
         self.chemiPot = chemiPot
-        log.info(msg="-------------------------------------------------\n")
+        log.info(msg="-------------------------------------------------")
         if abs(self.chemiPot["lead_L"]-self.chemiPot["lead_R"]) > 5e-4: # non-zero bias case
             assert abs(self.stru_options["lead_L"]["voltage"]-self.stru_options["lead_R"]["voltage"]) > 5e-4, "This is a heterogeneous system, which is not supported in this version."
             E_ref = 0.5 * (self.chemiPot["lead_L"] + self.chemiPot["lead_R"]) 
@@ -690,7 +692,7 @@ def get_grid(self,grid_info,structase):
         grid = Grid(xg,yg,za,xa,ya,za) #TODO: change back to zg
         return grid     
 
-    def get_nel_atom_lead(self, struct_leads, poisson_options, doped_region):
+    def get_nel_atom_lead(self, struct_leads, charge:float=None):
         nel_atom = self.stru_options.get("nel_atom", None)
         if nel_atom is None:
             log.warning(msg="nel_atom is None, using valence electron number by default")
@@ -698,21 +700,19 @@ def get_nel_atom_lead(self, struct_leads, poisson_options, doped_region):
         for lead_tag in ["lead_L", "lead_R"]:
             nel_atom_lead[lead_tag] = {}
             unique_elements = struct_leads[lead_tag].get_chemical_symbols()
-            for ele in unique_elements:
+            for elem in unique_elements:
                 if nel_atom is None:
-                    if ele not in valence_electron:
-                        raise ValueError(f"Element {ele} is not in the valence electron dictionary")
-                    nel_atom_lead[lead_tag][ele] = valence_electron[ele]
+                    if elem not in valence_electron:
+                        raise ValueError(f"Element {elem} is not in the valence electron dictionary")
+                    nel_atom_lead[lead_tag][elem] = valence_electron[elem]
                 else:
-                    if ele not in nel_atom:
-                        raise ValueError(f"Element {ele} is not in the nel_atom dictionary")
-                    nel_atom_lead[lead_tag][ele] = nel_atom[ele]
-            # subtract dope charge if the lead is fully covered by a doped region
-            lead_region = poisson_options[lead_tag]
-            for doped in doped_region:
-                if is_fully_covered(lead_region, doped):
-                    for key in nel_atom_lead[lead_tag].keys():
-                        nel_atom_lead[lead_tag][key] -= float(doped["charge"])
+                    if elem not in nel_atom:
+                        raise ValueError(f"Element {elem} is not in the nel_atom dictionary")
+                    nel_atom_lead[lead_tag][elem] = nel_atom[elem]
+            # subtract dope charge if the lead is doped
+            if charge is not None:
+                nel_atom_lead[lead_tag] = {elem: nel_atom_lead[lead_tag][elem] - charge[lead_tag] for elem in nel_atom_lead[lead_tag]}
+
         return nel_atom_lead  
     
     def fermi_dirac(self, x) -> torch.Tensor:
diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index 791e535..e3de713 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1103,12 +1103,15 @@ def lead():
     doc_voltage=""
     doc_useBloch=""
     doc_bloch_factor=""
+    doc_kmesh_lead_Ef = "The kmesh for lead Fermi level calculation."
+    doc_charge = "The charge of the doped lead, used for Fermi level calculation."
     return [
         Argument("id", str, optional=False, doc=doc_id),
         Argument("voltage", [int, float], optional=False, doc=doc_voltage),
         Argument("useBloch", bool, optional=True, default=False, doc=doc_useBloch),
         Argument("bloch_factor", list, optional=True, default=[1,1,1], doc=doc_bloch_factor),
-        Argument("kmesh_lead_Ef", list, optional=True, doc="The kmesh for lead Fermi level calculation."),
+        Argument("kmesh_lead_Ef", list, optional=True, doc=doc_kmesh_lead_Ef),
+        Argument("charge", [int, float], optional=True, default=0.0, doc=doc_charge)
     ]
 
 def scf_options():

From 053d33128240c960a4ce5de19febde35d62d1311 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 26 May 2025 16:34:23 +0800
Subject: [PATCH 018/152] =?UTF-8?q?refactor:=20=E4=BC=98=E5=8C=96=E7=94=B5?=
 =?UTF-8?q?=E5=8C=96=E5=AD=A6=E5=8A=BF=E5=92=8C=E5=8F=82=E8=80=83=E8=83=BD?=
 =?UTF-8?q?=E9=87=8F=E7=9A=84=E8=AE=A1=E7=AE=97=E9=80=BB=E8=BE=91=EF=BC=8C?=
 =?UTF-8?q?=E5=A2=9E=E5=BC=BA=E5=AF=B9=E4=B8=8D=E5=90=8C=E8=BE=B9=E7=95=8C?=
 =?UTF-8?q?=E6=9D=A1=E4=BB=B6=E7=9A=84=E6=94=AF=E6=8C=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/runner/NEGF.py | 21 ++++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 4d14b72..b6fef59 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -176,7 +176,6 @@ def __init__(self,
             e_fermi["lead_L"] = self.e_fermi
             e_fermi["lead_R"] = self.e_fermi
             log.info(msg="Fermi level is set to {0} from input file".format(self.e_fermi))
-            log.warning(msg="This should be zero-bias system with homogeneous leads.")
         
         # calculate electrochemical potential
         for lead_tag in ["lead_L", "lead_R"]:
@@ -187,17 +186,24 @@ def __init__(self,
         log.info(msg="-------------------------------------------------")
         if abs(self.chemiPot["lead_L"]-self.chemiPot["lead_R"]) > 5e-4: # non-zero bias case
             assert abs(self.stru_options["lead_L"]["voltage"]-self.stru_options["lead_R"]["voltage"]) > 5e-4, "This is a heterogeneous system, which is not supported in this version."
-            E_ref = 0.5 * (self.chemiPot["lead_L"] + self.chemiPot["lead_R"]) 
+            if self.poisson_options["with_Dirichlet_leads"]:
+                E_ref = 0.5 * (self.chemiPot["lead_L"] + self.chemiPot["lead_R"]) 
+            else: # NanoTCAD style NEGF-Poisson SCF
+                E_ref = self.e_fermi["lead_L"]
+                # In NanoTCAD Vides, the reference energy is set to the Fermi level of the whole system. Here we set it to the Fermi level of lead_L.
+                # In homogeneous case, the Fermi level of lead_L and lead_R are the same, so it does not matter.
             log.info(msg="Non-zero bias case detected.")
-            log.info(msg="Electrochemical potential for lead_L: {0}".format(self.chemiPot["lead_L"]))
-            log.info(msg="Electrochemical potential for lead_R: {0}".format(self.chemiPot["lead_R"]))
             # In this version, dpnegf does not support the heterogeneous case, where the Fermi level is different in the leads
             # because left-lead and right-lead Fermi level are calculated separately, which may be erroneous due to different vaccum level
         else: # zero bias case
             E_ref = self.e_fermi["lead_L"]
             log.info(msg="Zero bias case detected.")
-            log.info(msg="Fermi level for lead_L: {0}".format(self.e_fermi["lead_L"]))
-            log.info(msg="Fermi level for lead_R: {0}".format(self.e_fermi["lead_R"]))
+
+        log.info(msg="Fermi level for lead_L: {0}".format(self.e_fermi["lead_L"]))
+        log.info(msg="Fermi level for lead_R: {0}".format(self.e_fermi["lead_R"]))    
+        log.info(msg="Electrochemical potential for lead_L: {0}".format(self.chemiPot["lead_L"]))
+        log.info(msg="Electrochemical potential for lead_R: {0}".format(self.chemiPot["lead_R"]))        
+        log.info(msg="Reference energy E_ref: {0}".format(E_ref))
         log.info(msg="=================================================\n")
 
         # initialize deviceprop and leadprop
@@ -497,7 +503,8 @@ def negf_compute(self,scf_require=False,Vbias=None):
                         with_Dirichlet_leads = self.poisson_options["with_Dirichlet_leads"],
                         free_charge = self.free_charge,
                         eta_lead = self.eta_lead,
-                        eta_device = self.eta_device
+                        eta_device = self.eta_device,
+                        E_ref = self.deviceprop.E_ref
                         )
                 else:
                     # TODO: add Ozaki support for NanoTCAD-style SCF

From d9577f9321104d4a67d60e0e97c8e4a20d9d1a5e Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 26 May 2025 16:34:33 +0800
Subject: [PATCH 019/152] =?UTF-8?q?refactor:=20=E6=9B=B4=E6=96=B0Fiori?=
 =?UTF-8?q?=E7=B1=BB=E4=B8=AD=E7=9A=84density=5Fintegrate=5FFiori=E6=96=B9?=
 =?UTF-8?q?=E6=B3=95=EF=BC=8C=E4=BD=BF=E7=94=A8E=5Fref=E4=BD=9C=E4=B8=BANE?=
 =?UTF-8?q?GF-Poisson=E8=AE=A1=E7=AE=97=E7=9A=84=E5=8F=82=E8=80=83?=
 =?UTF-8?q?=E8=83=BD=E9=87=8F=E7=82=B9?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/density.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/dpnegf/negf/density.py b/dpnegf/negf/density.py
index f1e2361..d2a190c 100644
--- a/dpnegf/negf/density.py
+++ b/dpnegf/negf/density.py
@@ -241,7 +241,7 @@ def __init__(self, n_gauss=None):
         self.e_grid_Fiori = None
 
     def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks,integrate_way,deviceprop,
-                                device_atom_norbs,potential_at_atom,with_Dirichlet_leads,free_charge,
+                                device_atom_norbs,potential_at_atom,with_Dirichlet_leads,free_charge,E_ref,
                                 eta_lead=1e-5, eta_device=1e-5):
         if integrate_way == "gauss":
             assert self.n_gauss is not None, "n_gauss must be set in the Fiori class with gauss integration"
@@ -299,9 +299,9 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
                 A_Rd = [torch.mm(torch.mm(deviceprop.gr_lc[i],gammaR[-x1:, -x1:]),deviceprop.gr_lc[i].conj().T) for i in range(len(deviceprop.gr_lc))]
             
             A_Ld = [1j*(deviceprop.grd[i]-deviceprop.grd[i].conj().T)-A_Rd[i] for i in range(len(A_Rd))]
-            # the chemical potential in fermi_dirac is always set as lead_L.mu, representing the source and drain fermi level
-            gnd = [A_Ld[i]*deviceprop.lead_L.fermi_dirac(e+deviceprop.lead_L.chemiPot_lead) \
-                    +A_Rd[i]*deviceprop.lead_R.fermi_dirac(e+deviceprop.lead_R.chemiPot_lead) for i in range(len(A_Ld))]
+            # E_ref is the reference energy point in NEGF-Poisson calculations
+            gnd = [A_Ld[i]*deviceprop.lead_L.fermi_dirac(e+E_ref) \
+                    +A_Rd[i]*deviceprop.lead_R.fermi_dirac(e+E_ref) for i in range(len(A_Ld))]
             gpd = [A_Ld[i] + A_Rd[i] - gnd[i] for i in range(len(A_Ld))]
                 
 

From 9c9c3f03c228949a380c5b658b19593f97a1deb1 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 27 May 2025 11:06:37 +0800
Subject: [PATCH 020/152] =?UTF-8?q?refactor:=20=E5=A2=9E=E5=BC=BA=E7=94=B5?=
 =?UTF-8?q?=E5=8E=8B=E4=B8=80=E8=87=B4=E6=80=A7=E6=A3=80=E6=9F=A5=EF=BC=8C?=
 =?UTF-8?q?=E7=A1=AE=E4=BF=9D=E5=9C=A8SCF=E8=AE=A1=E7=AE=97=E4=B8=AD?=
 =?UTF-8?q?=E7=94=B5=E5=8E=8B=E5=8F=82=E6=95=B0=E7=9A=84=E6=AD=A3=E7=A1=AE?=
 =?UTF-8?q?=E8=AE=BE=E7=BD=AE?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/runner/NEGF.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index b6fef59..a4a5d6d 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -118,9 +118,13 @@ def __init__(self,
         self.scf = scf
         self.block_tridiagonal = block_tridiagonal
         for lead_tag in ["lead_L", "lead_R"]:
+            assert "voltage" in self.stru_options[lead_tag], f"{lead_tag} voltage should be set in stru_options"
             if self.scf:
-                if "voltage" in self.poisson_options.get(lead_tag, {}) and "voltage" in self.stru_options[lead_tag]:
-                    assert self.stru_options[lead_tag]["voltage"]==self.poisson_options[lead_tag]["voltage"], f"{lead_tag} voltage should be consistent"
+                if lead_tag in self.poisson_options:
+                    if "voltage" in self.poisson_options.get(lead_tag, {}):
+                        assert self.stru_options[lead_tag]["voltage"]==self.poisson_options[lead_tag]["voltage"], f"{lead_tag} voltage should be consistent"
+                    else:
+                        self.poisson_options[lead_tag]["voltage"] = self.stru_options[lead_tag].get("voltage", None)
             else:
                 assert self.stru_options[lead_tag]["voltage"] == 0, f"{lead_tag} voltage should be 0 in non-scf calculation"
 

From ddc57195e652baa1554d7daaf8457c6d2879a2fb Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 27 May 2025 15:40:31 +0800
Subject: [PATCH 021/152] =?UTF-8?q?refactor:=20=E6=9B=B4=E6=96=B0torch.loa?=
 =?UTF-8?q?d=E6=96=B9=E6=B3=95=EF=BC=8C=E6=B7=BB=E5=8A=A0weights=5Fonly?=
 =?UTF-8?q?=E5=8F=82=E6=95=B0=E4=BB=A5=E4=BC=98=E5=8C=96NEGF=E8=AE=A1?=
 =?UTF-8?q?=E7=AE=97=E7=BB=93=E6=9E=9C=E5=8A=A0=E8=BD=BD?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/tests/test_negf_run.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/dpnegf/tests/test_negf_run.py b/dpnegf/tests/test_negf_run.py
index 4f98c7b..9e273b0 100644
--- a/dpnegf/tests/test_negf_run.py
+++ b/dpnegf/tests/test_negf_run.py
@@ -26,7 +26,7 @@ def test_negf_run_chain(root_directory):
     
     negf_out_path = output+"/results/negf.out.pth"
     assert os.path.exists(negf_out_path), "NEGF calculation output file not found"
-    negf_results = torch.load(negf_out_path)
+    negf_results = torch.load(negf_out_path,weights_only=False)
     trans = negf_results['T_avg']
     assert(abs(trans[int(len(trans)/2)]-1)<1e-5)  #compare with calculated transmission at efermi
 
@@ -48,7 +48,7 @@ def test_negf_run_orth(root_directory):
 
     negf_out_path = output+"/results/negf.out.pth"
     assert os.path.exists(negf_out_path), "NEGF calculation output file not found"
-    negf_results = torch.load(negf_out_path)
+    negf_results = torch.load(negf_out_path,weights_only=False)
     
     k_standard = np.array([[0. , 0. , 0.], [0. , 0.33333333, 0.]])
     k = negf_results['k']
@@ -115,7 +115,7 @@ def test_negf_run_S(root_directory):
 
     negf_out_path = output+"/results/negf.out.pth"
     assert os.path.exists(negf_out_path), "NEGF calculation output file not found"
-    negf_results = torch.load(negf_out_path)
+    negf_results = torch.load(negf_out_path,weights_only=False)
 
     k_standard = np.array([[0. , 0. , 0.], [0. , 0.33333333, 0.]])
     k = negf_results['k']
@@ -166,5 +166,5 @@ def test_negf_run_S(root_directory):
     T_avg_standard = torch.tensor(T_avg_standard)
     assert  abs(T_avg-T_avg_standard).max()<1e-4  #compare with calculated transmission at efermi
 
-    # if os.path.exists(output+"/results"):
-    #     os.system("rm -r "+output+"/results")
\ No newline at end of file
+    if os.path.exists(output+"/results"):
+        os.system("rm -r "+output+"/results")
\ No newline at end of file

From 58b95e4926e0a6bc8baa2292ef91036f355de3ef Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 27 May 2025 16:10:59 +0800
Subject: [PATCH 022/152] =?UTF-8?q?test:=20=E6=B7=BB=E5=8A=A0Fermi?=
 =?UTF-8?q?=E8=83=BD=E7=BA=A7=E8=AE=A1=E7=AE=97=E6=B5=8B=E8=AF=95=EF=BC=8C?=
 =?UTF-8?q?=E5=8C=85=E5=90=AB=E4=B8=8D=E5=90=8C=E7=9A=84=E5=B9=B3=E6=BB=91?=
 =?UTF-8?q?=E6=96=B9=E6=B3=95?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../tests/data/test_get_fermi/PRIMCELL.vasp   |   9 +++++++
 .../tests/data/test_get_fermi/nnsk.best.pth   | Bin 0 -> 7650 bytes
 dpnegf/tests/test_get_fermi.py                |  25 ++++++++++++++++++
 3 files changed, 34 insertions(+)
 create mode 100644 dpnegf/tests/data/test_get_fermi/PRIMCELL.vasp
 create mode 100644 dpnegf/tests/data/test_get_fermi/nnsk.best.pth
 create mode 100644 dpnegf/tests/test_get_fermi.py

diff --git a/dpnegf/tests/data/test_get_fermi/PRIMCELL.vasp b/dpnegf/tests/data/test_get_fermi/PRIMCELL.vasp
new file mode 100644
index 0000000..046c6f4
--- /dev/null
+++ b/dpnegf/tests/data/test_get_fermi/PRIMCELL.vasp
@@ -0,0 +1,9 @@
+Generated by VASPKIT code
+ 1.000000
+    0.0000000000000000    2.0391499996000002    2.0391499996000002
+    2.0391499996000002    0.0000000000000000    2.0391499996000002
+    2.0391499996000002    2.0391499996000002    0.0000000000000000
+   Au
+     1
+Cartesian
+    0.0000000000000000    0.0000000000000000    0.0000000000000000    Au001
diff --git a/dpnegf/tests/data/test_get_fermi/nnsk.best.pth b/dpnegf/tests/data/test_get_fermi/nnsk.best.pth
new file mode 100644
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diff --git a/dpnegf/tests/test_get_fermi.py b/dpnegf/tests/test_get_fermi.py
new file mode 100644
index 0000000..40a5c66
--- /dev/null
+++ b/dpnegf/tests/test_get_fermi.py
@@ -0,0 +1,25 @@
+from dpnegf.utils.elec_struc_cal import ElecStruCal
+from dptb.nn.build import build_model
+import os
+from pathlib import Path
+
+rootdir = os.path.join(Path(os.path.abspath(__file__)).parent, "data")
+
+
+def test_get_fermi():
+    ckpt = f"{rootdir}/test_get_fermi/nnsk.best.pth"  #  'hopping': {'method': 'poly2exp', 'rs': 5.0, 'w': 0.6},
+    stru_data = f"{rootdir}/test_get_fermi/PRIMCELL.vasp"
+
+    model = build_model(checkpoint=ckpt)
+    nel_atom = {"Au":11}
+
+    elec_cal = ElecStruCal(model=model,device='cpu')
+    _, efermi =elec_cal.get_fermi_level(data=stru_data, 
+                    nel_atom = nel_atom,smearing_method='FD',
+                meshgrid=[30,30,30])
+    assert abs(efermi  + 3.2262574434280395) < 1e-3
+
+    _, efermi =elec_cal.get_fermi_level(data=stru_data, 
+                    nel_atom = nel_atom,smearing_method='Gaussian',
+                meshgrid=[30,30,30])
+    assert abs(efermi  + 3.2262574434280395) < 1e-3

From 84f9c7921eb753124bc73a98bf04117892ac07d2 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 28 May 2025 10:25:28 +0800
Subject: [PATCH 023/152] refactor: for with_Dirichlet_leads case, self-energy
 calculation is moved up.

---
 dpnegf/negf/density.py       | 45 +++++++++++++++++--------------
 dpnegf/negf/lead_property.py |  4 +--
 dpnegf/runner/NEGF.py        | 52 ++++++++++++++++++++++++------------
 3 files changed, 62 insertions(+), 39 deletions(-)

diff --git a/dpnegf/negf/density.py b/dpnegf/negf/density.py
index d2a190c..3c6916a 100644
--- a/dpnegf/negf/density.py
+++ b/dpnegf/negf/density.py
@@ -233,16 +233,15 @@ def get_density_onsite(self, deviceprop, DM):
 
 class Fiori(Density):
 
-    def __init__(self, n_gauss=None):
+    def __init__(self, n_gauss=None, integrate_way:str="direct", e_grid=None):
         super(Fiori, self).__init__()
         self.n_gauss = n_gauss
+        self.e_grid = e_grid
+        self.integrate_way = integrate_way
         self.xs = None
         self.wlg = None
         self.e_grid_Fiori = None
 
-    def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks,integrate_way,deviceprop,
-                                device_atom_norbs,potential_at_atom,with_Dirichlet_leads,free_charge,E_ref,
-                                eta_lead=1e-5, eta_device=1e-5):
         if integrate_way == "gauss":
             assert self.n_gauss is not None, "n_gauss must be set in the Fiori class with gauss integration"
             if self.xs is None:
@@ -261,10 +260,16 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
             pre_factor = dE * torch.ones(len(e_grid))
         else:
             raise ValueError("integrate_way only supports 'gauss' and 'direct' in this version")
+        self.integrate_range = integrate_range
+        self.pre_factor = pre_factor
+        
 
 
+    def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks,integrate_way,deviceprop,
+                                device_atom_norbs,potential_at_atom,with_Dirichlet_leads,free_charge,E_ref,
+                                eta_lead=1e-5, eta_device=1e-5):
 
-        for eidx, e in enumerate(integrate_range):
+        for eidx, e in enumerate(self.integrate_range):
             if not with_Dirichlet_leads:
                 # Follow the NanoTCAD ViDES code:
                 #   Because the Dirichlet leads are not included, self-energy of the leads would be updated in each iteration
@@ -272,11 +277,11 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
                 deviceprop.lead_L.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=False)
                 deviceprop.lead_R.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=False)
 
-            else:
-                # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
-                # In each iteration, the self-energy of the leads is not updated.
-                deviceprop.lead_L.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=True)
-                deviceprop.lead_R.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=True)
+            # else:
+            #     # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
+            #     # In each iteration, the self-energy of the leads is not updated.
+            #     deviceprop.lead_L.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=True)
+            #     deviceprop.lead_R.self_energy(kpoint=kpoint, energy=e, eta_lead=eta_lead, save=True)
             
              
             deviceprop.cal_green_function(energy=e, kpoint=kpoint, block_tridiagonal=block_tridiagonal,\
@@ -313,46 +318,46 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
                 if e >= Ei_at_atom: 
                     if not block_tridiagonal:
                         free_charge[str(kpoint)][atom_index] +=\
-                            pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[0][pre_orbs:last_orbs,pre_orbs:last_orbs])                          
+                            self.pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[0][pre_orbs:last_orbs,pre_orbs:last_orbs])                          
                     else:
                         block_indexs,orb_start,orb_end = self.get_subblock_index(subblocks,atom_index,device_atom_norbs)
                         if len(block_indexs) == 1:
                             free_charge[str(kpoint)][atom_index] += \
-                            pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[block_indexs[0]][orb_start:orb_end,orb_start:orb_end])
+                            self.pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[block_indexs[0]][orb_start:orb_end,orb_start:orb_end])
                         else:
                             for bindex in block_indexs:
                                 if bindex == block_indexs[0]:
                                     free_charge[str(kpoint)][atom_index] += \
-                                    pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[bindex][orb_start:,orb_start:])
+                                    self.pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[bindex][orb_start:,orb_start:])
                                 elif bindex == block_indexs[-1]:
                                     free_charge[str(kpoint)][atom_index] += \
-                                    pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[bindex][:orb_end,:orb_end])
+                                    self.pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[bindex][:orb_end,:orb_end])
                                 else:
                                     free_charge[str(kpoint)][atom_index] += \
-                                    pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[bindex])
+                                    self.pre_factor[eidx]*2*(-1)/2/torch.pi*torch.trace(gnd[bindex])
                 # hole density
                 else:
                     if not block_tridiagonal:                      
                         free_charge[str(kpoint)][atom_index] +=\
-                        pre_factor[eidx]*2/2/torch.pi*torch.trace(gpd[0][pre_orbs:last_orbs,pre_orbs:last_orbs])
+                        self.pre_factor[eidx]*2/2/torch.pi*torch.trace(gpd[0][pre_orbs:last_orbs,pre_orbs:last_orbs])
                         # free_charge[str(kpoint)][atom_index] += pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[0][pre_orbs:last_orbs,pre_orbs:last_orbs])
         
                     else:
                         block_indexs,orb_start,orb_end = self.get_subblock_index(subblocks,atom_index,device_atom_norbs)
                         if len(block_indexs) == 1:
                             free_charge[str(kpoint)][atom_index] += \
-                            pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[block_indexs[0]][orb_start:orb_end,orb_start:orb_end])
+                            self.pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[block_indexs[0]][orb_start:orb_end,orb_start:orb_end])
                         else:
                             for bindex in block_indexs:
                                 if bindex == block_indexs[0]:
                                     free_charge[str(kpoint)][atom_index] += \
-                                    pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[bindex][orb_start:,orb_start:])
+                                    self.pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[bindex][orb_start:,orb_start:])
                                 elif bindex == block_indexs[-1]:
                                     free_charge[str(kpoint)][atom_index] += \
-                                    pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[bindex][:orb_end,:orb_end])
+                                    self.pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[bindex][:orb_end,:orb_end])
                                 else:
                                     free_charge[str(kpoint)][atom_index] += \
-                                    pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[bindex])
+                                    self.pre_factor[eidx]*2*1/2/torch.pi*torch.trace(gpd[bindex])
                             
     def get_subblock_index(self,subblocks,atom_index,device_atom_norbs):
         # print('atom_index:',atom_index)
diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 3053589..0bd4208 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -142,7 +142,7 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                 save_path = os.path.join(save_path, \
                                         f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
                 assert os.path.exists(save_path), f"Cannot find the self energy file {save_path}"
-            self.se = torch.load(save_path)
+            self.se = torch.load(save_path,weights_only=False)
             return
         else:
             if se_info_display:
@@ -290,7 +290,7 @@ def sigmaLR2Gamma(self, se):
         Returns
         -------
         Gamma
-            The Gamma function, $\Gamma = 1j(se-se^\dagger)$.
+            The Gamma function, Gamma = 1j(se-se^dagger).
         
         '''
         return 1j * (se - se.conj().T)
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index a4a5d6d..556c51a 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -246,21 +246,6 @@ def __init__(self,
             )
         )
 
-        # initialize density class
-        self.density_options = density_options
-        if self.density_options["method"] == "Ozaki":
-            self.density = Ozaki(R=self.density_options["R"], 
-                                 M_cut=self.density_options["M_cut"], 
-                                 n_gauss=self.density_options["n_gauss"])
-            
-        elif self.density_options["method"] == "Fiori":
-            if self.density_options["integrate_way"] == "gauss":
-                assert self.density_options["n_gauss"] is not None, "n_gauss should be set for Fiori method using gauss integration"
-                self.density = Fiori(n_gauss=self.density_options["n_gauss"])
-            else:
-                self.density = Fiori() #calculate the density by integrating the energy window in direct way
-        else:
-            raise ValueError
 
         # number of orbitals on atoms in device region
         self.device_atom_norbs = self.negf_hamiltonian.atom_norbs[self.negf_hamiltonian.device_id[0]:self.negf_hamiltonian.device_id[1]]
@@ -283,9 +268,28 @@ def __init__(self,
         self.out_ldos = out_ldos
         self.out_lcurrent = out_lcurrent
         assert not (self.out_lcurrent and self.block_tridiagonal)
-        self.generate_energy_grid()
         self.out = {}
-
+        # initialize density class
+        self.density_options = density_options
+        self.generate_energy_grid()
+        if self.density_options["method"] == "Ozaki":
+            self.density = Ozaki(R=self.density_options["R"], 
+                                 M_cut=self.density_options["M_cut"], 
+                                 n_gauss=self.density_options["n_gauss"])
+            
+        elif self.density_options["method"] == "Fiori":
+            if self.density_options["integrate_way"] == "gauss":
+                assert self.density_options["n_gauss"] is not None, "n_gauss should be set for Fiori method using gauss integration"
+                self.density = Fiori(n_gauss=self.density_options["n_gauss"],
+                                     integrate_way=self.density_options["integrate_way"],
+                                     e_grid=self.uni_grid)
+            elif self.density_options["integrate_way"] == "direct":
+                self.density = Fiori(integrate_way=self.density_options["integrate_way"],
+                                     e_grid=self.uni_grid) #calculate the density by integrating the energy window in direct way
+            else:
+                raise ValueError("integrate_way should be 'gauss' or 'direct' for Fiori method")
+        else:
+            raise ValueError
 
 
 
@@ -462,6 +466,20 @@ def negf_compute(self,scf_require=False,Vbias=None):
 
         self.out['k']=[];self.out['wk']=[]
         if hasattr(self, "uni_grid"): self.out["uni_grid"] = self.uni_grid
+
+        if scf_require and self.poisson_options["with_Dirichlet_leads"]:
+            # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
+            # In each iteration, the self-energy of the leads is not updated.
+            for ik, k in enumerate(self.kpoints):
+                for e in self.density.integrate_range:
+                    self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+                    self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+        elif not self.scf:
+            # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
+            for ik, k in enumerate(self.kpoints): 
+                for e in self.uni_grid:
+                    self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+                    self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
     
         for ik, k in enumerate(self.kpoints):
 

From 2848c62b8ede33a3193b8debd17e31b8123c6874 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 28 May 2025 11:31:27 +0800
Subject: [PATCH 024/152] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0=E7=89=88?=
 =?UTF-8?q?=E6=9C=AC=E4=BF=A1=E6=81=AF=E8=8E=B7=E5=8F=96=E5=8A=9F=E8=83=BD?=
 =?UTF-8?q?=EF=BC=8C=E8=AE=B0=E5=BD=95DPNEGF=E5=92=8CDeePTB=E7=9A=84?=
 =?UTF-8?q?=E7=89=88=E6=9C=AC=E4=BF=A1=E6=81=AF?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/utils/loggers.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/dpnegf/utils/loggers.py b/dpnegf/utils/loggers.py
index 06b1178..66bc032 100644
--- a/dpnegf/utils/loggers.py
+++ b/dpnegf/utils/loggers.py
@@ -4,6 +4,24 @@
 import os
 from typing import TYPE_CHECKING, Optional
 
+# Get version info (synchronized with pyproject.toml)
+try:
+    from importlib.metadata import version, PackageNotFoundError
+except ImportError:
+    from importlib_metadata import version, PackageNotFoundError  # For Python <3.8
+
+def get_version_dpnegf():
+    try:
+        return version("dpnegf")
+    except PackageNotFoundError:
+        return "unknown"
+
+def get_version_dptb():
+    try:
+        return version("dptb")
+    except PackageNotFoundError:
+        return "unknown"
+
 if TYPE_CHECKING:
     from pathlib import Path
 logging.getLogger(__name__)
@@ -112,3 +130,8 @@ def set_log_handles(
             fh.setLevel(level)
             fh.addFilter(_AppFilter())
             root_log.addHandler(fh)
+
+    # print version info
+    logger = logging.getLogger(__name__)
+    logger.info(f"DPNEGF version: {get_version_dpnegf()}")
+    logger.info(f"DeePTB version: {get_version_dptb()}")
\ No newline at end of file

From dc52a83a26a71ea643f804942fec658b91c76e33 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 28 May 2025 11:45:25 +0800
Subject: [PATCH 025/152] =?UTF-8?q?docs:=20=E6=9B=B4=E6=96=B0README.md?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 15aae6d..fc56ce0 100644
--- a/README.md
+++ b/README.md
@@ -1,14 +1,14 @@
 # DPNEGF
 
-**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green’s Function (**NEGF**) method, enabling efficient quantum transport simulations with first-principles accuracy. 
+**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green’s Function (**NEGF**) method, establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy. 
 
-By using DeePTB-SK or DeePTB-E3—both available within the DeePTB package—DeePTB-NEGF can compute quantum transport properties in open-boundary systems with either environment-corrected **Slater-Koster tight-binding Hamiltonian** or **linear combination of atomic orbitals (LCAO) Kohn-Sham Hamiltonian**.
+By using DeePTB-SK or DeePTB-E3—both available within the DeePTB package—DeePTB-NEGF can compute quantum transport properties in open-boundary systems with either environment-corrected **Slater-Koster TB Hamiltonian** or **linear combination of atomic orbitals (LCAO) Kohn-Sham Hamiltonian**.
 
 
 For more details, see our papers:
   1. [DeePTB-NEGF: arXiv:2411.08800v2](https://arxiv.org/abs/2411.08800v2)
   2. [DeePTB-SK: Nat Commun 15, 6772 (2024)](https://doi.org/10.1038/s41467-024-51006-4)
-  3. [DeePTB-E3: arXiv:2407.06053](https://arxiv.org/pdf/2407.06053)
+  3. [DeePTB-E3: ICLR 2025 Spotlight](https://openreview.net/forum?id=kpq3IIjUD3)
 
 
 ## Installation
@@ -17,18 +17,26 @@ Installing **DPNEGF** is straightforward. We recommend using a virtual environme
 
 - **Requirements**
   - Git
-  - DeePTB(https://github.com/deepmodeling/DeePTB) 
+  - DeePTB(https://github.com/deepmodeling/DeePTB) ≥ 2.2.1
 
 - **From Source**
     1. Clone the repository:
         ```bash
         git clone https://github.com/DeePTB-Lab/dpnegf.git
         ```
-    2. Navigate to the root directory and install DeePTB-NEGF:
+    2. Navigate to the root directory and install DPNEGF:
         ```bash
-        cd DeePTB-negf
+        cd dpnegf
         pip install .
         ```
+## Test code 
+
+To ensure the code is correctly installed, please run the unit tests first:
+```bash
+pytest ./dpnegf/tests/
+```
+Be careful if not all tests pass!
+
 
 ## How to cite
 

From 29850705f350358c6361be54ff85a552db793ba8 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 28 May 2025 12:02:53 +0800
Subject: [PATCH 026/152] =?UTF-8?q?fix:=20=E6=9B=B4=E6=96=B0DeePTB?=
 =?UTF-8?q?=E7=89=88=E6=9C=AC=E8=A6=81=E6=B1=82=E4=B8=BA=E2=89=A52.1.1?=
 =?UTF-8?q?=EF=BC=8C=E4=BC=98=E5=8C=96=E7=89=88=E6=9C=AC=E4=BF=A1=E6=81=AF?=
 =?UTF-8?q?=E6=97=A5=E5=BF=97=E6=A0=BC=E5=BC=8F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md               | 2 +-
 dpnegf/utils/loggers.py | 8 ++++++--
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index fc56ce0..c01a91a 100644
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ Installing **DPNEGF** is straightforward. We recommend using a virtual environme
 
 - **Requirements**
   - Git
-  - DeePTB(https://github.com/deepmodeling/DeePTB) ≥ 2.2.1
+  - DeePTB(https://github.com/deepmodeling/DeePTB) ≥ 2.1.1
 
 - **From Source**
     1. Clone the repository:
diff --git a/dpnegf/utils/loggers.py b/dpnegf/utils/loggers.py
index 66bc032..a51158b 100644
--- a/dpnegf/utils/loggers.py
+++ b/dpnegf/utils/loggers.py
@@ -133,5 +133,9 @@ def set_log_handles(
 
     # print version info
     logger = logging.getLogger(__name__)
-    logger.info(f"DPNEGF version: {get_version_dpnegf()}")
-    logger.info(f"DeePTB version: {get_version_dptb()}")
\ No newline at end of file
+    logger.info("=" * 80)
+    logger.info(f"{'Version Info':^80}")
+    logger.info("-" * 80)
+    logger.info(f"{'DPNEGF':<20} : {get_version_dpnegf()}")
+    logger.info(f"{'DeePTB':<20} : {get_version_dptb()}")
+    logger.info("=" * 80 + "\n")
\ No newline at end of file

From e00516e52bf20a9aa4525b08917a2a8301baae56 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 29 May 2025 09:53:16 +0800
Subject: [PATCH 027/152] =?UTF-8?q?test:=20=E8=B0=83=E6=95=B4Fermi?=
 =?UTF-8?q?=E8=83=BD=E7=BA=A7=E8=AE=A1=E7=AE=97=E7=9A=84=E7=B2=BE=E5=BA=A6?=
 =?UTF-8?q?=E8=A6=81=E6=B1=82=EF=BC=8C=E6=9B=B4=E6=96=B0=E6=96=AD=E8=A8=80?=
 =?UTF-8?q?=E5=AE=B9=E5=B7=AE?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/tests/test_get_fermi.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dpnegf/tests/test_get_fermi.py b/dpnegf/tests/test_get_fermi.py
index 40a5c66..35b097a 100644
--- a/dpnegf/tests/test_get_fermi.py
+++ b/dpnegf/tests/test_get_fermi.py
@@ -17,9 +17,9 @@ def test_get_fermi():
     _, efermi =elec_cal.get_fermi_level(data=stru_data, 
                     nel_atom = nel_atom,smearing_method='FD',
                 meshgrid=[30,30,30])
-    assert abs(efermi  + 3.2262574434280395) < 1e-3
+    assert abs(efermi  + 3.2257686853408813) < 1e-6
 
     _, efermi =elec_cal.get_fermi_level(data=stru_data, 
                     nel_atom = nel_atom,smearing_method='Gaussian',
                 meshgrid=[30,30,30])
-    assert abs(efermi  + 3.2262574434280395) < 1e-3
+    assert abs(efermi  + 3.2267462015151978) < 1e-6

From fd11df83726c78511fca7a7ab01c35a74782ecb5 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 29 May 2025 16:47:31 +0800
Subject: [PATCH 028/152] =?UTF-8?q?fix:=20=E4=BF=AE=E6=AD=A3=E5=AF=BC?=
 =?UTF-8?q?=E5=85=A5=E8=B7=AF=E5=BE=84=E4=BB=A5=E7=A1=AE=E4=BF=9D=E6=AD=A3?=
 =?UTF-8?q?=E7=A1=AE=E5=BC=95=E7=94=A8anglrMId=E5=B8=B8=E9=87=8F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/data/interfaces/ham_to_feature.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/data/interfaces/ham_to_feature.py b/dpnegf/data/interfaces/ham_to_feature.py
index effb0ab..502be86 100644
--- a/dpnegf/data/interfaces/ham_to_feature.py
+++ b/dpnegf/data/interfaces/ham_to_feature.py
@@ -3,7 +3,7 @@
 import numpy as np
 import re
 import logging
-from ...constants import anglrMId
+from dpnegf.utils.constants import anglrMId
 from .. import AtomicData, AtomicDataDict
 
 log = logging.getLogger(__name__)

From 91521a50a8aced6e1888327ffebd5f01d3d4c57b Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 4 Jun 2025 17:38:15 +0800
Subject: [PATCH 029/152] =?UTF-8?q?fix:=20=E4=BF=AE=E6=AD=A3Fermi=E8=83=BD?=
 =?UTF-8?q?=E7=BA=A7=E8=AE=A1=E7=AE=97=E4=B8=ADfixed=5Fcharge=E6=AD=A3?=
 =?UTF-8?q?=E8=B4=9F=E5=92=8C=E6=8E=BA=E6=9D=82=E7=B1=BB=E5=9E=8B=E7=9A=84?=
 =?UTF-8?q?=E5=85=B3=E7=B3=BB?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/runner/NEGF.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 556c51a..aea301e 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -160,7 +160,7 @@ def __init__(self,
         log.info(msg="-------------Fermi level calculation-------------")
         e_fermi = {}; chemiPot = {}
         # calculate Fermi level
-        if not self.e_fermi:        
+        if  self.e_fermi is None:        
             elec_cal = ElecStruCal(model=model,device=self.torch_device)
             nel_atom_lead = self.get_nel_atom_lead(
                                 struct_leads, 
@@ -740,7 +740,17 @@ def get_nel_atom_lead(self, struct_leads, charge:float=None):
                     nel_atom_lead[lead_tag][elem] = nel_atom[elem]
             # subtract dope charge if the lead is doped
             if charge is not None:
-                nel_atom_lead[lead_tag] = {elem: nel_atom_lead[lead_tag][elem] - charge[lead_tag] for elem in nel_atom_lead[lead_tag]}
+                assert charge.get(lead_tag) is not None, f"Charge for {lead_tag} is not provided"
+                if isinstance(charge[lead_tag], (int, float)):
+                    if charge[lead_tag] < 0:
+                        log.info(msg=f"p doping detected in {lead_tag}, fixed_charge = {charge[lead_tag]}")
+                    elif charge[lead_tag] > 0:
+                        log.info(msg=f"n doping detected in {lead_tag}, fixed_charge = {charge[lead_tag]}")
+                    else:
+                        log.warning(msg=f"No doping detected in {lead_tag}, fixed_charge = {charge[lead_tag]}")
+                else:
+                    raise ValueError(f"Charge for {lead_tag} should be a number, got {type(charge[lead_tag])}")
+                nel_atom_lead[lead_tag] = {elem: nel_atom_lead[lead_tag][elem] + charge[lead_tag] for elem in nel_atom_lead[lead_tag]}
 
         return nel_atom_lead  
     

From 45a2c26bb9ecb0c8932e35d2469e9fb17c4642c6 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 8 Jun 2025 17:34:46 +0800
Subject: [PATCH 030/152] =?UTF-8?q?feat:=20=E6=B7=BB=E5=8A=A0get=5Fblock?=
 =?UTF-8?q?=5Ftridiagonal=20docstring?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/negf_hamiltonian_init.py | 53 +++++++++++++++++++++++-----
 1 file changed, 44 insertions(+), 9 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index f31274f..c033f22 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -571,6 +571,45 @@ def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         return stru_lead, stru_lead_fold, bloch_sorted_indice, bloch_R_list
 
     def get_block_tridiagonal(self,HK,SK,structase:ase.Atoms,leftmost_size:int,rightmost_size:int):
+        """
+        Block-tridiagonalizes the Hamiltonian (HK) and overlap (SK) matrices for a given atomic structure.
+        This method splits the input matrices into block tridiagonal form based on the atomic structure along the z-axis.
+        Note that the splitting process is performed on HK[0] and SK[0], which are the matrices for Gamma k-point.
+        The function returns the diagonal, upper-diagonal, and lower-diagonal blocks for both HK and SK, as well as the subblock sizes.
+        Parameters
+        ----------
+        HK : np.ndarray
+            The Hamiltonian matrix (k-point resolved), shape (nk, n_orb, n_orb).
+        SK : np.ndarray
+            The overlap matrix (k-point resolved), shape (nk, n_orb, n_orb).
+        structase : ase.Atoms
+            The atomic structure, used to determine block boundaries along the z-axis.
+        leftmost_size : int or None
+            Number of orbitals in the leftmost block. If None, it is determined from the structure.
+        rightmost_size : int or None
+            Number of orbitals in the rightmost block. If None, it is determined from the structure.
+        Returns
+        -------
+        hd : list
+            List of diagonal blocks of HK for each k-point.
+        hu : list
+            List of upper-diagonal blocks of HK for each k-point.
+        hl : list
+            List of lower-diagonal blocks of HK for each k-point.
+        sd : list
+            List of diagonal blocks of SK for each k-point.
+        su : list
+            List of upper-diagonal blocks of SK for each k-point.
+        sl : list
+            List of lower-diagonal blocks of SK for each k-point.
+        subblocks : list
+            List of subblock sizes (number of orbitals in each block).
+        Notes
+        -----
+        - Uses optimized or fallback block splitting depending on the structure.
+        - Logs information about the block structure and occupation.
+        - Calls `show_blocks` to visualize the block structure.
+        """
 
 
         # return hd in format: (k_index,block_index, orb, orb)
@@ -699,8 +738,10 @@ def get_hs_device(self, kpoint=[0,0,0], V=None, block_tridiagonal=False, only_su
             # log.info(msg="The HS_device.pth exists in the saved path {}.".format(self.saved_HS_path))
             HS_device_path = HS_device_path_pth
             HS_device = torch.load(HS_device_path)
-        
-
+        else:
+            raise FileNotFoundError(f"Neither HS_device.pth nor HS_device.h5 found in {self.saved_HS_path}. " )
+                  
+                
         if only_subblocks:
             if "subblocks" not in HS_device:
                 log.warning(msg=" 'subblocks' might not be saved in the HS_device.pth for old version.")
@@ -839,13 +880,7 @@ def get_hs_lead(self, kpoint, tab, v):
                                HS_leads["SL"][ik_bloch], HS_leads["SLL"][ik_bloch]
             hDL,sDL = HS_leads["HDL"][ik], HS_leads["SDL"][ik]
 
-        return hL-v*sL, hLL-v*sLL, hDL, sL, sLL, sDL 
-
-    def attach_potential():
-        pass
-
-    def write(self):
-        pass
+        return hL-v*sL, hLL-v*sLL, hDL, sL, sLL, sDL         
 
     @property
     def device_norbs(self):

From b44f96e93ceaa7e60a28d0ae25ded33882de6cba Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 8 Jun 2025 17:37:34 +0800
Subject: [PATCH 031/152] =?UTF-8?q?fix:=20=E4=BF=AE=E6=AD=A3lead=5Fpropert?=
 =?UTF-8?q?y=E4=B8=AD=E8=87=AA=E8=83=BD=E8=AE=A1=E7=AE=97=E7=9A=84?=
 =?UTF-8?q?=E5=BD=A2=E7=8A=B6=EF=BC=8C=E4=BB=A5=E5=8C=B9=E9=85=8D=E5=9D=97?=
 =?UTF-8?q?=E5=AF=B9=E8=A7=92=E5=8C=96=E7=BB=93=E6=9E=9C?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 dpnegf/negf/density.py                        |  4 +-
 dpnegf/negf/lead_property.py                  | 40 +++++++++++++------
 dpnegf/negf/split_btd.py                      |  2 +-
 dpnegf/tests/test_negf_device_property.py     | 18 ++++++++-
 .../tests/test_negf_negf_hamiltonian_init.py  | 19 ++++++++-
 5 files changed, 63 insertions(+), 20 deletions(-)

diff --git a/dpnegf/negf/density.py b/dpnegf/negf/density.py
index 3c6916a..4b06486 100644
--- a/dpnegf/negf/density.py
+++ b/dpnegf/negf/density.py
@@ -288,8 +288,8 @@ def density_integrate_Fiori(self,e_grid,kpoint,Vbias,block_tridiagonal,subblocks
                                           eta_device=eta_device,Vbias = Vbias)
 
             tx, ty = deviceprop.g_trans.shape
-            lx, ly = deviceprop.lead_L.se.shape
-            rx, ry = deviceprop.lead_R.se.shape
+            lx= deviceprop.lead_L.se.shape[0]
+            rx= deviceprop.lead_R.se.shape[0]
             x0 = min(lx, tx)
             x1 = min(rx, ty)
 
diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 0bd4208..2708bf9 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -150,7 +150,7 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                 log.info(f"Not find stored {self.tab} self energy. Calculating it at kpoint {kpoint} and energy {energy}.")
                 log.info("-"*50)
 
-
+        subblocks = self.hamiltonian.get_hs_device(kpoint, only_subblocks=True)
         # calculate self energy
         if not self.useBloch:
             if not hasattr(self, "HL") or abs(self.voltage_old-self.voltage)>1e-6 or max(abs(self.kpoint-torch.tensor(kpoint)))>1e-6:
@@ -159,7 +159,8 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
                 self.voltage_old = self.voltage
                 self.kpoint = torch.tensor(kpoint)
 
-            HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk)
+            
+            HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk,subblocks)
             
             self.se, _ = selfEnergy(
                 ee=energy,
@@ -177,7 +178,9 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             # torch.save(self.se, os.path.join(self.results_path, f"se_nobloch_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_{energy}.pth"))
         
         else:
-            if not hasattr(self, "HL") or abs(self.voltage_old-self.voltage)>1e-6 or max(abs(self.kpoint-torch.tensor(kpoint)))>1e-6:
+            if not hasattr(self, "HL") \
+                or abs(self.voltage_old-self.voltage)>1e-6 \
+                or max(abs(self.kpoint-torch.tensor(kpoint)))>1e-6:
                 self.kpoint = torch.tensor(kpoint)
                 self.voltage_old = self.voltage
 
@@ -217,18 +220,17 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             b = self.HDLk.shape[1] # size of lead hamiltonian
 
             # reduce the Hamiltonian and overlap matrix based on the non-zero range of HDL
-            HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk) 
-            # HDL_reduced, SDL_reduced = self.HDL, self.SDL
+            HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk,subblocks) 
             if not isinstance(energy, torch.Tensor):
                 eeshifted = torch.scalar_tensor(energy, dtype=torch.complex128) + self.E_ref
             else:
                 eeshifted = energy + self.E_ref
-            # self.se = (eeshifted*self.SDL-self.HDL) @ sgf_k[:b,:b] @ (eeshifted*self.SDL.conj().T-self.HDL.conj().T)
             self.se = (eeshifted*SDL_reduced-HDL_reduced) @ sgf_k[:b,:b] @ (eeshifted*SDL_reduced.conj().T-HDL_reduced.conj().T)
-
+            # In subblocks case, the self energy shape of left/right lead should be consistent with subblocks[0] and subblocks[-1]
         if not HS_inmem:
             del self.HLk, self.HLLk, self.HDLk, self.SLk, self.SLLk, self.SDLk
 
+
         if save:
             assert save_path is not None, "Please specify the path to save the self energy."
             if se_info_display: log.info(f"Saving self energy to {save_path}")
@@ -239,9 +241,9 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             #     torch.save(self.se, os.path.join(self.results_path, f"se_nobloch_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_{energy}.pth"))
 
     @staticmethod
-    def HDL_reduced(HDL: torch.Tensor, SDL: torch.Tensor) -> torch.Tensor:
+    def HDL_reduced(HDL: torch.Tensor, SDL: torch.Tensor, subblocks: np.ndarray) -> torch.Tensor:
         '''This function takes in Hamiltonian/Overlap matrix between lead and device and reduces 
-        it based on the non-zero range of the Hamiltonian matrix.
+        it based on the subblocks results or non-zero range of the Hamiltonian matrix.
 
             When the device part has only one orbital, the Hamiltonian matrix is not reduced.
         
@@ -263,16 +265,28 @@ def HDL_reduced(HDL: torch.Tensor, SDL: torch.Tensor) -> torch.Tensor:
         assert HDL.shape == SDL.shape, "The shape of HDL and SDL should be the same."
 
         HDL_nonzero_range = (HDL.nonzero().min(dim=0).values, HDL.nonzero().max(dim=0).values)
+        if subblocks is None:
+            cut_range = HDL_nonzero_range
+        else:
+            cut_range = ((subblocks[-1],subblocks[-1]), (subblocks[0],subblocks[0]))
         # HDL_nonzero_range is a tuple((min_row,min_col),(max_row,max_col))
         if HDL.shape[0] == 1: # Only 1 orbital in the device
             HDL_reduced = HDL
             SDL_reduced = SDL
         elif HDL_nonzero_range[0][0] > 0: # Right lead
-            HDL_reduced = HDL[HDL_nonzero_range[0][0]:, :]
-            SDL_reduced = SDL[HDL_nonzero_range[0][0]:, :]
+            if subblocks is None:
+                HDL_reduced = HDL[cut_range[0][0]:, :]
+                SDL_reduced = SDL[cut_range[0][0]:, :]
+            else:
+                HDL_reduced = HDL[-1*cut_range[0][0]:, :]
+                SDL_reduced = SDL[-1*cut_range[0][0]:, :]
         else: # Left lead
-            HDL_reduced = HDL[:HDL_nonzero_range[1][0]+1, :]
-            SDL_reduced = SDL[:HDL_nonzero_range[1][0]+1, :]
+            if subblocks is None:
+                HDL_reduced = HDL[:cut_range[1][0]+1, :]
+                SDL_reduced = SDL[:cut_range[1][0]+1, :]
+            else:
+                HDL_reduced = HDL[:cut_range[1][0], :]
+                SDL_reduced = SDL[:cut_range[1][0], :]
 
         return HDL_reduced, SDL_reduced
 
diff --git a/dpnegf/negf/split_btd.py b/dpnegf/negf/split_btd.py
index d3cd25b..77e3bdf 100644
--- a/dpnegf/negf/split_btd.py
+++ b/dpnegf/negf/split_btd.py
@@ -1316,7 +1316,7 @@ def show_blocks(subblocks, input_mat, results_path):
     plt.xlim(input_mat.shape[0] - 0.5, -1.0)
     plt.ylim(-1.0, input_mat.shape[0] - 0.5)
     plt.axis('off')
-    plt.savefig(results_path +'/subblocks.png', dpi=300)
+    plt.savefig(results_path +'/subblocks_HK0.png', dpi=300)
 
 
 # if __name__ == "__main__":
diff --git a/dpnegf/tests/test_negf_device_property.py b/dpnegf/tests/test_negf_device_property.py
index c89d248..45ada2b 100644
--- a/dpnegf/tests/test_negf_device_property.py
+++ b/dpnegf/tests/test_negf_device_property.py
@@ -100,8 +100,22 @@ def test_negf_Device(root_directory):
                 )
 
         # check left and right leads' self-energy
-    lead_L_se_standard=torch.tensor([[-3.3171e-07-0.6096j]], dtype=torch.complex128)
-    lead_R_se_standard=torch.tensor([[-3.3171e-07-0.6096j]], dtype=torch.complex128)
+    lead_L_se_standard=torch.tensor([[-3.3171e-07-0.6096j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j]], dtype=torch.complex128)
+    lead_R_se_standard=torch.tensor([[0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+        -3.3171e-07-0.6096j]], dtype=torch.complex128)
     print('device.lead_L.se:',device.lead_L.se)
     print('device.lead_R.se:',device.lead_R.se)
     assert  abs(device.lead_L.se-lead_L_se_standard).max()<1e-5
diff --git a/dpnegf/tests/test_negf_negf_hamiltonian_init.py b/dpnegf/tests/test_negf_negf_hamiltonian_init.py
index e5269e1..573983c 100644
--- a/dpnegf/tests/test_negf_negf_hamiltonian_init.py
+++ b/dpnegf/tests/test_negf_negf_hamiltonian_init.py
@@ -82,9 +82,24 @@ def test_negf_Hamiltonian(root_directory):
     print("lead_L self energy:",deviceprop.lead_L.se)
     print("lead_R self energy:",deviceprop.lead_R.se)
 
-    lead_L_se_standard = torch.tensor([[1.8103e-08-0.6096j]], dtype=torch.complex128)
+    lead_L_se_standard = torch.tensor([[1.8103e-08-0.6096j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+         [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j]], dtype=torch.complex128)
     assert abs(deviceprop.lead_L.se-lead_L_se_standard).max()<1e-5
-    lead_R_se_standard = torch.tensor([[1.8103e-08-0.6096j]], dtype=torch.complex128)
+
+    lead_R_se_standard = torch.tensor([[0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+         0.0000e+00+0.0000j],
+        [0.0000e+00+0.0000j, 0.0000e+00+0.0000j, 0.0000e+00+0.0000j,
+        1.8103e-08-0.6096j]], dtype=torch.complex128)
     assert abs(deviceprop.lead_R.se-lead_R_se_standard).max()<1e-5
 
     #check device's Hamiltonian

From 65a7764c0296604c9a23948e9bf031e0e42d1562 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 9 Jun 2025 17:23:26 +0800
Subject: [PATCH 032/152] feat: add comand line function

---
 dpnegf/__init__.py         |   3 +
 dpnegf/__main__.py         |  32 +++++++++
 dpnegf/entrypoints/main.py | 143 +++++++++++++++++++++++++++++++++++++
 pyproject.toml             |   2 +
 4 files changed, 180 insertions(+)
 create mode 100644 dpnegf/__main__.py
 create mode 100644 dpnegf/entrypoints/main.py

diff --git a/dpnegf/__init__.py b/dpnegf/__init__.py
index b6e690f..b1fdac0 100644
--- a/dpnegf/__init__.py
+++ b/dpnegf/__init__.py
@@ -1 +1,4 @@
 from . import *
+import importlib.metadata
+
+__version__ = importlib.metadata.version("dpnegf")
\ No newline at end of file
diff --git a/dpnegf/__main__.py b/dpnegf/__main__.py
new file mode 100644
index 0000000..be6c361
--- /dev/null
+++ b/dpnegf/__main__.py
@@ -0,0 +1,32 @@
+from dpnegf.entrypoints.main import main as entry_main
+import logging
+import pyfiglet
+from dpnegf import __version__
+
+logging.basicConfig(level=logging.INFO, format='%(message)s')
+log = logging.getLogger(__name__)
+
+def print_logo():
+    f = pyfiglet.Figlet(font='starwars')  # 可选字体: 'standard', 'doom', 'starwars', 'slant'
+    logo = f.renderText("DPNEGF").rstrip()
+
+    banner_width = 80
+    separator = "=" * banner_width
+
+    log.info(separator)
+    for line in logo.splitlines():
+        log.info(line.center(banner_width))
+    
+    version_info = f"DPNEGF version {__version__}"
+    log.info("-" * banner_width)
+    log.info(version_info.center(banner_width))
+    log.info(separator)
+def main() -> None:
+    """
+    The main entry point for the dpnegf package.
+    """
+    print_logo()
+    entry_main()
+
+if __name__ == '__main__':
+    main()
diff --git a/dpnegf/entrypoints/main.py b/dpnegf/entrypoints/main.py
new file mode 100644
index 0000000..e77be25
--- /dev/null
+++ b/dpnegf/entrypoints/main.py
@@ -0,0 +1,143 @@
+import argparse
+import logging
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from dpnegf.entrypoints.run import run
+from dpnegf.utils.loggers import set_log_handles
+
+from dpnegf import __version__
+
+
+
+def get_ll(log_level: str) -> int:
+    """Convert string to python logging level.
+
+    Parameters
+    ----------
+    log_level : str
+        allowed input values are: DEBUG, INFO, WARNING, ERROR, 3, 2, 1, 0
+
+    Returns
+    -------
+    int
+        one of python logging module log levels - 10, 20, 30 or 40
+    """
+    if log_level.isdigit():
+        int_level = (4 - int(log_level)) * 10
+    else:
+        int_level = getattr(logging, log_level)
+
+    return int_level
+
+def main_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="DPNEGF: A NEGF Python package compatible to DeePTB method for efficient quantum transport simulations",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser.add_argument('-v', '--version', 
+                        action='version', version=f'%(prog)s {__version__}', help="show the DPNEGF's version number and exit")
+
+
+    subparsers = parser.add_subparsers(title="Valid subcommands", dest="command")
+
+    # log parser
+    parser_log = argparse.ArgumentParser(
+        add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser_log.add_argument(
+        "-ll",
+        "--log-level",
+        choices=["DEBUG", "3", "INFO", "2", "WARNING", "1", "ERROR", "0"],
+        default="INFO",
+        help="set verbosity level by string or number, 0=ERROR, 1=WARNING, 2=INFO "
+             "and 3=DEBUG",
+    )
+
+    parser_log.add_argument(
+        "-lp",
+        "--log-path",
+        type=str,
+        default=None,
+        help="set log file to log messages to disk, if not specified, the logs will "
+             "only be output to console",
+    )
+
+
+    # run parser
+    parser_run = subparsers.add_parser(
+        "run",
+        parents=[parser_log],
+        help="run the TB with a model.",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+
+    parser_run.add_argument(
+        "INPUT", help="the input parameter file for postprocess run in json format",
+        type=str,
+        default=None
+    )
+
+    parser_run.add_argument(
+        "-i",
+        "--init-model",
+        type=str,
+        default=None,
+        help="Initialize the model by the provided checkpoint.",
+    )
+
+    parser_run.add_argument(
+        "-stu",
+        "--structure",
+        type=str,
+        default=None,
+        help="the structure file name wiht its suffix of format, such as, .vasp, .cif etc., prior to the model_ckpt tags in the input json. "
+    )
+
+    parser_run.add_argument(
+        "-o",
+        "--output",
+        type=str,
+        default="./",
+        help="The output files in postprocess run."
+    )
+
+    return parser
+
+def parse_args(args: Optional[List[str]] = None) -> argparse.Namespace:
+    """Parse arguments and convert argument strings to objects.
+
+    Parameters
+    ----------
+    args: List[str]
+        list of command line arguments, main purpose is testing default option None
+        takes arguments from sys.argv
+
+    Returns
+    -------
+    argparse.Namespace
+        the populated namespace
+    """
+    parser = main_parser()
+    parsed_args = parser.parse_args(args=args)
+    if parsed_args.command is None:
+        parser.print_help()
+    else:
+        parsed_args.log_level = get_ll(parsed_args.log_level)
+
+    return parsed_args
+
+def main():
+    args = parse_args()
+
+    if args.command not in (None, "run"):
+        set_log_handles(args.log_level, Path(args.log_path) if args.log_path else None)
+
+    dict_args = vars(args)
+    
+
+    if args.command == 'run':
+        run(**dict_args)
+
+
diff --git a/pyproject.toml b/pyproject.toml
index f36accf..76e467d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,6 +62,8 @@ optional = true
 [tool.poetry.group.pybinding.dependencies]
 pybinding = "*"
 
+[tool.poetry.scripts]
+dpnegf = "dpnegf.__main__:main"
 
 [build-system]
 requires = ["poetry-core", "poetry-dynamic-versioning"]

From dedf9dfb20d89da0a332c59d94993ff920f66bc9 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 9 Jun 2025 20:05:23 +0800
Subject: [PATCH 033/152] Add examples for dpnegf in API and CLI cases

---
 .../input_files/chain.vasp                    |   0
 .../input_files/negf_chain_new.json           |   0
 .../input_files/nnsk_C_new.json               |   0
 .../run.ipynb                                 |  31 ++-
 .../atomic_chain_cli/input_files/chain.vasp   |  20 ++
 .../input_files/negf_chain_new.json           |  49 ++++
 .../input_files/nnsk_C_new.json               |  39 +++
 examples/atomic_chain_cli/run.ipynb           | 229 ++++++++++++++++++
 8 files changed, 362 insertions(+), 6 deletions(-)
 rename examples/{atomic_chain => atomic_chain_api}/input_files/chain.vasp (100%)
 rename examples/{atomic_chain => atomic_chain_api}/input_files/negf_chain_new.json (100%)
 rename examples/{atomic_chain => atomic_chain_api}/input_files/nnsk_C_new.json (100%)
 rename examples/{atomic_chain => atomic_chain_api}/run.ipynb (97%)
 create mode 100644 examples/atomic_chain_cli/input_files/chain.vasp
 create mode 100644 examples/atomic_chain_cli/input_files/negf_chain_new.json
 create mode 100644 examples/atomic_chain_cli/input_files/nnsk_C_new.json
 create mode 100644 examples/atomic_chain_cli/run.ipynb

diff --git a/examples/atomic_chain/input_files/chain.vasp b/examples/atomic_chain_api/input_files/chain.vasp
similarity index 100%
rename from examples/atomic_chain/input_files/chain.vasp
rename to examples/atomic_chain_api/input_files/chain.vasp
diff --git a/examples/atomic_chain/input_files/negf_chain_new.json b/examples/atomic_chain_api/input_files/negf_chain_new.json
similarity index 100%
rename from examples/atomic_chain/input_files/negf_chain_new.json
rename to examples/atomic_chain_api/input_files/negf_chain_new.json
diff --git a/examples/atomic_chain/input_files/nnsk_C_new.json b/examples/atomic_chain_api/input_files/nnsk_C_new.json
similarity index 100%
rename from examples/atomic_chain/input_files/nnsk_C_new.json
rename to examples/atomic_chain_api/input_files/nnsk_C_new.json
diff --git a/examples/atomic_chain/run.ipynb b/examples/atomic_chain_api/run.ipynb
similarity index 97%
rename from examples/atomic_chain/run.ipynb
rename to examples/atomic_chain_api/run.ipynb
index 51b317a..e7fbe22 100644
--- a/examples/atomic_chain/run.ipynb
+++ b/examples/atomic_chain_api/run.ipynb
@@ -31,6 +31,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO                                      Version Info                                  \n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev12+49c5b45\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev43+b666d17\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "\n",
       "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n"
      ]
     }
@@ -58,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "830d67a4",
    "metadata": {},
    "outputs": [
@@ -74,13 +81,24 @@
       "DPNEGF INFO    k-points weights: [1.]\n",
       "DPNEGF INFO    --------------------------------\n",
       "DPNEGF INFO    The AtomicData_options is {'r_max': 2.0}\n",
-      "/opt/mamba/envs/deeptb-dev/lib/python3.10/site-packages/torch/nested/__init__.py:58: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
-      "  return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)\n",
+      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
       "DPNEGF INFO    The coupling width of lead_L is 1.\n",
       "DPNEGF INFO    The coupling width of lead_R is 1.\n",
       "DPNEGF INFO    --------------------------------------------------------------------------------\n",
       "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
       "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
+      "DPNEGF INFO    Fermi level is set to -13.638587951660156 from input file\n",
+      "DPNEGF INFO    -------------------------------------------------\n",
+      "DPNEGF INFO    Zero bias case detected.\n",
+      "DPNEGF INFO    Fermi level for lead_L: -13.638587951660156\n",
+      "DPNEGF INFO    Fermi level for lead_R: -13.638587951660156\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -13.638587951660156\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -13.638587951660156\n",
+      "DPNEGF INFO    Reference energy E_ref: -13.638587951660156\n",
+      "DPNEGF INFO    =================================================\n",
+      "\n",
       "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
       "DPNEGF INFO    computing green's function at e = -2.000\n",
       "DPNEGF INFO    computing green's function at e = -1.599\n",
@@ -91,7 +109,8 @@
       "DPNEGF INFO    computing green's function at e = 0.406\n",
       "DPNEGF INFO    computing green's function at e = 0.807\n",
       "DPNEGF INFO    computing green's function at e = 1.208\n",
-      "DPNEGF INFO    computing green's function at e = 1.609\n"
+      "DPNEGF INFO    computing green's function at e = 1.609\n",
+      "DPNEGF WARNING The Fermi energy of the left and right leads should be equal in nscf current calculation.\n"
      ]
     }
    ],
@@ -193,7 +212,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "deeptb-dev",
+   "display_name": "dpnegf-dev",
    "language": "python",
    "name": "python3"
   },
@@ -207,7 +226,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/examples/atomic_chain_cli/input_files/chain.vasp b/examples/atomic_chain_cli/input_files/chain.vasp
new file mode 100644
index 0000000..81a1ab2
--- /dev/null
+++ b/examples/atomic_chain_cli/input_files/chain.vasp
@@ -0,0 +1,20 @@
+long_chain
+1.0
+        10.000000000        0.0000000000         0.0000000000
+        0.0000000000        10.0000000000         0.0000000000
+        0.0000000000         0.0000000000        19.2000000000
+    C
+   12
+Cartesian
+        0.000000000         0.000000000         3.200000000
+        0.000000000         0.000000000         4.800000000
+        0.000000000         0.000000000         0.000000000
+        0.000000000         0.000000000         1.600000000
+        0.000000000         0.000000000         6.400000000
+        0.000000000         0.000000000         8.000000000
+        0.000000000         0.000000000         9.600000000
+        0.000000000         0.000000000         11.20000000
+        0.000000000         0.000000000         12.80000000
+        0.000000000         0.000000000         14.40000000
+        0.000000000         0.000000000         16.00000000
+        0.000000000         0.000000000         17.60000000
\ No newline at end of file
diff --git a/examples/atomic_chain_cli/input_files/negf_chain_new.json b/examples/atomic_chain_cli/input_files/negf_chain_new.json
new file mode 100644
index 0000000..fdffbf4
--- /dev/null
+++ b/examples/atomic_chain_cli/input_files/negf_chain_new.json
@@ -0,0 +1,49 @@
+{
+    "task_options": {
+        "task": "negf",
+        "scf": false,
+        "block_tridiagonal": false,
+        "ele_T": 300,
+        "unit": "eV",
+        "stru_options":{
+            "gamma_center": true,
+            "time_reversal_symmetry": true,
+            "kmesh":[1,1,1],
+            "pbc":[false, false, false],
+            "device":{
+                "id":"4-8",
+                "sort": true
+            },
+            "lead_L":{
+                "id":"0-4",
+                "voltage":0.0,
+                "kmesh_lead_Ef":[1,1,20],
+                "useBloch": false
+            },
+            "lead_R":{
+                "id":"8-12",
+                "voltage":0.0,
+                "kmesh_lead_Ef":[1,1,20],
+                "useBloch": false
+            }
+        },
+        "density_options": {
+            "method": "Fiori",
+            "integrate_way": "direct"
+        },
+        "sgf_solver": "Sancho-Rubio",
+        "espacing": 0.01,
+        "emin": -2,
+        "emax": 2,
+        "eta_lead":1e-5,
+        "eta_device":0.0,
+        "out_dos": true,
+        "out_tc": true,
+        "out_ldos": true,
+        "out_current_nscf": true
+},    
+"AtomicData_options" :{
+    "r_max": 2.0
+},
+"structure":"./chain.vasp"
+}
diff --git a/examples/atomic_chain_cli/input_files/nnsk_C_new.json b/examples/atomic_chain_cli/input_files/nnsk_C_new.json
new file mode 100644
index 0000000..65f5e13
--- /dev/null
+++ b/examples/atomic_chain_cli/input_files/nnsk_C_new.json
@@ -0,0 +1,39 @@
+{ "version":2,
+"model_params": {
+    "onsite": {},
+    "hopping": {
+        "C-C-2s-2s-0": [
+            1.0884529828109601,
+            1.0
+        ]
+    }
+},
+"unit": "eV",
+"model_options": {
+    "nnsk": {
+        "onsite": {
+            "method": "none"
+        },
+        "hopping": {
+            "method": "powerlaw",
+            "rs": 1.6,
+            "w": 0.3
+        },
+        "soc": {},
+        "freeze": false,
+        "push": false,
+        "std": 0.01
+    }
+},
+"common_options": {
+    "basis": {
+        "C": [
+            "2s"
+        ]
+    },
+    "dtype": "float32",
+    "device": "cpu",
+    "overlap": false
+}
+}
+
diff --git a/examples/atomic_chain_cli/run.ipynb b/examples/atomic_chain_cli/run.ipynb
new file mode 100644
index 0000000..978f896
--- /dev/null
+++ b/examples/atomic_chain_cli/run.ipynb
@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "432a442f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "540efa50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_cli\n",
+      "sisl is not installed.Thus the input for TBtrans can not be generated, please install it first!\n",
+      "tbtrans is not in the Environment PATH. Thus the input for TBtrans can be generated but not run.\n",
+      "================================================================================\n",
+      "            _______  .______   .__   __.  _______   _______  _______            \n",
+      "           |       \\ |   _  \\  |  \\ |  | |   ____| /  _____||   ____|           \n",
+      "           |  .--.  ||  |_)  | |   \\|  | |  |__   |  |  __  |  |__              \n",
+      "           |  |  |  ||   ___/  |  . `  | |   __|  |  | |_ | |   __|             \n",
+      "           |  '--'  ||  |      |  |\\   | |  |____ |  |__| | |  |                \n",
+      "             |_______/ | _|      |__| \\__| |_______| \\______| |__|              \n",
+      "--------------------------------------------------------------------------------\n",
+      "                       DPNEGF version 0.1.1.dev12+49c5b45                       \n",
+      "================================================================================\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO                                      Version Info                                  \n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev12+49c5b45\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev43+b666d17\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "\n",
+      "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n",
+      "DPNEGF INFO    ------ k-point for NEGF -----\n",
+      "DPNEGF INFO    Gamma Center: True\n",
+      "DPNEGF INFO    Time Reversal: True\n",
+      "DPNEGF INFO    k-points Num: 1\n",
+      "DPNEGF INFO    k-points: [[0 0 0]]\n",
+      "DPNEGF INFO    k-points weights: [1.]\n",
+      "DPNEGF INFO    --------------------------------\n",
+      "DPNEGF INFO    The AtomicData_options is {'r_max': 2.0, 'er_max': None, 'oer_max': None}\n",
+      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+      "DPNEGF INFO    The coupling width of lead_L is 1.\n",
+      "DPNEGF INFO    The coupling width of lead_R is 1.\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
+      "DPNEGF WARNING nel_atom is None, using valence electron number by default\n",
+      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0.0\n",
+      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0.0\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 4.0}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 4.0}\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 50 iterations.\n",
+      "DPNEGF INFO    q_cal: 7.998605108261107, total_electrons: 16.0, diff q: 8.001394891738894\n",
+      "DPNEGF INFO    Estimated E_fermi: -12.374099200188748 based on the valence electrons setting nel_atom : {'C': 4.0} .\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 50 iterations.\n",
+      "DPNEGF INFO    q_cal: 7.998605275154112, total_electrons: 16.0, diff q: 8.001394724845888\n",
+      "DPNEGF INFO    Estimated E_fermi: -12.374099200188748 based on the valence electrons setting nel_atom : {'C': 4.0} .\n",
+      "DPNEGF INFO    -------------------------------------------------\n",
+      "DPNEGF INFO    Zero bias case detected.\n",
+      "DPNEGF INFO    Fermi level for lead_L: -12.374099200188748\n",
+      "DPNEGF INFO    Fermi level for lead_R: -12.374099200188748\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -12.374099200188748\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -12.374099200188748\n",
+      "DPNEGF INFO    Reference energy E_ref: -12.374099200188748\n",
+      "DPNEGF INFO    =================================================\n",
+      "\n",
+      "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -2.000\n",
+      "DPNEGF INFO    computing green's function at e = -1.599\n",
+      "DPNEGF INFO    computing green's function at e = -1.198\n",
+      "DPNEGF INFO    computing green's function at e = -0.797\n",
+      "DPNEGF INFO    computing green's function at e = -0.396\n",
+      "DPNEGF INFO    computing green's function at e = 0.005\n",
+      "DPNEGF INFO    computing green's function at e = 0.406\n",
+      "DPNEGF INFO    computing green's function at e = 0.807\n",
+      "DPNEGF INFO    computing green's function at e = 1.208\n",
+      "DPNEGF INFO    computing green's function at e = 1.609\n",
+      "DPNEGF WARNING The Fermi energy of the left and right leads should be equal in nscf current calculation.\n",
+      "DPNEGF INFO    negf calculation successfully completed.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Command line for DPNEGF\n",
+    "! pwd\n",
+    "! dpnegf run input_files/negf_chain_new.json  -i input_files/nnsk_C_new.json -stu input_files/chain.vasp -o output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "83d39465",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1559358/1401031374.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  negf_out = torch.load('./output/results/negf.out.pth')\n"
+     ]
+    }
+   ],
+   "source": [
+    "negf_out = torch.load('./output/results/negf.out.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6fd4754f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negf_out.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5dd516a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('Transmission')\n",
+    "plt.title('Transmission vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "92a9dada",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['DOS'][str(negf_out['k'][0])])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('DOS')\n",
+    "plt.title('DOS vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dpnegf-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 93a2e8e9f7f68d9ae95f5919bd6b951b83c58c96 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 10 Jun 2025 19:29:12 +0800
Subject: [PATCH 034/152] feat(SCF): add PDIIS and Broyden' 2nd method

---
 dpnegf/negf/field.py         |   0
 dpnegf/negf/poisson_init.py  |   4 +-
 dpnegf/negf/scf_method.py    | 382 ++++++++++++++++++++---------------
 dpnegf/negf/scf_method_bk.py | 179 ++++++++++++++++
 dpnegf/runner/NEGF.py        |  36 ++--
 5 files changed, 418 insertions(+), 183 deletions(-)
 delete mode 100644 dpnegf/negf/field.py
 create mode 100644 dpnegf/negf/scf_method_bk.py

diff --git a/dpnegf/negf/field.py b/dpnegf/negf/field.py
deleted file mode 100644
index e69de29..0000000
diff --git a/dpnegf/negf/poisson_init.py b/dpnegf/negf/poisson_init.py
index 60ea742..ece3dd3 100644
--- a/dpnegf/negf/poisson_init.py
+++ b/dpnegf/negf/poisson_init.py
@@ -256,11 +256,11 @@ def solve_poisson_NRcycle(self,method='pyamg',tolerance=1e-7,dtype:str='float64'
             Jacobian,B = self.to_scipy_Jac_B(dtype=dtype)
             norm_B = np.linalg.norm(B)
            
-            if method == 'scipy':   
+            if method == 'scipy':   #TODO: rename to 'Direct
                 if NR_cycle_step == 0:
                     log.info(msg="Solve Poisson equation by scipy")
                 delta_phi = spsolve(Jacobian,B)
-            elif method == 'pyamg':
+            elif method == 'pyamg': #TODO: rename to 'AMG'
                 if NR_cycle_step == 0:
                     log.info(msg="Solve Poisson equation by pyamg")
                 delta_phi = self.solver_pyamg(Jacobian,B,tolerance=1e-5)
diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 2a98be6..6f32a09 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -1,179 +1,225 @@
-import torch
-from torch.optim import LBFGS, Adam
-from xitorch.linalg.solve import solve
-from xitorch.grad.jachess import jac
-
-class SCFMethod(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, fcn, x0, scf_options, method='PDIIS', *params):
-        # with torch.no_grad():
-        #     x_ = fcn(x0, *params)
-        max_iter = scf_options["max_iter"]
-        abs_err = scf_options["abs_err"]
-
-        x_ = fcn(x0, *params)
-
-        if method == "default":
-            it = 0
-            old_x = x0
-            while (x_-old_x).norm() > abs_err and it < max_iter:
-                it += 1
-                old_x = x_
-                x_ = fcn(x_, *params)
-
-        elif method == 'GD':
-            x_ = x_.detach().requires_grad_()
-            temp_p = [p.detach() for p in params]
-            it = 0
-            loss = 1
-
-            def new_fcn(x_):
-
-                loss = (x_ - fcn(x_, *temp_p)).abs().sum()
-                print(loss)
-                return loss
-
-            with torch.enable_grad():
-                while it < max_iter and loss > abs_err:
-                    it += 1
-                    loss = new_fcn(x_)
-                    x_ = x_ - 1e-3 * torch.autograd.grad(loss, (x_,))[0]
-
-        elif method == 'Adam':
-            # x = torch.randn(200,1, dtype=torch.float64)
-            # x = x / x.norm()
-            # x_ = x_.unsqueeze(1) @ x.T
-            x_ = x_.detach().requires_grad_()
-            temp_p = [p.detach() for p in params]
-            optim = Adam(params=[x_], lr=1e-3)
-            def new_fcn(x_):
-                loss = (x_ - fcn(x_, *temp_p)).norm()
-                print(loss)
-                return loss
-            i = 0
-            loss = 1
-            with torch.enable_grad():
-                while i < max_iter and loss > abs_err:
-                    optim.zero_grad()
-                    loss = new_fcn(x_)
-                    loss.backward()
-                    optim.step()
-
-
-        elif method == "PDIIS":
-            with torch.no_grad():
-                x_ = PDIIS(lambda x: fcn(x, *params), p0=x_, **scf_options)
-
-        elif method == 'LBFGS':
-            x_ = x_.detach().requires_grad_()
-            temp_p = [p.detach() for p in params]
-            optim = LBFGS(params=[x_], lr=1e-2)
-
-            def new_fcn():
-                optim.zero_grad()
-                loss = (x_ - fcn(x_, *temp_p)).norm()
-                loss.backward()
-                print(loss)
-                return loss
-
-            with torch.enable_grad():
-                for i in range(max_iter):
-                    optim.step(new_fcn)
-                    print(x_)
+import numpy as np
+import logging
 
+log = logging.getLogger(__name__)
+class PDIISMixer:
+    """
+    Periodic Direct Inversion in the Iterative Subspace (PDIIS) mixer for accelerating SCF convergence.
+
+    Parameters
+    ----------
+    init_p : np.ndarray
+        Initial potential or state vector for SCF iterations.
+    mix_rate : float, optional
+        Mixing rate (step size) for linear update. Default is 0.05.
+    n_history : int, optional
+        Number of history steps to store for Pulay extrapolation. Default is 6.
+    mixing_period : int, optional
+        Frequency (in iterations) to apply DIIS mixing instead of linear mixing. Default is 3.
+    verbose : bool, optional
+        If True, print debug information. Default is False.
+    """
+    def __init__(self, init_p, mix_rate=0.05, n_history=4, mixing_period=2, verbose=True):
+        assert isinstance(init_p, np.ndarray), "init_p must be a numpy array"
+        
+        self.mix_rate = mix_rate
+        self.n_history = n_history
+        self.mixing_period = mixing_period
+        self.verbose = verbose
+        
+        self.iter_count = 0
+        self.p = init_p.copy()
+        self.f = None
+        self.R = [None for _ in range(n_history)]
+        self.F = [None for _ in range(n_history)]
+
+    def reset(self, new_init_p=None):
+        """Reset the mixer, optionally with a new initial potential."""
+        self.iter_count = 0
+        self.f = None
+        self.R = [None for _ in range(self.n_history)]
+        self.F = [None for _ in range(self.n_history)]
+        if new_init_p is not None:
+            assert isinstance(new_init_p, np.ndarray), "new_init_p must be a numpy array"
+            self.p = new_init_p.copy()
+
+    def update(self, p_new):
+        """
+        Perform one PDIIS mixing update based on the new input p_new.
+
+        Parameters
+        ----------
+        p_new : np.ndarray
+            Newly computed state (e.g., electrostatic potential).
+
+        Returns
+        -------
+        p_next : np.ndarray
+            The next mixed state.
+        """
+        assert isinstance(p_new, np.ndarray), "p_new must be a numpy array"
+        assert p_new.shape == self.p.shape, "Shape mismatch in p_new and current state"
+        
+        p_new = p_new.copy()
+        f_new = p_new - self.p
+
+        if self.f is not None:
+            idx = self.iter_count % self.n_history
+            self.R[idx] = p_new - self.p # Residual vector
+            self.F[idx] = f_new - self.f # Difference in residuals
+
+        
+
+        do_pdiis = (self.iter_count + 1) % self.mixing_period == 0
+        p_next = None
+
+        if do_pdiis and all(f is not None for f in self.F):
+            if self.verbose:
+                log.info(msg=f"[PDIIS] Performing DIIS mixing at iter {self.iter_count + 1}")
+            F_mat = np.stack(self.F, axis=1)
+            R_mat = np.stack(self.R, axis=1)
+
+            FtF = F_mat.T @ F_mat
+
+            try:
+                cond_FtF = np.linalg.cond(FtF)
+                if cond_FtF > 1e10:
+                    log.info(f"[PDIIS DEBUG] cond(FtF) = {cond_FtF:.2e}")
+                    log.info(f"[PDIIS DEBUG] Norms of F vectors: {[np.linalg.norm(f) for f in self.F]}")
+                    log.info(f"[PDIIS DEBUG] Rank of F_mat: {np.linalg.matrix_rank(F_mat)}")
+                    log.info(msg=f"[PDIIS] Warning: FtF matrix condition number too high ({cond_FtF:.2e}). Skipping DIIS.")
+                    raise RuntimeError("Ill-conditioned FtF matrix in PDIIS")
+
+                correction = (R_mat + self.mix_rate * F_mat) @ np.linalg.solve(FtF, F_mat.T @ f_new)
+                p_next = self.p + self.mix_rate * f_new - correction
+
+            except RuntimeError as e:
+                # This was manually raised due to condition number
+                if self.verbose:
+                    log.info(msg=f"[PDIIS] {e} Falling back to linear mixing.")
+                p_next = self.p + self.mix_rate * f_new
+
+            except np.linalg.LinAlgError as e:
+                # This is actual numerical failure in np.linalg.solve
+                if self.verbose:
+                    log.info(msg=f"[PDIIS] np.linalg.solve failed: {e}. Falling back to linear mixing.")
+                p_next = self.p + self.mix_rate * f_new
         else:
-            raise ValueError
-
-        print("Convergence achieved !")
-        x_ = x_ + 0j
-        ctx.save_for_backward(x_, *params)
-        ctx.fcn = fcn
-
-        return x_
-
-    @staticmethod
-    def backward(ctx, grad_outputs):
-        x_ = ctx.saved_tensors[0].detach().requires_grad_()
-        params = ctx.saved_tensors[1:]
-
-        idx = [i for i in range(len(params)) if params[i].requires_grad]
-
-
-        fcn = ctx.fcn
-        def new_fcn(x, *params):
-            return x - fcn(x, *params)
-
-        with torch.enable_grad():
-            grad = jac(fcn=new_fcn, params=(x_, *params), idxs=[0])[0]
+            if self.verbose:
+                log.info(msg=f"[PDIIS] Using linear mixing at iteration {self.iter_count + 1} (not periodic time step or not enough history).")
+            p_next = self.p + self.mix_rate * f_new
 
-        # pre = solve(grad.H, -grad_outputs.reshape(-1, 1))
-        pre = solve(grad.H, -grad_outputs.reshape(-1, 1).type_as(x_))
-        pre = pre.reshape(grad_outputs.shape)
 
 
-        with torch.enable_grad():
-            params_copy = [p.detach().requires_grad_() for p in params]
-            yfcn = new_fcn(x_, *params_copy)
+        # Update state
+        self.f = f_new.copy()
+        self.p = p_next.copy()
+        self.iter_count += 1
 
-        grad = torch.autograd.grad(yfcn, [params_copy[i] for i in idx], grad_outputs=pre,
-                                   create_graph=torch.is_grad_enabled(),
-                                   allow_unused=True)
-        grad_out = [None for _ in range(len(params))]
-        for i in range(len(idx)):
-            grad_out[idx[i]] = grad[i]
+        return p_next
+    
 
 
-        return None, None, None, None, None, None, *grad_out
-
-
-def PDIIS(fn, p0, step_size=0.05, n_history=6, max_iter=100, mixing_period=3, abs_err=1e-6, rel_err=1e-3, **options):
-    """The periodic pully mixing from https://doi.org/10.1016/j.cplett.2016.01.033.
-
-    Args:
-        fn (function): the iterative functions
-        p0 (_type_): the initial point
-        step_size (float, optional): the mixing beta value, or step size. Defaults to 0.05.
-        n_history (int, optional): the size of the storage of history to compute the pesuedo hessian matrix. Defaults to 6.
-        max_iter (int, optional): the maximum iteration. Defaults to 100.
-        mixing_period (int, optional): the period of conducting pully mixing. The algorithm will conduct pully mixing every k iterations. Defaults to 3.
-        abs_err (_type_, optional): the absolute err tolerance. Defaults to 1e-6.
-        rel_err (_type_, optional): the relative err tolerance. Defaults to 1e-3.
-
-    Returns:
-        p _type_: the stable point
+class BroydenSecondMixer:
+    """
+    Implements Broyden's Second Method (also known as "good Broyden")
+    for accelerating fixed-point iterations such as those arising
+    in SCF (self-consistent field) procedures.
+
+    This mixer constructs an approximation to the inverse Jacobian of the residual
+    using a low-rank update formula and applies it to iteratively improve convergence.
+
+    The method uses limited-memory rank-1 updates:
+        x_{n+1} = x_n - B_n * r_n
+    where B_n ≈ J^{-1} is the inverse Jacobian built from the update history.
+
+    Attributes:
+        alpha (float): Initial mixing parameter for the first step.
+        max_hist (int): Maximum number of update pairs (u, r) stored for low-rank updates.
+        eps (float): Threshold to avoid numerical instability in inner products.
+        B0 (ndarray): Initial inverse Jacobian approximation (scaled identity).
+        u_hist (list): History of update vectors u_n = s_n - B_n * delta_r_n.
+        r_hist (list): History of delta_r_n = r_n - r_{n-1}.
     """
-    i = 0
-    f = fn(p0) - p0
-    p = p0
-    R = [None for _ in range(n_history)]
-    F = [None for _ in range(n_history)]
-    print("SCF iter 0 abs err {0} | rel err {1}: ".format( 
-            f.abs().max().detach().numpy(), 
-            (f.abs() / p.abs()).max().detach().numpy())
-            )
-    while (f.abs().max() > abs_err or (f.abs() / p.abs()).max() > rel_err) and i < max_iter:
-        if not (i+1) % mixing_period:
-            F_ = torch.stack([t for t in F if t != None])
-            R_ = torch.stack([t for t in R if t != None])
-            p_ = p + step_size*f - (R_.T+step_size*F_.T)@(F_ @ F_.T).inverse() @ F_ @ f
-        else:
-            p_ = p + step_size * f
-
-        f_ = fn(p_) - p_
-        F[i % n_history] = f_ - f
-        R[i % n_history] = p_ - p
-
-        p = p_.clone()
-        f = f_.clone()
-        i += 1
-
-        print("SCF iter {0} abs err {1} | rel err {2}: ".format(
-            i, 
-            f.abs().max().detach().numpy(), 
-            (f.abs() / p.abs()).max().detach().numpy())
-            )
 
-    if i == max_iter:
-        print("Not Converged very well here.")
+    def __init__(self, shape, max_hist=8, alpha=0.1, eps=1e-12):
+        self.alpha = alpha        # Initial mixing factor
+        self.max_hist = max_hist  # Max number of correction terms
+        self.eps = eps            # Numerical stability threshold
+        self.reset(shape)
+
+    def reset(self, shape):
+        self.iter = 0
+        self.x_last = np.zeros(shape)
+        self.r_last = np.zeros(shape)
+        dim = np.prod(shape)
+        self.B0 = -self.alpha * np.eye(dim)  # Initial inverse Jacobian guess
+        self.u_hist = []  # History of update vectors u_n = s_n - B delta_r
+        self.r_hist = []  # Corresponding delta_r vectors
+
+    def update(self, x, r):
+        """
+        Perform one Broyden update step: x_{n+1} = x_n - B_n * r_n.
+
+        This function applies the approximate inverse Jacobian B_n
+        to the current residual r_n to compute the next guess x_{n+1}.
+        The internal approximation B_n is updated based on the history
+        of residual differences and solution updates.
+
+        Args:
+            x (np.ndarray): Current solution guess (arbitrary shape).
+            r (np.ndarray): Residual vector at the current guess.
+
+        Returns:
+            np.ndarray: Updated solution guess (same shape as input x).
+        """
+        x = x.ravel()
+        r = r.ravel()
+
+        if self.iter == 0:
+            x_new = x - self.B0 @ r
+            self.x_last = x.copy()
+            self.r_last = r.copy()
+            self.iter += 1
+            return x_new.reshape(self.x_last.shape)
+
+        # Step 1: Compute s_n = x - x_last, delta_r = r - r_last
+        s_n = x - self.x_last
+        delta_r = r - self.r_last
+
+        # Step 2: Build B * delta_r incrementally
+        B_delta_r = self.B0 @ delta_r
+        for u_j, r_j in zip(self.u_hist, self.r_hist):
+            rj_dot = np.dot(r_j, delta_r)
+            norm2 = np.dot(r_j, r_j)
+            if norm2 > self.eps:
+                B_delta_r += u_j * (rj_dot / (norm2 + self.eps))
+
+        # Step 3: u_n = s_n - B delta_r (corrected formula)
+        u_n = s_n - B_delta_r
+
+        # Step 4: Truncate history if needed
+        if len(self.u_hist) >= self.max_hist:
+            self.u_hist.pop(0)
+            self.r_hist.pop(0)
+        self.u_hist.append(u_n)
+        self.r_hist.append(delta_r)
+
+        # Step 5: Apply B_n * r using low-rank update
+        B_r = self.B0 @ r
+        for u_j, r_j in zip(self.u_hist, self.r_hist):
+            rj_dot = np.dot(r_j, r)
+            norm2 = np.dot(r_j, r_j)
+            if norm2 > self.eps:
+                B_r += u_j * (rj_dot / (norm2 + self.eps))
+
+        # Step 6: Final update
+        x_new = x - B_r
+
+        # Step 7: Cache for next iteration
+        self.x_last = x.copy()
+        self.r_last = r.copy()
+        self.iter += 1
+
+        return x_new.reshape(self.x_last.shape)
 
-    return p
\ No newline at end of file
diff --git a/dpnegf/negf/scf_method_bk.py b/dpnegf/negf/scf_method_bk.py
new file mode 100644
index 0000000..2a98be6
--- /dev/null
+++ b/dpnegf/negf/scf_method_bk.py
@@ -0,0 +1,179 @@
+import torch
+from torch.optim import LBFGS, Adam
+from xitorch.linalg.solve import solve
+from xitorch.grad.jachess import jac
+
+class SCFMethod(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, fcn, x0, scf_options, method='PDIIS', *params):
+        # with torch.no_grad():
+        #     x_ = fcn(x0, *params)
+        max_iter = scf_options["max_iter"]
+        abs_err = scf_options["abs_err"]
+
+        x_ = fcn(x0, *params)
+
+        if method == "default":
+            it = 0
+            old_x = x0
+            while (x_-old_x).norm() > abs_err and it < max_iter:
+                it += 1
+                old_x = x_
+                x_ = fcn(x_, *params)
+
+        elif method == 'GD':
+            x_ = x_.detach().requires_grad_()
+            temp_p = [p.detach() for p in params]
+            it = 0
+            loss = 1
+
+            def new_fcn(x_):
+
+                loss = (x_ - fcn(x_, *temp_p)).abs().sum()
+                print(loss)
+                return loss
+
+            with torch.enable_grad():
+                while it < max_iter and loss > abs_err:
+                    it += 1
+                    loss = new_fcn(x_)
+                    x_ = x_ - 1e-3 * torch.autograd.grad(loss, (x_,))[0]
+
+        elif method == 'Adam':
+            # x = torch.randn(200,1, dtype=torch.float64)
+            # x = x / x.norm()
+            # x_ = x_.unsqueeze(1) @ x.T
+            x_ = x_.detach().requires_grad_()
+            temp_p = [p.detach() for p in params]
+            optim = Adam(params=[x_], lr=1e-3)
+            def new_fcn(x_):
+                loss = (x_ - fcn(x_, *temp_p)).norm()
+                print(loss)
+                return loss
+            i = 0
+            loss = 1
+            with torch.enable_grad():
+                while i < max_iter and loss > abs_err:
+                    optim.zero_grad()
+                    loss = new_fcn(x_)
+                    loss.backward()
+                    optim.step()
+
+
+        elif method == "PDIIS":
+            with torch.no_grad():
+                x_ = PDIIS(lambda x: fcn(x, *params), p0=x_, **scf_options)
+
+        elif method == 'LBFGS':
+            x_ = x_.detach().requires_grad_()
+            temp_p = [p.detach() for p in params]
+            optim = LBFGS(params=[x_], lr=1e-2)
+
+            def new_fcn():
+                optim.zero_grad()
+                loss = (x_ - fcn(x_, *temp_p)).norm()
+                loss.backward()
+                print(loss)
+                return loss
+
+            with torch.enable_grad():
+                for i in range(max_iter):
+                    optim.step(new_fcn)
+                    print(x_)
+
+        else:
+            raise ValueError
+
+        print("Convergence achieved !")
+        x_ = x_ + 0j
+        ctx.save_for_backward(x_, *params)
+        ctx.fcn = fcn
+
+        return x_
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        x_ = ctx.saved_tensors[0].detach().requires_grad_()
+        params = ctx.saved_tensors[1:]
+
+        idx = [i for i in range(len(params)) if params[i].requires_grad]
+
+
+        fcn = ctx.fcn
+        def new_fcn(x, *params):
+            return x - fcn(x, *params)
+
+        with torch.enable_grad():
+            grad = jac(fcn=new_fcn, params=(x_, *params), idxs=[0])[0]
+
+        # pre = solve(grad.H, -grad_outputs.reshape(-1, 1))
+        pre = solve(grad.H, -grad_outputs.reshape(-1, 1).type_as(x_))
+        pre = pre.reshape(grad_outputs.shape)
+
+
+        with torch.enable_grad():
+            params_copy = [p.detach().requires_grad_() for p in params]
+            yfcn = new_fcn(x_, *params_copy)
+
+        grad = torch.autograd.grad(yfcn, [params_copy[i] for i in idx], grad_outputs=pre,
+                                   create_graph=torch.is_grad_enabled(),
+                                   allow_unused=True)
+        grad_out = [None for _ in range(len(params))]
+        for i in range(len(idx)):
+            grad_out[idx[i]] = grad[i]
+
+
+        return None, None, None, None, None, None, *grad_out
+
+
+def PDIIS(fn, p0, step_size=0.05, n_history=6, max_iter=100, mixing_period=3, abs_err=1e-6, rel_err=1e-3, **options):
+    """The periodic pully mixing from https://doi.org/10.1016/j.cplett.2016.01.033.
+
+    Args:
+        fn (function): the iterative functions
+        p0 (_type_): the initial point
+        step_size (float, optional): the mixing beta value, or step size. Defaults to 0.05.
+        n_history (int, optional): the size of the storage of history to compute the pesuedo hessian matrix. Defaults to 6.
+        max_iter (int, optional): the maximum iteration. Defaults to 100.
+        mixing_period (int, optional): the period of conducting pully mixing. The algorithm will conduct pully mixing every k iterations. Defaults to 3.
+        abs_err (_type_, optional): the absolute err tolerance. Defaults to 1e-6.
+        rel_err (_type_, optional): the relative err tolerance. Defaults to 1e-3.
+
+    Returns:
+        p _type_: the stable point
+    """
+    i = 0
+    f = fn(p0) - p0
+    p = p0
+    R = [None for _ in range(n_history)]
+    F = [None for _ in range(n_history)]
+    print("SCF iter 0 abs err {0} | rel err {1}: ".format( 
+            f.abs().max().detach().numpy(), 
+            (f.abs() / p.abs()).max().detach().numpy())
+            )
+    while (f.abs().max() > abs_err or (f.abs() / p.abs()).max() > rel_err) and i < max_iter:
+        if not (i+1) % mixing_period:
+            F_ = torch.stack([t for t in F if t != None])
+            R_ = torch.stack([t for t in R if t != None])
+            p_ = p + step_size*f - (R_.T+step_size*F_.T)@(F_ @ F_.T).inverse() @ F_ @ f
+        else:
+            p_ = p + step_size * f
+
+        f_ = fn(p_) - p_
+        F[i % n_history] = f_ - f
+        R[i % n_history] = p_ - p
+
+        p = p_.clone()
+        f = f_.clone()
+        i += 1
+
+        print("SCF iter {0} abs err {1} | rel err {2}: ".format(
+            i, 
+            f.abs().max().detach().numpy(), 
+            (f.abs() / p.abs()).max().detach().numpy())
+            )
+
+    if i == max_iter:
+        print("Not Converged very well here.")
+
+    return p
\ No newline at end of file
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index aea301e..3b1d1ea 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -14,6 +14,7 @@
 from dpnegf.utils.make_kpoints import kmesh_sampling_negf
 import logging
 from dpnegf.negf.poisson_init import Grid,Interface3D,Dirichlet,Dielectric
+from dpnegf.negf.scf_method import PDIISMixer,BroydenSecondMixer
 from typing import Optional, Union
 # from pyinstrument import Profiler
 
@@ -387,29 +388,34 @@ def compute(self):
         else:
             self.negf_compute(scf_require=False,Vbias=None)
 
-    def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_iter=1000,mix_rate=0.3,tolerance=1e-7):
+    def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_iter=1000,
+                         mix_method:str='PDIIS', mix_rate:float=0.3, tolerance:float=1e-7):
 
         
         # profiler.start() 
-        max_diff_phi = 1e30; max_diff_list = [] 
+        max_diff_phi = 1e30
+        max_diff_list = [] 
         iter_count=0
+        if mix_method == 'PDIIS':
+            mixer = PDIISMixer(init_p=interface_poisson.phi.copy(), mix_rate=mix_rate)
+            log.info(msg="Using PDIIS mixing method for NEGF-Poisson SCF")
+        elif mix_method == 'linear':
+            log.info(msg="Using linear mixing method for NEGF-Poisson SCF")
+        elif mix_method == 'BroydenSecond':
+            mixer = BroydenSecondMixer(shape=interface_poisson.phi.shape, max_hist=10, alpha=mix_rate)
+            log.info(msg="Using Broyden's second method for NEGF-Poisson SCF")
+        else:
+            raise ValueError("mix_method should be 'linear' or 'PDIIS'")
+
         # Gummel type iteration
         while max_diff_phi > err:
             # update Hamiltonian by modifying onsite energy with potential
             self.potential_at_atom = interface_poisson.phi[atom_gridpoint_index]
             self.potential_at_orb = torch.cat([torch.full((norb,), p) for p, norb\
-                                                in zip(self.potential_at_atom, self.device_atom_norbs)])
-            
-                      
+                                                in zip(self.potential_at_atom, self.device_atom_norbs)])              
             self.negf_compute(scf_require=True,Vbias=self.potential_at_orb)
             # Vbias makes sense for orthogonal basis as in NanoTCAD
             # TODO: check if Vbias makes sense for non-orthogonal basis 
-
-            # update electron density for solving Poisson equation SCF
-            # DM_eq,DM_neq = self.out["DM_eq"], self.out["DM_neq"]
-            # elec_density = torch.diag(DM_eq+DM_neq)
-            
-
             # TODO: check the sign of free_charge
             # TODO: check the spin degenracy
             # TODO: add k summation operation
@@ -423,8 +429,12 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
             max_diff_phi = interface_poisson.solve_poisson_NRcycle(method=self.poisson_options['solver'],\
                                                                 tolerance=tolerance,\
                                                                 dtype=self.poisson_options['poisson_dtype'])
-            interface_poisson.phi = interface_poisson.phi + mix_rate*(interface_poisson.phi_old-interface_poisson.phi)
-            
+            if mix_method == 'linear':
+                interface_poisson.phi = interface_poisson.phi + mix_rate*(interface_poisson.phi_old-interface_poisson.phi)
+            elif mix_method == 'PDIIS':
+                interface_poisson.phi = mixer.update(interface_poisson.phi.copy())
+            elif mix_method == 'BroydenSecond':
+                interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), interface_poisson.phi-interface_poisson.phi_old)
 
             iter_count += 1 # Gummel type iteration
             log.info(msg="Poisson-NEGF iteration: {}    Potential Diff Maximum: {}\n".format(iter_count,max_diff_phi))

From 0986f9fbc75f94358371b6f428c6e617ccb17312 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 10 Jun 2025 20:53:41 +0800
Subject: [PATCH 035/152] feat(SCF): add BroydenFirstMixer

---
 dpnegf/negf/scf_method.py | 81 ++++++++++++++++++++++++++++++++++++++-
 dpnegf/runner/NEGF.py     |  9 ++++-
 2 files changed, 87 insertions(+), 3 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 6f32a09..e649861 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -118,11 +118,90 @@ def update(self, p_new):
 
         return p_next
     
+class BroydenFirstMixer:
+    """
+    Limited-memory Broyden's First Method ("good Broyden").
+
+    Uses low-rank updates to approximate the Jacobian J (not its inverse).
+    Applies Sherman-Morrison-Woodbury formula for efficient inverse computations.
+
+    Attributes:
+        alpha (float): Initial mixing parameter (inverse scaling of initial J).
+        max_hist (int): Maximum number of stored update pairs (Δx, Δr).
+        eps (float): Numerical stability threshold.
+        J0 (ndarray): Initial Jacobian approximation (scaled identity).
+        dx_hist (list): History of Δx = x_n - x_{n-1}.
+        dr_hist (list): History of Δr = r_n - r_{n-1}.
+    """
+
+    def __init__(self, shape, max_hist=8, alpha=0.1, eps=1e-12):
+        self.alpha = alpha
+        self.max_hist = max_hist
+        self.eps = eps
+        self.reset(shape)
+
+    def reset(self, shape):
+        self.iter = 0
+        self.x_last = np.zeros(shape)
+        self.r_last = np.zeros(shape)
+        dim = np.prod(shape)
+        self.J0 = np.eye(dim) / self.alpha  # Initial J0 ≈ I/alpha
+        self.dx_hist = []  # delta x history
+        self.dr_hist = []  # delta r history
+
+    def update(self, x, r):
+        x = x.ravel()
+        r = r.ravel()
+
+        if self.iter == 0:
+            delta = np.linalg.solve(self.J0, r)
+            x_new = x - delta
+            self.x_last = x.copy()
+            self.r_last = r.copy()
+            self.iter += 1
+            return x_new.reshape(x.shape)
+
+        dx = x - self.x_last
+        dr = r - self.r_last
+
+        if len(self.dx_hist) >= self.max_hist:
+            self.dx_hist.pop(0)
+            self.dr_hist.pop(0)
+        self.dx_hist.append(dx)
+        self.dr_hist.append(dr)
+
+        J0_dx_list = [self.J0 @ dx for dx in self.dx_hist]
+        norm_dx_sq = [np.dot(dx, dx) for dx in self.dx_hist]
+
+        U = np.column_stack([
+            (dr - J0_dx) / (ndx + self.eps)
+            for dr, J0_dx, ndx in zip(self.dr_hist, J0_dx_list, norm_dx_sq)
+        ])  
+        V = np.column_stack(self.dx_hist)
+
+        J0_inv_r = self.alpha * r
+
+        M = np.eye(len(self.dx_hist)) + self.alpha * (V.T @ U)
+        rhs = V.T @ J0_inv_r
+        try:
+            y = np.linalg.solve(M, rhs)
+        except np.linalg.LinAlgError:
+            y = np.zeros_like(rhs)
+
+        delta = J0_inv_r - self.alpha * (U @ y)
+
+        x_new = x - delta
+
+        self.x_last = x.copy()
+        self.r_last = r.copy()
+        self.iter += 1
+
+        return x_new.reshape(self.x_last.shape)
 
 
 class BroydenSecondMixer:
     """
-    Implements Broyden's Second Method (also known as "good Broyden")
+    Implements Broyden's Second Method (also known as "bad Broyden")
     for accelerating fixed-point iterations such as those arising
     in SCF (self-consistent field) procedures.
 
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 3b1d1ea..b35b004 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -14,7 +14,7 @@
 from dpnegf.utils.make_kpoints import kmesh_sampling_negf
 import logging
 from dpnegf.negf.poisson_init import Grid,Interface3D,Dirichlet,Dielectric
-from dpnegf.negf.scf_method import PDIISMixer,BroydenSecondMixer
+from dpnegf.negf.scf_method import PDIISMixer,BroydenFirstMixer,BroydenSecondMixer
 from typing import Optional, Union
 # from pyinstrument import Profiler
 
@@ -401,8 +401,11 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
             log.info(msg="Using PDIIS mixing method for NEGF-Poisson SCF")
         elif mix_method == 'linear':
             log.info(msg="Using linear mixing method for NEGF-Poisson SCF")
+        elif mix_method == 'BroydenFirst':
+            mixer = BroydenFirstMixer(shape=interface_poisson.phi.shape, max_hist=8, alpha=mix_rate)
+            log.info(msg="Using Broyden's first method for NEGF-Poisson SCF")
         elif mix_method == 'BroydenSecond':
-            mixer = BroydenSecondMixer(shape=interface_poisson.phi.shape, max_hist=10, alpha=mix_rate)
+            mixer = BroydenSecondMixer(shape=interface_poisson.phi.shape, max_hist=8, alpha=mix_rate)
             log.info(msg="Using Broyden's second method for NEGF-Poisson SCF")
         else:
             raise ValueError("mix_method should be 'linear' or 'PDIIS'")
@@ -433,6 +436,8 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
                 interface_poisson.phi = interface_poisson.phi + mix_rate*(interface_poisson.phi_old-interface_poisson.phi)
             elif mix_method == 'PDIIS':
                 interface_poisson.phi = mixer.update(interface_poisson.phi.copy())
+            elif mix_method == 'BroydenFirst':
+                interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), interface_poisson.phi-interface_poisson.phi_old)
             elif mix_method == 'BroydenSecond':
                 interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), interface_poisson.phi-interface_poisson.phi_old)
 

From f3fed468f841b3aba9e914fdac160fa66e221579 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 10 Jun 2025 21:20:34 +0800
Subject: [PATCH 036/152] feat(SCF): update BroydenFirstMixer for efficiency

---
 dpnegf/negf/scf_method.py | 77 +++++++++++++++++----------------------
 1 file changed, 34 insertions(+), 43 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index e649861..6d22b89 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -120,18 +120,16 @@ def update(self, p_new):
     
 class BroydenFirstMixer:
     """
-    Limited-memory Broyden's First Method ("good Broyden").
+    Accelerated Broyden's First Method ("good Broyden") with improved memory and performance.
 
-    Uses low-rank updates to approximate the Jacobian J (not its inverse).
-    Applies Sherman-Morrison-Woodbury formula for efficient inverse computations.
+    Uses low-rank updates to approximate the Jacobian J.
+    Applies Sherman-Morrison-Woodbury formula for efficient inverse Jacobian application.
 
     Attributes:
         alpha (float): Initial mixing parameter (inverse scaling of initial J).
         max_hist (int): Maximum number of stored update pairs (Δx, Δr).
         eps (float): Numerical stability threshold.
-        J0 (ndarray): Initial Jacobian approximation (scaled identity).
-        dx_hist (list): History of Δx = x_n - x_{n-1}.
-        dr_hist (list): History of Δr = r_n - r_{n-1}.
+        shape (tuple): Shape of the input vectors.
     """
 
     def __init__(self, shape, max_hist=8, alpha=0.1, eps=1e-12):
@@ -142,61 +140,54 @@ def __init__(self, shape, max_hist=8, alpha=0.1, eps=1e-12):
 
     def reset(self, shape):
         self.iter = 0
-        self.x_last = np.zeros(shape)
-        self.r_last = np.zeros(shape)
-        dim = np.prod(shape)
-        self.J0 = np.eye(dim) / self.alpha  # Initial J0 ≈ I/alpha
-        self.dx_hist = []  # delta x history
-        self.dr_hist = []  # delta r history
+        self.shape = shape
+        self.size = np.prod(shape)
+        self.x_last = np.zeros(self.size)
+        self.r_last = np.zeros(self.size)
+        self.dx_hist = []
+        self.dr_hist = []
 
     def update(self, x, r):
         x = x.ravel()
         r = r.ravel()
 
         if self.iter == 0:
-            delta = np.linalg.solve(self.J0, r)
+            delta = self.alpha * r
             x_new = x - delta
-            self.x_last = x.copy()
-            self.r_last = r.copy()
-            self.iter += 1
-            return x_new.reshape(x.shape)
-
-        dx = x - self.x_last
-        dr = r - self.r_last
-
-        if len(self.dx_hist) >= self.max_hist:
-            self.dx_hist.pop(0)
-            self.dr_hist.pop(0)
-        self.dx_hist.append(dx)
-        self.dr_hist.append(dr)
+        else:
+            dx = x - self.x_last
+            dr = r - self.r_last
 
-        J0_dx_list = [self.J0 @ dx for dx in self.dx_hist]
-        norm_dx_sq = [np.dot(dx, dx) for dx in self.dx_hist]
+            if len(self.dx_hist) >= self.max_hist:
+                self.dx_hist.pop(0)
+                self.dr_hist.pop(0)
+            self.dx_hist.append(dx)
+            self.dr_hist.append(dr)
 
-        U = np.column_stack([
-            (dr - J0_dx) / (ndx + self.eps)
-            for dr, J0_dx, ndx in zip(self.dr_hist, J0_dx_list, norm_dx_sq)
-        ])  
-        V = np.column_stack(self.dx_hist)
+            dx_mat = np.column_stack(self.dx_hist)
+            dr_mat = np.column_stack(self.dr_hist)
 
-        J0_inv_r = self.alpha * r
+            J0_inv_r = self.alpha * r
 
-        M = np.eye(len(self.dx_hist)) + self.alpha * (V.T @ U)
-        rhs = V.T @ J0_inv_r
-        try:
-            y = np.linalg.solve(M, rhs)
-        except np.linalg.LinAlgError:
-            y = np.zeros_like(rhs)
+            dJ = dr_mat - self.alpha * dx_mat
+            VTJr = dx_mat.T @ J0_inv_r
+            VTdJ = dx_mat.T @ dJ
 
-        delta = J0_inv_r - self.alpha * (U @ y)
+            try:
+                y = np.linalg.solve(np.eye(len(self.dx_hist)) + VTdJ, VTJr)
+                correction = dJ @ y
+            except np.linalg.LinAlgError:
+                log.warning("Broyden's first method: Singular matrix encountered, falling back to linear mixing in this step.")
+                correction = 0.0
 
-        x_new = x - delta
+            delta = J0_inv_r - correction
+            x_new = x - delta
 
         self.x_last = x.copy()
         self.r_last = r.copy()
         self.iter += 1
 
-        return x_new.reshape(self.x_last.shape)
+        return x_new.reshape(self.shape)
 
 
 class BroydenSecondMixer:

From 44a617bea3ee4ef8abcbff8cc8d05c43108067c9 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 10 Jun 2025 23:33:41 +0800
Subject: [PATCH 037/152] feat(SCF): optimize Broyden's First Method for
 improved performance and memory usage

---
 dpnegf/negf/scf_method.py | 45 +++++++++++++++++++--------------------
 1 file changed, 22 insertions(+), 23 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 6d22b89..f57cee4 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -120,18 +120,14 @@ def update(self, p_new):
     
 class BroydenFirstMixer:
     """
-    Accelerated Broyden's First Method ("good Broyden") with improved memory and performance.
-
-    Uses low-rank updates to approximate the Jacobian J.
-    Applies Sherman-Morrison-Woodbury formula for efficient inverse Jacobian application.
+    Efficient Broyden's First Method (good Broyden) using low-rank updates and
+    the Sherman-Morrison-Woodbury formula. Optimized to avoid redundant computation.
 
     Attributes:
-        alpha (float): Initial mixing parameter (inverse scaling of initial J).
-        max_hist (int): Maximum number of stored update pairs (Δx, Δr).
+        alpha (float): Initial mixing parameter (J0 = I/alpha).
+        max_hist (int): Number of stored update pairs (Δx, Δr).
         eps (float): Numerical stability threshold.
-        shape (tuple): Shape of the input vectors.
     """
-
     def __init__(self, shape, max_hist=8, alpha=0.1, eps=1e-12):
         self.alpha = alpha
         self.max_hist = max_hist
@@ -140,10 +136,11 @@ def __init__(self, shape, max_hist=8, alpha=0.1, eps=1e-12):
 
     def reset(self, shape):
         self.iter = 0
+        self.x_last = np.zeros(shape)
+        self.r_last = np.zeros(shape)
+        self.dim = np.prod(shape)
         self.shape = shape
-        self.size = np.prod(shape)
-        self.x_last = np.zeros(self.size)
-        self.r_last = np.zeros(self.size)
+        self.J0 = np.eye(self.dim) / self.alpha  # Jacobian approximation
         self.dx_hist = []
         self.dr_hist = []
 
@@ -152,7 +149,7 @@ def update(self, x, r):
         r = r.ravel()
 
         if self.iter == 0:
-            delta = self.alpha * r
+            delta = np.linalg.solve(self.J0, r)
             x_new = x - delta
         else:
             dx = x - self.x_last
@@ -164,23 +161,25 @@ def update(self, x, r):
             self.dx_hist.append(dx)
             self.dr_hist.append(dr)
 
-            dx_mat = np.column_stack(self.dx_hist)
-            dr_mat = np.column_stack(self.dr_hist)
+            dx_mat = np.column_stack(self.dx_hist)  # shape: (dim, hist)
+            dr_mat = np.column_stack(self.dr_hist)  # shape: (dim, hist)
 
-            J0_inv_r = self.alpha * r
+            norm_dx_sq = np.sum(dx_mat * dx_mat, axis=0)  # shape: (hist,)
+            J0_dx_mat = dx_mat / self.alpha
+            U = (dr_mat - J0_dx_mat) / (norm_dx_sq + self.eps)
+            V = dx_mat
 
-            dJ = dr_mat - self.alpha * dx_mat
-            VTJr = dx_mat.T @ J0_inv_r
-            VTdJ = dx_mat.T @ dJ
+            J0_inv_r = self.alpha * r
+            VT_r = V.T @ J0_inv_r
+            M = np.eye(len(self.dx_hist)) + self.alpha * (V.T @ U)
 
             try:
-                y = np.linalg.solve(np.eye(len(self.dx_hist)) + VTdJ, VTJr)
-                correction = dJ @ y
+                y = np.linalg.solve(M, VT_r)
             except np.linalg.LinAlgError:
-                log.warning("Broyden's first method: Singular matrix encountered, falling back to linear mixing in this step.")
-                correction = 0.0
+                log.warning("Matrix inversion failed in Broyden's First Method. Falling back to linear mixing in this step.")
+                y = np.zeros_like(VT_r)
 
-            delta = J0_inv_r - correction
+            delta = J0_inv_r - self.alpha * (U @ y)
             x_new = x - delta
 
         self.x_last = x.copy()

From c92501c6eacebe3263df8f3776f5a4f4b5957c28 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 12 Jun 2025 13:50:52 +0800
Subject: [PATCH 038/152] feat(SCF): update BroydenFirstMixer in correct form

---
 dpnegf/negf/scf_method.py | 97 +++++++++++++++++++--------------------
 dpnegf/runner/NEGF.py     | 16 +++++--
 dpnegf/utils/tools.py     | 25 +++++++++-
 3 files changed, 83 insertions(+), 55 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index f57cee4..e452760 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -120,74 +120,73 @@ def update(self, p_new):
     
 class BroydenFirstMixer:
     """
-    Efficient Broyden's First Method (good Broyden) using low-rank updates and
-    the Sherman-Morrison-Woodbury formula. Optimized to avoid redundant computation.
+    Efficient Broyden's First Method (good Broyden) using the Sherman-Morrison-Woodbury formula.
 
     Attributes:
         alpha (float): Initial mixing parameter (J0 = I/alpha).
-        max_hist (int): Number of stored update pairs (Δx, Δr).
         eps (float): Numerical stability threshold.
     """
-    def __init__(self, shape, max_hist=8, alpha=0.1, eps=1e-12):
+    def __init__(self, init_x, alpha=0.1):
+        self.init_x = init_x
         self.alpha = alpha
-        self.max_hist = max_hist
-        self.eps = eps
-        self.reset(shape)
+        self.beta = 1 # Adaptive mixing factor
+        self.reset(init_x.shape)
+        
+        self.eps = 1e-12  # Numerical stability threshold
 
     def reset(self, shape):
         self.iter = 0
-        self.x_last = np.zeros(shape)
-        self.r_last = np.zeros(shape)
+        self.x_n = np.zeros(shape)
+        self.x_nm1 = np.zeros(shape)
         self.dim = np.prod(shape)
         self.shape = shape
-        self.J0 = np.eye(self.dim) / self.alpha  # Jacobian approximation
-        self.dx_hist = []
-        self.dr_hist = []
+        self.J0 = -np.eye(self.dim) / self.alpha  # Jacobian approximation
+        self.J_inv = np.zeros_like(self.J0)  # Inverse Jacobian
 
-    def update(self, x, r):
-        x = x.ravel()
-        r = r.ravel()
-
-        if self.iter == 0:
-            delta = np.linalg.solve(self.J0, r)
-            x_new = x - delta
-        else:
-            dx = x - self.x_last
-            dr = r - self.r_last
 
-            if len(self.dx_hist) >= self.max_hist:
-                self.dx_hist.pop(0)
-                self.dr_hist.pop(0)
-            self.dx_hist.append(dx)
-            self.dr_hist.append(dr)
+    def update(self, f):
 
-            dx_mat = np.column_stack(self.dx_hist)  # shape: (dim, hist)
-            dr_mat = np.column_stack(self.dr_hist)  # shape: (dim, hist)
+        linear_warm_range = 3  # Number of iterations to use linear mixing before switching to Broyden's method
 
-            norm_dx_sq = np.sum(dx_mat * dx_mat, axis=0)  # shape: (hist,)
-            J0_dx_mat = dx_mat / self.alpha
-            U = (dr_mat - J0_dx_mat) / (norm_dx_sq + self.eps)
-            V = dx_mat
-
-            J0_inv_r = self.alpha * r
-            VT_r = V.T @ J0_inv_r
-            M = np.eye(len(self.dx_hist)) + self.alpha * (V.T @ U)
-
-            try:
-                y = np.linalg.solve(M, VT_r)
-            except np.linalg.LinAlgError:
-                log.warning("Matrix inversion failed in Broyden's First Method. Falling back to linear mixing in this step.")
-                y = np.zeros_like(VT_r)
+        if self.iter == 0:
+            x_new = self.init_x + self.alpha * f  
+            self.J_inv = -np.eye(self.dim) * self.alpha  # Initial inverse Jacobian
+            self.x_nm1 = self.init_x.copy()
+        
+        elif self.iter < linear_warm_range:
+            x_new = self.x_n + self.alpha * f  # Linear mixing for first few iterations
+            self.J_inv = -np.eye(self.dim) * self.alpha  # Reset inverse Jacobian
+            self.x_nm1 = self.x_n.copy()  # Store previous x
 
-            delta = J0_inv_r - self.alpha * (U @ y)
-            x_new = x - delta
+        else:
+            dx = self.x_n - self.x_nm1  
+            df = f - self.f_last
+
+            # df_norm = np.linalg.norm(df)
+            # if self.iter == linear_warm_range:
+            #     self.last_df_norm = df_norm
+            #     self.beta = 1.0  # Initial beta value for adaptive mixing
+            # else:
+            #     if df_norm > self.last_df_norm:
+            #         self.beta = max(0.1, self.beta * 0.5)
+            #     else:
+            #         self.beta = min(1.0, self.beta * 1.2)
+            # self.last_df_norm = df_norm
+
+            J_inv_df = self.J_inv @ df  # J^{-1} * df
+            numerator = np.outer(dx - J_inv_df, self.J_inv @ dx)  # (dim, 1) x (1, dim) = (dim, dim)
+            denominator = dx.T @ (self.J_inv @ df)  + self.eps  # (1, dim) @ (dim, 1) = (1, 1) 
+            self.J_inv = self.J_inv + numerator / denominator
+
+            x_new = self.x_n - self.beta*self.J_inv @ f  # Update x using the new inverse Jacobian
+            self.x_nm1 = self.x_n.copy()  # Store previous x
 
-        self.x_last = x.copy()
-        self.r_last = r.copy()
+        # Update state
+        self.x_n = x_new.copy() 
+        self.f_last = f.copy()  
         self.iter += 1
 
-        return x_new.reshape(self.shape)
-
+        return x_new
 
 class BroydenSecondMixer:
     """
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index b35b004..d19bfc2 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -16,6 +16,7 @@
 from dpnegf.negf.poisson_init import Grid,Interface3D,Dirichlet,Dielectric
 from dpnegf.negf.scf_method import PDIISMixer,BroydenFirstMixer,BroydenSecondMixer
 from typing import Optional, Union
+from dpnegf.utils.tools import apply_gaussian_filter_3d
 # from pyinstrument import Profiler
 
 log = logging.getLogger(__name__)
@@ -389,7 +390,7 @@ def compute(self):
             self.negf_compute(scf_require=False,Vbias=None)
 
     def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_iter=1000,
-                         mix_method:str='PDIIS', mix_rate:float=0.3, tolerance:float=1e-7):
+                         mix_method:str='linear', mix_rate:float=0.3, tolerance:float=1e-7,Gaussian_sigma:float=3.0):
 
         
         # profiler.start() 
@@ -402,7 +403,7 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
         elif mix_method == 'linear':
             log.info(msg="Using linear mixing method for NEGF-Poisson SCF")
         elif mix_method == 'BroydenFirst':
-            mixer = BroydenFirstMixer(shape=interface_poisson.phi.shape, max_hist=8, alpha=mix_rate)
+            mixer = BroydenFirstMixer(init_x=interface_poisson.phi, alpha=mix_rate)
             log.info(msg="Using Broyden's first method for NEGF-Poisson SCF")
         elif mix_method == 'BroydenSecond':
             mixer = BroydenSecondMixer(shape=interface_poisson.phi.shape, max_hist=8, alpha=mix_rate)
@@ -437,9 +438,16 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
             elif mix_method == 'PDIIS':
                 interface_poisson.phi = mixer.update(interface_poisson.phi.copy())
             elif mix_method == 'BroydenFirst':
-                interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), interface_poisson.phi-interface_poisson.phi_old)
+                residual = interface_poisson.phi - interface_poisson.phi_old
+                # residual_filter = apply_gaussian_filter_3d(residual, 
+                #                                            shape=(interface_poisson.grid.shape[0],
+                #                                                    interface_poisson.grid.shape[1],
+                #                                                    interface_poisson.grid.shape[2]), 
+                #                                            sigma=Gaussian_sigma)
+                interface_poisson.phi = mixer.update(f = residual) # fixed point problem: f defined as F(\phi)-\phi =0
             elif mix_method == 'BroydenSecond':
-                interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), interface_poisson.phi-interface_poisson.phi_old)
+                residual = interface_poisson.phi - interface_poisson.phi_old
+                interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), residual)
 
             iter_count += 1 # Gummel type iteration
             log.info(msg="Poisson-NEGF iteration: {}    Potential Diff Maximum: {}\n".format(iter_count,max_diff_phi))
diff --git a/dpnegf/utils/tools.py b/dpnegf/utils/tools.py
index 8c8e5f1..d976900 100644
--- a/dpnegf/utils/tools.py
+++ b/dpnegf/utils/tools.py
@@ -29,6 +29,7 @@
 import urllib
 import zipfile
 import sys
+from scipy.ndimage import gaussian_filter
 
 
 log = logging.getLogger(__name__)
@@ -779,5 +780,25 @@ def extract_zip(path, folder, log=True):
     with zipfile.ZipFile(path, "r") as f:
         f.extractall(folder)
 
-if __name__ == '__main__':
-    print(get_neuron_config(nl=[0,1,2,3,4,5,6,7]))
+
+
+def apply_gaussian_filter_3d(phi_vector, shape, sigma):
+    """
+    Apply a 3D Gaussian filter to flattened phi_vector stored in (Nx, Ny, Nz) order.
+
+    Parameters:
+        phi_vector : 1D numpy array of shape (Nx*Ny*Nz,)
+        shape      : tuple (Nx, Ny, Nz)
+        sigma      : float or tuple of floats in grid units
+
+    Returns:
+        Filtered phi_vector (1D array of same shape)
+    """
+    # Reshape from 1D to 3D (x-major order)
+    phi_3d = phi_vector.reshape(shape)  # shape = (Nx, Ny, Nz)
+
+    # Apply 3D Gaussian filter
+    phi_3d_filtered = gaussian_filter(phi_3d, sigma=sigma)
+
+    # Flatten back to 1D
+    return phi_3d_filtered.ravel()

From bb74d47d3a0c357346382a6e3cdf666604bac03c Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 12 Jun 2025 14:53:33 +0800
Subject: [PATCH 039/152] feat(SCF): enhance convergence checks in Poisson-NEGF
 iteration

---
 dpnegf/runner/NEGF.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index d19bfc2..f0b7835 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -455,6 +455,10 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
 
             if max_diff_phi <= err:
                 log.info(msg="Poisson-NEGF SCF Converges Successfully!")
+            elif max_diff_phi > 1e5:
+                log.warning(msg="Warning! Poisson-NEGF iteration may diverge, max_diff_phi = {}".format(max_diff_phi))
+            elif np.isnan(max_diff_phi):
+                raise RuntimeError("Poisson-NEGF iteration diverges, max_diff_phi = {}".format(max_diff_phi))
                 
 
             if iter_count > max_iter:

From 1d8ea52d5a24ae52f3b8008108262e3211128291 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 12 Jun 2025 22:40:09 +0800
Subject: [PATCH 040/152] feat(SCF): update BroydenFirstMixer

---
 dpnegf/negf/scf_method.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index e452760..fdd7a9f 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -162,6 +162,9 @@ def update(self, f):
             dx = self.x_n - self.x_nm1  
             df = f - self.f_last
 
+            dx = dx.reshape(-1, 1)  # Ensure dx is a column vector
+            df = df.reshape(-1, 1)  # Ensure df is a column vector
+
             # df_norm = np.linalg.norm(df)
             # if self.iter == linear_warm_range:
             #     self.last_df_norm = df_norm
@@ -174,11 +177,10 @@ def update(self, f):
             # self.last_df_norm = df_norm
 
             J_inv_df = self.J_inv @ df  # J^{-1} * df
-            numerator = np.outer(dx - J_inv_df, self.J_inv @ dx)  # (dim, 1) x (1, dim) = (dim, dim)
-            denominator = dx.T @ (self.J_inv @ df)  + self.eps  # (1, dim) @ (dim, 1) = (1, 1) 
+            numerator = (dx - J_inv_df) @ (dx.T @ self.J_inv)  # (dim,1) @ (1, dim) = (dim, dim)
+            denominator = dx.T @ J_inv_df  + self.eps  # (1, dim) @ (dim, 1) = (1, 1) 
             self.J_inv = self.J_inv + numerator / denominator
-
-            x_new = self.x_n - self.beta*self.J_inv @ f  # Update x using the new inverse Jacobian
+            x_new = self.x_n -self.J_inv @ f  # Update x using the new inverse Jacobian
             self.x_nm1 = self.x_n.copy()  # Store previous x
 
         # Update state

From 3914f029757b619436efe3e7ee44f0b0ffeb4d3d Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 13 Jun 2025 15:20:30 +0800
Subject: [PATCH 041/152] feat(example): update nel_atom values for lead
 electrons in run notebook and JSON input

---
 .../input_files/negf_chain_new.json           |  1 +
 examples/atomic_chain_cli/run.ipynb           | 43 +++++++++----------
 2 files changed, 22 insertions(+), 22 deletions(-)

diff --git a/examples/atomic_chain_cli/input_files/negf_chain_new.json b/examples/atomic_chain_cli/input_files/negf_chain_new.json
index fdffbf4..cfe7012 100644
--- a/examples/atomic_chain_cli/input_files/negf_chain_new.json
+++ b/examples/atomic_chain_cli/input_files/negf_chain_new.json
@@ -8,6 +8,7 @@
         "stru_options":{
             "gamma_center": true,
             "time_reversal_symmetry": true,
+            "nel_atom": {"C": 1.0},
             "kmesh":[1,1,1],
             "pbc":[false, false, false],
             "device":{
diff --git a/examples/atomic_chain_cli/run.ipynb b/examples/atomic_chain_cli/run.ipynb
index 978f896..2b42cd0 100644
--- a/examples/atomic_chain_cli/run.ipynb
+++ b/examples/atomic_chain_cli/run.ipynb
@@ -32,12 +32,12 @@
       "           |  '--'  ||  |      |  |\\   | |  |____ |  |__| | |  |                \n",
       "             |_______/ | _|      |__| \\__| |_______| \\______| |__|              \n",
       "--------------------------------------------------------------------------------\n",
-      "                       DPNEGF version 0.1.1.dev12+49c5b45                       \n",
+      "                       DPNEGF version 0.1.1.dev21+7918d32                       \n",
       "================================================================================\n",
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO                                      Version Info                                  \n",
       "DPNEGF INFO    --------------------------------------------------------------------------------\n",
-      "DPNEGF INFO    DPNEGF               : 0.1.1.dev12+49c5b45\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev21+7918d32\n",
       "DPNEGF INFO    DeePTB               : 2.1.2.dev43+b666d17\n",
       "DPNEGF INFO    ================================================================================\n",
       "\n",
@@ -58,36 +58,35 @@
       "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO    -------------Fermi level calculation-------------\n",
-      "DPNEGF WARNING nel_atom is None, using valence electron number by default\n",
       "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0.0\n",
       "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0.0\n",
-      "DPNEGF INFO    Number of electrons in lead_L: {'C': 4.0}\n",
-      "DPNEGF INFO    Number of electrons in lead_R: {'C': 4.0}\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 1.0}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 1.0}\n",
       "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
       "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
       "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
       "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
       "DPNEGF INFO    Getting eigenvalues from the model.\n",
       "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
-      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 50 iterations.\n",
-      "DPNEGF INFO    q_cal: 7.998605108261107, total_electrons: 16.0, diff q: 8.001394891738894\n",
-      "DPNEGF INFO    Estimated E_fermi: -12.374099200188748 based on the valence electrons setting nel_atom : {'C': 4.0} .\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
+      "DPNEGF INFO    q_cal: 3.999994485346824, total_electrons: 4.0, diff q: 5.514653175886508e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.638588428497314 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
       "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
       "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
       "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
       "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
       "DPNEGF INFO    Getting eigenvalues from the model.\n",
       "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
-      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 50 iterations.\n",
-      "DPNEGF INFO    q_cal: 7.998605275154112, total_electrons: 16.0, diff q: 8.001394724845888\n",
-      "DPNEGF INFO    Estimated E_fermi: -12.374099200188748 based on the valence electrons setting nel_atom : {'C': 4.0} .\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
+      "DPNEGF INFO    q_cal: 4.000002782981872, total_electrons: 4.0, diff q: 2.7829818716185173e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.638587474822998 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
       "DPNEGF INFO    -------------------------------------------------\n",
       "DPNEGF INFO    Zero bias case detected.\n",
-      "DPNEGF INFO    Fermi level for lead_L: -12.374099200188748\n",
-      "DPNEGF INFO    Fermi level for lead_R: -12.374099200188748\n",
-      "DPNEGF INFO    Electrochemical potential for lead_L: -12.374099200188748\n",
-      "DPNEGF INFO    Electrochemical potential for lead_R: -12.374099200188748\n",
-      "DPNEGF INFO    Reference energy E_ref: -12.374099200188748\n",
+      "DPNEGF INFO    Fermi level for lead_L: -13.638588428497314\n",
+      "DPNEGF INFO    Fermi level for lead_R: -13.638587474822998\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -13.638588428497314\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -13.638587474822998\n",
+      "DPNEGF INFO    Reference energy E_ref: -13.638588428497314\n",
       "DPNEGF INFO    =================================================\n",
       "\n",
       "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
@@ -114,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "83d39465",
    "metadata": {},
    "outputs": [
@@ -122,7 +121,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_1559358/1401031374.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "/tmp/ipykernel_727394/1401031374.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
       "  negf_out = torch.load('./output/results/negf.out.pth')\n"
      ]
     }
@@ -133,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "6fd4754f",
    "metadata": {},
    "outputs": [
@@ -143,7 +142,7 @@
        "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "id": "5dd516a0",
    "metadata": {},
    "outputs": [
@@ -180,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "id": "92a9dada",
    "metadata": {},
    "outputs": [

From 1620003b7f3c266e1baac4287ced847a4027ef3e Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 13 Jun 2025 15:25:31 +0800
Subject: [PATCH 042/152] update json file in API example

---
 .../input_files/negf_chain_new.json           |  4 +-
 examples/atomic_chain_api/run.ipynb           | 68 ++++++++++++++-----
 2 files changed, 55 insertions(+), 17 deletions(-)

diff --git a/examples/atomic_chain_api/input_files/negf_chain_new.json b/examples/atomic_chain_api/input_files/negf_chain_new.json
index a69668f..2a2002e 100644
--- a/examples/atomic_chain_api/input_files/negf_chain_new.json
+++ b/examples/atomic_chain_api/input_files/negf_chain_new.json
@@ -17,6 +17,7 @@
         "stru_options":{
             "gamma_center": true,
             "time_reversal_symmetry": true,
+            "nel_atom": {"C": 1.0},
             "kmesh":[1,1,1],
             "pbc":[false, false, false],
             "device":{
@@ -26,11 +27,13 @@
             "lead_L":{
                 "id":"0-4",
                 "voltage":0.0,
+                "kmesh_lead_Ef":[1,1,20],
                 "useBloch": false
             },
             "lead_R":{
                 "id":"8-12",
                 "voltage":0.0,
+                "kmesh_lead_Ef":[1,1,20],
                 "useBloch": false
             }
         },
@@ -42,7 +45,6 @@
         "espacing": 0.01,
         "emin": -2,
         "emax": 2,
-        "e_fermi": -13.638587951660156,
         "density_options": {
             "method": "Fiori",
             "integrate_way": "direct"
diff --git a/examples/atomic_chain_api/run.ipynb b/examples/atomic_chain_api/run.ipynb
index e7fbe22..6baae97 100644
--- a/examples/atomic_chain_api/run.ipynb
+++ b/examples/atomic_chain_api/run.ipynb
@@ -33,8 +33,14 @@
      "text": [
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO                                      Version Info                                  \n",
-      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
-      "DPNEGF INFO    DPNEGF               : 0.1.1.dev12+49c5b45\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev21+7918d32\n",
       "DPNEGF INFO    DeePTB               : 2.1.2.dev43+b666d17\n",
       "DPNEGF INFO    ================================================================================\n",
       "\n",
@@ -89,14 +95,35 @@
       "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO    -------------Fermi level calculation-------------\n",
-      "DPNEGF INFO    Fermi level is set to -13.638587951660156 from input file\n",
+      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0\n",
+      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 1.0}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 1.0}\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
+      "DPNEGF INFO    q_cal: 3.999994485346824, total_electrons: 4.0, diff q: 5.514653175886508e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.638588428497314 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
+      "DPNEGF INFO    q_cal: 4.000002782981872, total_electrons: 4.0, diff q: 2.7829818716185173e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.638587474822998 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
       "DPNEGF INFO    -------------------------------------------------\n",
       "DPNEGF INFO    Zero bias case detected.\n",
-      "DPNEGF INFO    Fermi level for lead_L: -13.638587951660156\n",
-      "DPNEGF INFO    Fermi level for lead_R: -13.638587951660156\n",
-      "DPNEGF INFO    Electrochemical potential for lead_L: -13.638587951660156\n",
-      "DPNEGF INFO    Electrochemical potential for lead_R: -13.638587951660156\n",
-      "DPNEGF INFO    Reference energy E_ref: -13.638587951660156\n",
+      "DPNEGF INFO    Fermi level for lead_L: -13.638588428497314\n",
+      "DPNEGF INFO    Fermi level for lead_R: -13.638587474822998\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -13.638588428497314\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -13.638587474822998\n",
+      "DPNEGF INFO    Reference energy E_ref: -13.638588428497314\n",
       "DPNEGF INFO    =================================================\n",
       "\n",
       "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
@@ -128,17 +155,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "db275dee",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_772487/1724458665.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  negf_out = torch.load('./output/negf.out.pth')\n"
+     ]
+    }
+   ],
    "source": [
     "negf_out = torch.load('./output/negf.out.pth')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "7a29b9ff",
    "metadata": {},
    "outputs": [
@@ -148,7 +184,7 @@
        "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -159,13 +195,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "8eb092f6",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -185,13 +221,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "4a2fe762",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]

From 6524c9e6a04643a224cb06b61045d0ee52db80b2 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 13 Jun 2025 22:24:20 +0800
Subject: [PATCH 043/152] feat(SCF): implement Block Broyden's first mixer for
 back up

---
 dpnegf/negf/scf_method.py | 90 +++++++++++++++++++++++++++++++++++++++
 dpnegf/runner/NEGF.py     | 12 ++++--
 2 files changed, 99 insertions(+), 3 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index fdd7a9f..da4919d 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -190,6 +190,96 @@ def update(self, f):
 
         return x_new
 
+# class Block_BroydenFirstMixer:
+#     """
+#     Efficient Broyden's First Method (good Broyden) using the Sherman-Morrison-Woodbury formula.
+
+#     Attributes:
+#         alpha (float): Initial mixing parameter (J0 = I/alpha).
+#         eps (float): Numerical stability threshold.
+#     """
+#     def __init__(self, init_x, alpha:float=0.1, k:int=None):
+        
+#         assert init_x.ndim == 1, "init_x must be a 1D array"
+#         self.init_x = init_x
+#         self.alpha = alpha
+#         self.beta = 1 # Adaptive mixing factor
+#         self.eps = 1e-12  # Numerical stability threshold
+
+#         if k is None:
+#             self.k = int(len(init_x)/10)
+#         else:
+#             assert k > 0, "k must be a positive integer"
+#             assert k <= len(init_x), "k must be less than or equal to the length of init_x"
+#             self.k = k
+
+        
+#         self.reset(init_x.shape)
+        
+#     def reset(self, shape):
+#         self.iter = 0
+#         self.x_n = np.zeros(shape)
+#         self.x_nm1 = np.zeros(shape)
+#         self.dim = np.prod(shape)
+#         self.shape = shape
+#         self.J0 = -np.eye(self.dim) / self.alpha  # Jacobian approximation
+#         self.Bm = np.zeros_like(self.J0)  # Inverse Jacobian
+
+#     @staticmethod
+#     def get_partial_jacobian(df, dx, column_indices, eps=1e-12):
+    
+#         assert df.ndim == 1, "df must be a 1D array"
+#         assert dx.ndim == 1, "dx must be a 1D array"
+#         assert df.shape == dx.shape, "df and dx must have the same shape"
+
+#         dx_norm_sq = np.dot(dx, dx) + eps  # scalar
+#         d_outer = np.outer(df, dx) / dx_norm_sq  # full rank-1 Jacobian approx (d x d)
+
+#         # Select only the desired columns
+#         return d_outer[:, column_indices]
+
+
+#     def update(self, f):
+    
+#         linear_warm_range = 3  # Number of iterations to use linear mixing before switching to Broyden's method
+
+#         if self.iter == 0:
+#             x_new = self.init_x + self.alpha * f  
+#             self.Bm = -np.eye(self.dim) * self.alpha  # Initial inverse Jacobian
+#             self.x_nm1 = self.init_x.copy()
+        
+#         elif self.iter < linear_warm_range:
+#             x_new = self.x_n + self.alpha * f  # Linear mixing for first few iterations
+#             self.Bm = -np.eye(self.dim) * self.alpha  # Reset inverse Jacobian
+#             self.x_nm1 = self.x_n.copy()  # Store previous x
+
+#         else:
+#             dx = self.x_n - self.x_nm1  
+#             df = f - self.f_last
+
+#             x_new = self.x_n -self.Bm @ f 
+
+#             # Randomly select k directions
+#             rng = np.random.default_rng()
+#             idx_seq = rng.choice(len(self.init_x), size=self.k, replace=False)
+#             U = np.eye(len(self.init_x))[:, idx_seq] # Randomly select k directions
+
+#             # Compute the partial Jacobian using finite differences
+#             J_U = self.get_partial_jacobian(df, dx, idx_seq, eps=self.eps)  # Compute partial Jacobian
+#             Bm_J_U = self.Bm @ J_U  # Apply the current inverse Jacobian to the partial Jacobian
+#             UT_Bm_J_U = U.T @ Bm_J_U  # Compute U^T * B_m * J_U
+#             M = np.linalg.solve(UT_Bm_J_U, U.T @ self.Bm) 
+#             # Update the inverse Jacobian using the Sherman-Morrison-Woodbury formula
+#             self.Bm = self.Bm - (Bm_J_U - U) @ M
+
+#             self.x_nm1 = self.x_n.copy()  # Store previous x
+
+#         # Update state
+#         self.x_n = x_new.copy() 
+#         self.f_last = f.copy()  
+#         self.iter += 1
+
+#         return x_new
 class BroydenSecondMixer:
     """
     Implements Broyden's Second Method (also known as "bad Broyden")
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index f0b7835..b61303e 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -408,6 +408,9 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
         elif mix_method == 'BroydenSecond':
             mixer = BroydenSecondMixer(shape=interface_poisson.phi.shape, max_hist=8, alpha=mix_rate)
             log.info(msg="Using Broyden's second method for NEGF-Poisson SCF")
+        # elif mix_method == 'Block_BroydenFirst':
+        #     mixer = Block_BroydenFirstMixer(init_x=interface_poisson.phi, alpha=mix_rate)
+        #     log.info(msg="Using Block Broyden's first method for NEGF-Poisson SCF")
         else:
             raise ValueError("mix_method should be 'linear' or 'PDIIS'")
 
@@ -448,6 +451,9 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
             elif mix_method == 'BroydenSecond':
                 residual = interface_poisson.phi - interface_poisson.phi_old
                 interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), residual)
+            # elif mix_method == 'Block_BroydenFirst':
+            #     residual = interface_poisson.phi - interface_poisson.phi_old
+            #     interface_poisson.phi = mixer.update(f = residual)
 
             iter_count += 1 # Gummel type iteration
             log.info(msg="Poisson-NEGF iteration: {}    Potential Diff Maximum: {}\n".format(iter_count,max_diff_phi))
@@ -455,9 +461,9 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
 
             if max_diff_phi <= err:
                 log.info(msg="Poisson-NEGF SCF Converges Successfully!")
-            elif max_diff_phi > 1e5:
-                log.warning(msg="Warning! Poisson-NEGF iteration may diverge, max_diff_phi = {}".format(max_diff_phi))
-            elif np.isnan(max_diff_phi):
+            if max_diff_phi > 1e8:
+                raise RuntimeError("Poisson-NEGF iteration diverges, max_diff_phi = {}".format(max_diff_phi))
+            if np.isnan(max_diff_phi):
                 raise RuntimeError("Poisson-NEGF iteration diverges, max_diff_phi = {}".format(max_diff_phi))
                 
 

From d9b149562b7c4b51c99ef30c9bd5e3fdc57cce10 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sat, 14 Jun 2025 16:43:19 +0800
Subject: [PATCH 044/152] feat(SCF): add Anderson acceleration method

---
 dpnegf/negf/scf_method.py | 88 +++++++++++++++++++++++++++++++++++++++
 dpnegf/runner/NEGF.py     |  7 +++-
 2 files changed, 94 insertions(+), 1 deletion(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index da4919d..e0c6bd8 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -383,3 +383,91 @@ def update(self, x, r):
 
         return x_new.reshape(self.x_last.shape)
 
+
+class AndersonMixer:
+    def __init__(self, m=5, alpha=0.1, verbose=False):
+        """
+        Parameters:
+        - m: number of history steps to retain
+        - alpha: mixing parameter (0 < alpha <= 1)
+        - verbose: if True, print internal details for debugging
+        """
+        self.m = m
+        self.alpha = alpha
+        self.verbose = verbose
+        self.dx_hist = []  #  xk - x_{k-1}
+        self.df_hist = []  #  f(x_k) - f(x_{k-1})
+        self.first_three = True  # Flag to handle first three iterations separately
+        self.iter = 0  # Iteration counter
+        self.xkm1 = None  # x_{k-1}
+        self.fkm1 = None  # f(x_{k-1})
+
+        self.beta = 1 # damping factor
+
+    def reset(self):
+        """Clear history (e.g. after SCF reset)."""
+        self.dx_hist.clear()
+        self.df_hist.clear()
+        self.first_three = True
+        self.iter = 0
+        self.xkm1 = None  # Reset previous x_{k-1}
+        self.fkm1 = None  # Reset previous f(x_{k-1})
+
+    def update(self, fk, xk):
+        """
+        Perform Anderson mixing.
+
+        Parameters:
+        - fk: output f(x_k) from fixed-point iteration
+        - xk: input x_k
+
+        Returns:
+        - xkp1: new guess using Anderson mixing
+        """
+
+        assert isinstance(fk, np.ndarray), "fk must be a numpy array"
+        assert isinstance(xk, np.ndarray), "xk must be a numpy array"
+        assert fk.shape == xk.shape, "fk and xk must have the same shape"
+
+        if self.first_three:
+            if self.iter < 3:
+                # self.dx_list.append(dx.copy())
+                # self.df_hist.append(df.copy())
+                self.iter += 1
+                x_new = xk + self.alpha * (fk - xk)  # Linear mixing for first three iterations
+                self.xkm1 = xk.copy()  # Store x_k for next iteration
+                self.fkm1 = fk.copy()
+                return x_new  # linear mixing
+            else:
+                self.first_three = False
+            
+
+        dx = xk - self.xkm1  # dx = x_k - x_{k-1}
+        df = fk - self.fkm1  # df = f(x_k) - f(x_{k-1})
+        self.xkm1 = xk.copy()  # Store x_k for next iteration
+        self.fkm1 = fk.copy()  # Store f_k for next iteration            
+
+        # Keep only last m entries
+        if len(self.dx_hist) >= self.m:
+            self.dx_hist.pop(0)
+            self.df_hist.pop(0)
+
+        self.dx_hist.append(dx.copy())
+        self.df_hist.append(df.copy())
+
+        # Construct matrix R = [r1, r2, ..., rn]
+        
+        try:
+            Gk = np.column_stack([df_i - dx_i for df_i, dx_i in zip(self.df_hist, self.dx_hist)])# [... gk-1 - gk-2, gk - gk-1 ]
+            c = np.linalg.lstsq(Gk, (fk-xk), rcond=None)[0] # Solve least squares: min ||Gk @ c - (fk-xk)||
+            correction = sum(c_i * df_i for c_i, df_i in zip(c, self.df_hist))
+            xkp1 =  xk + self.beta * ((fk - xk) - correction) # Update x_k+1
+
+        except np.linalg.LinAlgError:
+            # Fallback to linear mixing if R is rank-deficient
+            log.info("[Anderson] Linear algebra error, fallback to linear mixing.")
+            xkp1 = xk + self.alpha * (fk - xk)
+
+        self.iter += 1
+        return xkp1.reshape(fk.shape)
+
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index b61303e..837cef1 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -14,7 +14,7 @@
 from dpnegf.utils.make_kpoints import kmesh_sampling_negf
 import logging
 from dpnegf.negf.poisson_init import Grid,Interface3D,Dirichlet,Dielectric
-from dpnegf.negf.scf_method import PDIISMixer,BroydenFirstMixer,BroydenSecondMixer
+from dpnegf.negf.scf_method import PDIISMixer,BroydenFirstMixer,BroydenSecondMixer,AndersonMixer
 from typing import Optional, Union
 from dpnegf.utils.tools import apply_gaussian_filter_3d
 # from pyinstrument import Profiler
@@ -411,6 +411,9 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
         # elif mix_method == 'Block_BroydenFirst':
         #     mixer = Block_BroydenFirstMixer(init_x=interface_poisson.phi, alpha=mix_rate)
         #     log.info(msg="Using Block Broyden's first method for NEGF-Poisson SCF")
+        elif mix_method == 'Anderson':
+            mixer = AndersonMixer(m=5, alpha=0.2)
+            log.info(msg="Using Anderson mixing method for NEGF-Poisson SCF")
         else:
             raise ValueError("mix_method should be 'linear' or 'PDIIS'")
 
@@ -454,6 +457,8 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
             # elif mix_method == 'Block_BroydenFirst':
             #     residual = interface_poisson.phi - interface_poisson.phi_old
             #     interface_poisson.phi = mixer.update(f = residual)
+            elif mix_method == 'Anderson':
+                interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), interface_poisson.phi_old.copy())
 
             iter_count += 1 # Gummel type iteration
             log.info(msg="Poisson-NEGF iteration: {}    Potential Diff Maximum: {}\n".format(iter_count,max_diff_phi))

From 7fe76fd8fa296b8c19d456276f6c0dc5be073860 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sat, 14 Jun 2025 16:57:43 +0800
Subject: [PATCH 045/152] feat(SCF): add docstring for AndersonMixer

---
 dpnegf/negf/scf_method.py | 72 +++++++++++++++++++++++++++++++--------
 1 file changed, 58 insertions(+), 14 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index e0c6bd8..6964ad4 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -385,12 +385,38 @@ def update(self, x, r):
 
 
 class AndersonMixer:
+    """
+    AndersonMixer implements Anderson mixing for accelerating fixed-point iterations,
+    commonly used in self-consistent field (SCF) calculations.
+    Attributes:
+        m (int): Number of history steps to retain for mixing.
+        alpha (float): Mixing parameter for linear mixing (0 < alpha <= 1).
+        verbose (bool): If True, prints internal details for debugging.
+        dx_hist (list): History of differences in input vectors (xk - x_{k-1}).
+        df_hist (list): History of differences in function outputs (f(xk) - f(x_{k-1})).
+        first_three (bool): Flag to handle the first three iterations with linear mixing.
+        iter (int): Iteration counter.
+        xkm1 (np.ndarray or None): Previous input vector x_{k-1}.
+        fkm1 (np.ndarray or None): Previous function output f(x_{k-1}).
+        beta (float): Damping factor for the Anderson update.
+    """
     def __init__(self, m=5, alpha=0.1, verbose=False):
         """
-        Parameters:
-        - m: number of history steps to retain
-        - alpha: mixing parameter (0 < alpha <= 1)
-        - verbose: if True, print internal details for debugging
+        Initializes the SCF method parameters.
+
+        Args:
+            m (int, optional): Number of previous iterations to store for history-based methods. Defaults to 5.
+            alpha (float, optional): Mixing parameter or step size for the update. Defaults to 0.1.
+            verbose (bool, optional): If True, enables verbose output for debugging or logging. Defaults to False.
+
+        Attributes:
+            dx_hist (list): History of differences between consecutive x values (x_k - x_{k-1}).
+            df_hist (list): History of differences between consecutive function values (f(x_k) - f(x_{k-1})).
+            first_three (bool): Flag to handle the first three iterations separately.
+            iter (int): Iteration counter.
+            xkm1: Previous x value (x_{k-1}).
+            fkm1: Previous function value (f(x_{k-1})).
+            beta (float): Damping factor for the update, initialized to 1.
         """
         self.m = m
         self.alpha = alpha
@@ -405,7 +431,13 @@ def __init__(self, m=5, alpha=0.1, verbose=False):
         self.beta = 1 # damping factor
 
     def reset(self):
-        """Clear history (e.g. after SCF reset)."""
+        """
+        Resets the internal state of the SCF method.
+        This method clears the history of variable and function differences,
+        resets iteration counters, and sets previous step variables to None,
+        preparing the object for a fresh SCF cycle.
+        """
+        
         self.dx_hist.clear()
         self.df_hist.clear()
         self.first_three = True
@@ -415,16 +447,28 @@ def reset(self):
 
     def update(self, fk, xk):
         """
-        Perform Anderson mixing.
-
-        Parameters:
-        - fk: output f(x_k) from fixed-point iteration
-        - xk: input x_k
-
-        Returns:
-        - xkp1: new guess using Anderson mixing
+        Update the current estimate using Anderson mixing or linear mixing.
+        Parameters
+        ----------
+        fk : np.ndarray
+            The current function value (e.g., residual or fixed-point map at xk).
+        xk : np.ndarray
+            The current estimate of the solution.
+        Returns
+        -------
+        np.ndarray
+            The updated estimate after applying Anderson or linear mixing.
+        Raises
+        ------
+        AssertionError
+            If `fk` or `xk` are not numpy arrays or if their shapes do not match.
+        Notes
+        -----
+        - For the first three iterations, linear mixing is used.
+        - After the first three iterations, Anderson mixing is applied using the history of previous steps.
+        - If the Anderson mixing matrix is rank-deficient, falls back to linear mixing.
         """
-
+        # Ensure fk and xk are numpy arrays and have the same shape
         assert isinstance(fk, np.ndarray), "fk must be a numpy array"
         assert isinstance(xk, np.ndarray), "xk must be a numpy array"
         assert fk.shape == xk.shape, "fk and xk must have the same shape"

From cee6e73835adf8ab1208dca8db55ebdbf11d4743 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sat, 14 Jun 2025 23:33:32 +0800
Subject: [PATCH 046/152] fix(SCF): update variable names for clarity and
 adjust AndersonMixer damping factor

---
 dpnegf/negf/scf_method.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 6964ad4..4a70801 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -366,15 +366,15 @@ def update(self, x, r):
         self.r_hist.append(delta_r)
 
         # Step 5: Apply B_n * r using low-rank update
-        B_r = self.B0 @ r
+        H_r = self.B0 @ r
         for u_j, r_j in zip(self.u_hist, self.r_hist):
             rj_dot = np.dot(r_j, r)
             norm2 = np.dot(r_j, r_j)
             if norm2 > self.eps:
-                B_r += u_j * (rj_dot / (norm2 + self.eps))
+                H_r += u_j * (rj_dot / (norm2 + self.eps))
 
         # Step 6: Final update
-        x_new = x - B_r
+        x_new = x - H_r
 
         # Step 7: Cache for next iteration
         self.x_last = x.copy()
@@ -400,7 +400,7 @@ class AndersonMixer:
         fkm1 (np.ndarray or None): Previous function output f(x_{k-1}).
         beta (float): Damping factor for the Anderson update.
     """
-    def __init__(self, m=5, alpha=0.1, verbose=False):
+    def __init__(self, m:int=5, alpha:float=0.2, beta:float=1, verbose=False):
         """
         Initializes the SCF method parameters.
 
@@ -428,7 +428,7 @@ def __init__(self, m=5, alpha=0.1, verbose=False):
         self.xkm1 = None  # x_{k-1}
         self.fkm1 = None  # f(x_{k-1})
 
-        self.beta = 1 # damping factor
+        self.beta = beta # damping factor (0 < beta <= 1) for the Anderson update, default is 1 (no damping)
 
     def reset(self):
         """

From 663133b3a321f0e1d2cb87713dc0e7668ed40131 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 15 Jun 2025 10:29:16 +0800
Subject: [PATCH 047/152] feat(SCF): enhance AndersonMixer to include history
 info of  linear warmup iterations

---
 dpnegf/negf/scf_method.py | 29 +++++++++++++++++------------
 1 file changed, 17 insertions(+), 12 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 4a70801..7d1eb08 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -400,7 +400,7 @@ class AndersonMixer:
         fkm1 (np.ndarray or None): Previous function output f(x_{k-1}).
         beta (float): Damping factor for the Anderson update.
     """
-    def __init__(self, m:int=5, alpha:float=0.2, beta:float=1, verbose=False):
+    def __init__(self, m:int=5, alpha:float=0.2, beta:float=1, num_linear_warmup:int=3 , verbose=False):
         """
         Initializes the SCF method parameters.
 
@@ -412,7 +412,8 @@ def __init__(self, m:int=5, alpha:float=0.2, beta:float=1, verbose=False):
         Attributes:
             dx_hist (list): History of differences between consecutive x values (x_k - x_{k-1}).
             df_hist (list): History of differences between consecutive function values (f(x_k) - f(x_{k-1})).
-            first_three (bool): Flag to handle the first three iterations separately.
+            first_linear (bool): Flag to handle the first linear mixing iterations separately.
+            num_linear_warmup (int): Number of iterations to use linear mixing before switching to Anderson mixing.
             iter (int): Iteration counter.
             xkm1: Previous x value (x_{k-1}).
             fkm1: Previous function value (f(x_{k-1})).
@@ -423,7 +424,8 @@ def __init__(self, m:int=5, alpha:float=0.2, beta:float=1, verbose=False):
         self.verbose = verbose
         self.dx_hist = []  #  xk - x_{k-1}
         self.df_hist = []  #  f(x_k) - f(x_{k-1})
-        self.first_three = True  # Flag to handle first three iterations separately
+        self.first_linear = True  # Flag to handle first three iterations separately
+        self.num_linear_warmup = num_linear_warmup  # Number of iterations to use linear mixing before switching to Anderson mixing
         self.iter = 0  # Iteration counter
         self.xkm1 = None  # x_{k-1}
         self.fkm1 = None  # f(x_{k-1})
@@ -440,7 +442,7 @@ def reset(self):
         
         self.dx_hist.clear()
         self.df_hist.clear()
-        self.first_three = True
+        self.first_linear = True
         self.iter = 0
         self.xkm1 = None  # Reset previous x_{k-1}
         self.fkm1 = None  # Reset previous f(x_{k-1})
@@ -473,17 +475,20 @@ def update(self, fk, xk):
         assert isinstance(xk, np.ndarray), "xk must be a numpy array"
         assert fk.shape == xk.shape, "fk and xk must have the same shape"
 
-        if self.first_three:
-            if self.iter < 3:
-                # self.dx_list.append(dx.copy())
-                # self.df_hist.append(df.copy())
-                self.iter += 1
-                x_new = xk + self.alpha * (fk - xk)  # Linear mixing for first three iterations
+        if self.first_linear:
+            if self.iter < self.num_linear_warmup:
+                xkp1 = xk + self.alpha * (fk - xk)  # Linear mixing for first three iterations
+                if self.iter > 0:
+                    dx = xk - self.xkm1
+                    df = fk - self.fkm1
+                    self.dx_hist.append(dx.copy())
+                    self.df_hist.append(df.copy())
                 self.xkm1 = xk.copy()  # Store x_k for next iteration
                 self.fkm1 = fk.copy()
-                return x_new  # linear mixing
+                self.iter += 1
+                return xkp1  # linear mixing
             else:
-                self.first_three = False
+                self.first_linear = False
             
 
         dx = xk - self.xkm1  # dx = x_k - x_{k-1}

From 4fdb06d28f3b6f8508fb877dcb9d01842da9cefd Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 09:54:36 +0800
Subject: [PATCH 048/152] refactor(SCF): update BroydenSecondMixer

---
 dpnegf/negf/scf_method.py | 86 ++++++++++++++-------------------------
 1 file changed, 30 insertions(+), 56 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 7d1eb08..1960597 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -290,98 +290,72 @@ class BroydenSecondMixer:
     using a low-rank update formula and applies it to iteratively improve convergence.
 
     The method uses limited-memory rank-1 updates:
-        x_{n+1} = x_n - B_n * r_n
-    where B_n ≈ J^{-1} is the inverse Jacobian built from the update history.
+        x_{n+1} = x_n - H_n * f_n
+    where H_n ≈ J^{-1} is the inverse Jacobian.
 
     Attributes:
         alpha (float): Initial mixing parameter for the first step.
-        max_hist (int): Maximum number of update pairs (u, r) stored for low-rank updates.
         eps (float): Threshold to avoid numerical instability in inner products.
-        B0 (ndarray): Initial inverse Jacobian approximation (scaled identity).
-        u_hist (list): History of update vectors u_n = s_n - B_n * delta_r_n.
-        r_hist (list): History of delta_r_n = r_n - r_{n-1}.
+        H0 (ndarray): Initial inverse Jacobian approximation (scaled identity).
+        df_hist (list): History of delta_df_n = f_n - f_{n-1}.
     """
 
-    def __init__(self, shape, max_hist=8, alpha=0.1, eps=1e-12):
+    def __init__(self, shape, alpha=0.1, eps=1e-12):
         self.alpha = alpha        # Initial mixing factor
-        self.max_hist = max_hist  # Max number of correction terms
         self.eps = eps            # Numerical stability threshold
         self.reset(shape)
 
     def reset(self, shape):
         self.iter = 0
         self.x_last = np.zeros(shape)
-        self.r_last = np.zeros(shape)
+        self.f_last = np.zeros(shape)
         dim = np.prod(shape)
-        self.B0 = -self.alpha * np.eye(dim)  # Initial inverse Jacobian guess
-        self.u_hist = []  # History of update vectors u_n = s_n - B delta_r
-        self.r_hist = []  # Corresponding delta_r vectors
+        self.H0 = -self.alpha * np.eye(dim)  # Initial inverse Jacobian guess
+        self.df_hist = []  # Corresponding delta_r vectors
 
-    def update(self, x, r):
+    def update(self, x, f):
         """
-        Perform one Broyden update step: x_{n+1} = x_n - B_n * r_n.
+        Perform one Broyden update step: x_{n+1} = x_n - H_n * f_n.
 
-        This function applies the approximate inverse Jacobian B_n
-        to the current residual r_n to compute the next guess x_{n+1}.
+        This function applies the approximate inverse Jacobian H_n
+        to the current residual f_n to compute the next guess x_{n+1}.
         The internal approximation B_n is updated based on the history
         of residual differences and solution updates.
 
         Args:
             x (np.ndarray): Current solution guess (arbitrary shape).
-            r (np.ndarray): Residual vector at the current guess.
+            f (np.ndarray): Residual vector at the current guess.
 
         Returns:
             np.ndarray: Updated solution guess (same shape as input x).
         """
-        x = x.ravel()
-        r = r.ravel()
+        x = x.reshape(-1,1)
+        f = f.reshape(-1,1)
 
         if self.iter == 0:
-            x_new = x - self.B0 @ r
+            x_new = x - self.H0 @ f
             self.x_last = x.copy()
-            self.r_last = r.copy()
+            self.f_last = f.copy()
+            self.Hnm1 = self.H0.copy()  # Initial inverse Jacobian
             self.iter += 1
-            return x_new.reshape(self.x_last.shape)
+            return x_new.ravel()
 
         # Step 1: Compute s_n = x - x_last, delta_r = r - r_last
-        s_n = x - self.x_last
-        delta_r = r - self.r_last
-
-        # Step 2: Build B * delta_r incrementally
-        B_delta_r = self.B0 @ delta_r
-        for u_j, r_j in zip(self.u_hist, self.r_hist):
-            rj_dot = np.dot(r_j, delta_r)
-            norm2 = np.dot(r_j, r_j)
-            if norm2 > self.eps:
-                B_delta_r += u_j * (rj_dot / (norm2 + self.eps))
-
-        # Step 3: u_n = s_n - B delta_r (corrected formula)
-        u_n = s_n - B_delta_r
-
-        # Step 4: Truncate history if needed
-        if len(self.u_hist) >= self.max_hist:
-            self.u_hist.pop(0)
-            self.r_hist.pop(0)
-        self.u_hist.append(u_n)
-        self.r_hist.append(delta_r)
-
-        # Step 5: Apply B_n * r using low-rank update
-        H_r = self.B0 @ r
-        for u_j, r_j in zip(self.u_hist, self.r_hist):
-            rj_dot = np.dot(r_j, r)
-            norm2 = np.dot(r_j, r_j)
-            if norm2 > self.eps:
-                H_r += u_j * (rj_dot / (norm2 + self.eps))
-
-        # Step 6: Final update
-        x_new = x - H_r
+        dx = x - self.x_last
+        df = f - self.f_last
+
+        u_n = dx - self.Hnm1 @ df  # Update vector
+        norm_df = np.dot(df.T, df)
+        Hn = self.Hnm1 + np.outer(u_n, df) / (norm_df + self.eps)  # Update inverse Jacobian
+        x_new = x - Hn @ f  # Compute new solution guess
+        self.Hnm1 = Hn  # Update the last inverse Jacobian
 
         # Step 7: Cache for next iteration
         self.x_last = x.copy()
-        self.r_last = r.copy()
+        self.f_last = f.copy()
         self.iter += 1
 
-        return x_new.reshape(self.x_last.shape)
+        return x_new.ravel()
 
 
 class AndersonMixer:
@@ -514,7 +488,7 @@ def update(self, fk, xk):
 
         except np.linalg.LinAlgError:
             # Fallback to linear mixing if R is rank-deficient
-            log.info("[Anderson] Linear algebra error, fallback to linear mixing.")
+            log.warning("[Anderson] Linear algebra error, fallback to linear mixing.")
             xkp1 = xk + self.alpha * (fk - xk)
 
         self.iter += 1

From 3f1f06d7819735b923275e3053fcf9c11e2c6868 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 10:03:14 +0800
Subject: [PATCH 049/152] doc(SCF): enhance BroydenFirstMixer with detailed
 docstrings

---
 dpnegf/negf/scf_method.py | 39 ++++++++++++++++++++++++---------------
 1 file changed, 24 insertions(+), 15 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 1960597..de94072 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -120,12 +120,23 @@ def update(self, p_new):
     
 class BroydenFirstMixer:
     """
-    Efficient Broyden's First Method (good Broyden) using the Sherman-Morrison-Woodbury formula.
+    Implements the first Broyden mixing method for accelerating self-consistent field (SCF) iterations.
 
     Attributes:
-        alpha (float): Initial mixing parameter (J0 = I/alpha).
-        eps (float): Numerical stability threshold.
+        init_x (np.ndarray): Initial guess for the variable to be mixed.
+        alpha (float): Linear mixing parameter (default: 0.1).
+        beta (float): Adaptive mixing factor (currently unused, default: 1).
+        eps (float): Numerical stability threshold for denominator (default: 1e-12).
+        iter (int): Current iteration count.
+        x_n (np.ndarray): Current value of the variable.
+        x_nm1 (np.ndarray): Previous value of the variable.
+        dim (int): Flattened dimension of the variable.
+        shape (tuple): Shape of the variable.
+        J0 (np.ndarray): Initial Jacobian approximation.
+        J_inv (np.ndarray): Current inverse Jacobian approximation.
+        f_last (np.ndarray): Last residual vector.
     """
+
     def __init__(self, init_x, alpha=0.1):
         self.init_x = init_x
         self.alpha = alpha
@@ -145,6 +156,16 @@ def reset(self, shape):
 
 
     def update(self, f):
+        """
+        Update the solution vector using a combination of linear mixing and Broyden's method.
+        For the first few iterations(warm up), it uses simple linear mixing to stabilize convergence.
+        After a specified number of iterations, it switches to Broyden's first method to accelerate convergence 
+        by approximating the inverse Jacobian.
+        Args:
+            f (np.ndarray): The current residual or function value at the current solution vector.
+        Returns:
+            np.ndarray: The updated solution vector after applying the mixing or Broyden's update.
+        """
 
         linear_warm_range = 3  # Number of iterations to use linear mixing before switching to Broyden's method
 
@@ -161,21 +182,9 @@ def update(self, f):
         else:
             dx = self.x_n - self.x_nm1  
             df = f - self.f_last
-
             dx = dx.reshape(-1, 1)  # Ensure dx is a column vector
             df = df.reshape(-1, 1)  # Ensure df is a column vector
 
-            # df_norm = np.linalg.norm(df)
-            # if self.iter == linear_warm_range:
-            #     self.last_df_norm = df_norm
-            #     self.beta = 1.0  # Initial beta value for adaptive mixing
-            # else:
-            #     if df_norm > self.last_df_norm:
-            #         self.beta = max(0.1, self.beta * 0.5)
-            #     else:
-            #         self.beta = min(1.0, self.beta * 1.2)
-            # self.last_df_norm = df_norm
-
             J_inv_df = self.J_inv @ df  # J^{-1} * df
             numerator = (dx - J_inv_df) @ (dx.T @ self.J_inv)  # (dim,1) @ (1, dim) = (dim, dim)
             denominator = dx.T @ J_inv_df  + self.eps  # (1, dim) @ (dim, 1) = (1, 1) 

From fe046568bc59a519dfaeac8bd9437ad10751abed Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 14:20:04 +0800
Subject: [PATCH 050/152] feat(SCF): add DIIS

---
 dpnegf/negf/scf_method.py | 95 +++++++++++++++++++++++++++++++++++++++
 dpnegf/runner/NEGF.py     |  8 +++-
 2 files changed, 102 insertions(+), 1 deletion(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index de94072..f217e47 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -2,6 +2,98 @@
 import logging
 
 log = logging.getLogger(__name__)
+
+
+class DIISMixer:
+    """
+    DIIS (Pulay mixing) for SCF acceleration.
+
+    Attributes
+    ----------
+    max_hist : int
+        Maximum number of previous iterations to store.
+    """
+    def __init__(self, max_hist=6, alpha=0.2, linear_warmup=1):
+        self.max_hist = max_hist
+        self.alpha = alpha
+        self.linear_warmup = linear_warmup  # Number of iterations to use linear mixing before DIIS
+        
+        self.x_hist = []
+        self.r_hist = []
+        self.iter = 0  # Iteration counter
+
+    def reset(self):
+        """
+        Reset the DIIS mixer state.
+        Clears the history of x and r.
+        """
+        self.x_hist.clear()
+        self.r_hist.clear()
+        self.iter = 0
+        log.info("[DIIS] Mixer state reset.")
+
+    def update(self, x_new, r_new):
+        """
+        Apply DIIS to improve the estimate.
+
+        Parameters
+        ----------
+        x_new : ndarray
+            Current input (e.g., electrostatic potential).
+        r_new : ndarray
+            Current residual (e.g., phi_poisson - phi)
+
+        Returns
+        -------
+        x_mixed : ndarray
+            Mixed input for next iteration.
+        """
+
+        self.iter += 1
+
+        x_new = x_new.copy().reshape(-1,1)
+        r_new = r_new.copy().reshape(-1,1)
+
+        # Store new history
+        if len(self.x_hist) >= self.max_hist:
+            self.x_hist.pop(0)
+            self.r_hist.pop(0)
+
+        self.x_hist.append(x_new)
+        self.r_hist.append(r_new)
+
+        if self.iter <= self.linear_warmup : # The first iteration
+            x_new = (x_new - r_new) + self.alpha * r_new  # Linear mixing
+            return x_new.ravel() # Not enough history yet
+
+        # Construct B matrix (r_i · r_j)
+        n = len(self.r_hist)
+        B = np.empty((n + 1, n + 1))
+        B[-1, :] = 1
+        B[:, -1] = -1
+        B[-1, -1] = 0
+        for i in range(n):
+            for j in range(n):
+                B[i, j] = np.dot(self.r_hist[i].T, self.r_hist[j])
+
+        # Right-hand side of linear system
+        rhs = np.zeros(n + 1)
+        rhs[-1] = 1
+        rhs = rhs.reshape(-1, 1)
+
+        try:
+            coeffs = np.linalg.solve(B, rhs)[:-1]  # drop Lagrange multiplier
+        except np.linalg.LinAlgError:
+            log.warning("[DIIS] Singular matrix in DIIS mixing. Falling back to lienar mixing.")
+            # Fallback to linear mixing if B is singular
+            x_new = (x_new - r_new) + self.alpha * r_new  # Linear mixing
+            return x_new.ravel()
+
+        # Construct mixed x
+        x_mixed = sum(c * x for c, x in zip(coeffs, self.x_hist))
+        return x_mixed.ravel()  # Return as 1D array
+
+
 class PDIISMixer:
     """
     Periodic Direct Inversion in the Iterative Subspace (PDIIS) mixer for accelerating SCF convergence.
@@ -118,6 +210,9 @@ def update(self, p_new):
 
         return p_next
     
+
+
+
 class BroydenFirstMixer:
     """
     Implements the first Broyden mixing method for accelerating self-consistent field (SCF) iterations.
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 837cef1..8aa9e7c 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -14,7 +14,7 @@
 from dpnegf.utils.make_kpoints import kmesh_sampling_negf
 import logging
 from dpnegf.negf.poisson_init import Grid,Interface3D,Dirichlet,Dielectric
-from dpnegf.negf.scf_method import PDIISMixer,BroydenFirstMixer,BroydenSecondMixer,AndersonMixer
+from dpnegf.negf.scf_method import PDIISMixer,DIISMixer,BroydenFirstMixer,BroydenSecondMixer,AndersonMixer
 from typing import Optional, Union
 from dpnegf.utils.tools import apply_gaussian_filter_3d
 # from pyinstrument import Profiler
@@ -402,6 +402,9 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
             log.info(msg="Using PDIIS mixing method for NEGF-Poisson SCF")
         elif mix_method == 'linear':
             log.info(msg="Using linear mixing method for NEGF-Poisson SCF")
+        elif mix_method == 'DIIS':
+            mixer = DIISMixer(max_hist=6, alpha=0.2)
+            log.info(msg="Using DIIS mixing method for NEGF-Poisson SCF")
         elif mix_method == 'BroydenFirst':
             mixer = BroydenFirstMixer(init_x=interface_poisson.phi, alpha=mix_rate)
             log.info(msg="Using Broyden's first method for NEGF-Poisson SCF")
@@ -441,6 +444,9 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
                                                                 dtype=self.poisson_options['poisson_dtype'])
             if mix_method == 'linear':
                 interface_poisson.phi = interface_poisson.phi + mix_rate*(interface_poisson.phi_old-interface_poisson.phi)
+            elif mix_method == 'DIIS':
+                residual = interface_poisson.phi - interface_poisson.phi_old
+                interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), residual)
             elif mix_method == 'PDIIS':
                 interface_poisson.phi = mixer.update(interface_poisson.phi.copy())
             elif mix_method == 'BroydenFirst':

From 061776b095eaaa44fcf8e44c6f6fe2a293a211af Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 16:27:16 +0800
Subject: [PATCH 051/152] refactor(NEGF): refactor PDIIS mixing method
 selection and initialization for clarity

---
 dpnegf/negf/scf_method.py | 257 +++++++++++++++++++++++---------------
 dpnegf/runner/NEGF.py     |  48 +++----
 2 files changed, 174 insertions(+), 131 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index f217e47..76ca966 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -6,14 +6,29 @@
 
 class DIISMixer:
     """
-    DIIS (Pulay mixing) for SCF acceleration.
-
+    DIISMixer implements the Direct Inversion in the Iterative Subspace (DIIS) method 
+    for accelerating self-consistent field (SCF) convergence.
+    max_hist : int, optional
+        Maximum number of history vectors to store for DIIS extrapolation (default: 6).
+    alpha : float, optional
+        Mixing parameter for linear mixing during warmup (default: 0.2).
+    linear_warmup : int, optional
+        Number of initial iterations to use linear mixing before switching to DIIS (default: 1).
     Attributes
-    ----------
+    x_hist : list of ndarray
+        History of previous input vectors (e.g., potentials).
+    r_hist : list of ndarray
+        History of previous residual vectors.
+    iter : int
+        Iteration counter.
     max_hist : int
-        Maximum number of previous iterations to store.
+        Maximum number of history vectors.
+    alpha : float
+        Linear mixing parameter.
+    linear_warmup : int
+        Number of linear mixing iterations before DIIS.
     """
-    def __init__(self, max_hist=6, alpha=0.2, linear_warmup=1):
+    def __init__(self, max_hist:int=6, alpha:float=0.2, linear_warmup:int=1):
         self.max_hist = max_hist
         self.alpha = alpha
         self.linear_warmup = linear_warmup  # Number of iterations to use linear mixing before DIIS
@@ -24,31 +39,36 @@ def __init__(self, max_hist=6, alpha=0.2, linear_warmup=1):
 
     def reset(self):
         """
-        Reset the DIIS mixer state.
-        Clears the history of x and r.
+        Reset the internal state of the mixer by clearing the history of x and r,
+        setting the iteration counter to zero, and logging the reset action.
         """
         self.x_hist.clear()
         self.r_hist.clear()
         self.iter = 0
         log.info("[DIIS] Mixer state reset.")
 
-    def update(self, x_new, r_new):
+    def update(self, x_new:np.ndarray, r_new:np.ndarray):
         """
-        Apply DIIS to improve the estimate.
+        Update the current solution vector using DIIS (Direct Inversion in the Iterative Subspace) or linear mixing.
+
+        This method manages the iterative update of a solution vector `x_new` and its corresponding residual `r_new`.
+        It stores a history of previous solution and residual vectors up to `max_hist` entries. During the initial
+        `linear_warmup` iterations, it applies linear mixing. After that, it constructs the DIIS B matrix and solves
+        for optimal mixing coefficients to accelerate convergence. If the B matrix is singular, it falls back to 
+        linear mixing.
 
         Parameters
         ----------
-        x_new : ndarray
-            Current input (e.g., electrostatic potential).
-        r_new : ndarray
-            Current residual (e.g., phi_poisson - phi)
+        x_new : np.ndarray
+            The new solution vector to be mixed.
+        r_new : np.ndarray
+            The new residual vector.
 
         Returns
         -------
-        x_mixed : ndarray
-            Mixed input for next iteration.
+        np.ndarray
+            The updated (mixed) solution vector.
         """
-
         self.iter += 1
 
         x_new = x_new.copy().reshape(-1,1)
@@ -96,119 +116,152 @@ def update(self, x_new, r_new):
 
 class PDIISMixer:
     """
-    Periodic Direct Inversion in the Iterative Subspace (PDIIS) mixer for accelerating SCF convergence.
+    PDIISMixer implements the Periodic Direct Inversion in the Iterative Subspace (PDIIS) 
+    mixing scheme for accelerating self-consistent field (SCF) convergence.
 
-    Parameters
-    ----------
-    init_p : np.ndarray
-        Initial potential or state vector for SCF iterations.
+    init_f : np.ndarray
+        Initial state (e.g., potential or density) for the mixer.
     mix_rate : float, optional
-        Mixing rate (step size) for linear update. Default is 0.05.
-    n_history : int, optional
-        Number of history steps to store for Pulay extrapolation. Default is 6.
+        Linear mixing rate (default: 0.2).
+    max_hist : int, optional
+        Maximum number of history vectors to store for DIIS extrapolation (default: 4).
     mixing_period : int, optional
-        Frequency (in iterations) to apply DIIS mixing instead of linear mixing. Default is 3.
-    verbose : bool, optional
-        If True, print debug information. Default is False.
+        Number of iterations between DIIS mixing steps (default: 2).
+    Attributes
+    mix_rate : float
+        Linear mixing rate.
+    max_hist : int
+        Maximum number of history vectors.
+    mixing_period : int
+        Number of iterations between DIIS mixing steps.
+    iter_count : int
+        Current iteration count.
+    x : np.ndarray or None
+        Current mixed state.
+    x_last : np.ndarray or None
+        Previous mixed state.
+    f : np.ndarray or None
+        Current input state.
+    R : list of np.ndarray
+        History of differences in mixed states.
+    F : list of np.ndarray
+        History of differences in input states.
+    Methods
+    reset(new_init_f=None)
+        Reset the mixer, optionally with a new initial state.
+    update(f_new)
+        Perform one PDIIS mixing update based on the new input state.
+    Notes
+    -----
+    - The mixer alternates between linear mixing and DIIS mixing according to `mixing_period`.
+    - If the DIIS matrix is ill-conditioned or a numerical error occurs, the mixer falls back to linear mixing.
     """
-    def __init__(self, init_p, mix_rate=0.05, n_history=4, mixing_period=2, verbose=True):
-        assert isinstance(init_p, np.ndarray), "init_p must be a numpy array"
+    def __init__(self, init_x, mix_rate=0.2, max_hist=4, mixing_period=2):
+        assert isinstance(init_x, np.ndarray), "init_x must be a numpy array"
         
         self.mix_rate = mix_rate
-        self.n_history = n_history
+        self.max_hist = max_hist
         self.mixing_period = mixing_period
-        self.verbose = verbose
         
-        self.iter_count = 0
-        self.p = init_p.copy()
+        self.iter_count = 1
+        self.x = init_x.copy() # x_i
+        self.x_last = None  # x_{i-1}
         self.f = None
-        self.R = [None for _ in range(n_history)]
-        self.F = [None for _ in range(n_history)]
+        self.R = []
+        self.F = []
 
-    def reset(self, new_init_p=None):
+    def reset(self, new_init_f=None):
         """Reset the mixer, optionally with a new initial potential."""
-        self.iter_count = 0
+        self.iter_count = 1
+        self.x = None
         self.f = None
-        self.R = [None for _ in range(self.n_history)]
-        self.F = [None for _ in range(self.n_history)]
-        if new_init_p is not None:
-            assert isinstance(new_init_p, np.ndarray), "new_init_p must be a numpy array"
-            self.p = new_init_p.copy()
+        self.R = []
+        self.F = []
+        if new_init_f is not None:
+            assert isinstance(new_init_f, np.ndarray), "new_init_f must be a numpy array"
+            self.f = new_init_f.copy()
 
-    def update(self, p_new):
+    def update(self, f_new):
         """
-        Perform one PDIIS mixing update based on the new input p_new.
+        Perform one PDIIS mixing update based on the new input f_new.
 
         Parameters
         ----------
-        p_new : np.ndarray
+        f_new : np.ndarray
             Newly computed state (e.g., electrostatic potential).
 
         Returns
         -------
-        p_next : np.ndarray
+        x_next : np.ndarray
             The next mixed state.
         """
-        assert isinstance(p_new, np.ndarray), "p_new must be a numpy array"
-        assert p_new.shape == self.p.shape, "Shape mismatch in p_new and current state"
+        assert isinstance(f_new, np.ndarray), "f_new must be a numpy array"
         
-        p_new = p_new.copy()
-        f_new = p_new - self.p
-
-        if self.f is not None:
-            idx = self.iter_count % self.n_history
-            self.R[idx] = p_new - self.p # Residual vector
-            self.F[idx] = f_new - self.f # Difference in residuals
-
-        
-
-        do_pdiis = (self.iter_count + 1) % self.mixing_period == 0
-        p_next = None
-
-        if do_pdiis and all(f is not None for f in self.F):
-            if self.verbose:
-                log.info(msg=f"[PDIIS] Performing DIIS mixing at iter {self.iter_count + 1}")
-            F_mat = np.stack(self.F, axis=1)
-            R_mat = np.stack(self.R, axis=1)
-
-            FtF = F_mat.T @ F_mat
-
-            try:
-                cond_FtF = np.linalg.cond(FtF)
-                if cond_FtF > 1e10:
-                    log.info(f"[PDIIS DEBUG] cond(FtF) = {cond_FtF:.2e}")
-                    log.info(f"[PDIIS DEBUG] Norms of F vectors: {[np.linalg.norm(f) for f in self.F]}")
-                    log.info(f"[PDIIS DEBUG] Rank of F_mat: {np.linalg.matrix_rank(F_mat)}")
-                    log.info(msg=f"[PDIIS] Warning: FtF matrix condition number too high ({cond_FtF:.2e}). Skipping DIIS.")
-                    raise RuntimeError("Ill-conditioned FtF matrix in PDIIS")
-
-                correction = (R_mat + self.mix_rate * F_mat) @ np.linalg.solve(FtF, F_mat.T @ f_new)
-                p_next = self.p + self.mix_rate * f_new - correction
-
-            except RuntimeError as e:
-                # This was manually raised due to condition number
-                if self.verbose:
-                    log.info(msg=f"[PDIIS] {e} Falling back to linear mixing.")
-                p_next = self.p + self.mix_rate * f_new
-
-            except np.linalg.LinAlgError as e:
-                # This is actual numerical failure in np.linalg.solve
-                if self.verbose:
-                    log.info(msg=f"[PDIIS] np.linalg.solve failed: {e}. Falling back to linear mixing.")
-                p_next = self.p + self.mix_rate * f_new
-        else:
-            if self.verbose:
-                log.info(msg=f"[PDIIS] Using linear mixing at iteration {self.iter_count + 1} (not periodic time step or not enough history).")
-            p_next = self.p + self.mix_rate * f_new
-
+        f_new = f_new.copy()
+
+
+        if self.iter_count <= 2:
+            # First two iteration
+            x_next = self.x + self.mix_rate * (f_new - self.x)  # Linear mixing
+            if self.iter_count == 2:
+                self.x_last = self.x.copy()
+            self.x = x_next.copy()
+            self.f = f_new.copy()  # Update current state
+            self.iter_count += 1
+            return x_next  # Return the mixed state
+
+        else: # After the first two iterations, PDIIS can be used
+            assert f_new.shape == self.f.shape, "Shape mismatch in x_new and current state"
+            dx_i = self.x - self.x_last # dx_i = x_i - x_{i-1}
+            df_i = f_new - self.f  # df_i = f_i - f_{i-1}
+
+            # Store new history
+            if len(self.F) >= self.max_hist:
+                self.R.pop(0)
+                self.F.pop(0)
+            self.R.append(dx_i)  # Store the difference in potentials
+            self.F.append(df_i)
+
+            do_pdiis = self.iter_count  % self.mixing_period == 0
+            x_next = None # x_{i+1}
+            if do_pdiis:
+                log.info(msg=f"[PDIIS] Performing DIIS mixing at iter {self.iter_count}")
+                F_mat = np.column_stack(self.F)
+                R_mat = np.column_stack(self.R)
+                FtF = F_mat.T @ F_mat
+
+                try:
+                    cond_FtF = np.linalg.cond(FtF)
+                    if cond_FtF > 1e10:
+                        log.warning(msg=f"[PDIIS] Warning: FtF matrix condition number ({cond_FtF:.2e}) is too high. Skipping DIIS.")
+                        log.warning(f"[PDIIS DEBUG] cond(FtF) = {cond_FtF:.2e}")
+                        log.warning(f"[PDIIS DEBUG] Norms of F vectors: {[np.linalg.norm(f) for f in self.F]}")
+                        log.warning(f"[PDIIS DEBUG] Rank of F_mat: {np.linalg.matrix_rank(F_mat)}")
+                        raise RuntimeError("Ill-conditioned FtF matrix in PDIIS")
+
+                    correction = (R_mat + self.mix_rate * F_mat) @ np.linalg.solve(FtF, F_mat.T @ f_new)
+                    x_next = self.x + self.mix_rate * f_new - correction
+                except RuntimeError as e:
+                    # This was manually raised due to condition number
+                    log.warning(msg=f"[PDIIS] {e} Falling back to linear mixing.")
+                    x_next = self.x + self.mix_rate * (f_new - self.x) 
+
+                except np.linalg.LinAlgError as e:
+                    # Numerical failure in np.linalg.solve
+                    log.warning(msg=f"[PDIIS] np.linalg.solve failed: {e}. Falling back to linear mixing.")
+                    x_next = self.x + self.mix_rate * (f_new - self.x) 
 
+            else:
+                log.info(msg=f"[PDIIS] Using linear mixing at iteration {self.iter_count} (not periodic time step).")
+                x_next = self.x + self.mix_rate * (f_new - self.x)  # Linear mixing
 
-        # Update state
-        self.f = f_new.copy()
-        self.p = p_next.copy()
-        self.iter_count += 1
+            # Update state
+            self.f = f_new.copy()
+            self.x_last = self.x.copy()
+            self.x = x_next.copy()
+            self.iter_count += 1
 
-        return p_next
+            return x_next
     
 
 
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 8aa9e7c..750c135 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -397,28 +397,26 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
         max_diff_phi = 1e30
         max_diff_list = [] 
         iter_count=0
-        if mix_method == 'PDIIS':
-            mixer = PDIISMixer(init_p=interface_poisson.phi.copy(), mix_rate=mix_rate)
-            log.info(msg="Using PDIIS mixing method for NEGF-Poisson SCF")
-        elif mix_method == 'linear':
-            log.info(msg="Using linear mixing method for NEGF-Poisson SCF")
-        elif mix_method == 'DIIS':
-            mixer = DIISMixer(max_hist=6, alpha=0.2)
-            log.info(msg="Using DIIS mixing method for NEGF-Poisson SCF")
-        elif mix_method == 'BroydenFirst':
-            mixer = BroydenFirstMixer(init_x=interface_poisson.phi, alpha=mix_rate)
-            log.info(msg="Using Broyden's first method for NEGF-Poisson SCF")
-        elif mix_method == 'BroydenSecond':
-            mixer = BroydenSecondMixer(shape=interface_poisson.phi.shape, max_hist=8, alpha=mix_rate)
-            log.info(msg="Using Broyden's second method for NEGF-Poisson SCF")
-        # elif mix_method == 'Block_BroydenFirst':
-        #     mixer = Block_BroydenFirstMixer(init_x=interface_poisson.phi, alpha=mix_rate)
-        #     log.info(msg="Using Block Broyden's first method for NEGF-Poisson SCF")
-        elif mix_method == 'Anderson':
-            mixer = AndersonMixer(m=5, alpha=0.2)
-            log.info(msg="Using Anderson mixing method for NEGF-Poisson SCF")
+        mix_method_list = ['linear', 'PDIIS', 'DIIS', 'BroydenFirst', 'BroydenSecond', 'Anderson']
+        if mix_method not in mix_method_list:
+            raise ValueError("mix_method should be one of {}".format(mix_method_list))
         else:
-            raise ValueError("mix_method should be 'linear' or 'PDIIS'")
+        # initialize the mixer
+            log.info(msg="Using {} mixing method for NEGF-Poisson SCF".format(mix_method))
+            if mix_method == 'PDIIS':
+                mixer = PDIISMixer(init_x=interface_poisson.phi.copy(), mix_rate=mix_rate)
+            elif mix_method == 'DIIS':
+                mixer = DIISMixer(max_hist=6, alpha=0.2)
+            elif mix_method == 'BroydenFirst':
+                mixer = BroydenFirstMixer(init_x=interface_poisson.phi, alpha=mix_rate)
+            elif mix_method == 'BroydenSecond':
+                mixer = BroydenSecondMixer(shape=interface_poisson.phi.shape, max_hist=8, alpha=mix_rate)
+            elif mix_method == 'Anderson':
+                mixer = AndersonMixer(m=5, alpha=0.2)
+            elif mix_method == 'linear':
+                mixer = None
+
+   
 
         # Gummel type iteration
         while max_diff_phi > err:
@@ -451,18 +449,10 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
                 interface_poisson.phi = mixer.update(interface_poisson.phi.copy())
             elif mix_method == 'BroydenFirst':
                 residual = interface_poisson.phi - interface_poisson.phi_old
-                # residual_filter = apply_gaussian_filter_3d(residual, 
-                #                                            shape=(interface_poisson.grid.shape[0],
-                #                                                    interface_poisson.grid.shape[1],
-                #                                                    interface_poisson.grid.shape[2]), 
-                #                                            sigma=Gaussian_sigma)
                 interface_poisson.phi = mixer.update(f = residual) # fixed point problem: f defined as F(\phi)-\phi =0
             elif mix_method == 'BroydenSecond':
                 residual = interface_poisson.phi - interface_poisson.phi_old
                 interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), residual)
-            # elif mix_method == 'Block_BroydenFirst':
-            #     residual = interface_poisson.phi - interface_poisson.phi_old
-            #     interface_poisson.phi = mixer.update(f = residual)
             elif mix_method == 'Anderson':
                 interface_poisson.phi = mixer.update(interface_poisson.phi.copy(), interface_poisson.phi_old.copy())
 

From f51aea5e8bc0536f31f7a4db85547de59c759535 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 22:33:51 +0800
Subject: [PATCH 052/152] feat(PDIISMixer): enhance initialization and update
 methods, improve mixing parameters and documentation

---
 dpnegf/negf/scf_method.py | 43 ++++++++++++++++++++++-----------------
 1 file changed, 24 insertions(+), 19 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 76ca966..38a048c 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -119,6 +119,8 @@ class PDIISMixer:
     PDIISMixer implements the Periodic Direct Inversion in the Iterative Subspace (PDIIS) 
     mixing scheme for accelerating self-consistent field (SCF) convergence.
 
+    This PDIIS method is from https://doi.org/10.1016/j.cplett.2016.01.033.
+    
     init_f : np.ndarray
         Initial state (e.g., potential or density) for the mixer.
     mix_rate : float, optional
@@ -156,7 +158,7 @@ class PDIISMixer:
     - The mixer alternates between linear mixing and DIIS mixing according to `mixing_period`.
     - If the DIIS matrix is ill-conditioned or a numerical error occurs, the mixer falls back to linear mixing.
     """
-    def __init__(self, init_x, mix_rate=0.2, max_hist=4, mixing_period=2):
+    def __init__(self, init_x, mix_rate=0.2, max_hist=5, mixing_period=3):
         assert isinstance(init_x, np.ndarray), "init_x must be a numpy array"
         
         self.mix_rate = mix_rate
@@ -164,24 +166,24 @@ def __init__(self, init_x, mix_rate=0.2, max_hist=4, mixing_period=2):
         self.mixing_period = mixing_period
         
         self.iter_count = 1
-        self.x = init_x.copy() # x_i
+        self.x = init_x.copy().reshape(-1,1)  # x_i
         self.x_last = None  # x_{i-1}
         self.f = None
         self.R = []
         self.F = []
 
-    def reset(self, new_init_f=None):
+    def reset(self, new_init_x=None):
         """Reset the mixer, optionally with a new initial potential."""
         self.iter_count = 1
         self.x = None
         self.f = None
         self.R = []
         self.F = []
-        if new_init_f is not None:
-            assert isinstance(new_init_f, np.ndarray), "new_init_f must be a numpy array"
-            self.f = new_init_f.copy()
+        if new_init_x is not None:
+            assert isinstance(new_init_x, np.ndarray), "new_init_f must be a numpy array"
+            self.x = new_init_x.copy().reshape(-1,1)
 
-    def update(self, f_new):
+    def update(self, g_new):
         """
         Perform one PDIIS mixing update based on the new input f_new.
 
@@ -195,25 +197,28 @@ def update(self, f_new):
         x_next : np.ndarray
             The next mixed state.
         """
-        assert isinstance(f_new, np.ndarray), "f_new must be a numpy array"
+        assert isinstance(g_new, np.ndarray), "f_new must be a numpy array"
         
-        f_new = f_new.copy()
+        g_new = g_new.copy()
+        g_new = g_new.reshape(-1, 1)  # Ensure f_new is a column vector
 
 
         if self.iter_count <= 2:
             # First two iteration
-            x_next = self.x + self.mix_rate * (f_new - self.x)  # Linear mixing
+            x_next = self.x + self.mix_rate * (g_new - self.x)  # Linear mixing
             if self.iter_count == 2:
                 self.x_last = self.x.copy()
+            self.f = g_new - self.x
             self.x = x_next.copy()
-            self.f = f_new.copy()  # Update current state
             self.iter_count += 1
-            return x_next  # Return the mixed state
+            log.info(msg=f"[PDIIS] Using linear mixing warm up at iteration {self.iter_count}.")
+            return x_next.ravel()  # Return the mixed state
 
         else: # After the first two iterations, PDIIS can be used
-            assert f_new.shape == self.f.shape, "Shape mismatch in x_new and current state"
+            assert g_new.shape == self.f.shape, "Shape mismatch in f_new and current state"
+            f_new = g_new - self.x # f_i = g_i - x_i
             dx_i = self.x - self.x_last # dx_i = x_i - x_{i-1}
-            df_i = f_new - self.f  # df_i = f_i - f_{i-1}
+            df_i = f_new - self.f       # df_i = f_i - f_{i-1}
 
             # Store new history
             if len(self.F) >= self.max_hist:
@@ -244,24 +249,24 @@ def update(self, f_new):
                 except RuntimeError as e:
                     # This was manually raised due to condition number
                     log.warning(msg=f"[PDIIS] {e} Falling back to linear mixing.")
-                    x_next = self.x + self.mix_rate * (f_new - self.x) 
+                    x_next = self.x + self.mix_rate * (g_new - self.x) 
 
                 except np.linalg.LinAlgError as e:
                     # Numerical failure in np.linalg.solve
                     log.warning(msg=f"[PDIIS] np.linalg.solve failed: {e}. Falling back to linear mixing.")
-                    x_next = self.x + self.mix_rate * (f_new - self.x) 
+                    x_next = self.x + self.mix_rate * (g_new - self.x) 
 
             else:
                 log.info(msg=f"[PDIIS] Using linear mixing at iteration {self.iter_count} (not periodic time step).")
-                x_next = self.x + self.mix_rate * (f_new - self.x)  # Linear mixing
+                x_next = self.x + self.mix_rate * (g_new - self.x)  # Linear mixing
 
             # Update state
-            self.f = f_new.copy()
+            self.f = g_new.copy()
             self.x_last = self.x.copy()
             self.x = x_next.copy()
             self.iter_count += 1
 
-            return x_next
+            return x_next.ravel()
     
 
 

From 5c6bae988bc867c5df034ab015894be5ab8e80d1 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 22:43:19 +0800
Subject: [PATCH 053/152] test(AndersonMixer): add unit tests for linear mixing
 behavior, switching to Anderson mixing, reset functionality, and shape
 assertion

---
 dpnegf/tests/test_scf_method.py | 53 +++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 dpnegf/tests/test_scf_method.py

diff --git a/dpnegf/tests/test_scf_method.py b/dpnegf/tests/test_scf_method.py
new file mode 100644
index 0000000..6edfee7
--- /dev/null
+++ b/dpnegf/tests/test_scf_method.py
@@ -0,0 +1,53 @@
+import numpy as np
+import pytest
+from dpnegf.negf.scf_method import AndersonMixer
+
+def test_anderson_mixer_linear_mixing_behavior():
+    mixer = AndersonMixer(m=3, alpha=0.5, num_linear_warmup=2)
+    x0 = np.array([1.0, 2.0])
+    f0 = np.array([2.0, 4.0])
+    # First update: should use linear mixing
+    x1 = mixer.update(f0, x0)
+    expected_x1 = x0 + 0.5 * (f0 - x0)
+    np.testing.assert_allclose(x1, expected_x1)
+    # Second update: still linear mixing
+    f1 = np.array([3.0, 6.0])
+    x2 = mixer.update(f1, x1)
+    expected_x2 = x1 + 0.5 * (f1 - x1)
+    np.testing.assert_allclose(x2, expected_x2)
+
+def test_anderson_mixer_switches_to_anderson():
+    mixer = AndersonMixer(m=2, alpha=0.1, num_linear_warmup=1)
+    x0 = np.array([0.0, 0.0])
+    f0 = np.array([1.0, 1.0])
+    # First update: linear mixing
+    x1 = mixer.update(f0, x0)
+    np.testing.assert_allclose(x1, x0 + 0.1 * (f0 - x0))
+    # Second update: should switch to Anderson mixing
+    f1 = np.array([0.6, 0.4])
+    x2 = mixer.update(f1, x1)
+    # Anderson mixing should not raise and should return a numpy array of correct shape
+    assert isinstance(x2, np.ndarray)
+    assert x2.shape == x1.shape
+    x2_ = np.array([0.35135135, 0.02702703])
+    assert  abs(x2 - x2_).max() < 1e-8
+
+def test_anderson_mixer_reset():
+    mixer = AndersonMixer(m=2, alpha=0.3, num_linear_warmup=1)
+    x0 = np.array([1.0, 1.0])
+    f0 = np.array([2.0, 2.0])
+    mixer.update(f0, x0)
+    mixer.reset()
+    assert mixer.iter == 0
+    assert mixer.xkm1 is None
+    assert mixer.fkm1 is None
+    assert mixer.dx_hist == []
+    assert mixer.df_hist == []
+    assert mixer.first_linear is True
+
+def test_anderson_mixer_shape_assertion():
+    mixer = AndersonMixer()
+    x = np.array([1.0, 2.0])
+    f = np.array([1.0, 2.0, 3.0])
+    with pytest.raises(AssertionError):
+        mixer.update(f, x)

From 73f31079672b6f0f35e76ca8cb97c12cba4312a6 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 22:53:07 +0800
Subject: [PATCH 054/152] test(BroydenSecondMixer): add tests for linear
 mixing, second step update, reset functionality, and multiple iterations

---
 dpnegf/tests/test_scf_method.py | 56 +++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)

diff --git a/dpnegf/tests/test_scf_method.py b/dpnegf/tests/test_scf_method.py
index 6edfee7..9277800 100644
--- a/dpnegf/tests/test_scf_method.py
+++ b/dpnegf/tests/test_scf_method.py
@@ -1,6 +1,7 @@
 import numpy as np
 import pytest
 from dpnegf.negf.scf_method import AndersonMixer
+from dpnegf.negf.scf_method import BroydenSecondMixer
 
 def test_anderson_mixer_linear_mixing_behavior():
     mixer = AndersonMixer(m=3, alpha=0.5, num_linear_warmup=2)
@@ -51,3 +52,58 @@ def test_anderson_mixer_shape_assertion():
     f = np.array([1.0, 2.0, 3.0])
     with pytest.raises(AssertionError):
         mixer.update(f, x)
+
+def test_broyden_second_mixer_linear_first_step():
+    # Test that the first update is equivalent to linear mixing with -alpha
+    shape = (2,)
+    alpha = 0.3
+    mixer = BroydenSecondMixer(shape, alpha=alpha)
+    x0 = np.array([1.0, 2.0])
+    f0 = np.array([0.5, -1.0])
+    x1 = mixer.update(x0, f0)
+    expected = x0 + alpha * f0
+    np.testing.assert_allclose(x1, expected)
+
+def test_broyden_second_mixer_second_step_update():
+    # Test that the second update uses the Broyden formula and returns correct shape
+    shape = (2,)
+    alpha = 0.2
+    mixer = BroydenSecondMixer(shape, alpha=alpha)
+    x0 = np.array([0.0, 0.0])
+    f0 = np.array([1.0, -1.0])
+    x1 = mixer.update(x0, f0)
+    x2 = mixer.update(x1, np.array([0.5, -0.5]))
+    x2_ = np.array([0.4, -0.4])  # Expected value after second update
+    assert isinstance(x2, np.ndarray)
+    assert x2.shape == x0.shape
+    np.testing.assert_allclose(x2, x2_)
+
+def test_broyden_second_mixer_reset():
+    shape = (3,)
+    mixer = BroydenSecondMixer(shape, alpha=0.1)
+    x0 = np.array([1.0, 2.0, 3.0])
+    f0 = np.array([0.1, 0.2, 0.3])
+    mixer.update(x0, f0)
+    mixer.reset(shape)
+    assert mixer.iter == 0
+    np.testing.assert_array_equal(mixer.x_last, np.zeros(shape))
+    np.testing.assert_array_equal(mixer.f_last, np.zeros(shape))
+    assert isinstance(mixer.H0, np.ndarray)
+    assert mixer.df_hist == []
+
+def test_broyden_second_mixer_multiple_iterations():
+    # Test that the mixer can run for several iterations without error
+    shape = (2,)
+    mixer = BroydenSecondMixer(shape, alpha=0.15)
+    x = np.array([0.0, 0.0])
+    xlist_ = np.array([[0.15,-0.15],
+                       [1.5,-1.5],
+                       [12.3,-12.3],
+                       [87.8999999,-87.8999999],
+                       [541.4999999,-541.4999999]])
+    for i in range(5):
+        f = np.array([1.0 - 0.1 * i, -1.0 + 0.1 * i])
+        x = mixer.update(x, f)
+        assert x.shape == (2,)
+        assert np.allclose(x, xlist_[i])
+

From 721b2ba1b809945432374b1af20185317c09553e Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 23:01:58 +0800
Subject: [PATCH 055/152] test(BroydenFirstMixer): add tests for linear mixing,
 switching to Broyden update, reset functionality, and multiple iterations

---
 dpnegf/tests/test_scf_method.py | 83 +++++++++++++++++++++++++++++++++
 1 file changed, 83 insertions(+)

diff --git a/dpnegf/tests/test_scf_method.py b/dpnegf/tests/test_scf_method.py
index 9277800..db1466d 100644
--- a/dpnegf/tests/test_scf_method.py
+++ b/dpnegf/tests/test_scf_method.py
@@ -2,6 +2,7 @@
 import pytest
 from dpnegf.negf.scf_method import AndersonMixer
 from dpnegf.negf.scf_method import BroydenSecondMixer
+from dpnegf.negf.scf_method import BroydenFirstMixer
 
 def test_anderson_mixer_linear_mixing_behavior():
     mixer = AndersonMixer(m=3, alpha=0.5, num_linear_warmup=2)
@@ -107,3 +108,85 @@ def test_broyden_second_mixer_multiple_iterations():
         assert x.shape == (2,)
         assert np.allclose(x, xlist_[i])
 
+
+def test_broyden_first_mixer_linear_warmup():
+    # Test that the first three updates use linear mixing
+    init_x = np.array([1.0, 2.0])
+    alpha = 0.2
+    mixer = BroydenFirstMixer(init_x, alpha=alpha)
+    f0 = np.array([0.5, -1.0])
+    # First update: should use init_x + alpha * f
+    x1 = mixer.update(f0)
+    np.testing.assert_allclose(x1, init_x + alpha * f0)
+    # Second update: should use x_n + alpha * f
+    f1 = np.array([1.0, 1.0])
+    x2 = mixer.update(f1)
+    np.testing.assert_allclose(x2, x1 + alpha * f1)
+    # Third update: should use x_n + alpha * f
+    f2 = np.array([-0.5, 0.5])
+    x3 = mixer.update(f2)
+    np.testing.assert_allclose(x3, x2 + alpha * f2)
+
+def test_broyden_first_mixer_switches_to_broyden():
+    # After three iterations, should use Broyden's update
+    init_x = np.array([0.0, 0.0])
+    alpha = 0.1
+    mixer = BroydenFirstMixer(init_x, alpha=alpha)
+    f0 = np.array([1.0, 2.0])
+    x1 = mixer.update(f0)
+    np.testing.assert_allclose(x1, init_x + alpha * f0)
+    f1 = np.array([0.5, 1.5])
+    x2 = mixer.update(f1)
+    np.testing.assert_allclose(x2, x1 + alpha * f1)
+    f2 = np.array([0.2, 1.0])
+    x3 = mixer.update(f2)
+    np.testing.assert_allclose(x3, x2 + alpha * f2)
+    # Now, Broyden's update should be used
+    f3 = np.array([0.1, 0.5])
+    x4 = mixer.update(f3)
+    x4_ = np.array([0.19, 0.55])
+    # Check that the shape is correct and no error is raised
+    assert isinstance(x4, np.ndarray)
+    assert x4.shape == init_x.shape
+    np.testing.assert_allclose(x4, x4_)
+
+def test_broyden_first_mixer_reset():
+    init_x = np.array([1.0, 2.0, 3.0])
+    mixer = BroydenFirstMixer(init_x, alpha=0.3)
+    f = np.array([0.1, 0.2, 0.3])
+    mixer.update(f)
+    mixer.reset(init_x.shape)
+    assert mixer.iter == 0
+    np.testing.assert_array_equal(mixer.x_n, np.zeros(init_x.shape))
+    np.testing.assert_array_equal(mixer.x_nm1, np.zeros(init_x.shape))
+    assert mixer.J0.shape == (3, 3)
+    assert mixer.J_inv.shape == (3, 3)
+
+def test_broyden_first_mixer_multiple_iterations():
+    # Run several iterations and check for shape and no errors
+    init_x = np.array([0.0, 0.0])
+    mixer = BroydenFirstMixer(init_x, alpha=0.15)
+    x = init_x.copy()
+    x_last = np.array([ 1644.28499959, -1644.28499959])
+    for i in range(10):
+        f = np.array([1.0 - 0.1 * i, -1.0 + 0.1 * i])
+        x = mixer.update(f)
+        assert x.shape == (2,)
+        if i == 9:
+            np.testing.assert_allclose(x, x_last)
+ 
+
+def test_broyden_first_mixer_handles_zero_residual():
+    # Should not crash if residual is zero
+    init_x = np.array([1.0, 2.0])
+    mixer = BroydenFirstMixer(init_x, alpha=0.2)
+    f = np.zeros_like(init_x)
+    x1 = mixer.update(f)
+    x2 = mixer.update(f)
+    x3 = mixer.update(f)
+    x4 = mixer.update(f)
+    np.testing.assert_allclose(x4, x3)
+
+
+
+

From 79629c0b9962d17dcc5b5d02bf54432bf0a1c1b7 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 23:30:12 +0800
Subject: [PATCH 056/152] test(DIISMixer): add tests for linear mixing
 behavior, switching to DIIS, history limit, reset functionality, and
 shape/type assertions

---
 dpnegf/negf/scf_method.py       |  2 +-
 dpnegf/tests/test_scf_method.py | 72 +++++++++++++++++++++++++++++++++
 2 files changed, 73 insertions(+), 1 deletion(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index 38a048c..d88e221 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -94,7 +94,7 @@ def update(self, x_new:np.ndarray, r_new:np.ndarray):
         B[-1, -1] = 0
         for i in range(n):
             for j in range(n):
-                B[i, j] = np.dot(self.r_hist[i].T, self.r_hist[j])
+                B[i, j] = np.dot(self.r_hist[i].ravel(), self.r_hist[j].ravel())
 
         # Right-hand side of linear system
         rhs = np.zeros(n + 1)
diff --git a/dpnegf/tests/test_scf_method.py b/dpnegf/tests/test_scf_method.py
index db1466d..75b39ba 100644
--- a/dpnegf/tests/test_scf_method.py
+++ b/dpnegf/tests/test_scf_method.py
@@ -3,6 +3,7 @@
 from dpnegf.negf.scf_method import AndersonMixer
 from dpnegf.negf.scf_method import BroydenSecondMixer
 from dpnegf.negf.scf_method import BroydenFirstMixer
+from dpnegf.negf.scf_method import DIISMixer
 
 def test_anderson_mixer_linear_mixing_behavior():
     mixer = AndersonMixer(m=3, alpha=0.5, num_linear_warmup=2)
@@ -188,5 +189,76 @@ def test_broyden_first_mixer_handles_zero_residual():
     np.testing.assert_allclose(x4, x3)
 
 
+def test_diis_mixer_linear_mixing_warmup():
+    mixer = DIISMixer(max_hist=3, alpha=0.5, linear_warmup=2)
+    x0 = np.array([1.0, 2.0])
+    r0 = np.array([0.5, 1.0])
+    # First update: should use linear mixing
+    x1 = mixer.update(x0, r0)
+    expected_x1 = (x0 - r0) + 0.5 * r0
+    np.testing.assert_allclose(x1, expected_x1)
+    # Second update: still linear mixing
+    x2 = mixer.update(x1, r0)
+    expected_x2 = (x1 - r0) + 0.5 * r0
+    np.testing.assert_allclose(x2, expected_x2)
+
+def test_diis_mixer_switches_to_diis():
+    mixer = DIISMixer(max_hist=3, alpha=0.2, linear_warmup=1)
+    x0 = np.array([1.0, 2.0])
+    r0 = np.array([0.5, 1.0])
+    x1 = mixer.update(x0, r0)  # linear mixing
+    x2 = mixer.update(x1, r0)  # should switch to DIIS
+    # Should return a numpy array of correct shape
+    assert isinstance(x2, np.ndarray)
+    assert x2.shape == x0.shape
+    x2_ = np.array([0.2, 0.4])  # Expected value after DIIS mixing
+    assert abs(x2 - x2_).max() < 1e-8
+
+def test_diis_mixer_history_limit():
+    mixer = DIISMixer(max_hist=2, alpha=0.3, linear_warmup=1)
+    x = np.array([1.0, 2.0])
+    r = np.array([0.5, 1.0])
+    # Fill up history
+    mixer.update(x, r)
+    mixer.update(x, r)
+    mixer.update(x, r)
+    # Should not exceed max_hist
+    assert len(mixer.x_hist) <= 2
+    assert len(mixer.r_hist) <= 2
+
+def test_diis_mixer_reset():
+    mixer = DIISMixer(max_hist=2, alpha=0.3, linear_warmup=1)
+    x = np.array([1.0, 2.0])
+    r = np.array([0.5, 1.0])
+    mixer.update(x, r)
+    mixer.reset()
+    assert mixer.iter == 0
+    assert mixer.x_hist == []
+    assert mixer.r_hist == []
+
+def test_diis_mixer_fallback_on_singular_matrix(monkeypatch):
+    mixer = DIISMixer(max_hist=2, alpha=0.1, linear_warmup=0)
+    x = np.array([1.0, 2.0])
+    r = np.array([0.5, 1.0])
+    # Fill up history so that B will be singular
+    mixer.update(x, r)
+    mixer.update(x, r)
+    # Monkeypatch np.linalg.solve to raise LinAlgError
+    monkeypatch.setattr(np.linalg, "solve", lambda *a, **k: (_ for _ in ()).throw(np.linalg.LinAlgError))
+    x_new = mixer.update(x, r)
+    # Should fall back to linear mixing
+    expected = (x - r) + 0.1 * r
+    np.testing.assert_allclose(x_new, expected)
+
+def test_diis_mixer_shape_and_type():
+    mixer = DIISMixer(max_hist=3, alpha=0.2, linear_warmup=1)
+    x = np.array([1.0, 2.0, 3.0])
+    r = np.array([0.1, 0.2, 0.3])
+    out = mixer.update(x, r)
+    assert isinstance(out, np.ndarray)
+    assert out.shape == x.shape
+
+
+
 
 

From a40e573405e564d866a7788da165705333debe2d Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 16 Jun 2025 23:44:50 +0800
Subject: [PATCH 057/152] test(PDIISMixer): add tests for linear mixing
 behavior, switching to PDIIS, history limit, reset functionality, and shape
 assertion

---
 dpnegf/negf/scf_method.py       |  7 +--
 dpnegf/tests/test_scf_method.py | 82 +++++++++++++++++++++++++++++++++
 2 files changed, 86 insertions(+), 3 deletions(-)

diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index d88e221..abdcdb4 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -180,16 +180,16 @@ def reset(self, new_init_x=None):
         self.R = []
         self.F = []
         if new_init_x is not None:
-            assert isinstance(new_init_x, np.ndarray), "new_init_f must be a numpy array"
+            assert isinstance(new_init_x, np.ndarray), "new_init_x must be a numpy array"
             self.x = new_init_x.copy().reshape(-1,1)
 
     def update(self, g_new):
         """
-        Perform one PDIIS mixing update based on the new input f_new.
+        Perform one PDIIS mixing update based on the new input g_new.
 
         Parameters
         ----------
-        f_new : np.ndarray
+        g_new : np.ndarray
             Newly computed state (e.g., electrostatic potential).
 
         Returns
@@ -201,6 +201,7 @@ def update(self, g_new):
         
         g_new = g_new.copy()
         g_new = g_new.reshape(-1, 1)  # Ensure f_new is a column vector
+        assert g_new.shape == self.x.shape, "Shape mismatch in g_new and current state"
 
 
         if self.iter_count <= 2:
diff --git a/dpnegf/tests/test_scf_method.py b/dpnegf/tests/test_scf_method.py
index 75b39ba..fd83659 100644
--- a/dpnegf/tests/test_scf_method.py
+++ b/dpnegf/tests/test_scf_method.py
@@ -4,6 +4,7 @@
 from dpnegf.negf.scf_method import BroydenSecondMixer
 from dpnegf.negf.scf_method import BroydenFirstMixer
 from dpnegf.negf.scf_method import DIISMixer
+from dpnegf.negf.scf_method import PDIISMixer
 
 def test_anderson_mixer_linear_mixing_behavior():
     mixer = AndersonMixer(m=3, alpha=0.5, num_linear_warmup=2)
@@ -258,6 +259,87 @@ def test_diis_mixer_shape_and_type():
     assert isinstance(out, np.ndarray)
     assert out.shape == x.shape
 
+def test_pdiis_mixer_linear_warmup_behavior():
+    init_x = np.array([1.0, 2.0])
+    mixer = PDIISMixer(init_x, mix_rate=0.5, max_hist=3, mixing_period=2)
+    g0 = np.array([2.0, 4.0])
+    # First update: linear mixing
+    x1 = mixer.update(g0)
+    expected_x1 = init_x + 0.5 * (g0 - init_x)
+    np.testing.assert_allclose(x1, expected_x1)
+    # Second update: still linear mixing
+    g1 = np.array([3.0, 6.0])
+    x2 = mixer.update(g1)
+    expected_x2 = x1 + 0.5 * (g1 - x1)
+    np.testing.assert_allclose(x2, expected_x2)
+
+def test_pdiis_mixer_switches_to_pdiis_and_periodic():
+    init_x = np.array([0.0, 0.0])
+    mixer = PDIISMixer(init_x, mix_rate=0.2, max_hist=2, mixing_period=2)
+    g0 = np.array([1.0, 1.0])
+    x1 = mixer.update(g0)  # linear mixing
+    g1 = np.array([0.5, 0.5])
+    x2 = mixer.update(g1)  # linear mixing
+    g2 = np.array([0.2, 0.4])
+    x3 = mixer.update(g2)  # should use PDIIS (since iter_count == 3)
+    # Should return a numpy array of correct shape
+    assert isinstance(x3, np.ndarray)
+    assert x3.shape == init_x.shape
+
+def test_pdiis_mixer_history_limit():
+    init_x = np.array([1.0, 2.0])
+    mixer = PDIISMixer(init_x, mix_rate=0.3, max_hist=2, mixing_period=2)
+    g = np.array([2.0, 3.0])
+    # Fill up history
+    mixer.update(g)
+    mixer.update(g)
+    mixer.update(g)
+    mixer.update(g)
+    # Should not exceed max_hist
+    assert len(mixer.F) <= 2
+    assert len(mixer.R) <= 2
+
+def test_pdiis_mixer_reset():
+    init_x = np.array([1.0, 2.0])
+    mixer = PDIISMixer(init_x, mix_rate=0.3, max_hist=2, mixing_period=2)
+    g = np.array([2.0, 3.0])
+    mixer.update(g)
+    mixer.reset()
+    assert mixer.iter_count == 1
+    assert mixer.x is None or isinstance(mixer.x, np.ndarray)
+    assert mixer.f is None
+    assert mixer.R == []
+    assert mixer.F == []
+
+def test_pdiis_mixer_shape_assertion():
+    init_x = np.array([1.0, 2.0])
+    mixer = PDIISMixer(init_x)
+    g = np.array([1.0, 2.0, 3.0])
+    with pytest.raises(AssertionError):
+        mixer.update(g)
+
+def test_pdiis_mixer_fallback_on_singular_matrix(monkeypatch):
+    init_x = np.array([1.0, 2.0])
+    mixer = PDIISMixer(init_x, mix_rate=0.1, max_hist=2, mixing_period=3)
+    g = np.array([2.0, 3.0])
+    x1 = mixer.update(g)
+    x2 = mixer.update(g)
+    # Monkeypatch np.linalg.solve to raise LinAlgError
+    monkeypatch.setattr(np.linalg, "solve", lambda *a, **k: (_ for _ in ()).throw(np.linalg.LinAlgError))
+    # Third update triggers PDIIS and should fallback to linear mixing
+    x_new = mixer.update(g)
+    expected = mixer.x_last.ravel() + 0.1 * (g - mixer.x_last.ravel())
+    np.testing.assert_allclose(x_new, expected.ravel())
+
+def test_pdiis_mixer_output_shape_and_type():
+    init_x = np.array([1.0, 2.0, 3.0])
+    mixer = PDIISMixer(init_x, mix_rate=0.2, max_hist=2, mixing_period=2)
+    g = np.array([2.0, 3.0, 4.0])
+    out = mixer.update(g)
+    assert isinstance(out, np.ndarray)
+    assert out.shape == init_x.shape
+
+
 
 
 

From 98f673a6d1c7c03e55b5ecba743c2063256abcc3 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 17 Jun 2025 14:52:06 +0800
Subject: [PATCH 058/152] refactor: update mixer classes to inherit from object
 and improve docstrings

---
 dpnegf/negf/poisson_init.py | 315 +++++++++++++++++++++++++++++++++++-
 dpnegf/negf/scf_method.py   |  10 +-
 2 files changed, 319 insertions(+), 6 deletions(-)

diff --git a/dpnegf/negf/poisson_init.py b/dpnegf/negf/poisson_init.py
index ece3dd3..397e194 100644
--- a/dpnegf/negf/poisson_init.py
+++ b/dpnegf/negf/poisson_init.py
@@ -14,6 +14,41 @@
 log = logging.getLogger(__name__)
 
 class Grid(object):
+    """
+    Represents a 3D grid structure for spatial discretization.
+    Parameters
+    ----------
+    xg : array_like
+        1D array of grid point coordinates along the x-axis.
+    yg : array_like
+        1D array of grid point coordinates along the y-axis.
+    zg : array_like
+        1D array of grid point coordinates along the z-axis.
+    xa : array_like
+        1D array of atom coordinates along the x-axis. Atoms must be within the grid bounds.
+    ya : array_like
+        1D array of atom coordinates along the y-axis. Atoms must be within the grid bounds.
+    za : array_like
+        1D array of atom coordinates along the z-axis. Atoms must be within the grid bounds.
+    Attributes
+    ----------
+    xg, yg, zg : ndarray
+        Grid coordinates along x, y, z axes.
+    xall, yall, zall : ndarray
+        Unique coordinates of all grid and atom positions along each axis.
+    shape : tuple
+        Shape of the grid as (len(xall), len(yall), len(zall)).
+    grid_coord : ndarray
+        Array of shape (Np, 3) containing the coordinates of all grid points, sorted lexicographically.
+    Np : int
+        Total number of grid points.
+    Na : int
+        Number of atoms.
+    atom_index_dict : dict
+        Dictionary mapping atom indices to their corresponding grid point indices.
+    surface_grid : ndarray
+        Array of shape (Np, 3) containing the surface area of each grid point along x, y, z axes.
+    """
     # define the grid in 3D space
     def __init__(self,xg,yg,zg,xa,ya,za):
         # xg,yg,zg are the coordinates of the basic grid points
@@ -76,6 +111,18 @@ def __init__(self,xg,yg,zg,xa,ya,za):
         
 
     def get_atom_index(self,xa,ya,za):
+        """
+        Finds the indices of atoms in the grid based on their coordinates.
+        Parameters
+        ----------
+            xa (array-like): Array of x-coordinates of atoms.
+            ya (array-like): Array of y-coordinates of atoms.
+            za (array-like): Array of z-coordinates of atoms.
+        Returns
+        ---------
+            dict: A dictionary where keys are atom indices and values are the corresponding
+                  grid point indices in `self.grid_coord` that match the atom positions.
+        """
         # find the index of the atoms in the grid
         swap = {}
         for atom_index in range(self.Na):
@@ -87,6 +134,24 @@ def get_atom_index(self,xa,ya,za):
         return swap
     
     def cal_vorlen(self,x):
+        """
+        Compute the length of the Voronoi segment for each point in a one-dimensional array.
+
+        For each point in the input array `x`, this function calculates the length of the Voronoi segment,
+        which is defined as half the distance to the neighboring points. The endpoints are handled by taking
+        half the distance to their single neighbor.
+
+        Parameters
+        ----------
+        x : array_like
+            One-dimensional array of points (must be indexable and support len()).
+
+        Returns
+        -------
+        xd : numpy.ndarray
+            Array of the same length as `x`, where each element represents the Voronoi segment length
+            corresponding to each point in `x`.
+        """
         # compute the length of the Voronoi segment of a one-dimensional array x
         xd = np.zeros(len(x))
         xd[0] = abs(x[0]-x[1])/2
@@ -97,12 +162,50 @@ def cal_vorlen(self,x):
 
 
 class region(object):
+    """
+    A class representing a 3D rectangular region defined by ranges along the x, y, and z axes.
+    parameters
+    ----------
+    x_range : tuple or list
+        A sequence of two values specifying the minimum and maximum of the x-axis.
+    y_range : tuple or list
+        A sequence of two values specifying the minimum and maximum of the y-axis.
+    z_range : tuple or list
+        A sequence of two values specifying the minimum and maximum of the z-axis.
+    Attributes
+    ----------
+        xmin (float): Minimum value of the x-axis range.
+        xmax (float): Maximum value of the x-axis range.
+        ymin (float): Minimum value of the y-axis range.
+        ymax (float): Maximum value of the y-axis range.
+        zmin (float): Minimum value of the z-axis range.
+        zmax (float): Maximum value of the z-axis range.
+    """
     def __init__(self,x_range,y_range,z_range):
         self.xmin,self.xmax = float(x_range[0]),float(x_range[1])
         self.ymin,self.ymax = float(y_range[0]),float(y_range[1])
         self.zmin,self.zmax = float(z_range[0]),float(z_range[1])
     
 class Dirichlet(region):
+    """
+    Dirichlet boundary condition class for defining regions with fixed potential.
+
+    Inherits from the `region` class and defines a region in 3D space
+    with given x, y, and z ranges. The Dirichlet boundary condition is specified
+    by a Fermi level (`Ef`) which represents the potential at the boundary.
+    Parameters
+    ----------
+    x_range : tuple or list
+        The lower and upper bounds (min, max) for the x-coordinate range.
+    y_range : tuple or list
+        The lower and upper bounds (min, max) for the y-coordinate range.
+    z_range : tuple or list
+        The lower and upper bounds (min, max) for the z-coordinate range.
+    Attributes
+    ----------
+    Ef : float
+        Fermi level of the gate (in unit eV), representing the fixed potential at the boundary.
+    """
     def __init__(self,x_range,y_range,z_range):
         # Dirichlet boundary condition
         super().__init__(x_range,y_range,z_range)
@@ -111,6 +214,25 @@ def __init__(self,x_range,y_range,z_range):
 
 
 class Dielectric(region):
+    """
+    Represents a dielectric region with a specified permittivity.
+
+    Inherits from the `region` class and defines a region in 3D space
+    with given x, y, and z ranges. The dielectric permittivity (`eps`)
+    is initialized to 1.0 by default.
+    Parameters
+    ----------
+    x_range : tuple or list
+        The lower and upper bounds (min, max) for the x-coordinate range.
+    y_range : tuple or list
+        The lower and upper bounds (min, max) for the y-coordinate range.
+    z_range : tuple or list
+        The lower and upper bounds (min, max) for the z-coordinate range.
+    Attributes
+    ----------
+    eps : float
+        Dielectric permittivity of the region, default is 1.0.
+    """
     def __init__(self,x_range,y_range,z_range):
         # dielectric region
         super().__init__(x_range,y_range,z_range)
@@ -121,7 +243,68 @@ def __init__(self,x_range,y_range,z_range):
 
 
 class Interface3D(object):
+    """
+    Interface3D(grid, Dirichlet_group, dielectric_group)
+    A class to handle the initialization and solution of the 3D Poisson equation
+    on a structured grid with support for Dirichlet and dielectric regions.
+    Parameters
+    ----------
+    grid : Grid
+        An instance of the Grid class defining the spatial discretization.
+    Dirichlet_group : list of Dirichlet
+        List of Dirichlet region objects specifying boundary conditions.
+    dielectric_group : list of Dielectric
+        List of Dielectric region objects specifying spatially varying permittivity.
+    Attributes
+    ----------
+    Dirichlet_group : list
+        List of Dirichlet region objects.
+    dielectric_group : list
+        List of Dielectric region objects.
+    grid : Grid
+        The grid object used for discretization.
+    eps : np.ndarray
+        Dielectric permittivity at each grid point.
+    phi : np.ndarray
+        Electrostatic potential at each grid point.
+    phi_old : np.ndarray
+        Previous iteration's electrostatic potential.
+    free_charge : np.ndarray
+        Free charge density at each grid point.
+    fixed_charge : np.ndarray
+        Fixed charge density at each grid point.
+    Temperature : float
+        Temperature in Kelvin.
+    kBT : float
+        Thermal energy in eV.
+    boudnary_points : dict
+        Dictionary mapping grid point indices to boundary type or "in" for internal.
+    lead_gate_potential : np.ndarray
+        Potential values for lead/gate Dirichlet regions.
+    internal_NP : int
+        Number of internal (non-boundary) grid points.
+    """
     def __init__(self,grid,Dirichlet_group,dielectric_group):
+        """
+        Initializes the Poisson solver with the given grid, Dirichlet boundary regions, and dielectric regions.
+        Parameters:
+            grid (Grid): The computational grid object. Must be an instance of the Grid class.
+            Dirichlet_group (list of Dirichlet): List of Dirichlet boundary region objects. Each must be an instance of the Dirichlet class.
+            dielectric_group (list of Dielectric): List of dielectric region objects. Each must be an instance of the Dielectric class.
+        Attributes:
+            Dirichlet_group (list): Stores the Dirichlet boundary regions.
+            dielectric_group (list): Stores the dielectric regions.
+            grid (Grid): The computational grid.
+            eps (np.ndarray): Dielectric permittivity array, initialized to ones.
+            phi (np.ndarray): Potential array, initialized to zeros.
+            phi_old (np.ndarray): Previous potential array, initialized to zeros.
+            free_charge (np.ndarray): Free charge density array, initialized to zeros.
+            fixed_charge (np.ndarray): Fixed charge density array, initialized to zeros.
+            Temperature (float): Temperature in Kelvin, default is 300.0.
+            kBT (float): Thermal energy in eV.
+            boudnary_points (dict): Dictionary mapping grid point indices to boundary status ("in" or boundary type).
+            lead_gate_potential (np.ndarray): Lead or gate potential array, initialized to zeros.
+        """
         assert grid.__class__.__name__ == 'Grid'
 
         
@@ -151,6 +334,19 @@ def __init__(self,grid,Dirichlet_group,dielectric_group):
         
 
     def get_fixed_charge(self,x_range,y_range,z_range,molar_fraction,atom_gridpoint_index):
+        """
+        Sets the fixed charge density for grid points within the specified spatial ranges and atom indices.
+
+        Parameters:
+            x_range (tuple or list): The lower and upper bounds (min, max) for the x-coordinate range.
+            y_range (tuple or list): The lower and upper bounds (min, max) for the y-coordinate range.
+            z_range (tuple or list): The lower and upper bounds (min, max) for the z-coordinate range.
+            molar_fraction (float): The value to assign as the fixed charge density for the selected grid points.
+            atom_gridpoint_index (array-like): Indices of grid points corresponding to atom positions.
+
+        Modifies:
+            self.fixed_charge (np.ndarray): Updates the fixed charge density at the selected grid points.
+        """
         # set the fixed charge density
         mask = (
             (float(x_range[0]) <= self.grid.grid_coord[:, 0]) &
@@ -167,6 +363,18 @@ def get_fixed_charge(self,x_range,y_range,z_range,molar_fraction,atom_gridpoint_
 
 
     def get_boundary_points(self):
+        """
+        Identifies and labels the boundary points of the grid in the x, y, and z directions.
+        For each point in the grid, this method checks if the point lies on the minimum or maximum
+        boundary along the x, y, or z axes.
+        It assigns a corresponding label ("xmin", "xmax", "ymin", "ymax", "zmin", or "zmax") to 
+        the `self.boudnary_points` array for boundary points. Points that do not lie on any boundary 
+        are counted as internal points.
+        Updates:
+            - self.boudnary_points: Array with boundary labels for each grid point.
+            - self.internal_NP: Number of internal (non-boundary) grid points.
+        """
+
         # set the boundary points
         xmin,xmax = np.min(self.grid.xall),np.max(self.grid.xall)
         ymin,ymax = np.min(self.grid.yall),np.max(self.grid.yall)
@@ -183,7 +391,28 @@ def get_boundary_points(self):
                 
         self.internal_NP = internal_NP
     
-    def get_potential_eps(self,region_list):
+
+    def get_potential_eps(self, region_list):
+        """
+        Assigns potential values and dielectric permittivity to grid points based on the provided region list.
+        For each region in `region_list`, this method:
+            - Identifies the grid points that fall within the spatial boundaries of the region.
+            - If the region is of type 'Dirichlet', assigns the corresponding potential to those grid points and marks them as Dirichlet boundary points.
+            - If the region is of type 'Dielectric', assigns the region's dielectric permittivity to those grid points.
+            - Raises a ValueError if the region type is unknown.
+        Parameters
+        ----------
+        region_list : list
+            List of region objects, each with attributes defining spatial boundaries (xmin, xmax, ymin, ymax, zmin, zmax)
+            and either a potential (Ef) for Dirichlet regions or permittivity (eps) for Dielectric regions.
+        Raises
+        ------
+        ValueError
+            If a region in the list has an unknown type.
+        Logs
+        ----
+        The number of Dirichlet points assigned.
+        """
         # assign the potential of Dirichlet region and dielectric permittivity to the grid points
         Dirichlet_point = 0
         for i in range(len(region_list)):    
@@ -209,6 +438,21 @@ def get_potential_eps(self,region_list):
         
         
     def to_pyamg_Jac_B(self,dtype=np.float64):
+        """
+        Converts the current object's data into a Jacobian matrix and right-hand side vector suitable for use with PyAMG solvers.
+
+        Parameters:
+            dtype (data-type, optional): The desired data-type for the arrays. Default is numpy.float64.
+
+        Returns:
+            tuple:
+                - Jacobian (scipy.sparse.csr_matrix): The constructed Jacobian matrix in CSR (Compressed Sparse Row) format.
+                - B (numpy.ndarray): The right-hand side vector corresponding to the Jacobian matrix.
+
+        Notes:
+            This method initializes a zero Jacobian matrix and right-hand side vector, constructs their values using
+            the `NR_construct_Jac_B` method, and returns them in formats compatible with PyAMG.
+        """
         # convert to amg format A,b matrix
         # A = poisson(self.grid.shape,format='csr',dtype=dtype)
         Jacobian = csr_matrix(np.zeros((self.grid.Np,self.grid.Np),dtype=dtype))
@@ -221,6 +465,20 @@ def to_pyamg_Jac_B(self,dtype=np.float64):
     
     
     def to_scipy_Jac_B(self,dtype=np.float64):
+        """
+        Constructs the Jacobian matrix and right-hand side vector (B) for the Poisson equation in SciPy sparse format.
+        The method relies on the `NR_construct_Jac_B` method to fill in the Jacobian and B.
+        Parameters
+        ----------
+        dtype : data-type, optional
+            The desired data-type for the Jacobian and B arrays (default is np.float64).
+        Returns
+        -------
+        Jacobian : scipy.sparse.csr_matrix
+            The constructed Jacobian matrix in CSR sparse format.
+        B : numpy.ndarray
+            The right-hand side vector for the Poisson equation.
+        """
         # create the Jacobian and B for the Poisson equation in scipy sparse format
         
         # Jacobian = csr_matrix(np.zeros((self.grid.Np,self.grid.Np),dtype=dtype))
@@ -236,6 +494,40 @@ def to_scipy_Jac_B(self,dtype=np.float64):
 
 
     def solve_poisson_NRcycle(self,method='pyamg',tolerance=1e-7,dtype:str='float64'):
+        """
+        Solve the Poisson equation using the Newton-Raphson (NR) iterative method.
+        This NR method is inspired by NanoTCAD ViDES (http://vides.nanotcad.com/vides/),iteratively solving 
+        the nonlinear Poisson equation by updating the potential (`self.phi`). 
+        At each iteration, it constructs the Jacobian and right-hand side (B),
+        solves the resulting linear system using either a direct solver ('scipy') or an algebraic multigrid solver ('pyamg'),
+        and updates the potential. The process continues until the correction norm falls below a threshold or a maximum
+        number of iterations is reached. The method also includes a control mechanism to prevent divergence by monitoring
+        the norm of B after each update.
+        Parameters
+        ----------
+        method : str, optional
+            The linear solver to use for the Poisson equation. Options are:
+            - 'pyamg': Use algebraic multigrid solver (default).
+            - 'scipy': Use direct solver from scipy.
+        tolerance : float, optional
+            The tolerance for the linear solver (default: 1e-7).
+        dtype : str, optional
+            Data type for the computation, either 'float64' (default, recommended for stability) or 'float32'.
+        Returns
+        -------
+        max_diff : float
+            The maximum absolute difference between the updated potential (`self.phi`) and the previous potential (`self.phi_old`)
+            after the NR cycle.
+        Raises
+        ------
+        ValueError
+            If an unknown data type or Poisson solver method is specified.
+        Notes
+        -----
+        - The method logs progress and warnings during the NR cycle.
+        - Includes a control mechanism to avoid increasing the norm of B after an NR update.
+        - The NR cycle stops if the correction norm is below 1e-3 or after 100 iterations.
+        """
         # solve the Poisson equation with Newton-Raphson method
         # delta_phi: the correction on the potential
         # It has been tested that dtype='float64' is a more stable SCF choice.
@@ -325,6 +617,27 @@ def callback(x):
         return x
     
     def NR_construct_Jac_B(self,J,B):
+        """
+        Constructs the Jacobian matrix (J) and right-hand side vector (B) for the Newton-Raphson solution 
+        of the Poisson equation on a 3D grid, accounting for both interior and boundary grid points.
+        For interior points, the method computes flux contributions in the x, y, and z directions using 
+        local permittivity, potential, and grid geometry.
+        For boundary points, the method applies appropriate boundary conditions (Dirichlet or Neumann) by 
+        modifying J and B accordingly, based on the type of boundary (xmin, xmax, ymin, ymax, zmin, zmax, or Dirichlet).
+        After assembling the contributions, the sign of B is flipped for nonzero entries for the Newton-Raphson iteration.
+        Parameters
+        ----------
+        J : numpy.ndarray
+            The Jacobian matrix to be constructed/updated (shape: [Np, Np], where Np is the number of grid points).
+        B : numpy.ndarray
+            The right-hand side vector to be constructed/updated (shape: [Np]).
+        Notes
+        -----
+        - Assumes that self.grid, self.eps, self.phi, self.phi_old, self.free_charge, self.fixed_charge, 
+            self.kBT, self.boudnary_points, and self.lead_gate_potential are properly initialized.
+        - Uses constants such as eps0 and elementary_charge, which must be defined in the scope.
+        - The method modifies J and B in place.
+        """
         # construct the Jacobian and B for the Poisson equation
                
         Nx = self.grid.shape[0];Ny = self.grid.shape[1];Nz = self.grid.shape[2]
diff --git a/dpnegf/negf/scf_method.py b/dpnegf/negf/scf_method.py
index abdcdb4..c730a5e 100644
--- a/dpnegf/negf/scf_method.py
+++ b/dpnegf/negf/scf_method.py
@@ -4,7 +4,7 @@
 log = logging.getLogger(__name__)
 
 
-class DIISMixer:
+class DIISMixer(object):
     """
     DIISMixer implements the Direct Inversion in the Iterative Subspace (DIIS) method 
     for accelerating self-consistent field (SCF) convergence.
@@ -114,7 +114,7 @@ def update(self, x_new:np.ndarray, r_new:np.ndarray):
         return x_mixed.ravel()  # Return as 1D array
 
 
-class PDIISMixer:
+class PDIISMixer(object):
     """
     PDIISMixer implements the Periodic Direct Inversion in the Iterative Subspace (PDIIS) 
     mixing scheme for accelerating self-consistent field (SCF) convergence.
@@ -272,7 +272,7 @@ def update(self, g_new):
 
 
 
-class BroydenFirstMixer:
+class BroydenFirstMixer(object):
     """
     Implements the first Broyden mixing method for accelerating self-consistent field (SCF) iterations.
 
@@ -443,7 +443,7 @@ def update(self, f):
 #         self.iter += 1
 
 #         return x_new
-class BroydenSecondMixer:
+class BroydenSecondMixer(object):
     """
     Implements Broyden's Second Method (also known as "bad Broyden")
     for accelerating fixed-point iterations such as those arising
@@ -521,7 +521,7 @@ def update(self, x, f):
         return x_new.ravel()
 
 
-class AndersonMixer:
+class AndersonMixer(object):
     """
     AndersonMixer implements Anderson mixing for accelerating fixed-point iterations,
     commonly used in self-consistent field (SCF) calculations.

From 2b325803bd37ff67a41cab44d1e7f0f9e5778853 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 17 Jun 2025 15:01:04 +0800
Subject: [PATCH 059/152] test: add unit tests for Grid class properties and
 methods

---
 dpnegf/tests/test_poisson_init.py | 84 +++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 dpnegf/tests/test_poisson_init.py

diff --git a/dpnegf/tests/test_poisson_init.py b/dpnegf/tests/test_poisson_init.py
new file mode 100644
index 0000000..b64f953
--- /dev/null
+++ b/dpnegf/tests/test_poisson_init.py
@@ -0,0 +1,84 @@
+import numpy as np
+import pytest
+from dpnegf.negf.poisson_init import Grid
+
+def test_grid_basic_properties():
+    # Define a simple 2x2x2 grid and 2 atoms inside the grid
+    xg = np.array([0.0, 1.0])
+    yg = np.array([0.0, 1.0])
+    zg = np.array([0.0, 1.0])
+    xa = np.array([0.0, 1.0])
+    ya = np.array([0.0, 1.0])
+    za = np.array([0.0, 1.0])
+
+    grid = Grid(xg, yg, zg, xa, ya, za)
+
+    # Check grid properties
+    assert np.allclose(grid.xg, xg)
+    assert np.allclose(grid.yg, yg)
+    assert np.allclose(grid.zg, zg)
+    assert grid.Na == 2
+    assert grid.shape == (2, 2, 2)
+    assert grid.Np == 8
+    assert grid.grid_coord.shape == (8, 3)
+    assert isinstance(grid.atom_index_dict, dict)
+    assert set(grid.atom_index_dict.keys()) == {0, 1}
+    assert grid.surface_grid.shape == (8, 3)
+    
+    grid_surface_grid_std= [[0.25, 0.25, 0.25],
+                        [0.25, 0.25, 0.25],
+                        [0.25, 0.25, 0.25],
+                        [0.25, 0.25, 0.25],
+                        [0.25, 0.25, 0.25],
+                        [0.25, 0.25, 0.25],
+                        [0.25, 0.25, 0.25],
+                        [0.25, 0.25, 0.25]]
+    assert np.allclose(grid.surface_grid, grid_surface_grid_std)
+
+
+def test_grid_atom_index():
+    # Atoms at grid points
+    xg = np.array([0.0, 1.0, 2.0])
+    yg = np.array([0.0, 1.0])
+    zg = np.array([0.0, 1.0])
+    xa = np.array([1.0, 2.0])
+    ya = np.array([1.0, 0.0])
+    za = np.array([0.0, 1.0])
+
+    grid = Grid(xg, yg, zg, xa, ya, za)
+    # Check that atom_index_dict maps each atom to a valid grid point
+    for atom_idx, grid_idx in grid.atom_index_dict.items():
+        atom_pos = (xa[atom_idx], ya[atom_idx], za[atom_idx])
+        grid_pos = grid.grid_coord[grid_idx]
+        assert np.allclose(atom_pos, grid_pos, atol=1e-3)
+
+def test_grid_cal_vorlen_uniform():
+    # Uniform grid
+    x = np.array([0.0, 1.0, 2.0, 3.0])
+    grid = Grid(x, x, x, x, x, x)
+    vorlen = grid.cal_vorlen(x)
+    # Endpoints: half the distance to neighbor, middle: average of neighbors
+    assert np.isclose(vorlen[0], 0.5)
+    assert np.isclose(vorlen[-1], 0.5)
+    assert np.allclose(vorlen[1:-1], 1.0)
+
+def test_grid_cal_vorlen_nonuniform():
+    # Non-uniform grid
+    x = np.array([0.0, 1.0, 3.0, 6.0])
+    grid = Grid(x, x, x, x, x, x)
+    vorlen = grid.cal_vorlen(x)
+    assert np.isclose(vorlen[0], 0.5)
+    assert np.isclose(vorlen[1], (1.0 + 2.0) / 2)
+    assert np.isclose(vorlen[2], (2.0 + 3.0) / 2)
+    assert np.isclose(vorlen[3], 1.5)
+
+def test_grid_atom_outside_raises():
+    # Atom outside grid should raise AssertionError
+    xg = np.array([0.0, 1.0])
+    yg = np.array([0.0, 1.0])
+    zg = np.array([0.0, 1.0])
+    xa = np.array([-1.0])  # outside
+    ya = np.array([0.5])
+    za = np.array([0.5])
+    with pytest.raises(AssertionError):
+        Grid(xg, yg, zg, xa, ya, za)
\ No newline at end of file

From 311d4c25fd8e4b31688a0c250a825b455b5eb162 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 17 Jun 2025 15:06:19 +0800
Subject: [PATCH 060/152] test: add tests for region and Dirichlet/Dielectric
 classes

---
 dpnegf/tests/test_poisson_init.py | 98 ++++++++++++++++++++++++++++++-
 1 file changed, 97 insertions(+), 1 deletion(-)

diff --git a/dpnegf/tests/test_poisson_init.py b/dpnegf/tests/test_poisson_init.py
index b64f953..473087b 100644
--- a/dpnegf/tests/test_poisson_init.py
+++ b/dpnegf/tests/test_poisson_init.py
@@ -1,6 +1,9 @@
 import numpy as np
 import pytest
 from dpnegf.negf.poisson_init import Grid
+from dpnegf.negf.poisson_init import region
+from dpnegf.negf.poisson_init import Dirichlet
+from dpnegf.negf.poisson_init import Dielectric
 
 def test_grid_basic_properties():
     # Define a simple 2x2x2 grid and 2 atoms inside the grid
@@ -81,4 +84,97 @@ def test_grid_atom_outside_raises():
     ya = np.array([0.5])
     za = np.array([0.5])
     with pytest.raises(AssertionError):
-        Grid(xg, yg, zg, xa, ya, za)
\ No newline at end of file
+        Grid(xg, yg, zg, xa, ya, za)
+
+def test_region_attributes():
+    x_range = (0, 10)
+    y_range = (1, 5)
+    z_range = (-2, 2)
+    r = region(x_range, y_range, z_range)
+    assert r.xmin == 0.0
+    assert r.xmax == 10.0
+    assert r.ymin == 1.0
+    assert r.ymax == 5.0
+    assert r.zmin == -2.0
+    assert r.zmax == 2.0
+
+def test_region_float_input():
+    x_range = (0.5, 2.5)
+    y_range = (1.1, 3.3)
+    z_range = (4.4, 5.5)
+    r = region(x_range, y_range, z_range)
+    assert r.xmin == 0.5
+    assert r.xmax == 2.5
+    assert r.ymin == 1.1
+    assert r.ymax == 3.3
+    assert r.zmin == 4.4
+    assert r.zmax == 5.5
+
+def test_region_list_input():
+    x_range = [1, 2]
+    y_range = [3, 4]
+    z_range = [5, 6]
+    r = region(x_range, y_range, z_range)
+    assert r.xmin == 1.0
+    assert r.xmax == 2.0
+    assert r.ymin == 3.0
+    assert r.ymax == 4.0
+    assert r.zmin == 5.0
+    assert r.zmax == 6.0
+
+def test_dirichlet_inherits_region():
+    x_range = (0, 1)
+    y_range = (2, 3)
+    z_range = (4, 5)
+    d = Dirichlet(x_range, y_range, z_range)
+    # Check region attributes
+    assert d.xmin == 0.0
+    assert d.xmax == 1.0
+    assert d.ymin == 2.0
+    assert d.ymax == 3.0
+    assert d.zmin == 4.0
+    assert d.zmax == 5.0
+
+def test_dirichlet_default_ef():
+    d = Dirichlet((0, 1), (0, 1), (0, 1))
+    assert hasattr(d, "Ef")
+    assert d.Ef == 0.0
+
+def test_dirichlet_accepts_list_input():
+    d = Dirichlet([1, 2], [3, 4], [5, 6])
+    assert d.xmin == 1.0
+    assert d.xmax == 2.0
+    assert d.ymin == 3.0
+    assert d.ymax == 4.0
+    assert d.zmin == 5.0
+    assert d.zmax == 6.0
+
+def test_dielectric_inherits_region():
+    x_range = (0, 2)
+    y_range = (1, 3)
+    z_range = (4, 5)
+    d = Dielectric(x_range, y_range, z_range)
+    assert d.xmin == 0.0
+    assert d.xmax == 2.0
+    assert d.ymin == 1.0
+    assert d.ymax == 3.0
+    assert d.zmin == 4.0
+    assert d.zmax == 5.0
+
+def test_dielectric_default_eps():
+    d = Dielectric((0, 1), (0, 1), (0, 1))
+    assert hasattr(d, "eps")
+    assert d.eps == 1.0
+
+def test_dielectric_accepts_list_input():
+    d = Dielectric([1, 2], [3, 4], [5, 6])
+    assert d.xmin == 1.0
+    assert d.xmax == 2.0
+    assert d.ymin == 3.0
+    assert d.ymax == 4.0
+    assert d.zmin == 5.0
+    assert d.zmax == 6.0
+
+
+
+

From f08df5dbf723eda984b7bc83cfe1f477dddfde92 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 17 Jun 2025 15:49:01 +0800
Subject: [PATCH 061/152] test: add tests for Interface3D initialization,
 boundary points, and charge handling

---
 dpnegf/tests/test_poisson_init.py | 180 ++++++++++++++++++++++++++++++
 1 file changed, 180 insertions(+)

diff --git a/dpnegf/tests/test_poisson_init.py b/dpnegf/tests/test_poisson_init.py
index 473087b..650488e 100644
--- a/dpnegf/tests/test_poisson_init.py
+++ b/dpnegf/tests/test_poisson_init.py
@@ -4,6 +4,9 @@
 from dpnegf.negf.poisson_init import region
 from dpnegf.negf.poisson_init import Dirichlet
 from dpnegf.negf.poisson_init import Dielectric
+from dpnegf.negf.poisson_init import Grid, region, Dirichlet, Dielectric, Interface3D
+import sys
+from scipy.sparse import lil_matrix
 
 def test_grid_basic_properties():
     # Define a simple 2x2x2 grid and 2 atoms inside the grid
@@ -176,5 +179,182 @@ def test_dielectric_accepts_list_input():
     assert d.zmax == 6.0
 
 
+class DummyLog:
+    def info(self, msg): pass
+    def warning(self, msg): pass
+
+# Patch log and constants for Interface3D
+sys.modules['dpnegf.negf.poisson_init'].log = DummyLog()
+sys.modules['dpnegf.negf.poisson_init'].Boltzmann = 1.380649e-23
+sys.modules['dpnegf.negf.poisson_init'].eV2J = 1.602176634e-19
+sys.modules['dpnegf.negf.poisson_init'].eps0 = 8.854187817e-12
+sys.modules['dpnegf.negf.poisson_init'].elementary_charge = 1.602176634e-19
+from scipy.sparse import csr_matrix
+sys.modules['dpnegf.negf.poisson_init'].csr_matrix = lambda *a, **k: csr_matrix(*a, **k)
+sys.modules['dpnegf.negf.poisson_init'].spsolve = lambda A, b: np.linalg.solve(A.toarray(), b) if hasattr(A, 'toarray') else np.linalg.solve(A, b)
+
+def make_simple_grid():
+    xg = np.array([0.0, 1.0])
+    yg = np.array([0.0, 1.0])
+    zg = np.array([0.0, 1.0])
+    xa = np.array([0.0, 1.0])
+    ya = np.array([0.0, 1.0])
+    za = np.array([0.0, 1.0])
+    return Grid(xg, yg, zg, xa, ya, za)
+
+def test_interface3d_init_and_boundary_points():
+    grid = make_simple_grid()
+    d = Dirichlet((0, 0), (0, 1), (0, 1))
+    diel = Dielectric((0, 1), (0, 1), (0, 1))
+    iface = Interface3D(grid, [d], [diel])
+    # Check attributes
+    assert iface.grid is grid
+    assert iface.Dirichlet_group == [d]
+    assert iface.dielectric_group == [diel]
+    assert iface.eps.shape == (grid.Np,)
+    assert iface.phi.shape == (grid.Np,)
+    assert iface.free_charge.shape == (grid.Np,)
+    assert iface.fixed_charge.shape == (grid.Np,)
+    assert iface.lead_gate_potential.shape == (grid.Np,)
+    # Check boundary points
+    boundary_types = set(iface.boudnary_points.values())
+    assert "in" not in boundary_types
+    assert hasattr(iface, "internal_NP")
+    assert isinstance(iface.internal_NP, int)
+    boundary_std = {0: 'xmin', 1: 'xmax', 2: 'xmin', 3: 'xmax', 4: 'xmin', 5: 'xmax', 6: 'xmin', 7: 'xmax'}
+    assert iface.boudnary_points == boundary_std
+    assert iface.internal_NP == 0  # No internal points in this simple grid
+
+def test_interface3d_get_fixed_charge_sets_values():
+    grid = make_simple_grid()
+    d = Dirichlet((0, 0), (0, 1), (0, 1))
+    diel = Dielectric((0, 1), (0, 1), (0, 1))
+    iface = Interface3D(grid, [d], [diel])
+    # All atoms are at grid points 0 and 7
+    atom_indices = np.array(list(grid.atom_index_dict.values()))
+    iface.get_fixed_charge((0, 1), (0, 1), (0, 1), 0.5, atom_indices)
+    # Only grid points corresponding to atoms should be set
+    for i in range(grid.Np):
+        if i in atom_indices:
+            assert iface.fixed_charge[i] == 0.5
+        else:
+            assert iface.fixed_charge[i] == 0.0
+
+def test_interface3d_get_potential_eps_dirichlet_and_dielectric():
+    grid = make_simple_grid()
+    d = Dirichlet((0, 0), (0, 1), (0, 1))
+    d.Ef =2.0
+    diel = Dielectric((0, 1), (0, 1), (0, 1))
+    diel.eps = 5.0
+    iface = Interface3D(grid, [d], [diel])
+    iface.get_potential_eps([d, diel])
+    # Dirichlet region should set lead_gate_potential to -Ef at correct indices
+    idx = np.nonzero((d.xmin <= grid.grid_coord[:,0]) &
+                        (d.xmax >= grid.grid_coord[:,0]) &
+                        (d.ymin <= grid.grid_coord[:,1]) &
+                        (d.ymax >= grid.grid_coord[:,1]) &
+                        (d.zmin <= grid.grid_coord[:,2]) &
+                        (d.zmax >= grid.grid_coord[:,2]))[0]
+    assert np.allclose(iface.lead_gate_potential[idx], -2.0)
+    # Dielectric region should set eps to 5.0 everywhere
+    assert np.allclose(iface.eps, 5.0)
+
+def test_interface3d_get_potential_eps_raises_on_unknown_type():
+    grid = make_simple_grid()
+    class Dummy: xmin=0; xmax=1; ymin=0; ymax=1; zmin=0; zmax=1
+    iface = Interface3D(grid, [], [])
+    with pytest.raises(ValueError):
+        iface.get_potential_eps([Dummy()])
+
+def test_interface3d_to_scipy_Jac_B_shapes():
+    grid = make_simple_grid()
+    d = Dirichlet((0, 0), (0, 1), (0, 1))
+    diel = Dielectric((0, 1), (0, 1), (0, 1))
+    iface = Interface3D(grid, [d], [diel])
+    J, B = iface.to_scipy_Jac_B()
+    assert isinstance(J, csr_matrix)
+    print(f"J shape: {J.shape}")
+    print("J:", J.toarray())
+    J_std = np.array(   [[ 1., -1.,  0.,  0.,  0.,  0.,  0.,  0.],
+                    [-1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.],
+                    [ 0.,  0.,  1., -1.,  0.,  0.,  0.,  0.],
+                    [ 0.,  0., -1.,  1.,  0.,  0.,  0.,  0.],
+                    [ 0.,  0.,  0.,  0.,  1., -1.,  0.,  0.],
+                    [ 0.,  0.,  0.,  0., -1.,  1.,  0.,  0.],
+                    [ 0.,  0.,  0.,  0.,  0.,  0.,  1., -1.],
+                    [ 0.,  0.,  0.,  0.,  0.,  0., -1.,  1.],])
+    assert J.shape == (grid.Np, grid.Np)
+    assert J.toarray().shape == (grid.Np, grid.Np)
+    assert np.allclose(J.toarray(), J_std)
+    B_std = np.array([0, 0, 0. ,0. ,0. ,0. ,0. ,0.])
+    assert B.shape == (grid.Np,)
+    assert np.allclose(B, B_std)
+
+def test_interface3d_to_pyamg_Jac_B_shapes():
+    grid = make_simple_grid()
+    d = Dirichlet((0, 0), (0, 1), (0, 1))
+    diel = Dielectric((0, 1), (0, 1), (0, 1))
+    iface = Interface3D(grid, [d], [diel])
+    J, B = iface.to_pyamg_Jac_B()
+    J_std = np.array([  [ 1., -1.,  0.,  0.,  0.,  0.,  0.,  0.],
+                        [-1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.],
+                        [ 0.,  0.,  1., -1.,  0.,  0.,  0.,  0.],
+                        [ 0.,  0., -1.,  1.,  0.,  0.,  0.,  0.],
+                        [ 0.,  0.,  0.,  0.,  1., -1.,  0.,  0.],
+                        [ 0.,  0.,  0.,  0., -1.,  1.,  0.,  0.],
+                        [ 0.,  0.,  0.,  0.,  0.,  0.,  1., -1.],
+                        [ 0.,  0.,  0.,  0.,  0.,  0., -1.,  1.],])
+    B_std = np.array([0, 0, 0. ,0. ,0. ,0. ,0. ,0.])
+    assert isinstance(J, csr_matrix)
+    assert J.toarray().shape == (grid.Np, grid.Np)
+    assert np.allclose(J.toarray(), J_std)
+    assert isinstance(B, np.ndarray)
+    assert np.allclose(B, B_std)
+    assert J.shape == (grid.Np, grid.Np)
+    assert B.shape == (grid.Np,)
+
+def test_interface3d_solve_poisson_NRcycle_dtype_check():
+    grid = make_simple_grid()
+    d = Dirichlet((0, 0), (0, 1), (0, 1))
+    diel = Dielectric((0, 1), (0, 1), (0, 1))
+    iface = Interface3D(grid, [d], [diel])
+    with pytest.raises(ValueError):
+        iface.solve_poisson_NRcycle(dtype="unknown")
+
+def test_interface3d_solve_poisson_NRcycle_method_check():
+    grid = make_simple_grid()
+    d = Dirichlet((0, 0), (0, 1), (0, 1))
+    diel = Dielectric((0, 1), (0, 1), (0, 1))
+    iface = Interface3D(grid, [d], [diel])
+    with pytest.raises(ValueError):
+        iface.solve_poisson_NRcycle(method="unknown")
+
+def test_interface3d_NR_construct_Jac_B_boundary_and_internal():
+    grid = make_simple_grid()
+    d = Dirichlet((0, 0), (0, 1), (0, 1))
+    diel = Dielectric((0, 1), (0, 1), (0, 1))
+    iface = Interface3D(grid, [d], [diel])
+    J = lil_matrix((grid.Np, grid.Np))
+    B = np.zeros(grid.Np)
+    iface.NR_construct_Jac_B(J, B)
+    # Check that diagonal is set for all points
+    diag = J.diagonal()
+    assert np.all(diag == 1)
+    J_std = np.array([[ 1., -1.,  0.,  0.,  0.,  0.,  0.,  0.],
+                [-1.,  1.,  0.,  0.,  0.,  0.,  0.,  0.],
+                [ 0.,  0.,  1., -1.,  0.,  0.,  0.,  0.],
+                [ 0.,  0., -1.,  1.,  0.,  0.,  0.,  0.],
+                [ 0.,  0.,  0.,  0.,  1., -1.,  0.,  0.],
+                [ 0.,  0.,  0.,  0., -1.,  1.,  0.,  0.],
+                [ 0.,  0.,  0.,  0.,  0.,  0.,  1., -1.],
+                [ 0.,  0.,  0.,  0.,  0.,  0., -1.,  1.],])
+    assert np.allclose(J.toarray(), J_std)
+    # Check that B is a vector of correct shape
+    assert B.shape == (grid.Np,)
+    B_std = np.array([0., 0., 0., 0., 0. ,0. ,0., 0.])
+    assert np.allclose(B, B_std)
+
+
+
 
 

From 8156db7e462efc61d151b7ccb05ef89acf00156d Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 20 Jun 2025 14:35:37 +0800
Subject: [PATCH 062/152] feat: add eps_average_mode parameter to Interface3D
 for flexible dielectric averaging

---
 dpnegf/negf/poisson_init.py | 27 +++++++++++++++------------
 1 file changed, 15 insertions(+), 12 deletions(-)

diff --git a/dpnegf/negf/poisson_init.py b/dpnegf/negf/poisson_init.py
index 397e194..a34417c 100644
--- a/dpnegf/negf/poisson_init.py
+++ b/dpnegf/negf/poisson_init.py
@@ -284,7 +284,7 @@ class Interface3D(object):
     internal_NP : int
         Number of internal (non-boundary) grid points.
     """
-    def __init__(self,grid,Dirichlet_group,dielectric_group):
+    def __init__(self,grid,Dirichlet_group,dielectric_group,eps_average_mode:str='harmonic'):
         """
         Initializes the Poisson solver with the given grid, Dirichlet boundary regions, and dielectric regions.
         Parameters:
@@ -330,7 +330,7 @@ def __init__(self,grid,Dirichlet_group,dielectric_group):
         self.get_boundary_points()
 
         self.lead_gate_potential = np.zeros(grid.Np) # no lead or gate potential initially, all grid points are set to zero
-        
+        self.average_mode = eps_average_mode
         
 
     def get_fixed_charge(self,x_range,y_range,z_range,molar_fraction,atom_gridpoint_index):
@@ -639,31 +639,36 @@ def NR_construct_Jac_B(self,J,B):
         - The method modifies J and B in place.
         """
         # construct the Jacobian and B for the Poisson equation
-               
+        def average_eps(eps1, eps2, mode:str='harmonic'):
+            if mode == 'arithmetic':
+                return 0.5 * (eps1 + eps2)
+            elif mode == 'harmonic':
+                return 2.0 * eps1 * eps2 / (eps1 + eps2)
+        average_mode = self.average_mode
         Nx = self.grid.shape[0];Ny = self.grid.shape[1];Nz = self.grid.shape[2]
         for gp_index in range(self.grid.Np):
             if self.boudnary_points[gp_index] == "in":
-                flux_xm_J = self.grid.surface_grid[gp_index,0]*eps0*(self.eps[gp_index-1]+self.eps[gp_index])*0.5\
+                flux_xm_J = self.grid.surface_grid[gp_index,0]*eps0*average_eps(self.eps[gp_index-1],self.eps[gp_index],mode = average_mode)\
                 /abs(self.grid.grid_coord[gp_index,0]-self.grid.grid_coord[gp_index-1,0])
                 flux_xm_B = flux_xm_J*(self.phi[gp_index-1]-self.phi[gp_index])
 
-                flux_xp_J = self.grid.surface_grid[gp_index,0]*eps0*(self.eps[gp_index+1]+self.eps[gp_index])*0.5\
+                flux_xp_J = self.grid.surface_grid[gp_index,0]*eps0*average_eps(self.eps[gp_index+1],self.eps[gp_index],mode = average_mode)\
                 /abs(self.grid.grid_coord[gp_index+1,0]-self.grid.grid_coord[gp_index,0])
                 flux_xp_B = flux_xp_J*(self.phi[gp_index+1]-self.phi[gp_index])
                 
-                flux_ym_J = self.grid.surface_grid[gp_index,1]*eps0*(self.eps[gp_index-Nx]+self.eps[gp_index])*0.5\
+                flux_ym_J = self.grid.surface_grid[gp_index,1]*eps0*average_eps(self.eps[gp_index-Nx],self.eps[gp_index],mode = average_mode)\
                 /abs(self.grid.grid_coord[gp_index-Nx,1]-self.grid.grid_coord[gp_index,1])
                 flux_ym_B = flux_ym_J*(self.phi[gp_index-Nx]-self.phi[gp_index])
 
-                flux_yp_J = self.grid.surface_grid[gp_index,1]*eps0*(self.eps[gp_index+Nx]+self.eps[gp_index])*0.5\
+                flux_yp_J = self.grid.surface_grid[gp_index,1]*eps0*average_eps(self.eps[gp_index+Nx],self.eps[gp_index],mode = average_mode)\
                 /abs(self.grid.grid_coord[gp_index+Nx,1]-self.grid.grid_coord[gp_index,1])
                 flux_yp_B = flux_yp_J*(self.phi[gp_index+Nx]-self.phi[gp_index])
 
-                flux_zm_J = self.grid.surface_grid[gp_index,2]*eps0*(self.eps[gp_index-Nx*Ny]+self.eps[gp_index])*0.5\
+                flux_zm_J = self.grid.surface_grid[gp_index,2]*eps0*average_eps(self.eps[gp_index-Nx*Ny],self.eps[gp_index],mode = average_mode)\
                 /abs(self.grid.grid_coord[gp_index-Nx*Ny,2]-self.grid.grid_coord[gp_index,2])
                 flux_zm_B = flux_zm_J*(self.phi[gp_index-Nx*Ny]-self.phi[gp_index])
 
-                flux_zp_J = self.grid.surface_grid[gp_index,2]*eps0*(self.eps[gp_index+Nx*Ny]+self.eps[gp_index])*0.5\
+                flux_zp_J = self.grid.surface_grid[gp_index,2]*eps0*average_eps(self.eps[gp_index+Nx*Ny],self.eps[gp_index],mode = average_mode)\
                 /abs(self.grid.grid_coord[gp_index+Nx*Ny,2]-self.grid.grid_coord[gp_index,2])
                 flux_zp_B = flux_zp_J*(self.phi[gp_index+Nx*Ny]-self.phi[gp_index])
 
@@ -709,6 +714,4 @@ def NR_construct_Jac_B(self,J,B):
                     B[gp_index] = (self.phi[gp_index]-self.lead_gate_potential[gp_index])
 
             if B[gp_index]!=0: # for convenience change the sign of B in later NR iteration
-                B[gp_index] = -B[gp_index]
-        
-        
\ No newline at end of file
+                B[gp_index] = -B[gp_index]           
\ No newline at end of file

From da6906ac1be1fb8aefd000c2a5fd8b0645ac725e Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 2 Jul 2025 19:45:03 +0800
Subject: [PATCH 063/152] fix: correct type annotation for properties and fix
 typo in compute_current method

---
 dpnegf/runner/NEGF.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 750c135..ab2d4d4 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -797,7 +797,7 @@ def compute_properties(self, kpoint, properties):
         #     ik = update_kmap(self.results_path, kpoint=k)
         for p in properties:
             # log.info(msg="Computing {0} at k = {1}".format(p, k))
-            prop = self.out.setdefault(p, [])
+            prop: list = self.out.setdefault(p, [])
             prop.append(getattr(self, "compute_"+p)(kpoint))
 
 
@@ -820,7 +820,7 @@ def compute_density_Ozaki(self, kpoint,Vbias):
 
     def compute_current(self, kpoint):
         self.deviceprop.cal_green_function(e=self.int_grid, kpoint=kpoint, block_tridiagonal=self.block_tridiagonal)
-        return self.devidevicepropce.current
+        return self.deviceprop.current
     
     def compute_lcurrent(self, kpoint):
         return self.deviceprop.lcurrent

From 9fc9bc4e77e99547bff32bcba4035eeb39389b74 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 7 Jul 2025 10:53:29 +0800
Subject: [PATCH 064/152] feat: make gate parameters optional and add
 left/right gates in pyamg and scipy functions

---
 dpnegf/utils/argcheck.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index e3de713..58f49d1 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1201,8 +1201,10 @@ def pyamg():
         Argument("mix_rate", int, optional=True, default=0.25, doc=doc_mix_rate),
         Argument("poisson_dtype", str, optional=True, default='float64', doc=doc_poisson_dtype),
         Argument("grid", dict, optional=False, sub_fields=grid(), doc=doc_grid),
-        Argument("gate_top", dict, optional=False, sub_fields=Dirichlet_BC(), doc=doc_gate),
-        Argument("gate_bottom", dict, optional=False, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("gate_top", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("gate_bottom", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("gate_left", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("gate_right", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("dielectric_region", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region", dict, optional=False, sub_fields=doped(), doc=doc_doped)
     ]
@@ -1227,6 +1229,8 @@ def scipy():
         Argument("grid", dict, optional=True, sub_fields=grid(), doc=doc_grid),
         Argument("gate_top", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("gate_bottom", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("gate_left", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
+        Argument("gate_right", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("lead_L", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("lead_R", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("dielectric_region", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),

From 3de58204b355281988c81caa2a68d2729037ffae Mon Sep 17 00:00:00 2001
From: kirk0830 <yike.huang@aliyun.com>
Date: Mon, 7 Jul 2025 22:26:42 +0800
Subject: [PATCH 065/152] Refactor: tighten the translational equivalence check
 on lead structure

---
 dpnegf/negf/negf_hamiltonian_init.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index c033f22..5a26fcf 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -480,8 +480,18 @@ def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         stru_lead = self.structase[self.lead_ids[kk][0]:self.lead_ids[kk][1]]
         cell = np.array(stru_lead.cell)[:2]
         
+        # translational vector between two parts (so-called principal layers) of atoms of lead
         R_vec = stru_lead[int(natom/2):].positions - stru_lead[:int(natom/2)].positions
-        assert np.abs(R_vec[0] - R_vec[-1]).sum() < 1e-5
+        # require the structure to have translational symmetry, and the atoms are arranged in two 
+        # layers in the identical way
+        err_symm = np.linalg.norm(R_vec - np.mean(R_vec, axis=0))/natom
+        log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}')
+        if err_symm >= 1e-6: # hard-coded threshold
+            raise ValueError('The dpnegf requires two principal layers of lead have tight translational'
+                             ' equivalence, which means if there are 2N atoms, the second N atoms\''
+                             ' coordinates can be obtained by translating the first N atoms. Plus, the'
+                             ' second N atoms (near the device) are required to be placed in front of '
+                             'the first N atoms for performance reason.')
         R_vec = R_vec.mean(axis=0) * 2
         cell = np.concatenate([cell, R_vec.reshape(1,-1)])
         pbc_lead = self.pbc_negf.copy()

From db1d3fd20efdd1edb934fcd7e5608cfe47bca065 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 7 Jul 2025 22:35:36 +0800
Subject: [PATCH 066/152] correct translational symmetry error calculation in
 NEGFHamiltonianInit

---
 dpnegf/negf/negf_hamiltonian_init.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 5a26fcf..a711310 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -484,7 +484,7 @@ def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         R_vec = stru_lead[int(natom/2):].positions - stru_lead[:int(natom/2)].positions
         # require the structure to have translational symmetry, and the atoms are arranged in two 
         # layers in the identical way
-        err_symm = np.linalg.norm(R_vec - np.mean(R_vec, axis=0))/natom
+        err_symm = np.linalg.norm(R_vec - np.mean(R_vec, axis=0))/(natom/2)
         log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}')
         if err_symm >= 1e-6: # hard-coded threshold
             raise ValueError('The dpnegf requires two principal layers of lead have tight translational'

From 4fb84521a364ca501d7c126dbbe5cc8024635f95 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 7 Jul 2025 22:40:23 +0800
Subject: [PATCH 067/152] improve error message for lead principal layers
 translational equivalence check

---
 dpnegf/negf/negf_hamiltonian_init.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index a711310..3d8e81e 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -487,11 +487,11 @@ def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         err_symm = np.linalg.norm(R_vec - np.mean(R_vec, axis=0))/(natom/2)
         log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}')
         if err_symm >= 1e-6: # hard-coded threshold
-            raise ValueError('The dpnegf requires two principal layers of lead have tight translational'
-                             ' equivalence, which means if there are 2N atoms, the second N atoms\''
-                             ' coordinates can be obtained by translating the first N atoms. Plus, the'
+            raise ValueError('DPNEGF requires two principal layers of lead have tight translational'
+                             ' equivalence. It means that for leads with 2N atoms, the second N atoms\''
+                             ' coordinates can be obtained by translating the first N atoms. Moreover, the'
                              ' second N atoms (near the device) are required to be placed in front of '
-                             'the first N atoms for performance reason.')
+                             'the first N atoms for implementation reason.')
         R_vec = R_vec.mean(axis=0) * 2
         cell = np.concatenate([cell, R_vec.reshape(1,-1)])
         pbc_lead = self.pbc_negf.copy()

From 3dd5e281701035e2a216b7dfcfe4c0c0c8da19ab Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 7 Jul 2025 22:42:30 +0800
Subject: [PATCH 068/152] change hard-coded threshold to 1e-10

---
 dpnegf/negf/negf_hamiltonian_init.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 3d8e81e..c9cc714 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -486,7 +486,7 @@ def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         # layers in the identical way
         err_symm = np.linalg.norm(R_vec - np.mean(R_vec, axis=0))/(natom/2)
         log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}')
-        if err_symm >= 1e-6: # hard-coded threshold
+        if err_symm >= 1e-10: # hard-coded threshold
             raise ValueError('DPNEGF requires two principal layers of lead have tight translational'
                              ' equivalence. It means that for leads with 2N atoms, the second N atoms\''
                              ' coordinates can be obtained by translating the first N atoms. Moreover, the'

From 766cc7e0b8134a802122a26d2aa1f89d5d751d87 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 7 Jul 2025 22:51:07 +0800
Subject: [PATCH 069/152] improve error info

---
 dpnegf/negf/negf_hamiltonian_init.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index c9cc714..1f1e293 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -487,11 +487,11 @@ def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         err_symm = np.linalg.norm(R_vec - np.mean(R_vec, axis=0))/(natom/2)
         log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}')
         if err_symm >= 1e-10: # hard-coded threshold
-            raise ValueError('DPNEGF requires two principal layers of lead have tight translational'
-                             ' equivalence. It means that for leads with 2N atoms, the second N atoms\''
-                             ' coordinates can be obtained by translating the first N atoms. Moreover, the'
-                             ' second N atoms (near the device) are required to be placed in front of '
-                             'the first N atoms for implementation reason.')
+            raise ValueError('DPNEGF requires two principal layers of one lead to be translationally equivalent.'
+                             'For lead with 2N atoms, ensure the second N atoms are a direct translation '
+                             'of the first N. Moreover, the second N atoms (near the device) are required'
+                             'to be placed in front of the first N atoms for implementation reason.')
+
         R_vec = R_vec.mean(axis=0) * 2
         cell = np.concatenate([cell, R_vec.reshape(1,-1)])
         pbc_lead = self.pbc_negf.copy()

From 7509bc7faa71ed6d327ebe785b68c6b97d062a84 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 7 Jul 2025 22:54:10 +0800
Subject: [PATCH 070/152] check natom is even

---
 dpnegf/negf/negf_hamiltonian_init.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 1f1e293..ae5fcf0 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -479,7 +479,7 @@ def remove_bonds_nonpbc(data,pbc,overlap):
     def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):       
         stru_lead = self.structase[self.lead_ids[kk][0]:self.lead_ids[kk][1]]
         cell = np.array(stru_lead.cell)[:2]
-        
+        assert natom % 2 == 0, "The number of atoms in the lead should be even."
         # translational vector between two parts (so-called principal layers) of atoms of lead
         R_vec = stru_lead[int(natom/2):].positions - stru_lead[:int(natom/2)].positions
         # require the structure to have translational symmetry, and the atoms are arranged in two 

From 3b848d000a762ab555498f7a40cbf119cfe148f3 Mon Sep 17 00:00:00 2001
From: kirk0830 <yike.huang@aliyun.com>
Date: Mon, 7 Jul 2025 23:50:20 +0800
Subject: [PATCH 071/152] Refactor: extract the translational equivalence check
 from get_lead_structure

---
 dpnegf/negf/negf_hamiltonian_init.py | 73 ++++++++++++++++++++++------
 1 file changed, 58 insertions(+), 15 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index ae5fcf0..78a00a5 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -476,23 +476,66 @@ def remove_bonds_nonpbc(data,pbc,overlap):
                 if overlap:
                     data[AtomicDataDict.EDGE_OVERLAP_KEY] = data[AtomicDataDict.EDGE_OVERLAP_KEY][mask]
 
-    def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):       
+    @staticmethod
+    def calc_principal_layers_disp_vec(coords, thr=1e-6):
+        '''
+        calculate the displacement vector between two principal layers of lead structure,
+        by substracting the coordinates of the first half atoms from the second half atoms.
+        This function can also be used to check the translational equivalence of the 
+        coordinates between two principal layers.
+
+        Parameters
+        ----------
+        coords : np.ndarray
+            The coordinates of the atoms in the lead structure.
+        thr : float, optional
+            The threshold for the translational equivalence error, by default 1e-6.
+        
+        Returns
+        -------
+        np.ndarray
+            The displacement vector between the two principal layers of the lead structure.
+        
+        Raises
+        -------
+        ValueError
+            If the number of atoms in the lead structure is not even, or if the translational
+            equivalence error is larger than the threshold.
+        '''
+        nat = coords.shape[0]
+        if nat % 2 != 0:
+            raise ValueError('The number of atoms in the lead structure must be even for dividing '
+                             'into two principal layers.')
+        R_vec = coords[int(nat/2):] - coords[:int(nat/2)]
+        # require the structure to have translational symmetry, and the atoms are arranged in two
+        # layers in the identical way
+        R_vec_mean = np.mean(R_vec, axis=0)
+        err_symm = np.linalg.norm(R_vec - R_vec_mean)/nat
+        log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}'
+                 f' (threshold: {thr:<.6e})')
+        if err_symm >= thr:
+            log.info('The atom coordinates of the lead:')
+            for pos in coords:
+                log.info('(' + ', '.join([f'{x:>10.6f}' for x in pos])+')')
+            log.info('The translational vector between two principal layers of lead:')
+            for r1, r2, v in zip(coords[int(nat/2):], coords[:int(nat/2)], R_vec):
+                log.info('(' + ', '.join([f'{x:>10.6f}' for x in r1])+')' + ' ' + \
+                         '(' + ', '.join([f'{x:>10.6f}' for x in r2])+')' + ' ' + \
+                         '->' + ' ' + \
+                         '(' + ', '.join([f'{x:>10.6f}' for x in v ])+')')
+            raise ValueError('The dpnegf requires two principal layers of lead have tight translational'
+                             ' equivalence, which means if there are 2N atoms, the second N atoms\''
+                             ' coordinates can be obtained by translating the first N atoms. Plus, the'
+                             ' second N atoms (near the device) are required to be placed in front of '
+                             'the first N atoms for performance reason.')
+        return R_vec_mean
+
+    def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         stru_lead = self.structase[self.lead_ids[kk][0]:self.lead_ids[kk][1]]
         cell = np.array(stru_lead.cell)[:2]
-        assert natom % 2 == 0, "The number of atoms in the lead should be even."
-        # translational vector between two parts (so-called principal layers) of atoms of lead
-        R_vec = stru_lead[int(natom/2):].positions - stru_lead[:int(natom/2)].positions
-        # require the structure to have translational symmetry, and the atoms are arranged in two 
-        # layers in the identical way
-        err_symm = np.linalg.norm(R_vec - np.mean(R_vec, axis=0))/(natom/2)
-        log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}')
-        if err_symm >= 1e-10: # hard-coded threshold
-            raise ValueError('DPNEGF requires two principal layers of one lead to be translationally equivalent.'
-                             'For lead with 2N atoms, ensure the second N atoms are a direct translation '
-                             'of the first N. Moreover, the second N atoms (near the device) are required'
-                             'to be placed in front of the first N atoms for implementation reason.')
-
-        R_vec = R_vec.mean(axis=0) * 2
+        # @kirk0830 recover the threshold for the translational equivalence error
+        # to 1e-5, which is the default value in the original DPNEGF code.
+        R_vec = 2 * NEGFHamiltonianInit.calc_principal_layers_disp_vec(stru_lead.positions[:natom], 1e-5)
         cell = np.concatenate([cell, R_vec.reshape(1,-1)])
         pbc_lead = self.pbc_negf.copy()
         pbc_lead[2] = True

From f69adbdfec16b139495451743a2c91d90b5c0137 Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Tue, 8 Jul 2025 09:44:37 +0800
Subject: [PATCH 072/152] Fix the typo 'substracting' in the docstring to
 'subtracting'.

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 dpnegf/negf/negf_hamiltonian_init.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 78a00a5..81d5916 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -480,7 +480,7 @@ def remove_bonds_nonpbc(data,pbc,overlap):
     def calc_principal_layers_disp_vec(coords, thr=1e-6):
         '''
         calculate the displacement vector between two principal layers of lead structure,
-        by substracting the coordinates of the first half atoms from the second half atoms.
+        by subtracting the coordinates of the first half atoms from the second half atoms.
         This function can also be used to check the translational equivalence of the 
         coordinates between two principal layers.
 

From b8cf2dc0c8b6a5644c0fa2a39c3ec85f8185026a Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 8 Jul 2025 10:10:53 +0800
Subject: [PATCH 073/152] refactor: update some info in
 cal_principal_layers_disp_vec

---
 dpnegf/negf/negf_hamiltonian_init.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 81d5916..36ee77b 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -477,7 +477,7 @@ def remove_bonds_nonpbc(data,pbc,overlap):
                     data[AtomicDataDict.EDGE_OVERLAP_KEY] = data[AtomicDataDict.EDGE_OVERLAP_KEY][mask]
 
     @staticmethod
-    def calc_principal_layers_disp_vec(coords, thr=1e-6):
+    def calc_principal_layers_disp_vec(coords, thr=1e-5, lead:str=None):
         '''
         calculate the displacement vector between two principal layers of lead structure,
         by subtracting the coordinates of the first half atoms from the second half atoms.
@@ -514,7 +514,8 @@ def calc_principal_layers_disp_vec(coords, thr=1e-6):
         log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}'
                  f' (threshold: {thr:<.6e})')
         if err_symm >= thr:
-            log.info('The atom coordinates of the lead:')
+            log.warning(f'The atom coordinates of {lead} don\'t satisfy the translational equivalence condition.')
+            log.info(f'The atom coordinates in {lead} are:')
             for pos in coords:
                 log.info('(' + ', '.join([f'{x:>10.6f}' for x in pos])+')')
             log.info('The translational vector between two principal layers of lead:')
@@ -523,11 +524,10 @@ def calc_principal_layers_disp_vec(coords, thr=1e-6):
                          '(' + ', '.join([f'{x:>10.6f}' for x in r2])+')' + ' ' + \
                          '->' + ' ' + \
                          '(' + ', '.join([f'{x:>10.6f}' for x in v ])+')')
-            raise ValueError('The dpnegf requires two principal layers of lead have tight translational'
-                             ' equivalence, which means if there are 2N atoms, the second N atoms\''
-                             ' coordinates can be obtained by translating the first N atoms. Plus, the'
-                             ' second N atoms (near the device) are required to be placed in front of '
-                             'the first N atoms for performance reason.')
+            raise ValueError('DPNEGF requires two principal layers of one lead to be translationally equivalent.'
+                             'For lead with 2N atoms, ensure the second N atoms are a direct translation '
+                             'of the first N. Moreover, the second N atoms (near the device) are required'
+                             'to be placed in front of the first N atoms for implementation reason.')
         return R_vec_mean
 
     def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
@@ -535,7 +535,7 @@ def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         cell = np.array(stru_lead.cell)[:2]
         # @kirk0830 recover the threshold for the translational equivalence error
         # to 1e-5, which is the default value in the original DPNEGF code.
-        R_vec = 2 * NEGFHamiltonianInit.calc_principal_layers_disp_vec(stru_lead.positions[:natom], 1e-5)
+        R_vec = 2 * NEGFHamiltonianInit.calc_principal_layers_disp_vec(stru_lead.positions[:natom], thr=1e-5, lead=kk)
         cell = np.concatenate([cell, R_vec.reshape(1,-1)])
         pbc_lead = self.pbc_negf.copy()
         pbc_lead[2] = True

From b49908207c4e9670b3cd9ac45e71515727cd686e Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 8 Jul 2025 10:38:06 +0800
Subject: [PATCH 074/152] feat: add additional dielectric_region parameters for
 enhanced configuration

---
 dpnegf/utils/argcheck.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index e3de713..caaabf0 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1204,6 +1204,8 @@ def pyamg():
         Argument("gate_top", dict, optional=False, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("gate_bottom", dict, optional=False, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("dielectric_region", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("dielectric_region2", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("dielectric_region3", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region", dict, optional=False, sub_fields=doped(), doc=doc_doped)
     ]
 
@@ -1230,6 +1232,8 @@ def scipy():
         Argument("lead_L", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("lead_R", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("dielectric_region", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("dielectric_region2", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("dielectric_region3", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region1", dict, optional=True, sub_fields=doped(), doc=doc_doped),
         Argument("doped_region2", dict, optional=True, sub_fields=doped(), doc=doc_doped)
     ]

From 1606a804a4a758d08797e2a5b97bfbd2483de235 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 8 Jul 2025 10:39:01 +0800
Subject: [PATCH 075/152] feat: add dielectric_region4 parameter for enhanced
 dielectric configuration

---
 dpnegf/utils/argcheck.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index caaabf0..7abbe8d 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1206,6 +1206,7 @@ def pyamg():
         Argument("dielectric_region", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("dielectric_region2", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("dielectric_region3", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("dielectric_region4", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region", dict, optional=False, sub_fields=doped(), doc=doc_doped)
     ]
 
@@ -1234,6 +1235,7 @@ def scipy():
         Argument("dielectric_region", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("dielectric_region2", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("dielectric_region3", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("dielectric_region4", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region1", dict, optional=True, sub_fields=doped(), doc=doc_doped),
         Argument("doped_region2", dict, optional=True, sub_fields=doped(), doc=doc_doped)
     ]

From ef90ff482dc3ff2c0428435b0fc3f60eb45b1285 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 8 Jul 2025 10:40:08 +0800
Subject: [PATCH 076/152] feat: add dielectric_region5 parameter for enhanced
 dielectric configuration

---
 dpnegf/utils/argcheck.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index 7abbe8d..444760a 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1207,6 +1207,7 @@ def pyamg():
         Argument("dielectric_region2", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("dielectric_region3", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("dielectric_region4", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("dielectric_region5", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region", dict, optional=False, sub_fields=doped(), doc=doc_doped)
     ]
 
@@ -1236,6 +1237,7 @@ def scipy():
         Argument("dielectric_region2", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("dielectric_region3", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("dielectric_region4", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
+        Argument("dielectric_region5", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
         Argument("doped_region1", dict, optional=True, sub_fields=doped(), doc=doc_doped),
         Argument("doped_region2", dict, optional=True, sub_fields=doped(), doc=doc_doped)
     ]

From d971cca4d32446c3320826d5041e88f6f0ba86e2 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 8 Jul 2025 10:56:10 +0800
Subject: [PATCH 077/152] feat: refactor dielectric_region arguments for
 improved readability and maintainability

---
 dpnegf/utils/argcheck.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index f3149d4..25601d3 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1206,10 +1206,10 @@ def pyamg():
         Argument("gate_left", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("gate_right", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("dielectric_region", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
-        Argument("dielectric_region2", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
-        Argument("dielectric_region3", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
-        Argument("dielectric_region4", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
-        Argument("dielectric_region5", dict, optional=False, sub_fields=dielectric(), doc=doc_dielectric),
+        *[
+            Argument(f"dielectric_region{i}", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric)
+            for i in range(2, 6)
+        ],
         Argument("doped_region", dict, optional=False, sub_fields=doped(), doc=doc_doped)
     ]
 
@@ -1238,10 +1238,10 @@ def scipy():
         Argument("lead_L", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("lead_R", dict, optional=True, sub_fields=Dirichlet_BC(), doc=doc_gate),
         Argument("dielectric_region", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
-        Argument("dielectric_region2", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
-        Argument("dielectric_region3", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
-        Argument("dielectric_region4", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
-        Argument("dielectric_region5", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric),
+        *[
+            Argument(f"dielectric_region{i}", dict, optional=True, sub_fields=dielectric(), doc=doc_dielectric)
+            for i in range(2, 6)
+        ],
         Argument("doped_region1", dict, optional=True, sub_fields=doped(), doc=doc_doped),
         Argument("doped_region2", dict, optional=True, sub_fields=doped(), doc=doc_doped)
     ]

From fda3dd20c81013b953f13dda4be0e01c99216b44 Mon Sep 17 00:00:00 2001
From: kirk0830 <yike.huang@aliyun.com>
Date: Tue, 8 Jul 2025 11:08:53 +0800
Subject: [PATCH 078/152] change to check the symmerr for each atom-pair and
 add unittest

---
 dpnegf/negf/negf_hamiltonian_init.py | 97 +++++++++++++++++++---------
 1 file changed, 67 insertions(+), 30 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 36ee77b..f1eb233 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -1,23 +1,24 @@
-from typing import List
-import torch
-from ase.io import read,write
-import logging
 import os
-import numpy as np
+import re
+import h5py
+import logging
+import unittest
+from typing import Optional, Union, List
 
+import torch
+import numpy as np
 import ase
-from dptb.data import AtomicData, AtomicDataDict
-from typing import Optional, Union
-from dptb.nn.hr2hk import HR2HK
+from ase.io import read, write
 from ase import Atoms
 from ase.build import sort
+from scipy.spatial import KDTree
+from dptb.data import AtomicData, AtomicDataDict
+from dptb.nn.hr2hk import HR2HK
+
 from dpnegf.negf.bloch import Bloch
 from dpnegf.negf.sort_btd import sort_lexico, sort_projection, sort_capacitance
 from dpnegf.negf.split_btd import show_blocks,split_into_subblocks,split_into_subblocks_optimized
 from dpnegf.negf.negf_utils import natsorted
-from scipy.spatial import KDTree
-import h5py
-import re
 
 '''
 a Hamiltonian object  that initializes and manipulates device and  lead Hamiltonians for NEGF
@@ -477,7 +478,7 @@ def remove_bonds_nonpbc(data,pbc,overlap):
                     data[AtomicDataDict.EDGE_OVERLAP_KEY] = data[AtomicDataDict.EDGE_OVERLAP_KEY][mask]
 
     @staticmethod
-    def calc_principal_layers_disp_vec(coords, thr=1e-5, lead:str=None):
+    def calc_principal_layers_disp_vec(coords, thr=1e-5):
         '''
         calculate the displacement vector between two principal layers of lead structure,
         by subtracting the coordinates of the first half atoms from the second half atoms.
@@ -506,36 +507,44 @@ def calc_principal_layers_disp_vec(coords, thr=1e-5, lead:str=None):
         if nat % 2 != 0:
             raise ValueError('The number of atoms in the lead structure must be even for dividing '
                              'into two principal layers.')
-        R_vec = coords[int(nat/2):] - coords[:int(nat/2)]
+        Rvec = coords[int(nat/2):] - coords[:int(nat/2)]
         # require the structure to have translational symmetry, and the atoms are arranged in two
         # layers in the identical way
-        R_vec_mean = np.mean(R_vec, axis=0)
-        err_symm = np.linalg.norm(R_vec - R_vec_mean)/nat
-        log.info(f'Lead principal layers translational equivalence error: {err_symm:<.6e}'
+        Rvec_mean = np.mean(Rvec, axis=0)
+        err_symm = np.linalg.norm(Rvec - Rvec_mean, axis=1)
+        log.info(f'Lead principal layers translational equivalence error (on average): {np.mean(err_symm):<.6e}'
                  f' (threshold: {thr:<.6e})')
-        if err_symm >= thr:
-            log.warning(f'The atom coordinates of {lead} don\'t satisfy the translational equivalence condition.')
-            log.info(f'The atom coordinates in {lead} are:')
-            for pos in coords:
-                log.info('(' + ', '.join([f'{x:>10.6f}' for x in pos])+')')
-            log.info('The translational vector between two principal layers of lead:')
-            for r1, r2, v in zip(coords[int(nat/2):], coords[:int(nat/2)], R_vec):
-                log.info('(' + ', '.join([f'{x:>10.6f}' for x in r1])+')' + ' ' + \
-                         '(' + ', '.join([f'{x:>10.6f}' for x in r2])+')' + ' ' + \
-                         '->' + ' ' + \
-                         '(' + ', '.join([f'{x:>10.6f}' for x in v ])+')')
+        if any(e >= thr for e in err_symm): # check on each pair of corresponding atoms
+            log.info('The translational vector between corresponding atoms in two principal layers of lead '
+                     'that exceed the threshold are:')
+            log.info(f'{"atom indexes":<12} {"layer 1 coordinates":<36} {"layer 2 coordinates":<36}'
+                     f' -> {"displacement vector":<36}')
+            log.info('-'*(12 + 36 + 36 + 36 + 6))
+            for i, (r1, r2, v, e) in enumerate(zip(coords[int(nat/2):], coords[:int(nat/2)], Rvec, err_symm)):
+                if e >= thr:
+                    log.info('(' + ', '.join([f'{x:>10.6f}' for x in r1])+')' + ' ' + \
+                             '(' + ', '.join([f'{x:>10.6f}' for x in r2])+')' + ' ' + \
+                             '->' + ' ' + \
+                             '(' + ', '.join([f'{x:>10.6f}' for x in v ])+')')
+            log.info('-'*(12 + 36 + 36 + 36 + 6))
             raise ValueError('DPNEGF requires two principal layers of one lead to be translationally equivalent.'
                              'For lead with 2N atoms, ensure the second N atoms are a direct translation '
                              'of the first N. Moreover, the second N atoms (near the device) are required'
                              'to be placed in front of the first N atoms for implementation reason.')
-        return R_vec_mean
+        return Rvec_mean
 
     def get_lead_structure(self,kk,natom,useBloch=False,bloch_factor=None):
         stru_lead = self.structase[self.lead_ids[kk][0]:self.lead_ids[kk][1]]
         cell = np.array(stru_lead.cell)[:2]
         # @kirk0830 recover the threshold for the translational equivalence error
         # to 1e-5, which is the default value in the original DPNEGF code.
-        R_vec = 2 * NEGFHamiltonianInit.calc_principal_layers_disp_vec(stru_lead.positions[:natom], thr=1e-5, lead=kk)
+        try:
+            R_vec : np.ndarray = \
+                2 * NEGFHamiltonianInit.calc_principal_layers_disp_vec(stru_lead.positions[:natom], thr=1e-5)
+        except ValueError as e:
+            log.error(f"The atom coordinates of {kk} don\'t satisfy the "
+                      f"translational equivalence condition: {e}")
+            raise
         cell = np.concatenate([cell, R_vec.reshape(1,-1)])
         pbc_lead = self.pbc_negf.copy()
         pbc_lead[2] = True
@@ -946,7 +955,35 @@ def device_norbs(self):
 
     #     return hd, hu, hl, sd, su, sl
 
-
+# pytest for the newly introduced staticmethod `calc_principal_layers_disp_vec`
+class NEGFHamiltonianInitTest(unittest.TestCase):
+    def test_calc_principal_layers_disp_vec(self):
+        # the normal case with even number of atoms
+        coords = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 1]])
+        thr = 1e-5
+        Rvec_mean = NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords, thr)
+        self.assertTrue(np.allclose(Rvec_mean, np.array([0, 0, 1]), atol=1e-6))
+
+        # the case with odd number of atoms should raise ValueError
+        coords_odd = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1]])
+        with self.assertRaises(ValueError):
+            NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_odd, thr)
+        
+        # the case with translational equivalence error larger than threshold
+        coords_error = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 2]])
+        with self.assertRaises(ValueError):
+            NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_error, thr)
+        
+        # the case with translational equivalence error smaller than threshold
+        coords_equiv = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 1 + 1e-6]])
+        Rvec_mean = NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_equiv, thr)
+        self.assertTrue(np.allclose(Rvec_mean, np.array([0, 0, 1]), atol=1e-6))
+        # however if we lower the threshold, it should raise ValueError
+        with self.assertRaises(ValueError):
+            NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_equiv, thr=1e-7)
+        
+if __name__ == "__main__":
+    unittest.main()
 
 # class _NEGFHamiltonianInit(object):
 #     '''The Class for Hamiltonian object in negf module. 

From deb822fd118dadbe0799a64d331e1c76b9a4b277 Mon Sep 17 00:00:00 2001
From: kirk0830 <yike.huang@aliyun.com>
Date: Tue, 8 Jul 2025 11:20:20 +0800
Subject: [PATCH 079/152] modify errmsg stdout

---
 dpnegf/negf/negf_hamiltonian_init.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index f1eb233..4fcc971 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -517,12 +517,13 @@ def calc_principal_layers_disp_vec(coords, thr=1e-5):
         if any(e >= thr for e in err_symm): # check on each pair of corresponding atoms
             log.info('The translational vector between corresponding atoms in two principal layers of lead '
                      'that exceed the threshold are:')
-            log.info(f'{"atom indexes":<12} {"layer 1 coordinates":<36} {"layer 2 coordinates":<36}'
-                     f' -> {"displacement vector":<36}')
+            log.info(f'{"atom indexes":<12} {"principal layer 1 atom coordinates":>36} {"principal layer 2 atom coordinates":>36}'
+                     f'    {"displacement vector":>36}')
             log.info('-'*(12 + 36 + 36 + 36 + 6))
             for i, (r1, r2, v, e) in enumerate(zip(coords[int(nat/2):], coords[:int(nat/2)], Rvec, err_symm)):
                 if e >= thr:
-                    log.info('(' + ', '.join([f'{x:>10.6f}' for x in r1])+')' + ' ' + \
+                    log.info(f'{i:>5}, {i+int(nat/2):>5}' + ' ' + \
+                             '(' + ', '.join([f'{x:>10.6f}' for x in r1])+')' + ' ' + \
                              '(' + ', '.join([f'{x:>10.6f}' for x in r2])+')' + ' ' + \
                              '->' + ' ' + \
                              '(' + ', '.join([f'{x:>10.6f}' for x in v ])+')')
@@ -982,6 +983,16 @@ def test_calc_principal_layers_disp_vec(self):
         with self.assertRaises(ValueError):
             NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_equiv, thr=1e-7)
         
+        # then a function call to ensure the normal stdout
+        for c in [coords, coords_odd, coords_error, coords_equiv]:
+            try:
+                with self.assertLogs(level='DEBUG') as log:
+                    NEGFHamiltonianInit.calc_principal_layers_disp_vec(c, thr)
+            except ValueError as e:
+                print(f"Caught expected ValueError: {e}")
+                for o in log.output:
+                    print(o)
+        
 if __name__ == "__main__":
     unittest.main()
 

From d982128a5ce3392abcc212cf7845d28b3e8b91c1 Mon Sep 17 00:00:00 2001
From: kirk0830 <yike.huang@aliyun.com>
Date: Tue, 8 Jul 2025 14:21:09 +0800
Subject: [PATCH 080/152] move unittest

---
 dpnegf/negf/negf_hamiltonian_init.py          | 40 ---------------
 .../tests/test_negf_negf_hamiltonian_init.py  | 50 +++++++++++++++----
 2 files changed, 40 insertions(+), 50 deletions(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 4fcc971..ab6f0dd 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -956,46 +956,6 @@ def device_norbs(self):
 
     #     return hd, hu, hl, sd, su, sl
 
-# pytest for the newly introduced staticmethod `calc_principal_layers_disp_vec`
-class NEGFHamiltonianInitTest(unittest.TestCase):
-    def test_calc_principal_layers_disp_vec(self):
-        # the normal case with even number of atoms
-        coords = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 1]])
-        thr = 1e-5
-        Rvec_mean = NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords, thr)
-        self.assertTrue(np.allclose(Rvec_mean, np.array([0, 0, 1]), atol=1e-6))
-
-        # the case with odd number of atoms should raise ValueError
-        coords_odd = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1]])
-        with self.assertRaises(ValueError):
-            NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_odd, thr)
-        
-        # the case with translational equivalence error larger than threshold
-        coords_error = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 2]])
-        with self.assertRaises(ValueError):
-            NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_error, thr)
-        
-        # the case with translational equivalence error smaller than threshold
-        coords_equiv = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 1 + 1e-6]])
-        Rvec_mean = NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_equiv, thr)
-        self.assertTrue(np.allclose(Rvec_mean, np.array([0, 0, 1]), atol=1e-6))
-        # however if we lower the threshold, it should raise ValueError
-        with self.assertRaises(ValueError):
-            NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_equiv, thr=1e-7)
-        
-        # then a function call to ensure the normal stdout
-        for c in [coords, coords_odd, coords_error, coords_equiv]:
-            try:
-                with self.assertLogs(level='DEBUG') as log:
-                    NEGFHamiltonianInit.calc_principal_layers_disp_vec(c, thr)
-            except ValueError as e:
-                print(f"Caught expected ValueError: {e}")
-                for o in log.output:
-                    print(o)
-        
-if __name__ == "__main__":
-    unittest.main()
-
 # class _NEGFHamiltonianInit(object):
 #     '''The Class for Hamiltonian object in negf module. 
     
diff --git a/dpnegf/tests/test_negf_negf_hamiltonian_init.py b/dpnegf/tests/test_negf_negf_hamiltonian_init.py
index 573983c..58b24ac 100644
--- a/dpnegf/tests/test_negf_negf_hamiltonian_init.py
+++ b/dpnegf/tests/test_negf_negf_hamiltonian_init.py
@@ -1,16 +1,15 @@
 # Hamiltonian
-from dpnegf.utils.make_kpoints import kmesh_sampling
-from dpnegf.negf.device_property import DeviceProperty
-from dpnegf.negf.lead_property import LeadProperty
-from dptb.nn.build import build_model
 import json
 
-
 import numpy as np
 import torch
 import pytest
-from dpnegf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
+from dptb.nn.build import build_model
 
+from dpnegf.negf.negf_hamiltonian_init import NEGFHamiltonianInit
+from dpnegf.utils.make_kpoints import kmesh_sampling
+from dpnegf.negf.device_property import DeviceProperty
+from dpnegf.negf.lead_property import LeadProperty
 
 @pytest.fixture(scope='session', autouse=True)
 def root_directory(request):
@@ -176,10 +175,41 @@ def test_negf_Hamiltonian(root_directory):
     assert na == 4
     assert hamiltonian.device_norbs==device_norbs_standard
 
-   
-
-
-
+def test_calc_principal_layers_disp_vec():
+    '''
+    unittest for static method calc_principal_layers_disp_vec of class
+    NEGFHamiltonianInit
+    '''
+    symm_thr = 1e-5
+    # test the following cases
+    # case 1: normal case
+    coords = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 1]])
+    out = NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords, symm_thr)
+    assert np.allclose(out, np.array([0, 0, 1]), atol=1e-6)
+    
+    # case 2: with odd number of atoms
+    coords_nat_odd = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1]])
+    try:
+        NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_nat_odd, symm_thr)
+    except ValueError as e:
+        assert 'The number of atoms in the lead structure must be even for' in str(e)
+    
+    # case 3: with atoms have not consistent displacement vector
+    coords_error = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 2]])
+    try:
+        NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_error, symm_thr)
+    except ValueError as e:
+        assert 'principal layers of one lead to be translationally equivalent' in str(e)
+
+    # case 4: with atoms have not consistent displacement vector but with a small error
+    coords_equiv = np.array([[0, 0, 0], [1, 1, 0], [0, 0, 1], [1, 1, 1 + 1e-6]])
+    out = NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_equiv, symm_thr)
+    assert np.allclose(out, np.array([0, 0, 1]), atol=1e-6)
+    # case 4-1: however, if the error is larger than symm_thr, it will raise an error
+    try:
+        NEGFHamiltonianInit.calc_principal_layers_disp_vec(coords_equiv, 1e-7)
+    except ValueError as e:
+        assert 'principal layers of one lead to be translationally equivalent' in str(e)
 
 # def _test_negf_Hamiltonian(root_directory):
 

From 9a907a41ff111b984b696aab764101697a2b66c0 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 8 Jul 2025 14:28:27 +0800
Subject: [PATCH 081/152] remove unused unittest

---
 dpnegf/negf/negf_hamiltonian_init.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index ab6f0dd..4aa2b59 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -2,7 +2,6 @@
 import re
 import h5py
 import logging
-import unittest
 from typing import Optional, Union, List
 
 import torch

From c97fd29dc761cecd021831934e1015bbb20d84bf Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 8 Jul 2025 15:12:26 +0800
Subject: [PATCH 082/152] refactor(test_get_fermi): update root directory
 format

---
 dpnegf/tests/test_get_fermi.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/dpnegf/tests/test_get_fermi.py b/dpnegf/tests/test_get_fermi.py
index 35b097a..fea9fb1 100644
--- a/dpnegf/tests/test_get_fermi.py
+++ b/dpnegf/tests/test_get_fermi.py
@@ -1,14 +1,18 @@
+import pytest
 from dpnegf.utils.elec_struc_cal import ElecStruCal
 from dptb.nn.build import build_model
-import os
-from pathlib import Path
 
-rootdir = os.path.join(Path(os.path.abspath(__file__)).parent, "data")
 
+@pytest.fixture(scope='session', autouse=True)
+def root_directory(request):
+    """
+    :return:
+    """
+    return str(request.config.rootdir)
 
-def test_get_fermi():
-    ckpt = f"{rootdir}/test_get_fermi/nnsk.best.pth"  #  'hopping': {'method': 'poly2exp', 'rs': 5.0, 'w': 0.6},
-    stru_data = f"{rootdir}/test_get_fermi/PRIMCELL.vasp"
+def test_get_fermi(root_directory):
+    ckpt = f"{root_directory}/dpnegf/tests/data/test_get_fermi/nnsk.best.pth"  #  'hopping': {'method': 'poly2exp', 'rs': 5.0, 'w': 0.6},
+    stru_data = f"{root_directory}/dpnegf/tests/data/test_get_fermi/PRIMCELL.vasp"
 
     model = build_model(checkpoint=ckpt)
     nel_atom = {"Au":11}

From 2bd7e0281ed334fd1957d42ba08ef9591a08e3a4 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 11 Jul 2025 18:02:49 +0800
Subject: [PATCH 083/152] feat(NEGF): add support for AtomicData_options
 extraction from model

---
 dpnegf/negf/negf_hamiltonian_init.py |  1 -
 dpnegf/runner/NEGF.py                | 11 +++++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index 4aa2b59..b190a1d 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -73,7 +73,6 @@ def __init__(self,
         self.torch_device = torch_device   
         self.model = model
         self.AtomicData_options = AtomicData_options
-        log.info(msg="The AtomicData_options is {}".format(AtomicData_options))
         self.model.eval()
         
         # get bondlist with pbc in all directions for complete chemical environment
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index ab2d4d4..c71c99b 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -130,6 +130,17 @@ def __init__(self,
             else:
                 assert self.stru_options[lead_tag]["voltage"] == 0, f"{lead_tag} voltage should be 0 in non-scf calculation"
 
+        if AtomicData_options is None:
+            from dptb.utils.argcheck import get_cutoffs_from_model_options
+            # get the cutoffs from model options
+            r_max, er_max, oer_max  = get_cutoffs_from_model_options(model.model_options)
+            AtomicData_options = {'r_max': r_max, 'er_max': er_max, 'oer_max': oer_max}
+        else:
+            log.warning(msg="AtomicData_options is extracted from input file. " \
+                            "This may not be consistent with the model options. " \
+                            "Please be carefule and check the cutoffs.")
+            log.info(msg="The AtomicData_options is {}".format(AtomicData_options))
+
         # computing the hamiltonian
         self.negf_hamiltonian = NEGFHamiltonianInit(model=model,
                                                     AtomicData_options=AtomicData_options, 

From 914a60d318b26dd7c148d058b8dd3ae9667e84be Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sat, 12 Jul 2025 10:17:57 +0800
Subject: [PATCH 084/152] feat(NEGF): add JSON logging for AtomicData_options
 and adjust parameter handling

---
 dpnegf/runner/NEGF.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index c71c99b..58b27a3 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -13,6 +13,7 @@
 import numpy as np
 from dpnegf.utils.make_kpoints import kmesh_sampling_negf
 import logging
+import json
 from dpnegf.negf.poisson_init import Grid,Interface3D,Dirichlet,Dielectric
 from dpnegf.negf.scf_method import PDIISMixer,DIISMixer,BroydenFirstMixer,BroydenSecondMixer,AndersonMixer
 from typing import Optional, Union
@@ -35,7 +36,6 @@
 class NEGF(object):
     def __init__(self, 
                 model: torch.nn.Module,
-                AtomicData_options: dict, 
                 structure: Union[AtomicData, ase.Atoms, str],
                 ele_T: float,
                 emin: float, emax: float, espacing: float,
@@ -52,6 +52,7 @@ def __init__(self,
                 out_current: bool=False,out_current_nscf: bool=False,out_ldos: bool=False,out_lcurrent: bool=False,
                 results_path: Optional[str]=None,
                 torch_device: Union[str, torch.device]=torch.device('cpu'),
+                AtomicData_options: Optional[dict]=None, 
                 **kwargs):
         
         
@@ -137,9 +138,11 @@ def __init__(self,
             AtomicData_options = {'r_max': r_max, 'er_max': er_max, 'oer_max': oer_max}
         else:
             log.warning(msg="AtomicData_options is extracted from input file. " \
-                            "This may not be consistent with the model options. " \
+                            "This may be not consistent with the model options. " \
                             "Please be carefule and check the cutoffs.")
-            log.info(msg="The AtomicData_options is {}".format(AtomicData_options))
+        formatted = json.dumps(AtomicData_options, indent=4)
+        indented = '\n'.join('               ' + line for line in formatted.splitlines())
+        log.info("The AtomicData_options is:\n%s", indented)
 
         # computing the hamiltonian
         self.negf_hamiltonian = NEGFHamiltonianInit(model=model,

From d007bafb4360c6b9c60eeb80b815f1f68e856f44 Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Sat, 12 Jul 2025 16:01:15 +0800
Subject: [PATCH 085/152] Update dpnegf/runner/NEGF.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 dpnegf/runner/NEGF.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 58b27a3..d272865 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -139,7 +139,7 @@ def __init__(self,
         else:
             log.warning(msg="AtomicData_options is extracted from input file. " \
                             "This may be not consistent with the model options. " \
-                            "Please be carefule and check the cutoffs.")
+                            "Please be careful and check the cutoffs.")
         formatted = json.dumps(AtomicData_options, indent=4)
         indented = '\n'.join('               ' + line for line in formatted.splitlines())
         log.info("The AtomicData_options is:\n%s", indented)

From ef8dc1010fc9803e956992b27a3dfc6a632b1268 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sat, 12 Jul 2025 16:04:22 +0800
Subject: [PATCH 086/152] fix(NEGF): adjust indentation for logging
 AtomicData_options

---
 dpnegf/runner/NEGF.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index d272865..4c3b2f0 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -141,7 +141,7 @@ def __init__(self,
                             "This may be not consistent with the model options. " \
                             "Please be careful and check the cutoffs.")
         formatted = json.dumps(AtomicData_options, indent=4)
-        indented = '\n'.join('               ' + line for line in formatted.splitlines())
+        indented = '\n'.join(' ' * 15 + line for line in formatted.splitlines())
         log.info("The AtomicData_options is:\n%s", indented)
 
         # computing the hamiltonian

From 4929c02ecca92c852e66483279cdd49fb9290615 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 5 Aug 2025 10:49:05 +0800
Subject: [PATCH 087/152] backup torch-based surface_green and create
 numpy-based one

---
 dpnegf/negf/surface_green.py    | 430 ++++++++++++--------------------
 dpnegf/negf/surface_green_bk.py | 312 +++++++++++++++++++++++
 2 files changed, 475 insertions(+), 267 deletions(-)
 create mode 100644 dpnegf/negf/surface_green_bk.py

diff --git a/dpnegf/negf/surface_green.py b/dpnegf/negf/surface_green.py
index c339c79..61c55c9 100644
--- a/dpnegf/negf/surface_green.py
+++ b/dpnegf/negf/surface_green.py
@@ -1,312 +1,208 @@
-import torch
-import torch.linalg as tLA
-from xitorch.linalg.solve import solve
+import numpy as np
 import scipy.linalg as SLA
-import matplotlib.pyplot as plt
-from xitorch.grad.jachess import jac
-from torch.autograd.functional import jvp
 import logging
 
 log = logging.getLogger(__name__)
 
-
-class SurfaceGreen(torch.autograd.Function):
-    '''calculate surface green function
-
-    To realize AD-NEGF, this Class is designed manually to calculate the surface green function auto-differentiably.
-    
-    At this stage, we realized Lopez-Sancho scheme and  GEP scheme.
-    However, GEP scheme is not so stable, and we strongly recommended  to implement the Lopez-Sancho scheme.
-
-    '''
-
-    @staticmethod
-    def forward(ctx, H, h01, S, s01, ee, method='Lopez-Sancho'):
-        # '''
-        # gs = [A_l - A_{l,l-1} gs A_{l-1,l}]^{-1}
-        # H : HL
-        # h01 : HLL
-        # 1. ee can be a list, to handle a batch of samples
-        # '''
-
-        if method == 'GEP':
-            gs = calcg0(ee, H, S, h01, s01)
-        else:
-            h10 = h01.conj().T
-            s10 = s01.conj().T
-            alpha, beta = h10 - ee * s10, h01 - ee * s01
-            eps, epss = H.clone(), H.clone()
-            
-            converged = False
-            iteration = 0
-            while not converged:
-                iteration += 1
-                oldeps, oldepss = eps.clone(), epss.clone()
-                oldalpha, oldbeta = alpha.clone(), beta.clone()
-                tmpa = tLA.solve(ee * S - oldeps, oldalpha)
-                tmpb = tLA.solve(ee * S - oldeps, oldbeta)
-
-                alpha, beta = torch.mm(oldalpha, tmpa), torch.mm(oldbeta, tmpb)
-                eps = oldeps + torch.mm(oldalpha, tmpb) + torch.mm(oldbeta, tmpa)
-
-                epss = oldepss + torch.mm(oldbeta, tmpa)
-                LopezConvTest = torch.max(alpha.abs() + beta.abs())
-
-                if iteration == 101:
-                    log.error("Lopez-scheme not converged after 100 iteration.")
-
-                if LopezConvTest < 1.0e-40:
-                    gs = (ee * S - epss).inverse()
-
-                    test = ee * S - H - torch.mm(ee * s01 - h01, gs.mm(ee * s10 - h10))
-                    myConvTest = torch.max((test.mm(gs) - torch.eye(H.shape[0], dtype=h01.dtype)).abs())
-                    if myConvTest < 3.0e-5:
-                        converged = True
-                        if myConvTest > 1.0e-8:
-                            log.warning("Lopez-scheme not-so-well converged at E = %.4f eV:" % ee.real.item() + str(myConvTest.item()))
-                    else:
-                        log.error("Lopez-Sancho %.8f " % myConvTest.item() +
-                              "Error: gs iteration {0}".format(iteration))
-                        raise ArithmeticError("Criteria not met. Please check output...")
-                    
-        ctx.save_for_backward(gs, H, h01, S, s01, ee)
-        return gs
-
-    @staticmethod
-    def backward(ctx, grad_outputs):
-        gs_, H_, h01_, S_, s01_, ee_ = ctx.saved_tensors
-
-        def sgfn(gs, *params):
-            [H, h01, S, s01, ee] = params
-            return tLA.inv(ee*S - H - (ee*s01 - h01).matmul(gs).matmul(ee*s01.conj().T - h01.conj().T)) - gs
-
-        params = [H_, h01_, S_, s01_, ee_]
-        idx = [i for i in range(len(params)) if params[i].requires_grad]
-        params_copy = [p.detach().requires_grad_() for p in params]
-
-        with torch.enable_grad():
-
-            grad = jac(fcn=sgfn, params=(gs_, *params), idxs=[0])[0] # dfdz
-            pre = solve(A=grad.H, B=-grad_outputs.reshape(-1, 1))
-            pre = pre.reshape(grad_outputs.shape)
-            
-            yfcn = sgfn(gs_, *params_copy)
-
-            grad = torch.autograd.grad(yfcn, [params_copy[i] for i in idx], grad_outputs=pre,
-                                                         create_graph=torch.is_grad_enabled(),
-                                                         allow_unused=True)
-
-        # grad = torch.autograd.grad(yfcn, params_copy, grad_outputs=pre,
-        #                         create_graph=torch.is_grad_enabled(),
-        #                         allow_unused=True)
-
-            grad_out = [None for _ in range(len(params))]
-            for i in range(len(idx)):
-                grad_out[idx[i]] = grad[i]
-
-
-            '''
-            2. Is the matrix index direction correct? Also, is T necessarily becomes H when comes to complex matrix?
-            '''
-            # return *grad, None, None
-            return *grad_out, None
-
-    @staticmethod
-    def jvp(ctx, grad_input):
-        # should be of shape as [H, h01, S, s01, ee]
-        gs_, H_, h01_, S_, s01_, ee_ = ctx.saved_tensors
-        left = ctx.left
-
-        if left:
-            def sgfn(gs, *params):
-                [H, h01, S, s01, ee] = params
-                return tLA.inv(ee*S-H-(ee*s01.conj().T-h01.conj().T).matmul(gs).matmul(ee*s01-h01)) - gs
-        else:
-            def sgfn(gs, *params):
-                [H, h01, S, s01, ee] = params
-                return tLA.inv(ee*S - H - (ee*s01 - h01).matmul(gs).matmul(ee*s01.conj().T - h01.conj().T)) - gs
-        
-        yfcn = sgfn(gs_, *params_copy)
-
-        params = [H_, h01_, S_, s01_, ee_]
-        idx = [i for i in range(len(params)) if params[i].requires_grad]
-        params_copy = [p.detach().requires_grad_() for p in params]
-
-        with torch.enable_grad():
-            _, grad_fw = jvp(func=yfcn, inputs=[params_copy[i] for i in idx], v=[grad_input[i] for i in idx], create_graph=torch.is_grad_enabled())
-            dfdy = jac(fcn=sgfn, params=(gs_, *params), idxs=[0])[0]
-
-            out = [solve(A=dfdy, B=-gf.reshape(-1, 1)).conj().reshape(gf.shape) for gf in grad_fw]
-
-            return torch.mean(out, dim=0)
-
 def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=False, 
-               E_ref=0.0, dtype=torch.complex128, device='cpu', method='Lopez-Sancho'):
-    '''calculates the self-energy and surface Green's function for a given  Hamiltonian and overlap matrix.
-    
-    Parameters
-    ----------
-    hL
-        Hamiltonian matrix for one principal layer in Lead
-    hLL
-        Hamiltonian matrix between the most nearby principal layers in Lead
-    sL
-        Overlap matrix for one principal layer in Lead
-    sLL
-        Overlap matrix between the most nearby principal layers in Lead
-    ee
-        the given energy
-    hDL
-        Hamiltonian matrix between the lead and the device.   
-    sDL
-        Overlap matrix between the lead and the device.
-    etaLead
-        A small imaginary number that is used to avoid the singularity of the surface Green's function.
-    Bulk, optional
-        Ignore it, please.
-    chemiPot
-        the chemical potential of the lead.
-    dtype
-        the data type of the tensors used in the calculations. 
-    device
-        The "device" parameter specifies the device on which the calculations will be performed. It can be
-        set to 'cpu' for CPU computation or 'cuda' for GPU computation.
-    method
-        specify the method for calculating the surface Green's function.The available options 
-        are "Lopez-Sancho" and any other value will default to "Lopez-Sancho".
-    
-    Returns
-    -------
-        two values: Sig and SGF. The former is self-energy and the latter is surface Green's function.
-    
+                    E_ref=0.0, dtype=np.complex128, device='cpu', method='Lopez-Sancho'):
     '''
-    # if not isinstance(ee, torch.Tensor):
-    #     eeshifted = torch.scalar_tensor(ee, dtype=dtype) - voltage  # Shift of self energies due to voltage(V)
-    # else:
-    #     eeshifted = ee - voltage
-
-    if not isinstance(ee, torch.Tensor):
-        eeshifted = torch.scalar_tensor(ee, dtype=dtype) + E_ref
+    Calculates the self-energy and surface Green's function for a given Hamiltonian and overlap matrix.
+    NumPy-based rewrite of the PyTorch selfEnergy function.
+    '''
+    # 确保输入是NumPy数组
+    hL = np.array(hL)
+    sL = np.array(sL)
+    
+    # 处理 ee
+    if not isinstance(ee, np.ndarray):
+        eeshifted = np.array(ee, dtype=dtype) + E_ref
     else:
         eeshifted = ee + E_ref
-        
-
-    if hDL == None:
+    
+    # 添加一个小的虚部以避免奇点
+    eeshifted = eeshifted + 1j * etaLead
+    
+    if hDL is None:
         ESH = (eeshifted * sL - hL)
-        with torch.no_grad():
-            SGF = SurfaceGreen.apply(hL, hLL, sL, sLL, eeshifted + 1j * etaLead, method)
-
+        # 调用 NumPy 版本的 surface_green_numpy
+        SGF = surface_green(hL, hLL, sL, sLL, eeshifted, method)
+        
         if Bulk:
-            Sig = tLA.inv(SGF)  # SGF^1
+            Sig = np.linalg.inv(SGF)
         else:
-            Sig = ESH - tLA.inv(SGF)
+            Sig = ESH - np.linalg.inv(SGF)
     else:
+        hDL = np.array(hDL)
+        sDL = np.array(sDL)
         a, b = hDL.shape
-        with torch.no_grad():
-            SGF = SurfaceGreen.apply(hL, hLL, sL, sLL, eeshifted + 1j * etaLead, method)
-        #SGF = iterative_simple(eeshifted + 1j * etaLead, hL, hLL, sL, sLL, iter_max=1000)
+        SGF = surface_green(hL, hLL, sL, sLL, eeshifted, method)
+        
         Sig = (eeshifted*sDL-hDL) @ SGF[:b,:b] @ (eeshifted*sDL.conj().T-hDL.conj().T)
-    return Sig, SGF  # R(nuo, nuo)
+    
+    return Sig, SGF
 
 
-def calcg0(ee, h00, s00, h01, s01):
-    '''The `calcg0` function calculates the surface Green's function for a specific |k> , ref. Euro Phys J B 62, 381 (2008)
-        Inverse of : NOTE, setup for "right" lead.
-        e-h00 -h01  ...
-        -h10  e-h11 ...
-         .
-         .
-         .
+def surface_green(H, h01, S, s01, ee, method='Lopez-Sancho'):
+    '''
+    Calculate surface green function using NumPy.
 
-    Parameters
-    ----------
-    ee
-        The parameter `ee` represents the energy value for which the surface Green's function is
-    calculated. It is a complex number that determines the energy of the state being considered.
-    h00
-        hamiltonian matrix within principal layer
-    s00
-        overlap matrix within principal layer
-    h01
-        hamiltonian matrix between two adject principal layers
-    s01
-        overlap matrix between two adject principal layers
-    
-    Returns
-    -------
-        Surface Green's function `g00`.
+    This function is a NumPy-based rewrite of the PyTorch SurfaceGreen.forward method.
+    '''
     
-    ''' 
+    # 将输入的张量转换为NumPy数组
+    H = np.array(H)
+    h01 = np.array(h01)
+    S = np.array(S)
+    s01 = np.array(s01)
+    ee = np.array(ee)
+
+    # 确保 ee 是一个复数，以便处理复数运算
+    if not np.iscomplexobj(ee):
+        ee = np.complex128(ee)
+
+    if method == 'GEP':
+        # 调用 NumPy 版本的 calcg0
+        gs = calcg0_numpy(ee, H, S, h01, s01)
+    else:
+        h10 = h01.conj().T
+        s10 = s01.conj().T
+        alpha, beta = h10 - ee * s10, h01 - ee * s01
+        eps, epss = H.copy(), H.copy()
+        
+        converged = False
+        iteration = 0
+        while not converged:
+            iteration += 1
+            oldeps, oldepss = eps.copy(), epss.copy()
+            oldalpha, oldbeta = alpha.copy(), beta.copy()
+            
+            # 使用 numpy.linalg.solve 替换 torch.linalg.solve
+            tmpa = np.linalg.solve(ee * S - oldeps, oldalpha)
+            tmpb = np.linalg.solve(ee * S - oldeps, oldbeta)
+
+            # 使用 @ 运算符进行矩阵乘法
+            alpha, beta = oldalpha @ tmpa, oldbeta @ tmpb
+            eps = oldeps + oldalpha @ tmpb + oldbeta @ tmpa
+            epss = oldepss + oldbeta @ tmpa
+            
+            LopezConvTest = np.max(np.abs(alpha) + np.abs(beta))
+
+            if iteration == 101:
+                log.error("Lopez-scheme not converged after 100 iteration.")
+                raise RuntimeError("Lopez-scheme not converged.")
+
+            if LopezConvTest < 1.0e-40:
+                # 使用 numpy.linalg.inv 替换 tensor.inverse()
+                gs = np.linalg.inv(ee * S - epss)
+
+                test = ee * S - H - (ee * s01 - h01) @ gs @ (ee * s10 - h10)
+                myConvTest = np.max(np.abs(test @ gs - np.eye(H.shape[0], dtype=H.dtype)))
+                
+                if myConvTest < 3.0e-5:
+                    converged = True
+                    if myConvTest > 1.0e-8:
+                        log.warning("Lopez-scheme not-so-well converged at E = %.4f eV:" % ee.real.item() + str(myConvTest.item()))
+                else:
+                    log.error("Lopez-Sancho %.8f " % myConvTest.item() +
+                                "Error: gs iteration {0}".format(iteration))
+                    raise ArithmeticError("Criteria not met. Please check output...")
+                
+    return gs
 
 
-    NN, ee = h00.shape[0], ee.real + max(torch.max(ee.imag).item(), 1e-8) * 1.0j
+def calcg0_numpy(ee, h00, s00, h01, s01):
+    '''
+    The `calcg0_numpy` function calculates the surface Green's function for a specific |k> , ref. Euro Phys J B 62, 381 (2008)
+    NumPy-based rewrite of the PyTorch calcg0 function.
+    ''' 
+    # 将输入张量转换为 NumPy 数组
+    h00 = np.array(h00)
+    s00 = np.array(s00)
+    h01 = np.array(h01)
+    s01 = np.array(s01)
+    ee = np.array(ee)
+    
+    NN = h00.shape[0]
+    ee = ee.real + max(ee.imag, 1e-8) * 1.0j
 
     # Solve generalized eigen-problem
-    # ( e I - h00 , -I) (eps)          (h01 , 0) (eps)
-    # ( h10       ,  0) (xi ) = lambda (0   , I) (xi )
-    
-    a, b = torch.zeros((2 * NN, 2 * NN), dtype=h00.dtype), torch.zeros((2 * NN, 2 * NN),
-                                                                             dtype=h00.dtype)
+    a, b = np.zeros((2 * NN, 2 * NN), dtype=h00.dtype), np.zeros((2 * NN, 2 * NN), dtype=h00.dtype)
     
     a[0:NN, 0:NN] = ee * s00 - h00
-    a[0:NN, NN:2 * NN] = -torch.eye(NN, dtype=h00.dtype)
+    a[0:NN, NN:2 * NN] = -np.eye(NN, dtype=h00.dtype)
     a[NN:2 * NN, 0:NN] = h01.conj().T - ee * s01.conj().T
     b[0:NN, 0:NN] = h01 - ee * s01
-    b[NN:2 * NN, NN:2 * NN] = torch.eye(NN, dtype=h00.dtype)
-
-    
-
+    b[NN:2 * NN, NN:2 * NN] = np.eye(NN, dtype=h00.dtype)
 
+    # 使用 scipy.linalg.eig 替换 PyTorch 版本
     ev, evec = SLA.eig(a=a, b=b)
-    ev = torch.tensor(ev, dtype=h00.dtype)
-    evec = torch.tensor(evec, dtype=h00.dtype)
-    # ev = torch.complex(real=torch.tensor(ev.real), imag=torch.tensor(ev.imag))
-    # evec = torch.complex(real=torch.tensor(evec.real), imag=torch.tensor(evec.imag))
-
-    # Select lambda <0 and the eps part of the evec
-    ipiv = torch.where(ev.abs() < 1.)[0]
+    
+    # 选择 ev 绝对值小于 1 的特征值及其对应的特征向量
+    ipiv = np.where(np.abs(ev) < 1.)[0]
+    ev, evec = ev[ipiv], evec[:, ipiv]
 
-    ev, evec = ev[ipiv], evec[:NN, ipiv].T
     # Normalize evec
-    norm = torch.diag(torch.mm(evec, evec.conj().T)).sqrt()
-    evec = torch.mm(torch.diag(1.0 / norm), evec)
+    norm = np.sqrt(np.diag(evec.conj().T @ evec))
+    evec = evec @ np.diag(1.0 / norm)
 
     # E^+ Lambda_+ (E^+)^-1 --->>> g00
-    EP = evec.T
-    FP = EP.mm(torch.diag(ev)).mm(torch.inverse(torch.mm(EP.conj().T, EP))).mm(EP.conj().T)
-    g00 = torch.inverse(ee * s00 - h00 - torch.mm(h01 - ee * s01, FP))
-
-    g00 = iterative_gf(ee, g00, h00, h01, s00, s01, iter=3)
+    EP = evec
+    FP = EP @ np.diag(ev) @ np.linalg.inv(EP.conj().T @ EP) @ EP.conj().T
+    g00 = np.linalg.inv(ee * s00 - h00 - (h01 - ee * s01) @ FP)
+    
+    g00 = iterative_gf_numpy(ee, g00, h00, h01, s00, s01, iter=3)
 
-    # Check!
-    err = torch.max(torch.abs(g00 - torch.inverse(ee * s00 - h00 - \
-                                                  torch.mm(h01 - ee * s01, g00).mm(
-                                                      h01.conj().T - ee * s01.conj().T))))
+    err = np.max(np.abs(g00 - np.linalg.inv(ee * s00 - h00 - \
+                                            (h01 - ee * s01) @ g00 @ (h01.conj().T - ee * s01.conj().T))))
     if err > 1.0e-8:
-        print("WARNING: not-so-well converged for RIGHT electrode at E = {0} eV:".format(ee.real.numpy()), err.numpy())
+        print("WARNING: not-so-well converged for RIGHT electrode at E = {0} eV:".format(ee.real), err)
+    
     return g00
 
-
-def iterative_gf(ee, gs, h00, h01, s00, s01, iter=1):
+def iterative_gf_numpy(ee, gs, h00, h01, s00, s01, iter=1):
+    '''
+    NumPy-based rewrite of the PyTorch iterative_gf function.
+    '''
+    # 将输入张量转换为 NumPy 数组
+    gs = np.array(gs)
+    h00 = np.array(h00)
+    h01 = np.array(h01)
+    s00 = np.array(s00)
+    s01 = np.array(s01)
+    ee = np.array(ee)
+    
     for i in range(iter):
-        gs = ee*s00 - h00 - (ee * s01 - h01) @ gs @ (ee * s01.conj().T - h01.conj().T)
-        gs = tLA.pinv(gs)
-
+        gs_new = ee*s00 - h00 - (ee * s01 - h01) @ gs @ (ee * s01.conj().T - h01.conj().T)
+        gs = np.linalg.pinv(gs_new)
+    
     return gs
 
-def iterative_simple(ee, h00, h01, s00, s01, iter_max=1000):
-    gs =  torch.linalg.inv(ee*s00 - h00)
+def iterative_simple_numpy(ee, h00, h01, s00, s01, iter_max=1000):
+    '''
+    NumPy-based rewrite of the PyTorch iterative_simple function.
+    '''
+    # 将输入张量转换为 NumPy 数组
+    h00 = np.array(h00)
+    h01 = np.array(h01)
+    s00 = np.array(s00)
+    s01 = np.array(s01)
+    ee = np.array(ee)
+    
+    gs = np.linalg.inv(ee*s00 - h00)
     diff_gs = 1
-    iter = 0
+    iteration = 0
     while diff_gs > 1e-8:
-        iter +=1
-        gs = ee*s00 - h00 - (ee * s01 - h01) @ gs @ (ee * s01.conj().T - h01.conj().T)
-        # gs = tLA.pinv(gs)
-        gs = torch.linalg.inv(gs)
-        diff_gs = \
-        torch.max(torch.abs(gs - torch.inverse(ee * s00 - h00 - torch.mm(h01 - ee * s01, gs).mm(h01.conj().T - ee * s01.conj().T))))
-        if iter > iter_max:
+        iteration += 1
+        gs_prev = gs.copy()
+        
+        term = (ee * s01 - h01) @ gs_prev @ (ee * s01.conj().T - h01.conj().T)
+        gs = np.linalg.inv(ee*s00 - h00 - term)
+        
+        diff_gs = np.max(np.abs(gs - gs_prev))
+        
+        if iteration > iter_max:
             log.warning("iterative_simple not converged after 1000 iteration.")
             break
-
+            
     return gs
\ No newline at end of file
diff --git a/dpnegf/negf/surface_green_bk.py b/dpnegf/negf/surface_green_bk.py
new file mode 100644
index 0000000..c339c79
--- /dev/null
+++ b/dpnegf/negf/surface_green_bk.py
@@ -0,0 +1,312 @@
+import torch
+import torch.linalg as tLA
+from xitorch.linalg.solve import solve
+import scipy.linalg as SLA
+import matplotlib.pyplot as plt
+from xitorch.grad.jachess import jac
+from torch.autograd.functional import jvp
+import logging
+
+log = logging.getLogger(__name__)
+
+
+class SurfaceGreen(torch.autograd.Function):
+    '''calculate surface green function
+
+    To realize AD-NEGF, this Class is designed manually to calculate the surface green function auto-differentiably.
+    
+    At this stage, we realized Lopez-Sancho scheme and  GEP scheme.
+    However, GEP scheme is not so stable, and we strongly recommended  to implement the Lopez-Sancho scheme.
+
+    '''
+
+    @staticmethod
+    def forward(ctx, H, h01, S, s01, ee, method='Lopez-Sancho'):
+        # '''
+        # gs = [A_l - A_{l,l-1} gs A_{l-1,l}]^{-1}
+        # H : HL
+        # h01 : HLL
+        # 1. ee can be a list, to handle a batch of samples
+        # '''
+
+        if method == 'GEP':
+            gs = calcg0(ee, H, S, h01, s01)
+        else:
+            h10 = h01.conj().T
+            s10 = s01.conj().T
+            alpha, beta = h10 - ee * s10, h01 - ee * s01
+            eps, epss = H.clone(), H.clone()
+            
+            converged = False
+            iteration = 0
+            while not converged:
+                iteration += 1
+                oldeps, oldepss = eps.clone(), epss.clone()
+                oldalpha, oldbeta = alpha.clone(), beta.clone()
+                tmpa = tLA.solve(ee * S - oldeps, oldalpha)
+                tmpb = tLA.solve(ee * S - oldeps, oldbeta)
+
+                alpha, beta = torch.mm(oldalpha, tmpa), torch.mm(oldbeta, tmpb)
+                eps = oldeps + torch.mm(oldalpha, tmpb) + torch.mm(oldbeta, tmpa)
+
+                epss = oldepss + torch.mm(oldbeta, tmpa)
+                LopezConvTest = torch.max(alpha.abs() + beta.abs())
+
+                if iteration == 101:
+                    log.error("Lopez-scheme not converged after 100 iteration.")
+
+                if LopezConvTest < 1.0e-40:
+                    gs = (ee * S - epss).inverse()
+
+                    test = ee * S - H - torch.mm(ee * s01 - h01, gs.mm(ee * s10 - h10))
+                    myConvTest = torch.max((test.mm(gs) - torch.eye(H.shape[0], dtype=h01.dtype)).abs())
+                    if myConvTest < 3.0e-5:
+                        converged = True
+                        if myConvTest > 1.0e-8:
+                            log.warning("Lopez-scheme not-so-well converged at E = %.4f eV:" % ee.real.item() + str(myConvTest.item()))
+                    else:
+                        log.error("Lopez-Sancho %.8f " % myConvTest.item() +
+                              "Error: gs iteration {0}".format(iteration))
+                        raise ArithmeticError("Criteria not met. Please check output...")
+                    
+        ctx.save_for_backward(gs, H, h01, S, s01, ee)
+        return gs
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        gs_, H_, h01_, S_, s01_, ee_ = ctx.saved_tensors
+
+        def sgfn(gs, *params):
+            [H, h01, S, s01, ee] = params
+            return tLA.inv(ee*S - H - (ee*s01 - h01).matmul(gs).matmul(ee*s01.conj().T - h01.conj().T)) - gs
+
+        params = [H_, h01_, S_, s01_, ee_]
+        idx = [i for i in range(len(params)) if params[i].requires_grad]
+        params_copy = [p.detach().requires_grad_() for p in params]
+
+        with torch.enable_grad():
+
+            grad = jac(fcn=sgfn, params=(gs_, *params), idxs=[0])[0] # dfdz
+            pre = solve(A=grad.H, B=-grad_outputs.reshape(-1, 1))
+            pre = pre.reshape(grad_outputs.shape)
+            
+            yfcn = sgfn(gs_, *params_copy)
+
+            grad = torch.autograd.grad(yfcn, [params_copy[i] for i in idx], grad_outputs=pre,
+                                                         create_graph=torch.is_grad_enabled(),
+                                                         allow_unused=True)
+
+        # grad = torch.autograd.grad(yfcn, params_copy, grad_outputs=pre,
+        #                         create_graph=torch.is_grad_enabled(),
+        #                         allow_unused=True)
+
+            grad_out = [None for _ in range(len(params))]
+            for i in range(len(idx)):
+                grad_out[idx[i]] = grad[i]
+
+
+            '''
+            2. Is the matrix index direction correct? Also, is T necessarily becomes H when comes to complex matrix?
+            '''
+            # return *grad, None, None
+            return *grad_out, None
+
+    @staticmethod
+    def jvp(ctx, grad_input):
+        # should be of shape as [H, h01, S, s01, ee]
+        gs_, H_, h01_, S_, s01_, ee_ = ctx.saved_tensors
+        left = ctx.left
+
+        if left:
+            def sgfn(gs, *params):
+                [H, h01, S, s01, ee] = params
+                return tLA.inv(ee*S-H-(ee*s01.conj().T-h01.conj().T).matmul(gs).matmul(ee*s01-h01)) - gs
+        else:
+            def sgfn(gs, *params):
+                [H, h01, S, s01, ee] = params
+                return tLA.inv(ee*S - H - (ee*s01 - h01).matmul(gs).matmul(ee*s01.conj().T - h01.conj().T)) - gs
+        
+        yfcn = sgfn(gs_, *params_copy)
+
+        params = [H_, h01_, S_, s01_, ee_]
+        idx = [i for i in range(len(params)) if params[i].requires_grad]
+        params_copy = [p.detach().requires_grad_() for p in params]
+
+        with torch.enable_grad():
+            _, grad_fw = jvp(func=yfcn, inputs=[params_copy[i] for i in idx], v=[grad_input[i] for i in idx], create_graph=torch.is_grad_enabled())
+            dfdy = jac(fcn=sgfn, params=(gs_, *params), idxs=[0])[0]
+
+            out = [solve(A=dfdy, B=-gf.reshape(-1, 1)).conj().reshape(gf.shape) for gf in grad_fw]
+
+            return torch.mean(out, dim=0)
+
+def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=False, 
+               E_ref=0.0, dtype=torch.complex128, device='cpu', method='Lopez-Sancho'):
+    '''calculates the self-energy and surface Green's function for a given  Hamiltonian and overlap matrix.
+    
+    Parameters
+    ----------
+    hL
+        Hamiltonian matrix for one principal layer in Lead
+    hLL
+        Hamiltonian matrix between the most nearby principal layers in Lead
+    sL
+        Overlap matrix for one principal layer in Lead
+    sLL
+        Overlap matrix between the most nearby principal layers in Lead
+    ee
+        the given energy
+    hDL
+        Hamiltonian matrix between the lead and the device.   
+    sDL
+        Overlap matrix between the lead and the device.
+    etaLead
+        A small imaginary number that is used to avoid the singularity of the surface Green's function.
+    Bulk, optional
+        Ignore it, please.
+    chemiPot
+        the chemical potential of the lead.
+    dtype
+        the data type of the tensors used in the calculations. 
+    device
+        The "device" parameter specifies the device on which the calculations will be performed. It can be
+        set to 'cpu' for CPU computation or 'cuda' for GPU computation.
+    method
+        specify the method for calculating the surface Green's function.The available options 
+        are "Lopez-Sancho" and any other value will default to "Lopez-Sancho".
+    
+    Returns
+    -------
+        two values: Sig and SGF. The former is self-energy and the latter is surface Green's function.
+    
+    '''
+    # if not isinstance(ee, torch.Tensor):
+    #     eeshifted = torch.scalar_tensor(ee, dtype=dtype) - voltage  # Shift of self energies due to voltage(V)
+    # else:
+    #     eeshifted = ee - voltage
+
+    if not isinstance(ee, torch.Tensor):
+        eeshifted = torch.scalar_tensor(ee, dtype=dtype) + E_ref
+    else:
+        eeshifted = ee + E_ref
+        
+
+    if hDL == None:
+        ESH = (eeshifted * sL - hL)
+        with torch.no_grad():
+            SGF = SurfaceGreen.apply(hL, hLL, sL, sLL, eeshifted + 1j * etaLead, method)
+
+        if Bulk:
+            Sig = tLA.inv(SGF)  # SGF^1
+        else:
+            Sig = ESH - tLA.inv(SGF)
+    else:
+        a, b = hDL.shape
+        with torch.no_grad():
+            SGF = SurfaceGreen.apply(hL, hLL, sL, sLL, eeshifted + 1j * etaLead, method)
+        #SGF = iterative_simple(eeshifted + 1j * etaLead, hL, hLL, sL, sLL, iter_max=1000)
+        Sig = (eeshifted*sDL-hDL) @ SGF[:b,:b] @ (eeshifted*sDL.conj().T-hDL.conj().T)
+    return Sig, SGF  # R(nuo, nuo)
+
+
+def calcg0(ee, h00, s00, h01, s01):
+    '''The `calcg0` function calculates the surface Green's function for a specific |k> , ref. Euro Phys J B 62, 381 (2008)
+        Inverse of : NOTE, setup for "right" lead.
+        e-h00 -h01  ...
+        -h10  e-h11 ...
+         .
+         .
+         .
+
+    Parameters
+    ----------
+    ee
+        The parameter `ee` represents the energy value for which the surface Green's function is
+    calculated. It is a complex number that determines the energy of the state being considered.
+    h00
+        hamiltonian matrix within principal layer
+    s00
+        overlap matrix within principal layer
+    h01
+        hamiltonian matrix between two adject principal layers
+    s01
+        overlap matrix between two adject principal layers
+    
+    Returns
+    -------
+        Surface Green's function `g00`.
+    
+    ''' 
+
+
+    NN, ee = h00.shape[0], ee.real + max(torch.max(ee.imag).item(), 1e-8) * 1.0j
+
+    # Solve generalized eigen-problem
+    # ( e I - h00 , -I) (eps)          (h01 , 0) (eps)
+    # ( h10       ,  0) (xi ) = lambda (0   , I) (xi )
+    
+    a, b = torch.zeros((2 * NN, 2 * NN), dtype=h00.dtype), torch.zeros((2 * NN, 2 * NN),
+                                                                             dtype=h00.dtype)
+    
+    a[0:NN, 0:NN] = ee * s00 - h00
+    a[0:NN, NN:2 * NN] = -torch.eye(NN, dtype=h00.dtype)
+    a[NN:2 * NN, 0:NN] = h01.conj().T - ee * s01.conj().T
+    b[0:NN, 0:NN] = h01 - ee * s01
+    b[NN:2 * NN, NN:2 * NN] = torch.eye(NN, dtype=h00.dtype)
+
+    
+
+
+    ev, evec = SLA.eig(a=a, b=b)
+    ev = torch.tensor(ev, dtype=h00.dtype)
+    evec = torch.tensor(evec, dtype=h00.dtype)
+    # ev = torch.complex(real=torch.tensor(ev.real), imag=torch.tensor(ev.imag))
+    # evec = torch.complex(real=torch.tensor(evec.real), imag=torch.tensor(evec.imag))
+
+    # Select lambda <0 and the eps part of the evec
+    ipiv = torch.where(ev.abs() < 1.)[0]
+
+    ev, evec = ev[ipiv], evec[:NN, ipiv].T
+    # Normalize evec
+    norm = torch.diag(torch.mm(evec, evec.conj().T)).sqrt()
+    evec = torch.mm(torch.diag(1.0 / norm), evec)
+
+    # E^+ Lambda_+ (E^+)^-1 --->>> g00
+    EP = evec.T
+    FP = EP.mm(torch.diag(ev)).mm(torch.inverse(torch.mm(EP.conj().T, EP))).mm(EP.conj().T)
+    g00 = torch.inverse(ee * s00 - h00 - torch.mm(h01 - ee * s01, FP))
+
+    g00 = iterative_gf(ee, g00, h00, h01, s00, s01, iter=3)
+
+    # Check!
+    err = torch.max(torch.abs(g00 - torch.inverse(ee * s00 - h00 - \
+                                                  torch.mm(h01 - ee * s01, g00).mm(
+                                                      h01.conj().T - ee * s01.conj().T))))
+    if err > 1.0e-8:
+        print("WARNING: not-so-well converged for RIGHT electrode at E = {0} eV:".format(ee.real.numpy()), err.numpy())
+    return g00
+
+
+def iterative_gf(ee, gs, h00, h01, s00, s01, iter=1):
+    for i in range(iter):
+        gs = ee*s00 - h00 - (ee * s01 - h01) @ gs @ (ee * s01.conj().T - h01.conj().T)
+        gs = tLA.pinv(gs)
+
+    return gs
+
+def iterative_simple(ee, h00, h01, s00, s01, iter_max=1000):
+    gs =  torch.linalg.inv(ee*s00 - h00)
+    diff_gs = 1
+    iter = 0
+    while diff_gs > 1e-8:
+        iter +=1
+        gs = ee*s00 - h00 - (ee * s01 - h01) @ gs @ (ee * s01.conj().T - h01.conj().T)
+        # gs = tLA.pinv(gs)
+        gs = torch.linalg.inv(gs)
+        diff_gs = \
+        torch.max(torch.abs(gs - torch.inverse(ee * s00 - h00 - torch.mm(h01 - ee * s01, gs).mm(h01.conj().T - ee * s01.conj().T))))
+        if iter > iter_max:
+            log.warning("iterative_simple not converged after 1000 iteration.")
+            break
+
+    return gs
\ No newline at end of file

From c2e5990902ae727b828da7aa9459fa39be355460 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 5 Aug 2025 11:26:08 +0800
Subject: [PATCH 088/152] feat(surface_green): rewrite selfEnergy and
 surface_green functions to support NumPy and add conversion from torch
 tensors

---
 dpnegf/negf/surface_green.py | 152 ++++++++++++++++++++++++-----------
 1 file changed, 104 insertions(+), 48 deletions(-)

diff --git a/dpnegf/negf/surface_green.py b/dpnegf/negf/surface_green.py
index 61c55c9..925e973 100644
--- a/dpnegf/negf/surface_green.py
+++ b/dpnegf/negf/surface_green.py
@@ -1,69 +1,103 @@
 import numpy as np
 import scipy.linalg as SLA
 import logging
+import torch
 
 log = logging.getLogger(__name__)
 
 def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=False, 
                     E_ref=0.0, dtype=np.complex128, device='cpu', method='Lopez-Sancho'):
-    '''
-    Calculates the self-energy and surface Green's function for a given Hamiltonian and overlap matrix.
-    NumPy-based rewrite of the PyTorch selfEnergy function.
+    '''calculates the self-energy and surface Green's function for a given  Hamiltonian and overlap matrix.
+    
+    Parameters
+    ----------
+    hL
+        Hamiltonian matrix for one principal layer in Lead
+    hLL
+        Hamiltonian matrix between the most nearby principal layers in Lead
+    sL
+        Overlap matrix for one principal layer in Lead
+    sLL
+        Overlap matrix between the most nearby principal layers in Lead
+    ee
+        the given energy
+    hDL
+        Hamiltonian matrix between the lead and the device.   
+    sDL
+        Overlap matrix between the lead and the device.
+    etaLead
+        A small imaginary number that is used to avoid the singularity of the surface Green's function.
+    Bulk, optional
+        Ignore it, please.
+    chemiPot
+        the chemical potential of the lead.
+    dtype
+        the data type of the tensors used in the calculations. 
+    device
+        The "device" parameter specifies the device on which the calculations will be performed. It can be
+        set to 'cpu' for CPU computation or 'cuda' for GPU computation.
+    method
+        specify the method for calculating the surface Green's function.The available options 
+        are "Lopez-Sancho" and any other value will default to "Lopez-Sancho".
+    
+    Returns
+    -------
+        two values: Sig and SGF. The former is self-energy and the latter is surface Green's function.
+    
     '''
     # 确保输入是NumPy数组
-    hL = np.array(hL)
-    sL = np.array(sL)
-    
+    hL = convert_to_numpy(hL)
+    sL = convert_to_numpy(sL)
+    hLL = convert_to_numpy(hLL)
+    sLL = convert_to_numpy(sLL)
+    ee = convert_to_numpy(ee)
+    if hDL is not None:
+        hDL = convert_to_numpy(hDL)
+    if sDL is not None:
+        sDL = convert_to_numpy(sDL)
+    E_ref = convert_to_numpy(E_ref)
+
+
     # 处理 ee
     if not isinstance(ee, np.ndarray):
         eeshifted = np.array(ee, dtype=dtype) + E_ref
     else:
         eeshifted = ee + E_ref
     
-    # 添加一个小的虚部以避免奇点
-    eeshifted = eeshifted + 1j * etaLead
     
     if hDL is None:
         ESH = (eeshifted * sL - hL)
-        # 调用 NumPy 版本的 surface_green_numpy
-        SGF = surface_green(hL, hLL, sL, sLL, eeshifted, method)
+        SGF = surface_green(hL, hLL, sL, sLL, eeshifted + 1j * etaLead , method)
         
         if Bulk:
             Sig = np.linalg.inv(SGF)
         else:
             Sig = ESH - np.linalg.inv(SGF)
     else:
-        hDL = np.array(hDL)
-        sDL = np.array(sDL)
         a, b = hDL.shape
-        SGF = surface_green(hL, hLL, sL, sLL, eeshifted, method)
+        SGF = surface_green(hL, hLL, sL, sLL, eeshifted + 1j * etaLead , method)
         
         Sig = (eeshifted*sDL-hDL) @ SGF[:b,:b] @ (eeshifted*sDL.conj().T-hDL.conj().T)
     
+    Sig = torch.tensor(Sig, dtype=torch.complex128, device=device)
+    SGF = torch.tensor(SGF, dtype=torch.complex128, device=device)
+    
     return Sig, SGF
 
 
 def surface_green(H, h01, S, s01, ee, method='Lopez-Sancho'):
-    '''
-    Calculate surface green function using NumPy.
+    '''calculate surface green function
 
-    This function is a NumPy-based rewrite of the PyTorch SurfaceGreen.forward method.
-    '''
     
-    # 将输入的张量转换为NumPy数组
-    H = np.array(H)
-    h01 = np.array(h01)
-    S = np.array(S)
-    s01 = np.array(s01)
-    ee = np.array(ee)
+    At this stage, we realized Lopez-Sancho scheme and  GEP scheme.
+    However, GEP scheme is not so stable, and we strongly recommended  to implement the Lopez-Sancho scheme.
 
-    # 确保 ee 是一个复数，以便处理复数运算
+    '''
     if not np.iscomplexobj(ee):
         ee = np.complex128(ee)
 
     if method == 'GEP':
-        # 调用 NumPy 版本的 calcg0
-        gs = calcg0_numpy(ee, H, S, h01, s01)
+        gs = calcg0(ee, H, S, h01, s01)
     else:
         h10 = h01.conj().T
         s10 = s01.conj().T
@@ -76,16 +110,13 @@ def surface_green(H, h01, S, s01, ee, method='Lopez-Sancho'):
             iteration += 1
             oldeps, oldepss = eps.copy(), epss.copy()
             oldalpha, oldbeta = alpha.copy(), beta.copy()
-            
-            # 使用 numpy.linalg.solve 替换 torch.linalg.solve
             tmpa = np.linalg.solve(ee * S - oldeps, oldalpha)
             tmpb = np.linalg.solve(ee * S - oldeps, oldbeta)
 
-            # 使用 @ 运算符进行矩阵乘法
             alpha, beta = oldalpha @ tmpa, oldbeta @ tmpb
             eps = oldeps + oldalpha @ tmpb + oldbeta @ tmpa
-            epss = oldepss + oldbeta @ tmpa
             
+            epss = oldepss + oldbeta @ tmpa
             LopezConvTest = np.max(np.abs(alpha) + np.abs(beta))
 
             if iteration == 101:
@@ -111,17 +142,34 @@ def surface_green(H, h01, S, s01, ee, method='Lopez-Sancho'):
     return gs
 
 
-def calcg0_numpy(ee, h00, s00, h01, s01):
-    '''
-    The `calcg0_numpy` function calculates the surface Green's function for a specific |k> , ref. Euro Phys J B 62, 381 (2008)
-    NumPy-based rewrite of the PyTorch calcg0 function.
+def calcg0(ee, h00, s00, h01, s01):
+    '''The `calcg0` function calculates the surface Green's function for a specific |k> , ref. Euro Phys J B 62, 381 (2008)
+        Inverse of : NOTE, setup for "right" lead.
+        e-h00 -h01  ...
+        -h10  e-h11 ...
+         .
+         .
+         .
+
+    Parameters
+    ----------
+    ee
+        The parameter `ee` represents the energy value for which the surface Green's function is
+    calculated. It is a complex number that determines the energy of the state being considered.
+    h00
+        hamiltonian matrix within principal layer
+    s00
+        overlap matrix within principal layer
+    h01
+        hamiltonian matrix between two adject principal layers
+    s01
+        overlap matrix between two adject principal layers
+    
+    Returns
+    -------
+        Surface Green's function `g00`.
+    
     ''' 
-    # 将输入张量转换为 NumPy 数组
-    h00 = np.array(h00)
-    s00 = np.array(s00)
-    h01 = np.array(h01)
-    s01 = np.array(s01)
-    ee = np.array(ee)
     
     NN = h00.shape[0]
     ee = ee.real + max(ee.imag, 1e-8) * 1.0j
@@ -135,19 +183,18 @@ def calcg0_numpy(ee, h00, s00, h01, s01):
     b[0:NN, 0:NN] = h01 - ee * s01
     b[NN:2 * NN, NN:2 * NN] = np.eye(NN, dtype=h00.dtype)
 
-    # 使用 scipy.linalg.eig 替换 PyTorch 版本
+
     ev, evec = SLA.eig(a=a, b=b)
-    
-    # 选择 ev 绝对值小于 1 的特征值及其对应的特征向量
+
     ipiv = np.where(np.abs(ev) < 1.)[0]
-    ev, evec = ev[ipiv], evec[:, ipiv]
+    ev, evec = ev[ipiv], evec[:NN, ipiv].T
 
     # Normalize evec
-    norm = np.sqrt(np.diag(evec.conj().T @ evec))
-    evec = evec @ np.diag(1.0 / norm)
+    norm = np.sqrt(np.diag(evec @ evec.conj().T)) 
+    evec = np.diag(1.0 / norm) @ evec
 
     # E^+ Lambda_+ (E^+)^-1 --->>> g00
-    EP = evec
+    EP = evec.T
     FP = EP @ np.diag(ev) @ np.linalg.inv(EP.conj().T @ EP) @ EP.conj().T
     g00 = np.linalg.inv(ee * s00 - h00 - (h01 - ee * s01) @ FP)
     
@@ -205,4 +252,13 @@ def iterative_simple_numpy(ee, h00, h01, s00, s01, iter_max=1000):
             log.warning("iterative_simple not converged after 1000 iteration.")
             break
             
-    return gs
\ No newline at end of file
+    return gs
+
+def convert_to_numpy(data):
+    if isinstance(data, torch.Tensor):
+        return data.detach().numpy()
+    elif isinstance(data, np.ndarray):
+        return data
+    else:
+        log.error("Unsupported data type: {}".format(type(data)))
+        return data
\ No newline at end of file

From e6af1120c5adab57ec02bd9b50cb047d1a26ed68 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 5 Aug 2025 14:56:17 +0800
Subject: [PATCH 089/152] feat(surface_green): add Numba JIT compilation for
 surface Green's function calculations and fallback to NumPy

---
 dpnegf/negf/surface_green.py | 137 +++++++++++++++++++++++++++--------
 1 file changed, 107 insertions(+), 30 deletions(-)

diff --git a/dpnegf/negf/surface_green.py b/dpnegf/negf/surface_green.py
index 925e973..6fffd6a 100644
--- a/dpnegf/negf/surface_green.py
+++ b/dpnegf/negf/surface_green.py
@@ -2,6 +2,7 @@
 import scipy.linalg as SLA
 import logging
 import torch
+from numba import njit, float64, complex128, int64
 
 log = logging.getLogger(__name__)
 
@@ -85,24 +86,19 @@ def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=Fals
     return Sig, SGF
 
 
-def surface_green(H, h01, S, s01, ee, method='Lopez-Sancho'):
-    '''calculate surface green function
-
-    
-    At this stage, we realized Lopez-Sancho scheme and  GEP scheme.
-    However, GEP scheme is not so stable, and we strongly recommended  to implement the Lopez-Sancho scheme.
+_numba_available = False
 
-    '''
-    if not np.iscomplexobj(ee):
-        ee = np.complex128(ee)
+try:
+    from numba import njit, complex128
 
-    if method == 'GEP':
-        gs = calcg0(ee, H, S, h01, s01)
-    else:
-        h10 = h01.conj().T
-        s10 = s01.conj().T
+    @njit(complex128[:,:](complex128[:,:], complex128[:,:], complex128[:,:], complex128[:,:], complex128))
+    def _surface_green_numba_core(H, h01, S, s01, ee):
+        # 将 PyTorch 的 h10 = h01.conj().T 逻辑转换为 NumPy
+        h10 = np.conj(h01.T)
+        s10 = np.conj(s01.T)
         alpha, beta = h10 - ee * s10, h01 - ee * s01
-        eps, epss = H.copy(), H.copy()
+        eps = H.copy()
+        epss = H.copy()
         
         converged = False
         iteration = 0
@@ -112,36 +108,117 @@ def surface_green(H, h01, S, s01, ee, method='Lopez-Sancho'):
             oldalpha, oldbeta = alpha.copy(), beta.copy()
             tmpa = np.linalg.solve(ee * S - oldeps, oldalpha)
             tmpb = np.linalg.solve(ee * S - oldeps, oldbeta)
-
-            alpha, beta = oldalpha @ tmpa, oldbeta @ tmpb
-            eps = oldeps + oldalpha @ tmpb + oldbeta @ tmpa
             
+            alpha = oldalpha @ tmpa
+            beta = oldbeta @ tmpb
+            eps = oldeps + oldalpha @ tmpb + oldbeta @ tmpa
             epss = oldepss + oldbeta @ tmpa
             LopezConvTest = np.max(np.abs(alpha) + np.abs(beta))
 
-            if iteration == 101:
-                log.error("Lopez-scheme not converged after 100 iteration.")
-                raise RuntimeError("Lopez-scheme not converged.")
-
             if LopezConvTest < 1.0e-40:
-                # 使用 numpy.linalg.inv 替换 tensor.inverse()
+                # np.linalg.inv() 等价于 PyTorch 的 .inverse()
                 gs = np.linalg.inv(ee * S - epss)
-
-                test = ee * S - H - (ee * s01 - h01) @ gs @ (ee * s10 - h10)
-                myConvTest = np.max(np.abs(test @ gs - np.eye(H.shape[0], dtype=H.dtype)))
                 
+                test = ee * S - H - (ee * s01 - h01) @ gs @ (ee * s10 - h10)
+                myConvTest = np.max(np.abs((test @ gs) - np.eye(H.shape[0], dtype=h01.dtype)))
+
                 if myConvTest < 3.0e-5:
                     converged = True
                     if myConvTest > 1.0e-8:
-                        log.warning("Lopez-scheme not-so-well converged at E = %.4f eV:" % ee.real.item() + str(myConvTest.item()))
+                        # 返回结果和警告标志
+                        return gs, 1, myConvTest, ee.real
+                    else:
+                        # 返回结果和成功标志
+                        return gs, 0, 0, 0
                 else:
-                    log.error("Lopez-Sancho %.8f " % myConvTest.item() +
-                                "Error: gs iteration {0}".format(iteration))
-                    raise ArithmeticError("Criteria not met. Please check output...")
+                    raise ArithmeticError(f"Criteria not met with value {myConvTest:.8f} at iteration {iteration}.")
+        
+            if iteration >= 101:
+                raise RuntimeError("Lopez-scheme not converged after 100 iteration.")
                 
+        return gs
+
+    _numba_available = True
+    log.info("Numba is available and JIT functions are compiled.")
+
+except (ImportError, Exception) as e:
+    log.warning(f"Numba acceleration is not available. Falling back to pure NumPy. Error: {e}")
+    _numba_available = False
+
+# --- 纯 NumPy 版本核心函数（作为回退） ---
+def _surface_green_numpy_core(H, h01, S, s01, ee):
+    h10 = np.conj(h01.T)
+    s10 = np.conj(s01.T)
+    alpha, beta = h10 - ee * s10, h01 - ee * s01
+    
+    eps, epss = H.copy(), H.copy()
+    
+    converged = False
+    iteration = 0
+    
+    while not converged:
+        iteration += 1
+        oldeps, oldepss = eps.copy(), epss.copy()
+        oldalpha, oldbeta = alpha.copy(), beta.copy()
+        tmpa = np.linalg.solve(ee * S - oldeps, oldalpha)
+        tmpb = np.linalg.solve(ee * S - oldeps, oldbeta)
+        
+        alpha = oldalpha @ tmpa
+        beta = oldbeta @ tmpb
+        eps = oldeps + oldalpha @ tmpb + oldbeta @ tmpa
+        epss = oldepss + oldbeta @ tmpa
+        
+        LopezConvTest = np.max(np.abs(alpha) + np.abs(beta))
+
+        if LopezConvTest < 1.0e-40:
+            gs = np.linalg.inv(ee * S - epss)
+            
+            test = ee * S - H - (ee * s01 - h01) @ gs @ (ee * s10 - h10)
+            myConvTest = np.max(np.abs((test @ gs) - np.eye(H.shape[0], dtype=h01.dtype)))
+            
+            if myConvTest < 3.0e-5:
+                converged = True
+                if myConvTest > 1.0e-8:
+                    log.warning(f"Lopez-scheme not-so-well converged at E = {ee.real:.4f} eV: {myConvTest}")
+            else:
+                log.error(f"Lopez-Sancho {myConvTest:.8f} Error: gs iteration {iteration}")
+                raise ArithmeticError("Criteria not met. Please check output...")
+        
+        if iteration >= 101:
+            log.error("Lopez-scheme not converged after 100 iteration.")
+            raise RuntimeError("Lopez-scheme not converged.")
+            
     return gs
 
 
+def surface_green(H, h01, S, s01, ee, 
+                  method='Lopez-Sancho',
+                  numba_jit=True):
+    '''calculate surface green function
+    At this stage, we realized Lopez-Sancho scheme and  GEP scheme.
+    However, GEP scheme is not so stable, and we strongly recommended  to implement the Lopez-Sancho scheme.
+
+    '''
+
+    if method == 'GEP':
+        gs = calcg0(ee, H, S, h01, s01)
+        return gs
+    else: # Lopez-Sancho scheme
+        if numba_jit and _numba_available:
+            try:
+                gs, conv_flag, conv_test, e_real = _surface_green_numba_core(H, h01, S, s01, ee)
+                if conv_flag == 1:
+                    log.warning(f"Lopez-Sancho scheme not-so-well converged at E = {e_real:.4f} eV: {conv_test}")
+                return gs
+            except (RuntimeError, ArithmeticError) as e:
+                log.error(f"Numba JIT function failed at runtime. Falling back to NumPy. Error: {e}")
+                return _surface_green_numpy_core(H, h01, S, s01, ee)
+        else:
+            return _surface_green_numpy_core(H, h01, S, s01, ee)
+                
+
+
+
 def calcg0(ee, h00, s00, h01, s01):
     '''The `calcg0` function calculates the surface Green's function for a specific |k> , ref. Euro Phys J B 62, 381 (2008)
         Inverse of : NOTE, setup for "right" lead.

From 81bcc924067e74b0c73fdce81a0c94d601fec4c7 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 5 Aug 2025 16:47:18 +0800
Subject: [PATCH 090/152] feat(surface_green): enhance selfEnergy and
 surface_green functions with type and dimension checks for input validation

---
 dpnegf/negf/surface_green.py | 41 ++++++++++++++++++++++++++++--------
 1 file changed, 32 insertions(+), 9 deletions(-)

diff --git a/dpnegf/negf/surface_green.py b/dpnegf/negf/surface_green.py
index 6fffd6a..713b305 100644
--- a/dpnegf/negf/surface_green.py
+++ b/dpnegf/negf/surface_green.py
@@ -59,12 +59,13 @@ def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=Fals
     E_ref = convert_to_numpy(E_ref)
 
 
-    # 处理 ee
+    
     if not isinstance(ee, np.ndarray):
         eeshifted = np.array(ee, dtype=dtype) + E_ref
     else:
         eeshifted = ee + E_ref
     
+    eeshifted = eeshifted.item()
     
     if hDL is None:
         ESH = (eeshifted * sL - hL)
@@ -88,10 +89,13 @@ def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=Fals
 
 _numba_available = False
 
+
 try:
-    from numba import njit, complex128
+    from numba import njit, complex128, int64, float64
+    from numba.types import Tuple
+    NumbaReturnType = Tuple((complex128[:,:], int64, float64, float64))
 
-    @njit(complex128[:,:](complex128[:,:], complex128[:,:], complex128[:,:], complex128[:,:], complex128))
+    @njit(NumbaReturnType(complex128[:,:], complex128[:,:], complex128[:,:], complex128[:,:], complex128))
     def _surface_green_numba_core(H, h01, S, s01, ee):
         # 将 PyTorch 的 h10 = h01.conj().T 逻辑转换为 NumPy
         h10 = np.conj(h01.T)
@@ -131,10 +135,10 @@ def _surface_green_numba_core(H, h01, S, s01, ee):
                         # 返回结果和成功标志
                         return gs, 0, 0, 0
                 else:
-                    raise ArithmeticError(f"Criteria not met with value {myConvTest:.8f} at iteration {iteration}.")
+                    raise ArithmeticError
         
             if iteration >= 101:
-                raise RuntimeError("Lopez-scheme not converged after 100 iteration.")
+                raise RuntimeError
                 
         return gs
 
@@ -145,7 +149,7 @@ def _surface_green_numba_core(H, h01, S, s01, ee):
     log.warning(f"Numba acceleration is not available. Falling back to pure NumPy. Error: {e}")
     _numba_available = False
 
-# --- 纯 NumPy 版本核心函数（作为回退） ---
+# NumPy-based implementation of the surface Green's function calculation
 def _surface_green_numpy_core(H, h01, S, s01, ee):
     h10 = np.conj(h01.T)
     s10 = np.conj(s01.T)
@@ -206,6 +210,26 @@ def surface_green(H, h01, S, s01, ee,
     else: # Lopez-Sancho scheme
         if numba_jit and _numba_available:
             try:
+                # check
+                # 1. type check
+                assert isinstance(H, np.ndarray), "H must be a NumPy array."
+                assert isinstance(h01, np.ndarray), "h01 must be a NumPy array."
+                assert isinstance(S, np.ndarray), "S must be a NumPy array."
+                assert isinstance(s01, np.ndarray), "s01 must be a NumPy array."
+                assert isinstance(ee, (complex, float, int)), "ee must be a complex, float, or integer scalar."
+
+                # 2. dimension check
+                assert H.ndim == 2, "H must be a 2D array."
+                assert h01.ndim == 2, "h01 must be a 2D array."
+                assert S.ndim == 2, "S must be a 2D array."
+                assert s01.ndim == 2, "s01 must be a 2D array."
+
+                # 3. complex type check
+                assert np.iscomplexobj(H), "H must be a complex array."
+                assert np.iscomplexobj(h01), "h01 must be a complex array."
+                assert np.iscomplexobj(S), "S must be a complex array."
+                assert np.iscomplexobj(s01), "s01 must be a complex array."
+                assert isinstance(ee, complex), "ee must be a complex scalar."
                 gs, conv_flag, conv_test, e_real = _surface_green_numba_core(H, h01, S, s01, ee)
                 if conv_flag == 1:
                     log.warning(f"Lopez-Sancho scheme not-so-well converged at E = {e_real:.4f} eV: {conv_test}")
@@ -336,6 +360,5 @@ def convert_to_numpy(data):
         return data.detach().numpy()
     elif isinstance(data, np.ndarray):
         return data
-    else:
-        log.error("Unsupported data type: {}".format(type(data)))
-        return data
\ No newline at end of file
+    elif isinstance(data, float):
+        return np.array(data)

From 1826ba8c7a38e39af3294de4b92d5825e262bd28 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 5 Aug 2025 17:05:18 +0800
Subject: [PATCH 091/152] fix(negf_hamiltonian): prevent NaN values in
 translational equivalence error calculation and add error logging

---
 dpnegf/negf/negf_hamiltonian_init.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/dpnegf/negf/negf_hamiltonian_init.py b/dpnegf/negf/negf_hamiltonian_init.py
index b190a1d..28868cc 100644
--- a/dpnegf/negf/negf_hamiltonian_init.py
+++ b/dpnegf/negf/negf_hamiltonian_init.py
@@ -509,7 +509,12 @@ def calc_principal_layers_disp_vec(coords, thr=1e-5):
         # require the structure to have translational symmetry, and the atoms are arranged in two
         # layers in the identical way
         Rvec_mean = np.mean(Rvec, axis=0)
-        err_symm = np.linalg.norm(Rvec - Rvec_mean, axis=1)
+        err_symm = np.linalg.norm(Rvec - Rvec_mean + 1e-10, axis=1) # 1e-10 to avoid zero division
+        if np.any(np.isnan(err_symm)):
+            log.error("Calculation of translational equivalence error resulted in NaN values. "
+                    "This may result from wrong input settings.")
+            raise ValueError
+
         log.info(f'Lead principal layers translational equivalence error (on average): {np.mean(err_symm):<.6e}'
                  f' (threshold: {thr:<.6e})')
         if any(e >= thr for e in err_symm): # check on each pair of corresponding atoms

From 4ea294b5cc35973521c4d19d18db8e7c882fb982 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 6 Aug 2025 14:15:18 +0800
Subject: [PATCH 092/152] feat(surface_green): update NumPy-based surface
 Green's function implementation as scipy-based and adjust fallback logic for
 Numba JIT

---
 dpnegf/negf/surface_green.py | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/dpnegf/negf/surface_green.py b/dpnegf/negf/surface_green.py
index 713b305..e9d970d 100644
--- a/dpnegf/negf/surface_green.py
+++ b/dpnegf/negf/surface_green.py
@@ -97,7 +97,7 @@ def selfEnergy(hL, hLL, sL, sLL, ee, hDL=None, sDL=None, etaLead=1e-8, Bulk=Fals
 
     @njit(NumbaReturnType(complex128[:,:], complex128[:,:], complex128[:,:], complex128[:,:], complex128))
     def _surface_green_numba_core(H, h01, S, s01, ee):
-        # 将 PyTorch 的 h10 = h01.conj().T 逻辑转换为 NumPy
+
         h10 = np.conj(h01.T)
         s10 = np.conj(s01.T)
         alpha, beta = h10 - ee * s10, h01 - ee * s01
@@ -106,10 +106,12 @@ def _surface_green_numba_core(H, h01, S, s01, ee):
         
         converged = False
         iteration = 0
+        oldeps, oldepss = np.empty_like(eps), np.empty_like(epss)
+        oldalpha, oldbeta = np.empty_like(alpha), np.empty_like(beta)
         while not converged:
             iteration += 1
-            oldeps, oldepss = eps.copy(), epss.copy()
-            oldalpha, oldbeta = alpha.copy(), beta.copy()
+            oldeps[:], oldepss[:] = eps, epss
+            oldalpha[:], oldbeta[:] = alpha, beta
             tmpa = np.linalg.solve(ee * S - oldeps, oldalpha)
             tmpb = np.linalg.solve(ee * S - oldeps, oldbeta)
             
@@ -128,11 +130,9 @@ def _surface_green_numba_core(H, h01, S, s01, ee):
 
                 if myConvTest < 3.0e-5:
                     converged = True
-                    if myConvTest > 1.0e-8:
-                        # 返回结果和警告标志
+                    if myConvTest > 1.0e-8: # warning threshold
                         return gs, 1, myConvTest, ee.real
                     else:
-                        # 返回结果和成功标志
                         return gs, 0, 0, 0
                 else:
                     raise ArithmeticError
@@ -149,8 +149,8 @@ def _surface_green_numba_core(H, h01, S, s01, ee):
     log.warning(f"Numba acceleration is not available. Falling back to pure NumPy. Error: {e}")
     _numba_available = False
 
-# NumPy-based implementation of the surface Green's function calculation
-def _surface_green_numpy_core(H, h01, S, s01, ee):
+# Scipy-based implementation of the surface Green's function calculation
+def _surface_green_scipy_core(H, h01, S, s01, ee):
     h10 = np.conj(h01.T)
     s10 = np.conj(s01.T)
     alpha, beta = h10 - ee * s10, h01 - ee * s01
@@ -159,19 +159,19 @@ def _surface_green_numpy_core(H, h01, S, s01, ee):
     
     converged = False
     iteration = 0
-    
+    oldeps, oldepss = np.empty_like(eps), np.empty_like(epss)
+    oldalpha, oldbeta = np.empty_like(alpha), np.empty_like(beta)
     while not converged:
         iteration += 1
-        oldeps, oldepss = eps.copy(), epss.copy()
-        oldalpha, oldbeta = alpha.copy(), beta.copy()
-        tmpa = np.linalg.solve(ee * S - oldeps, oldalpha)
-        tmpb = np.linalg.solve(ee * S - oldeps, oldbeta)
+        oldeps[:], oldepss[:] = eps, epss
+        oldalpha[:], oldbeta[:] = alpha, beta
+        tmpa = SLA.solve(ee * S - oldeps, oldalpha)
+        tmpb = SLA.solve(ee * S - oldeps, oldbeta)
         
         alpha = oldalpha @ tmpa
         beta = oldbeta @ tmpb
         eps = oldeps + oldalpha @ tmpb + oldbeta @ tmpa
         epss = oldepss + oldbeta @ tmpa
-        
         LopezConvTest = np.max(np.abs(alpha) + np.abs(beta))
 
         if LopezConvTest < 1.0e-40:
@@ -197,7 +197,7 @@ def _surface_green_numpy_core(H, h01, S, s01, ee):
 
 def surface_green(H, h01, S, s01, ee, 
                   method='Lopez-Sancho',
-                  numba_jit=True):
+                  numba_jit=False):
     '''calculate surface green function
     At this stage, we realized Lopez-Sancho scheme and  GEP scheme.
     However, GEP scheme is not so stable, and we strongly recommended  to implement the Lopez-Sancho scheme.
@@ -236,9 +236,9 @@ def surface_green(H, h01, S, s01, ee,
                 return gs
             except (RuntimeError, ArithmeticError) as e:
                 log.error(f"Numba JIT function failed at runtime. Falling back to NumPy. Error: {e}")
-                return _surface_green_numpy_core(H, h01, S, s01, ee)
+                return _surface_green_scipy_core(H, h01, S, s01, ee)
         else:
-            return _surface_green_numpy_core(H, h01, S, s01, ee)
+            return _surface_green_scipy_core(H, h01, S, s01, ee)
                 
 
 

From a6f10b14a99749f55332ae959b4e3d7997a4db9a Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 6 Aug 2025 14:42:23 +0800
Subject: [PATCH 093/152] enable lead H/S in memory when calculating self
 energy

---
 dpnegf/negf/lead_property.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 2708bf9..ebbad7d 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -100,7 +100,7 @@ def __init__(self, tab, hamiltonian, structure, results_path, voltage, \
 
     def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-Sancho", \
                     save: bool=False, save_path: str=None, se_info_display: bool=False,
-                    HS_inmem: bool=False):
+                    HS_inmem: bool=True):
         '''calculate and loads the self energy and surface green function at the given kpoint and energy.
         
         Parameters

From 8d633ed0bbb4f8477658fdd8b4b778ba85414108 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 6 Aug 2025 14:44:04 +0800
Subject: [PATCH 094/152] feat(pyproject): add numba as a dependency

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 76e467d..9ebdff0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,6 +54,7 @@ h5py = ">=3.7.0,<=3.11.0,!=3.10.0"
 lmdb = "1.4.1"
 pyfiglet = "1.0.2"
 tensorboard = "*"
+numba = "*"
 
 
 [tool.poetry.group.pybinding]

From 02b829f2f578626f34f3682b4b6edcf81e1e67b5 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 6 Aug 2025 15:31:50 +0800
Subject: [PATCH 095/152] multiprocessing.Pool implementation

---
 dpnegf/runner/NEGF.py | 33 ++++++++++++++++++++++++++++-----
 1 file changed, 28 insertions(+), 5 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 4c3b2f0..d481777 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -18,7 +18,10 @@
 from dpnegf.negf.scf_method import PDIISMixer,DIISMixer,BroydenFirstMixer,BroydenSecondMixer,AndersonMixer
 from typing import Optional, Union
 from dpnegf.utils.tools import apply_gaussian_filter_3d
-# from pyinstrument import Profiler
+from pyinstrument import Profiler
+import os
+from multiprocessing import Pool, cpu_count
+from dpnegf.utils.tools import self_energy_worker
 
 log = logging.getLogger(__name__)
 
@@ -401,7 +404,13 @@ def compute(self):
             self.negf_compute(scf_require=False,Vbias=self.potential_at_orb)
         
         else:
+            profiler = Profiler()
+            profiler.start() 
             self.negf_compute(scf_require=False,Vbias=None)
+            profiler.stop()
+            output_path = os.path.join(self.results_path, "profile_report.html")
+            with open(output_path, 'w') as report_file:
+                report_file.write(profiler.output_html())
 
     def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_iter=1000,
                          mix_method:str='linear', mix_rate:float=0.3, tolerance:float=1e-7,Gaussian_sigma:float=3.0):
@@ -506,6 +515,18 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
         #     profiler.stop()
         #     with open('profile_report.html', 'w') as report_file:
         #         report_file.write(profiler.output_html())
+    
+
+    def compute_all_self_energy(self, kpoint_grid, energy_grid, n_processes=None):
+        if n_processes is None:
+            n_processes = min(4, cpu_count())  # 默认使用最多 4个进程，或者系统的CPU核心数
+        args_list = [
+            (k, e, self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R)
+            for k in kpoint_grid
+            for e in energy_grid
+        ]
+        with Pool(processes=n_processes) as pool:
+            pool.map(self_energy_worker, args_list)
 
     def negf_compute(self,scf_require=False,Vbias=None):
         
@@ -515,6 +536,7 @@ def negf_compute(self,scf_require=False,Vbias=None):
         self.out['k']=[];self.out['wk']=[]
         if hasattr(self, "uni_grid"): self.out["uni_grid"] = self.uni_grid
 
+
         if scf_require and self.poisson_options["with_Dirichlet_leads"]:
             # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
             # In each iteration, the self-energy of the leads is not updated.
@@ -524,10 +546,11 @@ def negf_compute(self,scf_require=False,Vbias=None):
                     self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
         elif not self.scf:
             # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
-            for ik, k in enumerate(self.kpoints): 
-                for e in self.uni_grid:
-                    self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
-                    self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+            # for ik, k in enumerate(self.kpoints): 
+            #     for e in self.uni_grid:
+            #         self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+            #         self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+            self.compute_all_self_energy(self.kpoints, self.uni_grid)
     
         for ik, k in enumerate(self.kpoints):
 

From 5266f7bd65a5ef01ee915acc3d4904235f70e215 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 6 Aug 2025 15:57:23 +0800
Subject: [PATCH 096/152] feat(NEGF): refactor self-energy computation to use
 joblib for parallel processing

---
 dpnegf/runner/NEGF.py | 43 ++++++++++++++++++++++++++-----------------
 dpnegf/utils/tools.py |  5 +++++
 2 files changed, 31 insertions(+), 17 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index d481777..2d8f9e3 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -22,6 +22,7 @@
 import os
 from multiprocessing import Pool, cpu_count
 from dpnegf.utils.tools import self_energy_worker
+from joblib import Parallel, delayed
 
 log = logging.getLogger(__name__)
 
@@ -517,16 +518,27 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
         #         report_file.write(profiler.output_html())
     
 
-    def compute_all_self_energy(self, kpoint_grid, energy_grid, n_processes=None):
-        if n_processes is None:
-            n_processes = min(4, cpu_count())  # 默认使用最多 4个进程，或者系统的CPU核心数
-        args_list = [
-            (k, e, self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R)
-            for k in kpoint_grid
+    def compute_all_self_energy(self, kpoints_grid, energy_grid, n_jobs=-1):
+        """
+        Compute the self-energy for all combinations of k-points and energy values in parallel using joblib.
+        Parameters:
+            kpoints_grid (Iterable): An iterable of k-point values to compute self-energy for.
+            energy_grid (Iterable): An iterable of energy values to compute self-energy for.
+            n_jobs (int, optional): The number of parallel jobs to run. Defaults to -1 (use all available cores).
+        Notes:
+            This method uses joblib's Parallel to distribute the computation of self-energy across multiple processes.
+            The worker function `self_energy_worker` must be serializable and defined at the top level.
+        """
+        eta = self.eta_lead
+        lead_L = self.deviceprop.lead_L
+        lead_R = self.deviceprop.lead_R
+
+        # joblib 要求 worker 函数是顶层函数或可序列化
+        Parallel(n_jobs=n_jobs, backend="loky")(
+            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+            for k in kpoints_grid
             for e in energy_grid
-        ]
-        with Pool(processes=n_processes) as pool:
-            pool.map(self_energy_worker, args_list)
+        )
 
     def negf_compute(self,scf_require=False,Vbias=None):
         
@@ -540,16 +552,13 @@ def negf_compute(self,scf_require=False,Vbias=None):
         if scf_require and self.poisson_options["with_Dirichlet_leads"]:
             # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
             # In each iteration, the self-energy of the leads is not updated.
-            for ik, k in enumerate(self.kpoints):
-                for e in self.density.integrate_range:
-                    self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
-                    self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
-        elif not self.scf:
-            # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
-            # for ik, k in enumerate(self.kpoints): 
-            #     for e in self.uni_grid:
+            # for ik, k in enumerate(self.kpoints):
+            #     for e in self.density.integrate_range:
             #         self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
             #         self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+            self.compute_all_self_energy(self.kpoints, self.density.integrate_range)
+        elif not self.scf:
+            # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
             self.compute_all_self_energy(self.kpoints, self.uni_grid)
     
         for ik, k in enumerate(self.kpoints):
diff --git a/dpnegf/utils/tools.py b/dpnegf/utils/tools.py
index d976900..4663f26 100644
--- a/dpnegf/utils/tools.py
+++ b/dpnegf/utils/tools.py
@@ -802,3 +802,8 @@ def apply_gaussian_filter_3d(phi_vector, shape, sigma):
 
     # Flatten back to 1D
     return phi_3d_filtered.ravel()
+
+
+def self_energy_worker(k, e, eta, lead_L, lead_R):
+    lead_L.self_energy(kpoint=k, energy=e, eta_lead=eta, save=True)
+    lead_R.self_energy(kpoint=k, energy=e, eta_lead=eta, save=True)
\ No newline at end of file

From bf240314532a096b085b0fce86826f9948a6828a Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 6 Aug 2025 15:57:55 +0800
Subject: [PATCH 097/152] feat(pyproject): add joblib as a dependency

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 9ebdff0..d3863e1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -55,6 +55,7 @@ lmdb = "1.4.1"
 pyfiglet = "1.0.2"
 tensorboard = "*"
 numba = "*"
+joblib = "*"
 
 
 [tool.poetry.group.pybinding]

From 8e09236626701daaab787a6f1c6c2a807b89c9b2 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 6 Aug 2025 19:56:40 +0800
Subject: [PATCH 098/152] Update output dir for test_run

---
 .../{out_negf_graphene => out_negf_graphene_orth}/show.ipynb  | 0
 dpnegf/tests/test_negf_run.py                                 | 4 ++--
 2 files changed, 2 insertions(+), 2 deletions(-)
 rename dpnegf/tests/data/test_negf/test_negf_run/{out_negf_graphene => out_negf_graphene_orth}/show.ipynb (100%)

diff --git a/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene/show.ipynb b/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene_orth/show.ipynb
similarity index 100%
rename from dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene/show.ipynb
rename to dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene_orth/show.ipynb
diff --git a/dpnegf/tests/test_negf_run.py b/dpnegf/tests/test_negf_run.py
index 9e273b0..516b6c3 100644
--- a/dpnegf/tests/test_negf_run.py
+++ b/dpnegf/tests/test_negf_run.py
@@ -38,7 +38,7 @@ def test_negf_run_chain(root_directory):
 
 def test_negf_run_orth(root_directory):
     INPUT_file =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene_new.json" 
-    output =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene"  
+    output =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene_orth"  
     checkfile =  root_directory +'/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_new.json'
     structure =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/graphene.xyz" 
 
@@ -106,7 +106,7 @@ def test_negf_run_orth(root_directory):
 
 def test_negf_run_S(root_directory):
     INPUT_file =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/negf_graphene_new.json" 
-    output =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene"  
+    output =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/out_negf_graphene_S"  
     checkfile =  root_directory +'/dpnegf/tests/data/test_negf/test_negf_run/nnsk_C_newS.json'
     structure =  root_directory +"/dpnegf/tests/data/test_negf/test_negf_run/graphene.xyz" 
 

From e2a429ade6776dbfa5d51b3ec4e50f11a61bcf3e Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Wed, 6 Aug 2025 21:18:18 +0800
Subject: [PATCH 099/152] refactor(NEGF): simplify loop by removing unnecessary
 enumeration of kpoints_bloch

---
 dpnegf/negf/lead_property.py |  2 +-
 dpnegf/runner/NEGF.py        | 21 ++++++++++++---------
 2 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index ebbad7d..10af2b8 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -188,7 +188,7 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             kpoints_bloch = bloch_unfolder.unfold_points(self.kpoint.tolist())
             sgf_k = []
             m_size = self.bloch_factor[1]*self.bloch_factor[0]
-            for ik_lead,k_bloch in enumerate(kpoints_bloch):
+            for k_bloch in kpoints_bloch:
                 k_bloch = torch.tensor(k_bloch)
                 self.HLk, self.HLLk, self.HDLk, self.SLk, self.SLLk, self.SDLk \
                     = self.hamiltonian.get_hs_lead(k_bloch, tab=self.tab, v=self.voltage)
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 2d8f9e3..bbf1aea 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -20,7 +20,6 @@
 from dpnegf.utils.tools import apply_gaussian_filter_3d
 from pyinstrument import Profiler
 import os
-from multiprocessing import Pool, cpu_count
 from dpnegf.utils.tools import self_energy_worker
 from joblib import Parallel, delayed
 
@@ -405,13 +404,13 @@ def compute(self):
             self.negf_compute(scf_require=False,Vbias=self.potential_at_orb)
         
         else:
-            profiler = Profiler()
-            profiler.start() 
+            # profiler = Profiler()
+            # profiler.start() 
             self.negf_compute(scf_require=False,Vbias=None)
-            profiler.stop()
-            output_path = os.path.join(self.results_path, "profile_report.html")
-            with open(output_path, 'w') as report_file:
-                report_file.write(profiler.output_html())
+            # profiler.stop()
+            # output_path = os.path.join(self.results_path, "profile_report.html")
+            # with open(output_path, 'w') as report_file:
+                # report_file.write(profiler.output_html())
 
     def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_iter=1000,
                          mix_method:str='linear', mix_rate:float=0.3, tolerance:float=1e-7,Gaussian_sigma:float=3.0):
@@ -532,8 +531,8 @@ def compute_all_self_energy(self, kpoints_grid, energy_grid, n_jobs=-1):
         eta = self.eta_lead
         lead_L = self.deviceprop.lead_L
         lead_R = self.deviceprop.lead_R
-
-        # joblib 要求 worker 函数是顶层函数或可序列化
+        # joblib's Parallel and delayed are used to parallelize the self-energy computation
+        # joblib requires worker function to be top-level or serializable
         Parallel(n_jobs=n_jobs, backend="loky")(
             delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
             for k in kpoints_grid
@@ -549,6 +548,10 @@ def negf_compute(self,scf_require=False,Vbias=None):
         if hasattr(self, "uni_grid"): self.out["uni_grid"] = self.uni_grid
 
 
+
+        selfen_parent_dir = os.path.join(self.results_path,"self_energy")
+        if not os.path.exists(selfen_parent_dir): 
+            os.makedirs(selfen_parent_dir)
         if scf_require and self.poisson_options["with_Dirichlet_leads"]:
             # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
             # In each iteration, the self-energy of the leads is not updated.

From e7b5c41db25ea93c5566acfe8600b0c6ac750f8d Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 7 Aug 2025 11:42:27 +0800
Subject: [PATCH 100/152] fix(run): set default log_level to 20 in run function
 signature

---
 dpnegf/entrypoints/run.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/entrypoints/run.py b/dpnegf/entrypoints/run.py
index 7f8b8b3..97ee269 100644
--- a/dpnegf/entrypoints/run.py
+++ b/dpnegf/entrypoints/run.py
@@ -22,8 +22,8 @@ def run(
         init_model: str,
         structure: str,
         output: str,
-        log_level: int,
         log_path: Optional[str],
+        log_level: int = 20,
         **kwargs
         ):
 

From cc98b45b0eb412d86d4f5419625931479eb3e2de Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 7 Aug 2025 14:41:31 +0800
Subject: [PATCH 101/152] refactor (self_energy): move compute_all_self_energy
 in lead_property.py

---
 dpnegf/negf/lead_property.py | 36 +++++++++++++++++++++++++++-----
 dpnegf/runner/NEGF.py        | 40 ++++++++----------------------------
 2 files changed, 39 insertions(+), 37 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 10af2b8..e2490e3 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -9,6 +9,8 @@
 from dpnegf.negf.bloch import Bloch
 import torch.profiler
 import ase
+from dpnegf.utils.tools import self_energy_worker
+from joblib import Parallel, delayed
 
 log = logging.getLogger(__name__)
 
@@ -128,11 +130,11 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             energy = torch.tensor(energy) # Energy relative to Ef
         
         if save_path is None:
-            save_path = os.path.join(self.results_path,"self_energy",\
-                                        f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
-            parent_dir = os.path.dirname(save_path)
-            if not os.path.exists(parent_dir): 
+            parent_dir = os.path.join(self.results_path, "self_energy")
+            if not os.path.exists(parent_dir):
                 os.makedirs(parent_dir)
+            save_path = os.path.join(parent_dir, f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
+
 
         # If the .pth file in save_path exists, then directly load it    
         if os.path.exists(save_path):
@@ -314,4 +316,28 @@ def fermi_dirac(self, x) -> torch.Tensor:
     
     @property
     def gamma(self):
-        return self.sigmaLR2Gamma(self.se)
\ No newline at end of file
+        return self.sigmaLR2Gamma(self.se)
+    
+
+
+def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jobs=-1):
+    """
+    Compute the self-energy for all combinations of k-points and energy values in parallel using joblib.
+    Parameters:
+        eta (float): The broadening parameter for calculating lead surface green function.
+        lead_L (LeadProperty): The left lead object.
+        lead_R (LeadProperty): The right lead object.
+        kpoints_grid (Iterable): An iterable of k-point values to compute self-energy for.
+        energy_grid (Iterable): An iterable of energy values to compute self-energy for.
+        n_jobs (int, optional): The number of parallel jobs to run. Defaults to -1 (use all available cores).
+    Notes:
+        This method uses joblib's Parallel to distribute the computation of self-energy across multiple processes.
+        The worker function `self_energy_worker` must be serializable and defined at the top level.
+    """
+    # joblib's Parallel and delayed are used to parallelize the self-energy computation
+    # joblib requires worker function to be top-level or serializable
+    Parallel(n_jobs=n_jobs, backend="loky")(
+        delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+        for k in kpoints_grid
+        for e in energy_grid
+    )
\ No newline at end of file
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index bbf1aea..4a3ea19 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -6,7 +6,7 @@
 from dpnegf.utils.elec_struc_cal import ElecStruCal
 from dpnegf.negf.density import Ozaki,Fiori
 from dpnegf.negf.device_property import DeviceProperty
-from dpnegf.negf.lead_property import LeadProperty
+from dpnegf.negf.lead_property import LeadProperty, compute_all_self_energy
 from dpnegf.negf.negf_utils import is_fully_covered
 import ase
 from dpnegf.utils.constants import Boltzmann, eV2J
@@ -20,8 +20,7 @@
 from dpnegf.utils.tools import apply_gaussian_filter_3d
 from pyinstrument import Profiler
 import os
-from dpnegf.utils.tools import self_energy_worker
-from joblib import Parallel, delayed
+
 
 log = logging.getLogger(__name__)
 
@@ -410,7 +409,7 @@ def compute(self):
             # profiler.stop()
             # output_path = os.path.join(self.results_path, "profile_report.html")
             # with open(output_path, 'w') as report_file:
-                # report_file.write(profiler.output_html())
+            #     report_file.write(profiler.output_html())
 
     def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_iter=1000,
                          mix_method:str='linear', mix_rate:float=0.3, tolerance:float=1e-7,Gaussian_sigma:float=3.0):
@@ -517,38 +516,13 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
         #         report_file.write(profiler.output_html())
     
 
-    def compute_all_self_energy(self, kpoints_grid, energy_grid, n_jobs=-1):
-        """
-        Compute the self-energy for all combinations of k-points and energy values in parallel using joblib.
-        Parameters:
-            kpoints_grid (Iterable): An iterable of k-point values to compute self-energy for.
-            energy_grid (Iterable): An iterable of energy values to compute self-energy for.
-            n_jobs (int, optional): The number of parallel jobs to run. Defaults to -1 (use all available cores).
-        Notes:
-            This method uses joblib's Parallel to distribute the computation of self-energy across multiple processes.
-            The worker function `self_energy_worker` must be serializable and defined at the top level.
-        """
-        eta = self.eta_lead
-        lead_L = self.deviceprop.lead_L
-        lead_R = self.deviceprop.lead_R
-        # joblib's Parallel and delayed are used to parallelize the self-energy computation
-        # joblib requires worker function to be top-level or serializable
-        Parallel(n_jobs=n_jobs, backend="loky")(
-            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
-            for k in kpoints_grid
-            for e in energy_grid
-        )
-
     def negf_compute(self,scf_require=False,Vbias=None):
         
-    
         assert scf_require is not None, "scf_require should be set to True or False"
-
         self.out['k']=[];self.out['wk']=[]
         if hasattr(self, "uni_grid"): self.out["uni_grid"] = self.uni_grid
 
-
-
+        # self energy calculation
         selfen_parent_dir = os.path.join(self.results_path,"self_energy")
         if not os.path.exists(selfen_parent_dir): 
             os.makedirs(selfen_parent_dir)
@@ -559,10 +533,12 @@ def negf_compute(self,scf_require=False,Vbias=None):
             #     for e in self.density.integrate_range:
             #         self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
             #         self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
-            self.compute_all_self_energy(self.kpoints, self.density.integrate_range)
+            compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
+                                    self.kpoints, self.density.integrate_range)
         elif not self.scf:
             # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
-            self.compute_all_self_energy(self.kpoints, self.uni_grid)
+            compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
+                                    self.kpoints, self.uni_grid)
     
         for ik, k in enumerate(self.kpoints):
 

From f2ac0e3f4d7e6bbad73045d72bae726d6d42b4ab Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 7 Aug 2025 21:39:47 +0800
Subject: [PATCH 102/152] feat(self_energy): enhance self-energy computation
 with HDF5 support and batch processing

---
 dpnegf/negf/lead_property.py | 190 ++++++++++++++++++++++++++---------
 dpnegf/utils/tools.py        |   5 +-
 2 files changed, 146 insertions(+), 49 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index e2490e3..2300a1d 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -9,8 +9,10 @@
 from dpnegf.negf.bloch import Bloch
 import torch.profiler
 import ase
-from dpnegf.utils.tools import self_energy_worker
 from joblib import Parallel, delayed
+from multiprocessing import Process, Queue
+import h5py
+
 
 log = logging.getLogger(__name__)
 
@@ -68,9 +70,9 @@ class LeadProperty(object):
         calculate the Gamma function from the self energy.
 
     '''
-    def __init__(self, tab, hamiltonian, structure, results_path, voltage, \
-                 structure_leads_fold:ase.Atoms=None,bloch_sorted_indice:torch.Tensor=None, useBloch: bool=False, \
-                    bloch_factor: List[int]=[1,1,1],bloch_R_list:List=None,\
+    def __init__(self, tab, hamiltonian, structure, results_path, voltage,
+                 structure_leads_fold:ase.Atoms=None,bloch_sorted_indice:torch.Tensor=None, useBloch: bool=False,
+                    bloch_factor: List[int]=[1,1,1],bloch_R_list:List=None,
                     e_T=300, efermi:float=0.0, E_ref:float=None) -> None:
         self.hamiltonian = hamiltonian
         self.structure = structure
@@ -100,8 +102,13 @@ def __init__(self, tab, hamiltonian, structure, results_path, voltage, \
             assert self.bloch_factor is not None
             assert self.structure_leads_fold is not None
 
-    def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-Sancho", \
-                    save: bool=False, save_path: str=None, se_info_display: bool=False,
+    def self_energy(self, kpoint, energy, 
+                    eta_lead: float=1e-5,
+                    method: str="Lopez-Sancho",
+                    save: bool=False, 
+                    save_path: str=None, 
+                    save_format: str="h5",
+                    se_info_display: bool=False,
                     HS_inmem: bool=True):
         '''calculate and loads the self energy and surface green function at the given kpoint and energy.
         
@@ -133,25 +140,62 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
             parent_dir = os.path.join(self.results_path, "self_energy")
             if not os.path.exists(parent_dir):
                 os.makedirs(parent_dir)
-            save_path = os.path.join(parent_dir, f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
-
+            if save_format == "pth":
+                save_path = os.path.join(parent_dir, 
+                                         f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
+            elif save_format == "h5":
+                if self.tab == "lead_L":
+                    save_path = os.path.join(parent_dir, "self_energy_leadL.h5")
+                elif self.tab == "lead_R":
+                    save_path = os.path.join(parent_dir, "self_energy_leadR.h5")
+                else:
+                    raise ValueError(f"Unsupported tab {self.tab} for saving self energy.")
+            else:
+                raise ValueError(f"Unsupported save format {save_format}. Only 'pth' and 'h5' are supported.")
 
-        # If the .pth file in save_path exists, then directly load it    
+        # If the file in save_path exists, then directly load it    
         if os.path.exists(save_path):
-            if se_info_display: log.info(f"Loading self energy from {save_path}")     
-            if not save_path.endswith(".pth"):
+            if se_info_display: 
+                log.info(f"Loading self energy from {save_path}")   
+
+            if os.path.isdir(save_path):
+                if save_format == "pth":
+                    save_path = os.path.join(save_path, f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
+                elif save_format == "h5":
+                    save_path = os.path.join(save_path, f"self_energy_{self.tab}.h5")
+                else:
+                    raise ValueError(f"Unsupported save format {save_format}. Only 'pth' and 'h5' are supported.")
+                
+
+            assert os.path.exists(save_path), f"Cannot find the self energy file {save_path}"
+            if save_path.endswith(".pth"):
                 # if the save_path is a directory, then the self energy file is stored in the directory
-                save_path = os.path.join(save_path, \
-                                        f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
-                assert os.path.exists(save_path), f"Cannot find the self energy file {save_path}"
-            self.se = torch.load(save_path,weights_only=False)
+                self.se = torch.load(save_path, weights_only=False)
+            elif save_path.endswith(".h5"):
+                try:
+                    self.se = read_from_hdf5(save_path, kpoint, energy)
+                    self.se = torch.from_numpy(self.se)
+                except KeyError as e:
+                    log.error(f"Cannot find the self energy for kpoint {kpoint} and energy {energy} in {save_path}.")
+                    raise e
+
             return
+            
         else:
             if se_info_display:
                 log.info("-"*50)
                 log.info(f"Not find stored {self.tab} self energy. Calculating it at kpoint {kpoint} and energy {energy}.")
                 log.info("-"*50)
+        
+        self.self_energy_cal(kpoint, energy, eta_lead=eta_lead, method=method,HS_inmem=HS_inmem)
 
+    def self_energy_cal(self, 
+                        kpoint, 
+                        energy, 
+                        eta_lead: float=1e-5,
+                        method: str="Lopez-Sancho",
+                        HS_inmem: bool=True):
+        
         subblocks = self.hamiltonian.get_hs_device(kpoint, only_subblocks=True)
         # calculate self energy
         if not self.useBloch:
@@ -232,15 +276,7 @@ def self_energy(self, kpoint, energy, eta_lead: float=1e-5, method: str="Lopez-S
         if not HS_inmem:
             del self.HLk, self.HLLk, self.HDLk, self.SLk, self.SLLk, self.SDLk
 
-
-        if save:
-            assert save_path is not None, "Please specify the path to save the self energy."
-            if se_info_display: log.info(f"Saving self energy to {save_path}")
-            torch.save(self.se, save_path)
-            # if self.useBloch:
-            #     torch.save(self.se, os.path.join(self.results_path, f"se_bloch_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_{energy}.pth"))
-            # else:
-            #     torch.save(self.se, os.path.join(self.results_path, f"se_nobloch_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_{energy}.pth"))
+        return self.se
 
     @staticmethod
     def HDL_reduced(HDL: torch.Tensor, SDL: torch.Tensor, subblocks: np.ndarray) -> torch.Tensor:
@@ -320,24 +356,88 @@ def gamma(self):
     
 
 
-def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jobs=-1):
-    """
-    Compute the self-energy for all combinations of k-points and energy values in parallel using joblib.
-    Parameters:
-        eta (float): The broadening parameter for calculating lead surface green function.
-        lead_L (LeadProperty): The left lead object.
-        lead_R (LeadProperty): The right lead object.
-        kpoints_grid (Iterable): An iterable of k-point values to compute self-energy for.
-        energy_grid (Iterable): An iterable of energy values to compute self-energy for.
-        n_jobs (int, optional): The number of parallel jobs to run. Defaults to -1 (use all available cores).
-    Notes:
-        This method uses joblib's Parallel to distribute the computation of self-energy across multiple processes.
-        The worker function `self_energy_worker` must be serializable and defined at the top level.
-    """
-    # joblib's Parallel and delayed are used to parallelize the self-energy computation
-    # joblib requires worker function to be top-level or serializable
-    Parallel(n_jobs=n_jobs, backend="loky")(
-        delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
-        for k in kpoints_grid
-        for e in energy_grid
-    )
\ No newline at end of file
+# def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jobs=-1):
+#     """
+#     Compute the self-energy for all combinations of k-points and energy values in parallel using joblib.
+#     Parameters:
+#         eta (float): The broadening parameter for calculating lead surface green function.
+#         lead_L (LeadProperty): The left lead object.
+#         lead_R (LeadProperty): The right lead object.
+#         kpoints_grid (Iterable): An iterable of k-point values to compute self-energy for.
+#         energy_grid (Iterable): An iterable of energy values to compute self-energy for.
+#         n_jobs (int, optional): The number of parallel jobs to run. Defaults to -1 (use all available cores).
+#     Notes:
+#         This method uses joblib's Parallel to distribute the computation of self-energy across multiple processes.
+#         The worker function `self_energy_worker` must be serializable and defined at the top level.
+#     """
+#     # joblib's Parallel and delayed are used to parallelize the self-energy computation
+#     # joblib requires worker function to be top-level or serializable
+#     Parallel(n_jobs=n_jobs, backend="loky")(
+#         delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+#         for k in kpoints_grid
+#         for e in energy_grid
+#     )
+
+
+def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jobs=-1, batch_size=200):
+
+    save_path_L = os.path.join(lead_L.results_path, "self_energy", "self_energy_leadL.h5")
+    save_path_R = os.path.join(lead_R.results_path, "self_energy", "self_energy_leadR.h5")
+    
+    total_tasks = [(k, e) for k in kpoints_grid for e in energy_grid]
+    print("Total tasks to compute:", len(total_tasks))
+    if len(total_tasks) <= batch_size:
+        results = Parallel(n_jobs=n_jobs, backend="loky")(
+            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+            for k, e in total_tasks
+        )
+        results_L = [(k, e, sigma_L) for (k, e, sigma_L, _) in results]
+        results_R = [(k, e, sigma_R) for (k, e, _, sigma_R) in results]
+
+        write_to_hdf5(save_path_L, results_L)
+        write_to_hdf5(save_path_R, results_R)
+    
+    else:
+        for i in range(0, len(total_tasks), batch_size):
+            batch = total_tasks[i:i+batch_size]
+            print("batch idx:", i , "Batch:", batch)
+            results = Parallel(n_jobs=n_jobs, backend="loky")(
+                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+                for k, e in batch
+            )
+
+            results_L = [(k, e, sigma_L) for (k, e, sigma_L, _) in results]
+            results_R = [(k, e, sigma_R) for (k, e, _, sigma_R) in results]
+
+            write_to_hdf5(save_path_L, results_L)
+            write_to_hdf5(save_path_R, results_R)
+
+
+
+def self_energy_worker(k, e, eta, lead_L, lead_R):
+    seL = lead_L.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
+    seR = lead_R.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
+    return k, e, seL, seR
+
+def write_to_hdf5(h5_path, results):
+    with h5py.File(h5_path, "a") as f:
+        for k, e, se in results:
+            group_name = f"k_{k[0]}_{k[1]}_{k[2]}"
+            dset_name = f"E_{e:.6f}"
+            grp = f.require_group(group_name)
+            if dset_name in grp:
+                log.warning(f"Dataset {dset_name} already exists in group {group_name}. Passing it.")
+                continue 
+            grp.create_dataset(dset_name, data=se.cpu().numpy(), compression="gzip")
+            print(f"Written self-energy for kpoint {k} and energy {e} to {h5_path}.")
+        f.flush()
+
+
+def read_from_hdf5(h5_path, kpoint, energy):
+    with h5py.File(h5_path, "r") as f:
+        group_name = f"k_{kpoint[0]}_{kpoint[1]}_{kpoint[2]}"
+        dset_name = f"E_{energy:.6f}"
+        if group_name in f and dset_name in f[group_name]:
+            return f[group_name][dset_name][:]
+        else:
+            raise KeyError(f"Data for kpoint {kpoint} and energy {energy} not found.")
\ No newline at end of file
diff --git a/dpnegf/utils/tools.py b/dpnegf/utils/tools.py
index 4663f26..dd4f29a 100644
--- a/dpnegf/utils/tools.py
+++ b/dpnegf/utils/tools.py
@@ -30,6 +30,7 @@
 import zipfile
 import sys
 from scipy.ndimage import gaussian_filter
+import h5py
 
 
 log = logging.getLogger(__name__)
@@ -803,7 +804,3 @@ def apply_gaussian_filter_3d(phi_vector, shape, sigma):
     # Flatten back to 1D
     return phi_3d_filtered.ravel()
 
-
-def self_energy_worker(k, e, eta, lead_L, lead_R):
-    lead_L.self_energy(kpoint=k, energy=e, eta_lead=eta, save=True)
-    lead_R.self_energy(kpoint=k, energy=e, eta_lead=eta, save=True)
\ No newline at end of file

From 39f53b74ccf97f87fd5bd95e6e64fe311066256d Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Thu, 7 Aug 2025 21:47:57 +0800
Subject: [PATCH 103/152] fix(test_negf): update emin and emax values in
 negf_chain_new.json for accuracy

---
 dpnegf/negf/lead_property.py                                  | 3 ---
 dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json | 4 ++--
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 2300a1d..e194b46 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -385,7 +385,6 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jo
     save_path_R = os.path.join(lead_R.results_path, "self_energy", "self_energy_leadR.h5")
     
     total_tasks = [(k, e) for k in kpoints_grid for e in energy_grid]
-    print("Total tasks to compute:", len(total_tasks))
     if len(total_tasks) <= batch_size:
         results = Parallel(n_jobs=n_jobs, backend="loky")(
             delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
@@ -400,7 +399,6 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jo
     else:
         for i in range(0, len(total_tasks), batch_size):
             batch = total_tasks[i:i+batch_size]
-            print("batch idx:", i , "Batch:", batch)
             results = Parallel(n_jobs=n_jobs, backend="loky")(
                 delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
                 for k, e in batch
@@ -429,7 +427,6 @@ def write_to_hdf5(h5_path, results):
                 log.warning(f"Dataset {dset_name} already exists in group {group_name}. Passing it.")
                 continue 
             grp.create_dataset(dset_name, data=se.cpu().numpy(), compression="gzip")
-            print(f"Written self-energy for kpoint {k} and energy {e} to {h5_path}.")
         f.flush()
 
 
diff --git a/dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json b/dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json
index d8140fb..0ded7dc 100644
--- a/dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json
+++ b/dpnegf/tests/data/test_negf/test_negf_run/negf_chain_new.json
@@ -36,8 +36,8 @@
         },
         "sgf_solver": "Sancho-Rubio",
         "espacing": 0.01,
-        "emin": -2,
-        "emax": 2,
+        "emin": -0.2,
+        "emax": 0.2,
         "e_fermi": -13.638587951660156,
         "density_options": {
             "method": "Ozaki",

From 64f1d951d96a6e037d4f6f888737b762e4180ab8 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 8 Aug 2025 10:19:48 +0800
Subject: [PATCH 104/152] refactor(lead_property): each worker outputs self
 energy in a public file.

---
 dpnegf/negf/lead_property.py | 68 +++++++++++++++++++++---------------
 1 file changed, 40 insertions(+), 28 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index e194b46..d114e9a 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -10,10 +10,10 @@
 import torch.profiler
 import ase
 from joblib import Parallel, delayed
-from multiprocessing import Process, Queue
+from multiprocessing import Lock
 import h5py
 
-
+write_lock = Lock()
 log = logging.getLogger(__name__)
 
 # """The data output of the intermidiate result should be like this:
@@ -383,57 +383,69 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jo
 
     save_path_L = os.path.join(lead_L.results_path, "self_energy", "self_energy_leadL.h5")
     save_path_R = os.path.join(lead_R.results_path, "self_energy", "self_energy_leadR.h5")
-    
+
+    # 在主进程中执行（不在 worker 中做）
+    if not os.path.exists(save_path_L):
+        with h5py.File(save_path_L, 'w') as f:
+            pass  # 创建空文件
+    else:
+        log.warning(f"File {save_path_L} already exists. It will be overwritten.")
+        os.remove(save_path_L)
+
+    if not os.path.exists(save_path_R):
+        with h5py.File(save_path_R, 'w') as f:
+            pass # 创建空文件
+    else:
+        log.warning(f"File {save_path_R} already exists. It will be overwritten.")
+        os.remove(save_path_R)
+
+
     total_tasks = [(k, e) for k in kpoints_grid for e in energy_grid]
     if len(total_tasks) <= batch_size:
-        results = Parallel(n_jobs=n_jobs, backend="loky")(
-            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+        Parallel(n_jobs=n_jobs, backend="loky")(
+            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, save_path_L, save_path_R)
             for k, e in total_tasks
         )
-        results_L = [(k, e, sigma_L) for (k, e, sigma_L, _) in results]
-        results_R = [(k, e, sigma_R) for (k, e, _, sigma_R) in results]
-
-        write_to_hdf5(save_path_L, results_L)
-        write_to_hdf5(save_path_R, results_R)
     
     else:
         for i in range(0, len(total_tasks), batch_size):
             batch = total_tasks[i:i+batch_size]
-            results = Parallel(n_jobs=n_jobs, backend="loky")(
-                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+            Parallel(n_jobs=n_jobs, backend="loky")(
+                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, save_path_L, save_path_R)
                 for k, e in batch
             )
 
-            results_L = [(k, e, sigma_L) for (k, e, sigma_L, _) in results]
-            results_R = [(k, e, sigma_R) for (k, e, _, sigma_R) in results]
 
-            write_to_hdf5(save_path_L, results_L)
-            write_to_hdf5(save_path_R, results_R)
 
 
+def self_energy_worker(k, e, eta, lead_L, lead_R, save_path_L, save_path_R):
 
-def self_energy_worker(k, e, eta, lead_L, lead_R):
     seL = lead_L.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
     seR = lead_R.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
-    return k, e, seL, seR
 
-def write_to_hdf5(h5_path, results):
+    with write_lock:
+
+        write_to_hdf5(save_path_L, k, e, seL)
+        write_to_hdf5(save_path_R, k, e, seR)
+
+
+
+def write_to_hdf5(h5_path, k, e, se):
     with h5py.File(h5_path, "a") as f:
-        for k, e, se in results:
-            group_name = f"k_{k[0]}_{k[1]}_{k[2]}"
-            dset_name = f"E_{e:.6f}"
-            grp = f.require_group(group_name)
-            if dset_name in grp:
-                log.warning(f"Dataset {dset_name} already exists in group {group_name}. Passing it.")
-                continue 
-            grp.create_dataset(dset_name, data=se.cpu().numpy(), compression="gzip")
+        group_name = f"k_{k[0]}_{k[1]}_{k[2]}"
+        dset_name = f"E_{e:.8f}"
+        grp = f.require_group(group_name)
+        if dset_name in grp:
+            log.warning(f"Dataset {dset_name} already exists in group {group_name}. Passing it.")
+        grp.create_dataset(dset_name, data=se.cpu().numpy(), compression="gzip")
         f.flush()
 
 
+
 def read_from_hdf5(h5_path, kpoint, energy):
     with h5py.File(h5_path, "r") as f:
         group_name = f"k_{kpoint[0]}_{kpoint[1]}_{kpoint[2]}"
-        dset_name = f"E_{energy:.6f}"
+        dset_name = f"E_{energy:.8f}"
         if group_name in f and dset_name in f[group_name]:
             return f[group_name][dset_name][:]
         else:

From 923a87d17c2811c5a24c8280a3ebac627edc2dfc Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 8 Aug 2025 11:09:26 +0800
Subject: [PATCH 105/152] refactor(lead_property): streamline self-energy
 computation in all h5 format

---
 dpnegf/negf/lead_property.py | 78 +++++++++++++++++++++++-------------
 dpnegf/runner/NEGF.py        |  2 +-
 2 files changed, 51 insertions(+), 29 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index d114e9a..b183330 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -12,6 +12,7 @@
 from joblib import Parallel, delayed
 from multiprocessing import Lock
 import h5py
+import glob
 
 write_lock = Lock()
 log = logging.getLogger(__name__)
@@ -381,29 +382,10 @@ def gamma(self):
 
 def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jobs=-1, batch_size=200):
 
-    save_path_L = os.path.join(lead_L.results_path, "self_energy", "self_energy_leadL.h5")
-    save_path_R = os.path.join(lead_R.results_path, "self_energy", "self_energy_leadR.h5")
-
-    # 在主进程中执行（不在 worker 中做）
-    if not os.path.exists(save_path_L):
-        with h5py.File(save_path_L, 'w') as f:
-            pass  # 创建空文件
-    else:
-        log.warning(f"File {save_path_L} already exists. It will be overwritten.")
-        os.remove(save_path_L)
-
-    if not os.path.exists(save_path_R):
-        with h5py.File(save_path_R, 'w') as f:
-            pass # 创建空文件
-    else:
-        log.warning(f"File {save_path_R} already exists. It will be overwritten.")
-        os.remove(save_path_R)
-
-
     total_tasks = [(k, e) for k in kpoints_grid for e in energy_grid]
     if len(total_tasks) <= batch_size:
         Parallel(n_jobs=n_jobs, backend="loky")(
-            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, save_path_L, save_path_R)
+            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
             for k, e in total_tasks
         )
     
@@ -411,23 +393,30 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jo
         for i in range(0, len(total_tasks), batch_size):
             batch = total_tasks[i:i+batch_size]
             Parallel(n_jobs=n_jobs, backend="loky")(
-                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, save_path_L, save_path_R)
+                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
                 for k, e in batch
             )
 
+    save_path_L = os.path.join(lead_L.results_path, "self_energy", "self_energy_leadL.h5")
+    save_path_R = os.path.join(lead_R.results_path, "self_energy", "self_energy_leadR.h5")
 
+    merge_hdf5_files(os.path.join(lead_L.results_path, "self_energy"), save_path_L, pattern="tmp_leadL_*.h5")
+    merge_hdf5_files(os.path.join(lead_R.results_path, "self_energy"), save_path_R, pattern="tmp_leadR_*.h5")
 
 
-def self_energy_worker(k, e, eta, lead_L, lead_R, save_path_L, save_path_R):
 
-    seL = lead_L.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
-    seR = lead_R.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
 
-    with write_lock:
 
-        write_to_hdf5(save_path_L, k, e, seL)
-        write_to_hdf5(save_path_R, k, e, seR)
+def self_energy_worker(k, e, eta, lead_L, lead_R):
 
+    save_tmp_L = os.path.join(lead_L.results_path, "self_energy", f"tmp_leadL_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
+    save_tmp_R = os.path.join(lead_R.results_path, "self_energy", f"tmp_leadR_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
+
+    seL = lead_L.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
+    seR = lead_R.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
+
+    write_to_hdf5(save_tmp_L, k, e, seL)
+    write_to_hdf5(save_tmp_R, k, e, seR)
 
 
 def write_to_hdf5(h5_path, k, e, se):
@@ -449,4 +438,37 @@ def read_from_hdf5(h5_path, kpoint, energy):
         if group_name in f and dset_name in f[group_name]:
             return f[group_name][dset_name][:]
         else:
-            raise KeyError(f"Data for kpoint {kpoint} and energy {energy} not found.")
\ No newline at end of file
+            raise KeyError(f"Data for kpoint {kpoint} and energy {energy} not found.")
+
+
+
+def merge_hdf5_files(tmp_dir, output_path, pattern, remove=True):
+
+    tmp_paths = sorted(glob.glob(os.path.join(tmp_dir, pattern)))
+    if not tmp_paths:
+        raise ValueError(f"No files matched pattern '{pattern}' in '{tmp_dir}'")
+
+    log.info(f"Merging {len(tmp_paths)} tmp self energy files into {output_path}")
+
+    with h5py.File(output_path, 'a') as fout:
+        for path in tmp_paths:
+            with h5py.File(path, 'r') as fin:
+                for group_name in fin:
+                    fin_group = fin[group_name]
+                    fout_group = fout.require_group(group_name)
+
+                    for dset_name in fin_group:
+                        if dset_name in fout_group:
+                            log.warning(f"Dataset '{dset_name}' already exists in group '{group_name}'. Skipping.")
+                            continue
+                        fin_group.copy(dset_name, fout_group)
+
+    log.info("Merge complete.")
+
+    if remove:
+        for path in tmp_paths:
+            try:
+                os.remove(path)
+                # log.info(f"Deleted tmp file: {path}")
+            except Exception as e:
+                log.warning(f"Failed to delete {path}: {e}")
\ No newline at end of file
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 4a3ea19..d8594ad 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -409,7 +409,7 @@ def compute(self):
             # profiler.stop()
             # output_path = os.path.join(self.results_path, "profile_report.html")
             # with open(output_path, 'w') as report_file:
-            #     report_file.write(profiler.output_html())
+                # report_file.write(profiler.output_html())
 
     def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_iter=1000,
                          mix_method:str='linear', mix_rate:float=0.3, tolerance:float=1e-7,Gaussian_sigma:float=3.0):

From 188fc44f5816040aa2566a973869713dd786e7ca Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 8 Aug 2025 14:44:50 +0800
Subject: [PATCH 106/152] refactor(lead_property, NEGF): swap group and dataset
 names in HDF5

---
 dpnegf/negf/lead_property.py | 12 ++++++------
 dpnegf/runner/NEGF.py        |  5 ++++-
 2 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index b183330..4f37583 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -421,8 +421,8 @@ def self_energy_worker(k, e, eta, lead_L, lead_R):
 
 def write_to_hdf5(h5_path, k, e, se):
     with h5py.File(h5_path, "a") as f:
-        group_name = f"k_{k[0]}_{k[1]}_{k[2]}"
-        dset_name = f"E_{e:.8f}"
+        group_name = f"E_{e:.8f}"
+        dset_name = f"k_{k[0]}_{k[1]}_{k[2]}"
         grp = f.require_group(group_name)
         if dset_name in grp:
             log.warning(f"Dataset {dset_name} already exists in group {group_name}. Passing it.")
@@ -431,14 +431,14 @@ def write_to_hdf5(h5_path, k, e, se):
 
 
 
-def read_from_hdf5(h5_path, kpoint, energy):
+def read_from_hdf5(h5_path, k, e):
     with h5py.File(h5_path, "r") as f:
-        group_name = f"k_{kpoint[0]}_{kpoint[1]}_{kpoint[2]}"
-        dset_name = f"E_{energy:.8f}"
+        group_name = f"E_{e:.8f}"
+        dset_name = f"k_{k[0]}_{k[1]}_{k[2]}"
         if group_name in f and dset_name in f[group_name]:
             return f[group_name][dset_name][:]
         else:
-            raise KeyError(f"Data for kpoint {kpoint} and energy {energy} not found.")
+            raise KeyError(f"Data for kpoint {k} and energy {e} not found.")
 
 
 
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index d8594ad..8887ca7 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -523,6 +523,7 @@ def negf_compute(self,scf_require=False,Vbias=None):
         if hasattr(self, "uni_grid"): self.out["uni_grid"] = self.uni_grid
 
         # self energy calculation
+        log.info(msg="------Self-energy calculation------")
         selfen_parent_dir = os.path.join(self.results_path,"self_energy")
         if not os.path.exists(selfen_parent_dir): 
             os.makedirs(selfen_parent_dir)
@@ -539,7 +540,9 @@ def negf_compute(self,scf_require=False,Vbias=None):
             # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
             compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
                                     self.kpoints, self.uni_grid)
-    
+        log.info(msg="-----------------------------------\n")
+
+
         for ik, k in enumerate(self.kpoints):
 
             self.out['k'].append(k)

From 86a5328aa96fad0ab0fc865374180a779e4bfc8e Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 8 Aug 2025 14:52:10 +0800
Subject: [PATCH 107/152] refactor(lead_property): remove unused imports and
 clean up code

---
 dpnegf/negf/lead_property.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 4f37583..57cc83e 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -2,19 +2,16 @@
 from typing import List
 from dpnegf.negf.surface_green import selfEnergy
 import logging
-from dpnegf.negf.negf_utils import update_kmap, update_temp_file
 import os
 from dpnegf.utils.constants import Boltzmann, eV2J
 import numpy as np
 from dpnegf.negf.bloch import Bloch
-import torch.profiler
 import ase
 from joblib import Parallel, delayed
-from multiprocessing import Lock
 import h5py
 import glob
 
-write_lock = Lock()
+
 log = logging.getLogger(__name__)
 
 # """The data output of the intermidiate result should be like this:

From 3b6e5bd12723da1762f9adc77744b468db80af53 Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Fri, 8 Aug 2025 14:54:07 +0800
Subject: [PATCH 108/152] update log message

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 dpnegf/negf/lead_property.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 57cc83e..2a9374a 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -422,7 +422,7 @@ def write_to_hdf5(h5_path, k, e, se):
         dset_name = f"k_{k[0]}_{k[1]}_{k[2]}"
         grp = f.require_group(group_name)
         if dset_name in grp:
-            log.warning(f"Dataset {dset_name} already exists in group {group_name}. Passing it.")
+            log.warning(f"Dataset {dset_name} already exists in group {group_name}. Skipping it.")
         grp.create_dataset(dset_name, data=se.cpu().numpy(), compression="gzip")
         f.flush()
 

From 5621a9c572a5ff6356f2bf721687cdfcd19cc17f Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Fri, 8 Aug 2025 14:56:08 +0800
Subject: [PATCH 109/152] remove unnecessary code

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 dpnegf/negf/lead_property.py | 20 --------------------
 1 file changed, 20 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 2a9374a..cf5258f 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -354,26 +354,6 @@ def gamma(self):
     
 
 
-# def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jobs=-1):
-#     """
-#     Compute the self-energy for all combinations of k-points and energy values in parallel using joblib.
-#     Parameters:
-#         eta (float): The broadening parameter for calculating lead surface green function.
-#         lead_L (LeadProperty): The left lead object.
-#         lead_R (LeadProperty): The right lead object.
-#         kpoints_grid (Iterable): An iterable of k-point values to compute self-energy for.
-#         energy_grid (Iterable): An iterable of energy values to compute self-energy for.
-#         n_jobs (int, optional): The number of parallel jobs to run. Defaults to -1 (use all available cores).
-#     Notes:
-#         This method uses joblib's Parallel to distribute the computation of self-energy across multiple processes.
-#         The worker function `self_energy_worker` must be serializable and defined at the top level.
-#     """
-#     # joblib's Parallel and delayed are used to parallelize the self-energy computation
-#     # joblib requires worker function to be top-level or serializable
-#     Parallel(n_jobs=n_jobs, backend="loky")(
-#         delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
-#         for k in kpoints_grid
-#         for e in energy_grid
 #     )
 
 

From e48a56129a3a96f4f1ee2eeee01662b19e5dcb29 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 1 Sep 2025 22:05:31 +0800
Subject: [PATCH 110/152] add cnt example notebook

---
 examples/CNT/cnt7_0.xyz             | 632 ++++++++++++++++++++++++++++
 examples/CNT/input.json             |  89 ++++
 examples/CNT/nnsk_dftb.json         |   1 +
 examples/CNT/run.ipynb              | 293 +++++++++++++
 examples/atomic_chain_api/run.ipynb |  28 +-
 examples/atomic_chain_cli/run.ipynb |  27 +-
 6 files changed, 1051 insertions(+), 19 deletions(-)
 create mode 100644 examples/CNT/cnt7_0.xyz
 create mode 100644 examples/CNT/input.json
 create mode 100644 examples/CNT/nnsk_dftb.json
 create mode 100644 examples/CNT/run.ipynb

diff --git a/examples/CNT/cnt7_0.xyz b/examples/CNT/cnt7_0.xyz
new file mode 100644
index 0000000..d32b42d
--- /dev/null
+++ b/examples/CNT/cnt7_0.xyz
@@ -0,0 +1,632 @@
+630
+Lattice="10.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 200.0" Properties=species:S:1:pos:R:3 Generation=T from=T NanoTCAD-ViDES=T pbc="F F F"
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+C        1.73248964       2.17247329      90.72000000
+C       -0.61831839       2.70902988      90.72000000
+C       -2.50352006       1.20563172      90.72000000
+C       -2.50352006      -1.20563172      90.72000000
+C       -0.61831839      -2.70902988      90.72000000
+C        1.73248964      -2.17247329      90.72000000
+C        2.77869763       0.00000000      92.16000000
+C        1.73248964       2.17247329      92.16000000
+C       -0.61831839       2.70902988      92.16000000
+C       -2.50352006       1.20563172      92.16000000
+C       -2.50352006      -1.20563172      92.16000000
+C       -0.61831839      -2.70902988      92.16000000
+C        1.73248964      -2.17247329      92.16000000
+C        2.50352006       1.20563172      92.88000000
+C        0.61831839       2.70902988      92.88000000
+C       -1.73248964       2.17247329      92.88000000
+C       -2.77869763       0.00000000      92.88000000
+C       -1.73248964      -2.17247329      92.88000000
+C        0.61831839      -2.70902988      92.88000000
+C        2.50352006      -1.20563172      92.88000000
+C        2.50352006       1.20563172      94.32000000
+C        0.61831839       2.70902988      94.32000000
+C       -1.73248964       2.17247329      94.32000000
+C       -2.77869763       0.00000000      94.32000000
+C       -1.73248964      -2.17247329      94.32000000
+C        0.61831839      -2.70902988      94.32000000
+C        2.50352006      -1.20563172      94.32000000
+C        2.77869763       0.00000000      95.04000000
+C        1.73248964       2.17247329      95.04000000
+C       -0.61831839       2.70902988      95.04000000
+C       -2.50352006       1.20563172      95.04000000
+C       -2.50352006      -1.20563172      95.04000000
+C       -0.61831839      -2.70902988      95.04000000
+C        1.73248964      -2.17247329      95.04000000
+C        2.77869763       0.00000000      96.48000000
+C        1.73248964       2.17247329      96.48000000
+C       -0.61831839       2.70902988      96.48000000
+C       -2.50352006       1.20563172      96.48000000
+C       -2.50352006      -1.20563172      96.48000000
+C       -0.61831839      -2.70902988      96.48000000
+C        1.73248964      -2.17247329      96.48000000
\ No newline at end of file
diff --git a/examples/CNT/input.json b/examples/CNT/input.json
new file mode 100644
index 0000000..1af6e7f
--- /dev/null
+++ b/examples/CNT/input.json
@@ -0,0 +1,89 @@
+{
+    "task_options": {
+        "task": "negf",
+        "scf": false,
+        "block_tridiagonal": true,
+        "ele_T": 300,
+        "unit": "eV",
+        "scf_options":{
+            "mode": "PDIIS",
+            "mixing_period": 3,
+            "step_size": 0.05,
+            "n_history": 6,
+            "abs_err": 1e-6,
+            "rel_err": 1e-4,
+            "max_iter": 100
+        },
+        "stru_options":{
+            "gamma_center": true,
+            "time_reversal_symmetry": true,
+            "nel_atom": {"C": 4.0},
+            "kmesh":[1,1,1],
+            "pbc":[true, true, false],
+            "device":{
+                "id":"56-574",
+                "sort": true
+            },
+            "lead_L":{
+                "id":"0-56",
+                "voltage":0,
+                "useBloch": false,
+                "kmesh_lead_Ef": [1,1,10]
+            },
+            "lead_R":{
+                "id":"574-630",
+                "voltage":0,
+                "useBloch": false,
+                "kmesh_lead_Ef": [1,1,10]
+            }
+        },
+        "poisson_options": {
+            "solver": "scipy",
+            "err": 1e-2,
+            "tolerance":1e-5,
+            "max_iter":1000,
+            "mix_rate":0.2,
+            "grid":{
+                "x_range":"-20:20:9",
+                "y_range":"-20:20:9",
+                "z_range":"8.6:88:2"
+                },
+            "gate_top":{
+                "x_range": "-20:-20",
+                "y_range": "-20:20",
+                "z_range": "38.64:58.64",
+                "voltage": 0
+            },
+            "gate_bottom":{
+                "x_range": "20:20",
+                "y_range": "-20:20",
+                "z_range": "38.64:58.64",
+                "voltage": 0
+            },
+            "dielectric_region":{            
+             "x_range": "-20:20",
+             "y_range": "-20:20",
+             "z_range": "8.6:88",
+             "relative permittivity": 20            
+            }
+
+        },
+        "sgf_solver": "Sancho-Rubio",
+        "espacing": 0.05,
+        "emin": -3,
+        "emax": 3,
+        "density_options": {
+            "method": "Fiori",
+            "integrate_way": "direct"
+        },
+        "eta_lead":1e-5,
+        "eta_device":1e-5,
+        "out_dos": true,
+        "out_tc": true,
+        "out_ldos": false,
+        "out_current_nscf": true,
+        "out_density": true
+},
+"structure": "./ready_struct.xyz",
+"AtomicData_options" : {"r_max": 4.6}
+}
\ No newline at end of file
diff --git a/examples/CNT/nnsk_dftb.json b/examples/CNT/nnsk_dftb.json
new file mode 100644
index 0000000..cd4eaa8
--- /dev/null
+++ b/examples/CNT/nnsk_dftb.json
@@ -0,0 +1 @@
+{"version": 2, "unit": "eV", "model_options": {"nnsk": {"onsite": {"method": "uniform"}, "hopping": {"method": "poly2exp", "rs": 4.0, "w": 0.2}, "soc": {}, "freeze": false, "push": false, "std": 0.01}}, "common_options": {"basis": {"C": ["2s", "2p"]}, "dtype": "float32", "device": "cpu", "overlap": true}, "model_params": {"onsite": {"C-2s-0": [-0.10686016082763672], "C-2p-0": [0.12751197814941406]}, "hopping": {"C-C-2s-2s-0": [-11.012248039245605, 14.645441055297852, -5.926220417022705, 0.4684220552444458], "C-C-2s-2p-0": [-8.646349906921387, 10.996883392333984, -4.372412204742432, 0.2622162103652954], "C-C-2p-2p-0": [7.325995445251465, -7.802810192108154, 2.9014127254486084, 0.20314724743366241], "C-C-2p-2p-1": [-8.129714012145996, 10.417790412902832, -4.3871870040893555, 0.8520817160606384]}, "overlap": {"C-C-2s-2s-0": [6.221240043640137, 0.6663751602172852, -1.448582649230957, 2.1097588539123535], "C-C-2s-2p-0": [7.143936634063721, 2.7403581142425537, -2.2482755184173584, -2.028411388397217], "C-C-2p-2p-0": [-7.474735736846924, -6.219860076904297, 2.989943742752075, 1.9392958879470825], "C-C-2p-2p-1": [5.751282691955566, 0.34485864639282227, -0.9181159734725952, 2.4460558891296387]}}}
\ No newline at end of file
diff --git a/examples/CNT/run.ipynb b/examples/CNT/run.ipynb
new file mode 100644
index 0000000..9392000
--- /dev/null
+++ b/examples/CNT/run.ipynb
@@ -0,0 +1,293 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8c5c64a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "\n",
+    "from dpnegf.runner.NEGF import NEGF\n",
+    "from dptb.nn.build import build_model\n",
+    "import json\n",
+    "\n",
+    "from dpnegf.utils.loggers import set_log_handles\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0f64a6b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "rm: cannot remove 'output': Directory not empty\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO                                      Version Info                                  \n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n"
+     ]
+    }
+   ],
+   "source": [
+    "INPUT_file =  \"./input.json\" \n",
+    "model =  \"./nnsk_dftb.json\"\n",
+    "structure =  \"./cnt7_0.xyz\" \n",
+    "output = \"output\"  \n",
+    "\n",
+    "if os.path.exists(output):\n",
+    "    os.system('rm -rf %s' % output)\n",
+    "\n",
+    "\n",
+    "negf_json = json.load(open(INPUT_file))\n",
+    "model_json = json.load(open(model))\n",
+    "\n",
+    "log_path = output+'/log'\n",
+    "log_level = logging.INFO\n",
+    "set_log_handles(log_level, Path(log_path) if log_path else None)\n",
+    "\n",
+    "model = build_model(model,model_options= model_json['model_options'],\n",
+    "                    common_options=model_json['common_options'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "830d67a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    ------ k-point for NEGF -----\n",
+      "DPNEGF INFO    Gamma Center: True\n",
+      "DPNEGF INFO    Time Reversal: True\n",
+      "DPNEGF INFO    k-points Num: 1\n",
+      "DPNEGF INFO    k-points: [[0. 0. 0.]]\n",
+      "DPNEGF INFO    k-points weights: [1.]\n",
+      "DPNEGF INFO    --------------------------------\n",
+      "DPNEGF WARNING AtomicData_options is extracted from input file. This may be not consistent with the model options. Please be careful and check the cutoffs.\n",
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": 4.6\n",
+      "               }\n",
+      "DPNEGF INFO    The structure is sorted lexicographically in this version!\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732035e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
+      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+      "DPNEGF INFO    The coupling width of lead_L is 112.\n",
+      "DPNEGF INFO    The coupling width of lead_R is 112.\n",
+      "DPNEGF INFO    The Hamiltonian is block tridiagonalized into 18 subblocks.\n",
+      "DPNEGF INFO       the number of elements in subblocks: 687922\n",
+      "DPNEGF INFO                   occupation of subblocks: 16.023585292407684 %\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
+      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0\n",
+      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 4.0}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 4.0}\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 6 kpoints\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF INFO    Fermi energy converged after 4 iterations.\n",
+      "DPNEGF INFO    q_cal: 224.0, total_electrons: 224.0, diff q: 0.0\n",
+      "DPNEGF INFO    Estimated E_fermi: -4.476735791109641 based on the valence electrons setting nel_atom : {'C': 4.0} .\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 6 kpoints\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF INFO    Fermi energy converged after 4 iterations.\n",
+      "DPNEGF INFO    q_cal: 224.0, total_electrons: 224.0, diff q: 0.0\n",
+      "DPNEGF INFO    Estimated E_fermi: -4.476750334642966 based on the valence electrons setting nel_atom : {'C': 4.0} .\n",
+      "DPNEGF INFO    -------------------------------------------------\n",
+      "DPNEGF INFO    Zero bias case detected.\n",
+      "DPNEGF INFO    Fermi level for lead_L: -4.476735791109641\n",
+      "DPNEGF INFO    Fermi level for lead_R: -4.476750334642966\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -4.476735791109641\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -4.476750334642966\n",
+      "DPNEGF INFO    Reference energy E_ref: -4.476735791109641\n",
+      "DPNEGF INFO    =================================================\n",
+      "\n",
+      "DPNEGF INFO    Merging 120 tmp self energy files into output/self_energy/self_energy_leadL.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Merging 120 tmp self energy files into output/self_energy/self_energy_leadR.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -3.000\n",
+      "DPNEGF INFO    computing green's function at e = -2.395\n",
+      "DPNEGF INFO    computing green's function at e = -1.790\n",
+      "DPNEGF INFO    computing green's function at e = -1.185\n",
+      "DPNEGF INFO    computing green's function at e = -0.580\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 0.630\n",
+      "DPNEGF INFO    computing green's function at e = 1.235\n",
+      "DPNEGF INFO    computing green's function at e = 1.840\n",
+      "DPNEGF INFO    computing green's function at e = 2.445\n",
+      "DPNEGF WARNING Fiori method is under test in this version.\n",
+      "DPNEGF WARNING Free charge output has some problems.\n",
+      "DPNEGF WARNING The Fermi energy of the left and right leads should be equal in nscf current calculation.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAFYpJREFUeJzt3bFvHHl/HvCHK5lXUAJS6HaWhKsAad7q/AIp8h+8KVKktisbhisbMOD3kMaAXRusg0PgIm2C9PdWAdIGeAEV/AMCG9LMnuxmVwUJcTcFKUq6k8Ql97e785v5fCpJ4A0HJ3GenXn2u9+j9Xq9DgAkmRz6BADoD6EAwB2hAMAdoQDAHaEAwB2hAMAdoQDAHaEAwJ2nm37h8d+8yP87/5ckyf/Nf8if53999ut+m7/NH+cfkyR/nX/M/85//MXXTJL8Pr/OWX6fJJnNTh963p/ouqRpktevtzoMwOgdbTrR3PzNUebnZb7pq0yTJGeZJ0mOj49zeXn56OOd3maKUADYTi8eH11dXeX58+eHPg2A0etFKCTJcrnMN998c+jTABi13oRCcnPHcHR0dOjTABitjUPhv/2PXZ7Gp548ebK/bwbAnY1D4d//0y5P41Or1cqjJIAD6NXjo48pnwH2b+NQ+E9/tsvT+DzlM8B+bRwK//RvdngWX6F8Btif3j4++jnlM8DuVRMKymeA3asmFBLlM8CuVRUKifIZYJc2DoV/92aXp/EwymeA3dg4FP7nf9/laTyO8hmgrOoeH31M+QxQ1sah8H/+7S5P4/Gurq7Sde2hTwNgEDYOhb/6z7s8je2s1+u0rQ07ANvaeB3nJMmrkzLfdPZ2nlU+bGDb3q8zzzRHRz9uvdrzvek0efmyyKEAqrFxKPz+h3LfdHWUTDZaArqZJt3dr9v2dZGdzwBjtHEonP1FklI7mm/vOM7ezsscL9MkqyRnSZJ//dcyO58Bxqbqdx99iTkGgMfZOBSePt34pqI3vF0V4GE2DoUX377Is2fPdnkuxfmsJICHedDjo8VikV/96le7OpedWC6XJp8BNvTgTuHi4iJN0+ziXHbG5DPAZh5VNLdtW10wKJ8B7vfodx+1bVvdo6RE+QzwNVu9JfXi4kL5DDAgW88pKJ8BhqPI8JryGWAYik00K58B6lf0Yy6UzwB1K/7ZR8pngHrt5APxlM8AddrZp6QqnwHqs9OPzq61fLbaExirne9TaNu2uo4hiWAARulovV5vtBhz8veTNM8e/6q/a7u8/1avfkiaZdIV2/l8s3dtXmzn8yTzTPNHR79L08yKHNHOZ6AGG98pbBMISdLMmrtFPfOTpCt487A6KnvL06TLNPOs1+sidwxdl8zLbB4F2KmN7xRKmc1m6f6ku/lN8Z3PhY53e8dxlpsr+fFxmZ3Prz2RAnpu7zua27bNZFLXamiTz8BYHOTqPG2mVV5kvV0VGLqDvWRvZk1170oy+QwM3UGf49Q6+VzjXQ7AJg7+cL/GyefEoyRgmA4eCkm9k88+KwkYml6EQlJnMPisJGBoehMKSZ0fiaF8BoakV6GQKJ8BDql3oZAonwEOpZehkNTZMSifgdr1NhSSOoNB+QzUrNehkCifAfap96GQKJ8B9qWKUEiUzwD7UE0oJHV2DHY+AzWpKhSSOoMhsfMZqMPeN68lyen5abplZ+fzlux9Bkp7eohvOj3Z/mLbzJq8+elN3r17l/n7MCgUb6ujZFIwKpvcrB99v/N5Njvd+phdt/UhAH7hIHcKJY1x53Ni7zOwG9V1Cj9X685nk89AH9V1Nf2CaTOtLhhMPgN9VNeV9CumzdTkM8CWBhMKiclngG0NKhSSm8nn2h4lJSafgX6o7+q5gevr6+oG3JTPQB8MMhSSOieflc/AoQ02FBIfuw3wUIMOhUT5DPAQgw+FRPkMsKn6rpSPpHwGuN9oQiGpt3z2KAnYl1GFQlJn+ZxE+QzsxehCIam3fNYxALs2ylBIbsrn2u4Yrq6uBAOwU6MNheTmjqG2jkH5DOzSqEMhqbd8tvMZ2IXqN68lY9z5nLzf+/ybWbklzXY+AwfZ0Vza2HY+Jx/2Ptv5DJQ0iDuFkp4/f57lXyxvftPTnc/Jp3uf7XwGShl9p/Bzi8Wiuo/EUD4DpdR19duTWnc+m3wGtlXXlW+Patz5nJh8BrYjFL7C5DMwNkLhHiafgTERChsw+QyMhVDYUK2Tz8pn4CGEwgO0bZvj4+NDn8aDKZ+BTQmFB7q8vFQ+A4MlFB5B+QwMlVB4JOUzMERCYQvKZ2BohMKWlM/AkAiFApTPwFAIhUKUz8AQCIWClM9A7YRCYbWWz3Y+A4lQ2Ilay+euaw99CsCBWcf5Gafnp+mWXZpn273if7/z+dUPSbNMupP7/5tNzd4mqyTzbL+f+sYk80zzXX4ssvM5SabT5OXLIocC9uTpoU+gj6YnZS60L759ka7tMj+5zd2C8bs6SiYFj9eku/t1277eOhi67v6vAfrHncIePPn+SVarVXJe7pivbu86zt4WOt7tHcdZ5kmSyWSS6+vrRx/v9DZTXqsqoCo6hT2odeezt6vC+NR1parYtJlW966kq6srk88wMkJhj9q2NfkM9JpQ2LNaJ599iB6Mg1A4gBonn5OYfIYREAoHUuvks0dJMGxC4YBqDAblMwybUDgw5TPQJ0KhB5TPQF8IhZ5QPgN9IBR6pMaOQfkMwyIUeqbGYFA+w3AIhR5SPgOHIhR6SvkMHIJQ6LHFYlHlBjflM9RLKPTc5eVldR2Dnc9QL6FQgRrL58TOZ6iRzWt7UHrnc5Lie5/tfAYSO5r3ovTO5/V6nfn7MCgU6XY+A4k7hSp98803ufrLq5vfFNr7bOczkOgUqnR5eWnnM7ATdV1ZuDNtptUFg8ln6L+6rip8YtpMTT4DRQmFypl8BkoSCgNg8hkoRSgMRK2Tzx4lQb8IhQGpcfLZoyToF6EwMDV+7HYSdwzQE0JhgGotn71dFQ5PKAzUYrGo7o5huVwqn+HAhMKAXVxcVNcxKJ/hsITCwCmfgYcQCiOgfAY2JRRGQvkMbEIojIjyGbiPUBiZWstnO59hP4TCCNVYPicRDLAHQmGklM/A51jHWanT89N0yy7Ns+1e8b/f+fzqh6RZJt3J/f/NJmZvk1WSecrsp04mmWea7/Jjjo6O0jSzIkedTpOXL4scCgbh6aFPgMeZnpS52DazJm9+epP5ybubPyj0EmF1lEwKvtxo0t39er1ep21fZzY73eqYXXf/18DYuFMgSfLk+ydZrVbJeZnjvbq94zh7W+h4t3ccZ5nf/dnx8XEuLy8ffczT20x5raqAOzoFktS789nkM5RV11WAnZo20+oG3BLlM5QkFPjEYrGoLhhMPkM5QoFfMPkM4yUU+KxaJ589SoLtCAW+qMbJZ+UzbEco8FVt21bXMSTKZ3gsocC9lM8wHkKBjSifYRyEAhtTPsPwCQUeRPkMwyYUeDDlMwyXUOBRlM8wTEKBR6u1fPYoCb5MKLCVGsvnxKMk+BKhwNZqLZ/tfIZfEgoUUWMwJBEM8DM2r5FkjDufk/d7n//o6Hd2PsMtO5pJMr6dz8mHvc92PsMH7hTYib7vfE5+uffZzmfQKbAjte589llJjF1dP7VUZdpMqyuffVYSYycU2KkaPxLD5DNjJhTYOZPPUA+hwF6YfIY6CAX2psYBN+UzYyMU2Ksag0H5zJgIBfZO+Qz9JRQ4COUz9JNQ4GAuLi6qG3BLlM8MW30/kQzK9fV1dR2D8pkhEwocnPIZ+kMo0AvKZ+gHoUBvKJ/h8IQCvaJ8hsOq76ePwau1fLbakyEQCvRSjeVzYucz9bN5jZ3o+87nZBd7n8vvfE7sfWa/7GhmJ/q+8zkpv/e59M7nxN5n9s+dAlWY/HaS9XpdbOdzUn7vc+mdz4m9z+yfToEqNLOmunclmXymRnX9lDFqte58NsdATYQCValx8jmJyWeqIRSoTq2TzwbcqIFQoEoXFxfV3TFcXV0JBnpPKFCtxWJRXcegfKbvhAJVq3HyWflMnwkFqqd8hnKEAoOgfIYyhAKDoXyG7QkFBkX5DNsRCgyO8hkeTygwSMpneByhwGApn+HhhAKDpnyGhxEKDJ7yGTYnFBiFWstnO5/ZN6HAaLRtm+Pj40OfxoN1XXvoU2BErOOkCqfnp+mWXZpn27/af/PTm7x79y6vfkiaZdKdFDjBJLO3ySrJPGX2U9+YZJ5pvsuPRXY+J8l0mrx8WeRQDNDTQ58AbGJ6Uu5C++LbF+naLvOT29dDhV4WrY6SSeGXWE26u1+37eutg6Hr7v8axs2dAqP15PsnWa1WyXmZ4726veM4e1vmeEny6vau4yzzJMlkMsn19fWjj3d6mymvVRV8gU6B0Zo200wmdf0ImHxm1+r6iYDCps20yvLZ5DO7IhQYvcvLS5PPcEsoQOqdfPYoidKEAtyqcfI5iclnihIK8JFaJ589SqIUoQA/U2MwXF1dKZ8pQijAZ7Rtq3xmlIQCfIHymTESCvAVymfGRijAPWrsGJTPPJZQgA3UGAzKZx5DKMCGlM+MgVCAB1A+M3RCAR5I+cyQCQV4hBo7Bjuf2YRQgEeqMRgSO5/5OpvXGK1Se593tfM52cXe5/I7nxN7n4fEjmZGq9Te513tfE7K730uvfM5sfd5aNwpQCFHvz26CYRCO5+T8nufS+98Tux9HhqdAhQym82q3PlsjoGP1fUvGHpu2kyrK59NPvMxoQCFmXymZkIBdsDkM7USCrAji8Uix8fHhz6NBzP5PG5CAXbo8vKyuo5B+TxuQgF2rMbJZ+XzeAkF2APlM7UQCrAnymdqIBRgj5TP9J1QgD1TPtNnQgEOoNby2aOk4RMKcCA1ls9J3DEMnFCAA6q1fPZ21eESCnBgi8WiujuG5XKpfB4ooQA9cHFxUV3HoHweJqEAPVFr+dy2tusMiVCAHqm1fBYMw2FHMxTULbucnm+59/hPk6P2KOv1OvkhaZYf1nJua/Z2nlU+rOUs49eZZ5rJ5Hdpmlmxo06nycuXxQ7HhoQCFDI9KXehbWZN3vz0JvOTdzd/UGiT+uoomRTeyt6kS5Ks1+u07evMZluGYpKu2/oQPNLRer0u/E8EKOXJ90+yWq2S8zLHe3/Hcfa2zPGSD3cdZ5knSY6Pj3N5ebnVMU9vc+W1p1J7p1OAHps200wmdf2YmnyuW13/2mCEps20yvLZ21XrJBSgAiaf2RehAJUw+cw+CAWoiMlndk0oQGVqnXxWPtdBKECF2ratrmNIlM81EApQqcViUV0wKJ/7TyhAxZTPlCYUoHLKZ0oSCjAAymdKEQowEMpnShAKMCDKZ7YlFGBglM9sQyjAACmfeSyhAAOlfOYxhAIMWK3ls53Ph2PzGvTY6flpumWX5tl2r/i7tst6vc6r253PXaGdz0kye5usksyL7n2eZJ5pfjMrt6TZzufN2NEMPVZq7/Oudj4nu937bOfz/rlTgBEpvfM52f3eZzuf90unACNS685nb1fdn7r+dQBbmzbT6t6V5O2q+yMUYIRqfFeSyef9EAowUrVOPptj2C2hACNW4+Rz4kP0dkkowMjVOvmsfN4NoQBUGQzK590QCkAS5TM3hAJwR/mMUAA+oXweN6EA/EKNHYPyuQyhAHxWjcGgfN6eUAC+SPk8PkIB+Crl87gIBeBeFxcX1X26aqJ8foz6/paBg7i+vq6uY1A+P5xQADZWa/ls5/PmhALwIDWWz0nSde2hT6EK1nHCiJyen6Zbdmmebf9q/81Pb/Lu3bu8+iFplkl3UuAEb83eJqsk85TZUZ1MMs803+XHIjufPzadJi9fFj3kQT099AkA+zM9KXWRTV58+yLt6zbz92FQ8OXl6iiZFDxek+7u1237ulgwdN39X1MbdwrAVp58/ySr1So5L3fMV7dBc/a20PFu7zjOMk+STCaTXF9fb33c09tseT2gykKnAGxl2kyre7uqyecvq+tvEuilaTOtrnw2+fx5QgEowuTzMAgFoBiTz/Wr728P6DWTz3UTCkBxtU4+e5QkFIAdqXXyeezls1AAdqbW8nnMHYNQAHbq4uKiujuGq6ur0QaDUAB2brFYVNcxjLV8FgrAXiif6yAUgL1RPvefUAD2Svncb0IB2Dvlc38JBeAglM/9JBSAg1E+949QAA5K+dwvNq8BWym19/n9zuckxfc+l9/5/N7N7uffzMotaT70zmc7moGtlNr7/OLbF+naLuv1uvje59I7n9/7ePdzCX3Y+exOAeiV2WyW7k9ur46F9j6X3vl8d9zbO48/nLwZzM5nnQLQK23bVreoZ0jlc13/54FRmDbTpMJr7BDKZ6EA9NJsNjP5fABCAegtk8/7JxSAXjP5vF9CAeg9k8/7IxSAKrRtm+Pj40OfxoPVVj4LBaAal5eXyucdEwpAVWotn2t5lCQUgOrUWD4nqaJ8FgpAlWotn/v+KEkoANWqMRiurq56XT4LBaBqbdsqnwsSCkD1lM/lCAVgEJTPZQgFYDBq7Bj6Vj4LBWBQagyGPpXPQgEYnFrL57Y94Mq1W0IBGKQay+ckBw8GO5qB3jk9P0237NI82/4x0Lyb55//6yrNMulOCpzcR2Zvk1WS+e2u5u1NMs803+XHzGanhY75wSa7n58W/64AW5qelLrI3qz2nJ+0N78p/BJ4dZRMCh6zSXf367Z9XTQYuu7+r0ncKQAjcHp+mnk3z+ofVkWP++r2zuPsbaHj3d5xnGWeJHn27FkWi0WRY5+ebnanoFMARmHaTKssn/f9dlWhAIxGjeXzviefhQIwKiafv04oAKNT44DbviafhQIwSjUGwz4mn4UCMFq1Tj7v8o5BKACjpnz+lFAARm+xWOT4+PjQp/FguyifhQJAksvLy+o6hl2Uz0IB4JbyWSgAfGLs5bNQAPiZMZfPQgHgM8ZaPgsFgC8YY/ksFAC+otby+bGPkoQCwD1qLJ+TPOqOQSgAbKDW8vmhb1cVCgAbWiwW1d0xLJfLB5XP1nECg3d6fppu2aV5VqYbmHfzrFarvPohaZZJd1LksJm9TVZJ5im3o/qDSU7X9+/jfLqD7wzQK9OTshfZaTPNvJtnfnK787nQS+vVUTLZ0cv0Jl2Ojo5y332AOwWAR/qD//IHeffuXXJe5nivbu84zt6WOd4nx05yltwbCjoFgEd68e2Lve5P3gehALCFZtZUUz5vMm8hFAC2dHFxUcWAW9d1936NUAAooMbJ588RCgCF1Dr5/DGhAFBQjZPPHxMKAIXVOPn8nlAA2IFayuefEwoAO1Jj+SwUAHaobduqOgahALBji8WimmAQCgB7UEv5LBQA9qSG8lkoAOxR38tnoQCwZ30un4UCwAH0tXwWCgAH0sfyWSgAHFDfymehAHBgfSqfnx76BABq1i27nJ6fbn2co98eJX+XNMubfcqlbRo5R+v7tjgDsDfPnz/Pcrm8+33TNBttTNvUfZd8oQDAHZ0CAHeEAgB3hAIAd4QCAHeEAgB3hAIAd4QCAHeEAgB3/j8/KOU/7axvnAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "negf = NEGF(\n",
+    "    model=model,\n",
+    "    AtomicData_options=negf_json['AtomicData_options'],\n",
+    "    structure=structure,\n",
+    "    results_path=output,  \n",
+    "    **negf_json['task_options']\n",
+    ")\n",
+    "   \n",
+    "negf.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "db275dee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_17948/1724458665.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  negf_out = torch.load('./output/negf.out.pth')\n"
+     ]
+    }
+   ],
+   "source": [
+    "negf_out = torch.load('./output/negf.out.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a29b9ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negf_out.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8eb092f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['DOS'][str(negf_out['k'][0])])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('DOS')\n",
+    "plt.title('DOS vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4a2fe762",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('Transmission')\n",
+    "plt.title('Transmission vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dpnegf-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/atomic_chain_api/run.ipynb b/examples/atomic_chain_api/run.ipynb
index 6baae97..5f182a3 100644
--- a/examples/atomic_chain_api/run.ipynb
+++ b/examples/atomic_chain_api/run.ipynb
@@ -33,15 +33,9 @@
      "text": [
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO                                      Version Info                                  \n",
-      "DPNEGF INFO    --------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DPNEGF INFO    DPNEGF               : 0.1.1.dev21+7918d32\n",
-      "DPNEGF INFO    DeePTB               : 2.1.2.dev43+b666d17\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
       "DPNEGF INFO    ================================================================================\n",
       "\n",
       "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n"
@@ -86,7 +80,13 @@
       "DPNEGF INFO    k-points: [[0 0 0]]\n",
       "DPNEGF INFO    k-points weights: [1.]\n",
       "DPNEGF INFO    --------------------------------\n",
-      "DPNEGF INFO    The AtomicData_options is {'r_max': 2.0}\n",
+      "DPNEGF WARNING AtomicData_options is extracted from input file. This may be not consistent with the model options. Please be careful and check the cutoffs.\n",
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": 2.0\n",
+      "               }\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732052e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
       "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
       "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
       "DPNEGF INFO    The coupling width of lead_L is 1.\n",
@@ -126,6 +126,10 @@
       "DPNEGF INFO    Reference energy E_ref: -13.638588428497314\n",
       "DPNEGF INFO    =================================================\n",
       "\n",
+      "DPNEGF INFO    Merging 400 tmp self energy files into output/self_energy/self_energy_leadL.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Merging 400 tmp self energy files into output/self_energy/self_energy_leadR.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
       "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
       "DPNEGF INFO    computing green's function at e = -2.000\n",
       "DPNEGF INFO    computing green's function at e = -1.599\n",
@@ -163,7 +167,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_772487/1724458665.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "/tmp/ipykernel_16165/1724458665.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
       "  negf_out = torch.load('./output/negf.out.pth')\n"
      ]
     }
@@ -201,7 +205,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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6NOLj45GVlYUFCxbAy8sLkyZNQnBwMKZPn4558+YhLCwMQUFBeOSRR5CYmGj1jCUiIiKSN5cGmUuXLmHSpEm4fv06mjdvjjvuuAN79uxB8+bNAQBLliyBUqlESkoKtFotkpOT8f7777uyyERERCQhLg0yX331Vb2PazQapKamIjU1tYlKRERERO5EUmNkiIiIiGzBIENERERui0GGiIiI3BaDDBEREbktBhmSrNJyvauLQEREEscgQ5K09cQVdHx+I97bcsrVRSEiN/bG5pMY8sY25JfK86KNxCBDEvX0N4cAAK9v/tPFJSEid/bultM4e7UYX+w+7+qikJMwyJAkKaBwdRGISEb0BleXgJyFQYaIiIjcFoMMERERuS0GGZIkBc8sERGRFRhkiIhI9vjhSL4YZIiIiMhtMciQJPHDExE5EtsU+WKQIUlSsB+YiByITYp8McgQERGR22KQISIiIrfFIENERERui0GGiIhkj+Pu5ItBhoiIiNwWgwwRERG5LQYZkiT2AhMRkTUYZEiSGGSIyJHYpsgXgwwRERG5LQYZIiIiclsMMiRJCl4ZhYiIrMAgQ0REsscPR/LFIENERERui0GGJIkzDIjIkdimyBeDDEkS2xwiciS2KfLFIENERERui0GGiIiI3BaDDEkSr1RLRETWYJAhIiLZ42cj+WKQISIiIrfFIEOSxA9PRORIXBBPvhhkSJrY5hARkRUYZIiISPY4Rka+GGSIiIjIbTHIEBGRLAkhXF0EagIMMiRJ7AUmosZijvEMDDJERCRLzDGegUGGiIhkiaeWPAODDEkSL1FARI1VPcawTZEvBhmSJDY5RNRYhmo9MmxT5ItBhoiIZIlnljwDgwwRERG5LQYZkiSeziaixmKPjGdgkCEiIlkyGyPDD0eyxSBDRESyxA4Zz8AgQ5Kk4BwDImokwVlLHoFBhiSJ3cBE1FjskfEMDDJERCRLwlD1PRfEky8GGSIikiXBPhmPwCBDRESyxOnXnoFBhoiIZMnAJOMRGGSIiEiWzC8a6bJikJMxyBARkSxV75Bh54x8MciQJHGGARE1VvXBvoJJRrYkE2RefvllKBQKzJkzx7StrKwMs2bNQrNmzRAQEICUlBTk5OS4rpDUZBhjiKixzHpkXFcMcjJJBJl9+/bhww8/RNeuXc22z507F+vXr8eaNWuwfft2ZGVlYfz48S4qJRERuROeWvIMLg8yRUVFmDx5Mv7zn/8gNDTUtD0/Px+ffPIJ3nzzTQwZMgQ9e/bEsmXL8Msvv2DPnj0uLDEREbkDs1NLLiwHOZe3qwswa9Ys3H333UhKSsK///1v0/b9+/dDp9MhKSnJtK1Dhw6Ii4vD7t270bdv31qPp9VqodVqTfcLCgoAADqdDjqdzmHlNh7LkceUGtfWsarZcdbr8z10f6yf+3NmHXW6CtP3er3eJT9Hub+Hzn3/rDumS4PMV199hd9//x379u2zeCw7Oxs+Pj4ICQkx2x4ZGYns7Ow6j7l48WIsXLjQYvvmzZvh5+fX6DLXlJaW5vBjSo0r6lhQ4AXjSJkNGzY49bX4Hro/1s/9OaOOuVrA+G/u2LFj2JB31OGvYS25v4fOqF9JSYlV+7ksyFy8eBGPPvoo0tLSoNFoHHbc+fPnY968eab7BQUFiI2NxbBhwxAUFOSw19HpdEhLS8PQoUOhUqkcdlwpcWUdPzy/GyguBACMHDnSKa/B99D9sX7uz5l1vHSjFAt/3wkA6NixI0b2b+XQ41tD7u+hM+tnPKPSEJcFmf379+PKlSvo0aOHaZter8eOHTvw3nvvYdOmTSgvL0deXp5Zr0xOTg6ioqLqPK5arYZarbbYrlKpnPJL5KzjSokr6qhUVs1bcvZr8z10f6yf+3NGHb29q05NKJVeLv0Zyv09dEb9rD2ey4LMXXfdhcOHD5ttmzZtGjp06ICnnnoKsbGxUKlUSE9PR0pKCgDg5MmTuHDhAhITE11RZGpCCk7AJqJGMp9+zeG+cuWyIBMYGIjOnTubbfP390ezZs1M26dPn4558+YhLCwMQUFBeOSRR5CYmFjnQF8iIiKj6tda4vRr+XL5rKX6LFmyBEqlEikpKdBqtUhOTsb777/v6mIREZEbEHV8T/IiqSCzbds2s/sajQapqalITU11TYGIiMhtCfbIeASXL4hHVBteaomIGsvAMTIegUGGiIhkiuHFEzDIEBGRLPFaS56BQYYkiWeWiKixmF08A4MMSRMHyRBRI5lPv2askSsGGSIikiWeWvIMDDJERCRL5iv7klwxyBARkSxVn3LNHhn5YpAhSeIIGSJqLIYXz8AgQ0REssSLRnoGBhmSpOqTljjbgIjswVNLnoFBhiSp+qklAxsgIrIDB/t6BgYZkjz2yBCRPQycf+0RGGRI8tj8EJE9RB3fk7wwyJDk8YMUEdmDHTKegUGGJElRbbSvgS0QEdmh+mlpzlqSLwYZIiKSJUYXz8AgQ5JUfdYSO2SIyB48teQZGGRI8tglTET2MD+1RHLFIEOSVH1BPK4jQ0T2MLBHxiMwyJDkcR0ZIrKH2cq+7JORLQYZkjw2P0RkFy4k4xEYZEjyhMHVJSAid8Qc4xkYZEiSFNXmLbFLmIjswTWoPAODDEke2yIisof59Gs2JHLFIEPSVG3WEpsfIrKH2aklNiSyxSBDksfuYSKyB9eR8QwMMiRNXP+BiBqJK/t6BgYZkiSu/0BEjcV2xDMwyJAkCc6bJKJGYo+MZ2CQIUmq3ubwEgVEZA+2HZ6BQYYkj13CRGQPTrn2DAwyJElmsw3YFhGRHcynX7MhkSsGGZIkDpEhosYyGyPjumKQkzHIkCRVb4AMPNFNRHZgz65nYJAhSWKbQ0SNZd6zy1ZFrhhkSJr4SYqIGonTrz0DgwxJkvn0a7ZARGQ78wXxSK4YZEjy2AARkT04vM4zMMiQJJl3CbM1IiLbcbCvZ2CQIUlilzARORZbErlikCFJYo8METUWB/t6BgYZkiQ2QETUWAaeWvIIDDIkSVzZl4gay3xlX7YkcsUgQ5LEQXpE1Fjm11pyWTHIyRhkSPK4jgwR2YNth2dgkCHJY1tERHbhRSM9AoMMSRLPbRNRY5kt48BmRLYYZEiS2AARUWPxA5FnYJAhSeL0ayJqLAOnP3oEBhmSJPP2hy0QEdmOK4R7BgYZkiROvyaixuIK4Z6BQYYkqXqTwymURGQPthyegUGGJI+NERHZw6xn14XlIOdikCFp4mBfImokThrwDAwyJEminntERNZgj4xnYJAhSareABnYAhGRHcyvtcSGRK4YZEiSeLE3Imqs6h+C2IzIF4MMSRKnTRJRYwnBJOMJXBpkPvjgA3Tt2hVBQUEICgpCYmIifvzxR9PjZWVlmDVrFpo1a4aAgACkpKQgJyfHhSWmpsKFrIiIyBouDTItW7bEyy+/jP379+O3337DkCFDMHbsWBw9ehQAMHfuXKxfvx5r1qzB9u3bkZWVhfHjx7uyyOQCXEeGiOxhMBvsy3ZErrxd+eKjR482u//SSy/hgw8+wJ49e9CyZUt88sknWLlyJYYMGQIAWLZsGTp27Ig9e/agb9++rigyNRGz7ML2h4jswOnXnsGlQaY6vV6PNWvWoLi4GImJidi/fz90Oh2SkpJM+3To0AFxcXHYvXt3nUFGq9VCq9Wa7hcUFAAAdDoddDqdw8prPJYjjyk1rqxj9XPbuooKp5SB76H7Y/3cnzPrWKHXm77XGwwu+TnK/T10Zv2sPaZCuHgk5eHDh5GYmIiysjIEBARg5cqVGDlyJFauXIlp06aZhRIA6N27NwYPHoxXXnml1uO98MILWLhwocX2lStXws/Pzyl1IMd7Yb8XbpQrAAD/7KhH+xB+nCIi2/yUqcD6C14AgK5hBkxvb3BxicgWJSUl+Otf/4r8/HwEBQXVuZ/Le2Tat2+PgwcPIj8/H19//TWmTJmC7du32328+fPnY968eab7BQUFiI2NxbBhw+r9QdhKp9MhLS0NQ4cOhUqlcthxpcSVdXz52A6gvAwAcHvvXhjQNtzhr8H30P2xfu7PmXW8sP0scOE0ACAyMgojR3Zz6PGtIff30Jn1M55RaYjLg4yPjw/atm0LAOjZsyf27duHt99+GxMmTEB5eTny8vIQEhJi2j8nJwdRUVF1Hk+tVkOtVltsV6lUTvklctZxpcTVdfTy8nbq67u6fk1B7nVk/dyfM+qo9PKquqNQuPRnKPf30Bn1s/Z4kltHxmAwQKvVomfPnlCpVEhPTzc9dvLkSVy4cAGJiYkuLCE1Ba4jQ0SNZXaJAjYjsuXSHpn58+djxIgRiIuLQ2FhIVauXIlt27Zh06ZNCA4OxvTp0zFv3jyEhYUhKCgIjzzyCBITEzljycOwASIie7Dt8AwuDTJXrlzBAw88gMuXLyM4OBhdu3bFpk2bMHToUADAkiVLoFQqkZKSAq1Wi+TkZLz//vuuLDI1EfMF8dgaEZHtzK/TxnZErlwaZD755JN6H9doNEhNTUVqamoTlYikgus/EFFjmX0gYjsiW5IbI0ME8KKRRNR4vNSSZ2CQIUmq3gDxEgVEZA/zwb5sR+SKQYYkiheNJKLG4ZVOPAODDEkSx8gQUWOxHfEMDDIkSaKee0RE1uCMR8/AIEOSZ2BbRER2MHCwr0dgkCFJ4oqcRNRYXCHcMzDIkCSZD9JjA0REtmPb4RkYZEiSOEiPiBqN7YhHYJAhSareDcx1ZIjIHtXbDvbOyBeDDEkSmxwiaiz27HoGBhmSJjZARNRIbDo8A4MMSR5PLRGRPdgj4xkYZEiSeNFIImosjpHxDAwyJElm68i4sBxEJA/8QCRfDDIkSeY9MmyBiMh2/EDkGRhkSJJ4bpuIGsvs8iZsR2SLQYYkqfr5bJ7bJiJ7sB3xDAwyJEnskSGixmI74hkYZEjy2P4QkT3YdngGBhmSpOoNENeRISJ7cLCvZ2CQIWlilzARNZL5qSU2JHLlbcvOFRUV0Ov1UKvVpm05OTlYunQpiouLMWbMGNxxxx0OLyR5HvNBekREtjMLMq4rBjmZTUFmxowZ8PHxwYcffggAKCwsRK9evVBWVobo6GgsWbIE69atw8iRI51SWPIcQtR1h4jIOmYfiNiMyJZNp5Z27dqFlJQU0/3PP/8cer0ep06dwh9//IF58+bhtddec3ghyfOYj5FxWTGIyI0Z2CPjEWwKMpmZmWjXrp3pfnp6OlJSUhAcHAwAmDJlCo4ePerYEpJHMhukx49SRGQH9ux6BpuCjEajQWlpqen+nj170KdPH7PHi4qKHFc6IvCTFBHZh4vgeQabgky3bt3wxRdfAAB27tyJnJwcDBkyxPT4mTNnEBMT49gSkkfiqSUiajSeWvIINg32ff755zFixAisXr0aly9fxtSpUxEdHW16/Ntvv0X//v0dXkjyPJw2SUSNZRAc7OsJbAoyAwcOxP79+7F582ZERUXh3nvvNXu8W7du6N27t0MLSEREZA/za0YyyciVTUEGADp27IiOHTvW+tjDDz/c6AIR1eyB4ScpIrIHr7XkGexa2XfNmjUYP348OnfujM6dO2P8+PH4+uuvHV028lA1GxxeooCI7MFTS57BpiBjMBgwYcIETJgwAceOHUPbtm3Rtm1bHD16FBMmTMDEiRM5noEareZvEH+jiMgeoo7vSV5sOrX09ttv46effsJ3332HUaNGmT323XffYdq0aXj77bcxZ84cR5aRPAxPLRGRQ7Dt8Ag29cgsW7YMr732mkWIAYAxY8bg1VdfxaeffuqwwhEBHKRHRPYxv0QB2xG5sinInDp1CklJSXU+npSUhFOnTjW6UOTZLE4tsf0hIjsYDK4uATUFm4KMr68v8vLy6ny8oKAAGo2msWUiD1czuPCTFBHZgxeN9Aw2BZnExER88MEHdT6empqKxMTERheKPFvNU0lsgIjIHmbTr3mKWrZsGuz77LPPYtCgQbh+/Toef/xxdOjQAUIIHD9+HG+88QbWrVuHrVu3Oqus5CEsemRcUwwicnO8ZqRnsCnI9OvXD6tWrcLDDz+M//3vf6btQgiEhYXhyy+/5CUKyOG4jgwR2aP6aWm2IvJl88q+99xzD5KTk7F582b8+eefAIBbbrkFw4YNg5+fn8MLSJ7HcoyMa8pBRO6N12zzDDYHGYPBgK+++grffPMNzp8/D4VCgYSEBBQUFOD++++HQqFwRjnJg7H5ISJ7sO3wDDYN9hVCYMyYMXjooYeQmZmJLl26oFOnTsjIyMDUqVNxzz33OKuc5EEsB/uyOSIi2/HUkmewqUdm+fLl2LFjB9LT0zF48GCzx7Zs2YJx48bh888/xwMPPODQQpJn4aklInIEA69R4BFs6pH58ssv8cwzz1iEGAAYMmQInn76aaxYscJhhSPPZHmtJbZARGQ75hjPYFOQOXToEIYPH17n4yNGjMAff/zR6EKRZ+O1lojIEcxOLbEhkS2bgkxubi4iIyPrfDwyMhI3btxodKHIs9Vsbgxsf4jIDuYL4pFc2RRk9Ho9vL3rHlbj5eWFioqKRheKPJvlgnhsgojIdrxEgWewabCvEAJTp06FWq2u9XGtVuuQQhGZYQNERHZgePEMNgWZKVOmNLgPZyxRo/ESBUTkALzWkmewKcgsW7bMWeUgMqnZ4Bg4SIaI7GAQPLXkCWwaI0PUFHjRSCJyBF400jMwyJDkWKwjwwaIiOzBtsMjMMiQ5FisI8PWiIjsYD5rie2IXDHIkOSwR4aIHMHAdWQ8AoMMSY7ltZbYBBGR7dh2eAYGGZI8NkVEZA8O9vUMDDIkORbTr9kCEZEduI6MZ3BpkFm8eDF69eqFwMBAREREYNy4cTh58qTZPmVlZZg1axaaNWuGgIAApKSkICcnx0UlpiZhcWrJNcUgIvcmuI6MR3BpkNm+fTtmzZqFPXv2IC0tDTqdDsOGDUNxcbFpn7lz52L9+vVYs2YNtm/fjqysLIwfP96FpSZnsxjs65JSEJG7E3V8T/Ji08q+jrZx40az+8uXL0dERAT279+PO++8E/n5+fjkk0+wcuVKDBkyBEDl6sIdO3bEnj170LdvX1cUm5zMcrCva8pBRO7N7NQS2xHZktQYmfz8fABAWFgYAGD//v3Q6XRISkoy7dOhQwfExcVh9+7dLikjOV/Nc9mceUBE9jAfX8d2RK5c2iNTncFgwJw5c9C/f3907twZAJCdnQ0fHx+EhISY7RsZGYns7Oxaj6PVas2uwl1QUAAA0Ol00Ol0Diuv8ViOPKbUuKqOOl2F2X29weCUMvA9dH+sn/tzZh2rX6fNIIRLfo5yfw+dWT9rjymZIDNr1iwcOXIEP//8c6OOs3jxYixcuNBi++bNm+Hn59eoY9cmLS3N4ceUmqau4w0tUP1X88KFi9iwIcNpr8f30P2xfu7PGXUsKPACoAAAlGvLsWHDBoe/hrXk/h46o34lJSVW7SeJIDN79mx8//332LFjB1q2bGnaHhUVhfLycuTl5Zn1yuTk5CAqKqrWY82fPx/z5s0z3S8oKEBsbCyGDRuGoKAgh5VZp9MhLS0NQ4cOhUqlcthxpcRVdczKK8ULv+803W8Z2xIjR3Z2+OvwPXR/rJ/7c2YdPzj7C1BSBABQ+fhg5MjBDj2+NeT+HjqzfsYzKg1xaZARQuCRRx7Bt99+i23btiEhIcHs8Z49e0KlUiE9PR0pKSkAgJMnT+LChQtITEys9ZhqtRpqtdpiu0qlcsovkbOOKyVNXUcv75rdiUqnvj7fQ/fH+rk/Z9RR3OyNqf4ariL399AZ9bP2eC4NMrNmzcLKlSuxbt06BAYGmsa9BAcHw9fXF8HBwZg+fTrmzZuHsLAwBAUF4ZFHHkFiYiJnLMmYxawlDtIjIjuYXTTSheUg53JpkPnggw8AAIMGDTLbvmzZMkydOhUAsGTJEiiVSqSkpECr1SI5ORnvv/9+E5eUXIotEBHZgdOvPYPLTy01RKPRIDU1FampqU1QIpICyx4ZIiLbmV9riS2JXElqHRkigNdaIiLHqN52sBWRLwYZkjzmGCKyC9sOj8AgQ5LDU0tE5AiizjskJwwyJDkWF41klwwR2UHw1JJHYJAhyakZXJhjiMgeBrNZS2xI5IpBhiTHokeGn6WIyA5cR8YzMMiQ5FiMkWELRER24DoynoFBhiSI06+JqPHMggz7ZGSLQYYkhz0yROQIHBfjGRhkSPLYFBGRPcxX9nVZMcjJGGRIciynX7ukGETk5sxPLZFcMciQ5FieWmITRES2MzDJeAQGGZKcmoPy2P4QkT3MTi2xJZEtBhmSHPbIEJEjcPq1Z2CQIcnhtZaIyDG4IJ4nYJAhyanZBWxgC0REdmDb4RkYZEjyeGqJiOxhdtFItiOyxSBDksP2hogcQdTxPckLgwxJHoMNEdmDg309A4MMSU7NBofXWiIie7Dt8AwMMiQ5FuvIsC0iIntwKQePwCBDkmM5/ZqNDxHZjpc78QwMMiQ5NdsaTqEkInvUPLXEpkSeGGRI+tj6EJEd2APjGRhkSHJqnsfmqSUisofleDu2JXLEIEOSU7Op0enZ+BCR7fQGnlryBAwyJDk1PzSV6fSuKQgRuS2d3mDxIYgdMvLEIEMSZN7aMMgQka1qazd4mlqeGGRIcmp+aiplkCEiG9XWbrBHRp4YZEhyarY1peUMMkRkm7Jyg6uLQE2EQYYkS+1d+etZpmODRES2MfbIGNsRki++wyQ5xu5fPx8vAEC53mAx+4CIqD7GMTLGdgTgqSW5YpAhyTGu9eDn423axgG/RGSLUlOQqWpHONhXnhhkSHKMTY1aVfXryQG/RGQLY5vhyx4Z2WOQIckxNjYKAL6qykaIA36JyBZlN9sMYxsCcEE8uWKQIckxdv8qFApoVMYBvwwyRGS92ntkGGXkiEGGpKe2HhkGGSKygSnIsEdG9hhkSHKMjY1CAWh8eGqJiGxnbDOqz1oiefJueBci11BAAV/jqaUKriVDRNbT3mwzONhX/tgjQ5JjGuyr4GBfIrJPrT0yDDKyxCBDklN9rQfjpykO9iUiW3AdGc/BIEOSU737V8PBvkRkB2OboVHx1JLcMciQ5FQN9lVUBRmeWiIiG5TVcmqJOUaeGGRIcoxrPVROv678FWWPDBHZotbp1+ySkSUGGZKc6tOvjY0Qx8gQkS1qXRDPVYUhp2KQIcmqvo4MgwwR2aK2q1+TPDHIkPSYVvZVcGVfIrJLqe7mOjIc7Ct7DDIkOVXXWqq+jgwXxCMi61W/aKRCUbmN06/liUGGJKf6pyauI0NE9jBNv+aCeLLHIEOSI6pdNFLjzVNLRGQ7U5Dx9sLNDhnmGJlikCHJMTU2CgUvGklEdjGdWvLxguLmuSWOkZEnBhmSHPN1ZNgjQ0S2K6uoNkbm5jaOkZEnBhmSLK4jQ0T20OkN0OkrQ0v1wb4kTwwyJDmmBfEA+PpU/ooyyBCRtaq3FxofJRTgqSU5Y5AhyTEN9q12raVijpEhIiuV3GwvlArAx0sJmKZfkxwxyJAEVY2RCfP3AQDklZTzOilEZJXc4nIAle2Hotp5JbYh8sQgQ5JTva0xBhmdXqCgrMJFJSIid1I9yACoGuzLHCNLDDIkOdUvGqn29kKA2hsAcL1I67pCEZHbuF4zyHCwr6wxyJDkiGrXWgKqGiPjpywiovrk3vzQ08xfDQAc7CtzLg0yO3bswOjRoxETEwOFQoG1a9eaPS6EwPPPP4/o6Gj4+voiKSkJp06dck1hqckIVFvaF1VB5jqDDBFZgT0ynsWlQaa4uBi33XYbUlNTa3381VdfxTvvvIOlS5di79698Pf3R3JyMsrKypq4pOQKxranGXtkiMgGFkHm5nYuiCdP3q588REjRmDEiBG1PiaEwFtvvYV//etfGDt2LADg888/R2RkJNauXYuJEyc2ZVGpCVVNv678ylNLRGSL3KLKtqJZgLFHhqeW5MylQaY+586dQ3Z2NpKSkkzbgoOD0adPH+zevbvOIKPVaqHVVg0KLSgoAADodDrodDqHlc94LEceU2pcVceKipuzk4SATqdDqF/lr+mVglK+hzaSex1ZP/fnjDpeK6rstQ9We0Gn05l6YnQVjv0/YA25v4fOrJ+1x5RskMnOzgYAREZGmm2PjIw0PVabxYsXY+HChRbbN2/eDD8/P8cWEkBaWprDjyk1TV3HA9cUALxw/fp1bNiwATlZlfePnDqPDRvOOvz1+B66P9bP/TmyjheveAFQ4NSRA9hwUUBfUXl/27btiPB12MvYRO7voTPqV1JSYtV+kg0y9po/fz7mzZtnul9QUIDY2FgMGzYMQUFBDnsdnU6HtLQ0DB06FCqVymHHlRJX1bHij8vAqcMIDw/HyJG3Q3sgC+syjkAT3BwjR/Z02OvwPXR/rJ/7c0YdX/hjKwAdRg4ZgHaRAfjXgS0o01fgzjsHonVzf4e8hrXk/h46s37GMyoNkWyQiYqKAgDk5OQgOjratD0nJwfdunWr83lqtRpqtdpiu0qlcsovkbOOKyVNXUcvr8rLEiiVCqhUKjQPrvwIdaNEx/fQTnKvI+vn/hxVxwq9AXmllackIkL8oFKpTIN9vby9XfZzlPt76Iz6WXs8ya4jk5CQgKioKKSnp5u2FRQUYO/evUhMTHRhycjZhOkSBZXNTzPT9GsuiEdE9btRooMQlZMFQv3MB/vyakvy5NIemaKiIpw+fdp0/9y5czh48CDCwsIQFxeHOXPm4N///jfatWuHhIQEPPfcc4iJicG4ceNcV2hqMrXNWhJCmF07hYioOuPsxhBfFbyUlW0Fmwx5c2mQ+e233zB48GDTfePYlilTpmD58uV48sknUVxcjIcffhh5eXm44447sHHjRmg0GlcVmZpAzSmS4QGVpwp1eoHc4nI0C7A8dUhEBADZBZUzlpoHVrUTvNaSvLk0yAwaNKjeq5EqFAosWrQIixYtasJSkatVrSNT2fxoVF6ICtIgu6AMGbklDDJEVKcL14sBAHFhVYN6TevIuKRE5GySHSNDnqu2xiauWeXU+QvXrZuOR0SeKeNmGxHfzHK5DfbIyBODDEmOsZeu+mnt+LCbQSaXQYaI6paRaxlkeIkCeWOQIckxNjXVB+gZG6UM9sgQUT2MvbaxYdWCzM22hD0y8sQgQ9JjfvFrAEBcs8rz3Rdyi5u+PETkFoQQpl7b+LDqp5Z4rSU5Y5Ahyao+zdrYKLFHhojqcrVQi1KdHkoF0DLUskeG5IlBhiSnakG8KsZTS1cKtSgt17ugVEQkdcbxMdHBvvDxrvr3xjEy8sYgQ5JTNf26aluInw+CfSuXqz53jaeXiMjSuauVbUPNGUscIyNvDDIkOXW1NR2jAwEAR7Lym64wROQ2jG3DrdGOu0AwSR+DDElO1acm8xPbnWOCAQBHMhlkiMjS4ZttQ5eWwWbbFRzsK2sMMiQ5pjEyNQboGRsnBhkiqqlCb8DxywUAgE4xNYKM8dQSx8jIEoMMSY6oZfo1AHRuUdk4HbtcgAq9oWkLRUSSduZqMcp0Bvj7eKF1uL/ZY7zWkrwxyJBk1eyRSWjmD38fL5TpDDhzlQN+iaiKsae2U0wwlErzxkPB+deyxiBDkmNa2bdGn4xSqTCdXtp3PreJS0VEUvZbRmWb0LXG+Jjq2CEjTwwyJD2i9jEyANCvTTgAYNfpa01ZIiKSuJ9vtgn924ZbPFY1/ZpRRo4YZEhy6mtq7mhX2Uj9cuY69AY2SkQEZFwvxsXcUqi8FOidEFbnfmwx5IlBhiSntgXxjLq2CEag2hv5pTrOXiIiAFW9Md3jQuGv9rZ4nAviyRuDDEmOsfu35hgZAPD2UiKxTTMAQPqJK01aLiKSpi3HK9uCO2o5rQRUb0uYZOSIQYYkp4718EyGd44CAHx/KIvnvIk8XH6JDjtOXQUAjLjZNtTEHhl5Y5AhyalrHRmjobdGwsdbibNXi3H8cmGTlYuIpGfT0Wzo9AIdogLRLjKw1n3YHyNvDDIkWXWt/RCoUWFw++YAgG8PXGrKIhGRxHx7IBMAMKprdJ37cB0ZeWOQIcmpWkembn/pGQsAWP3bJZSW651eJiKSnlM5hdh99jqUCmBc9xZ17seVfeWNQYYkx5pxL0M6RKBlqC/yS3VYdzCzCUpFRFLz2e7zACpPN7cM9Wtwf46pkycGGZKs+nqDvZQKPJAYDwD4aMdZXnuJyMNcKSjDmt8qTy1P6deq/p1NF40kOWKQIclpaLCv0V/7xCPUT4Wz14qx9mCW08tFRNLx/rYz0FYY0CMuBImtm9W7L08tyRuDDEmOgPESBfVHmQC1N2YObAMAeHPzSZSUVzi9bETkemevFmHl3gsAgMeGtW+wrTA+LtgnI0sMMiQ51vbIAMCUxFZoEeKLrPwyvJN+2qnlIiLXE0JgwXdHUa43YOAtzdGvTf29MUC1toQ5RpYYZEi6rEgyvj5eeGFMJwDAf3aexcGLec4tExG51Ff7LmLnqWvw8VZi4ZhOVk2t5uxreWOQIcmpmn5tXeuT1DECo7pGQ28QePSrA8gv0TmvcETkMqevFGLR+mMAgMeG3oJW4f5WPc/YlrBDRp4YZEhybB2Qp1Ao8NK4LmgR4ouM6yWY+d/fUF7BWUxEcpJfosOMz/ejVKdHvzbNMGNAa5uPwcG+8sQgQ5JTNdjX+ucE+6nw8ZTbEaD2xp6zuXj6m0NcM4JIJkrL9Xjo8304d60YLUJ88e6k7lAqrW8gTNdaYp+MLDHIkOTYMti3uo7RQUid3ANeSgW++T0Tz3x7BHoDGy4id1ZQpsOUZb9i3/kbCNR44+Mpt6NZgNquY/GzjTwxyJBk2TNAb+AtzfHy+C5QKoAvf72AWSt+R5mOlzAgckdXC7WY+OEe/HouF4Fqbyyb2gsdo4NsPk7V9GuSIwYZkhzjKSFrB/vWdO/tsUj9aw/4eCmx8Wg27vtwNy5cL3FkEYnIyX47n4sx7/2MY5cLEB7ggy8f7ovbW4XZdayqBfEYZeSIQYYkqzFTJkd0icbyab0Q7KvCoUv5uPvdnVj/RxYbMiKJ0+kNSN16GhM+2oPL+WVoHe6PNX/vh84tgu0+JqdfyxuDDEmOaYxMIxuffm3DseHRAegeF4LCsgo88uUBPLh8Hy7msneGSIoOXLiBMe/twmubTkJvEBjbLQbfPXIHEqycZl0XBa+1JGsMMiQ5VY1N4z9GtQjxxeqZifi/u9pB5aXA1pNXcdeb2/H/NhzHjZLyRh+fiBrvaikwd/Uh3PP+Lzh+uQAhfiq8fu9teGtCNwSovRt9fAWvGilrjf8NIXIwR5/9UXkpMW/oLRhzWwyeW3sEu89ex0c7zmLF3gz0ClOi641SJESoHPuiRNSgI5n5+M+OM1h/yAsGkQ0AGN+jBZ4d2dHumUn14fRreWKQIcmxZx0Za7SNCMDKGX2w7c+reG3jSRy7XIBtl5XYsWQnhneOwt/6xKNv62Y2rU9BRLYp0+mRfvwKPtt9Hr+ey725VYGB7cLxxPAOjRoLUxfTqSXmGFlikCHJsXcdGWsoFAoMbh+Bge2aI/34Zbz23X78ma/EhsPZ2HA4G9HBGozpFoNx3VqgQ1SgVddxIaL66Q0C+87nYu2BTPxw+DIKyyqvVO+tVGBE50jcIi7h7/f1gErlnJ7RqllLTjk8uRiDDEmO6VpLTswQSqUCg25pjpJbDWjToz9W7MvE94eycDm/DB9uP4sPt59FixBfDO7QHHd1iERim2bQqLycVyAimSko0+GX09eQduwKtpzIwY1q10CLDtZgfI8W+FvfeIT7eWPDhkvOLQzXkZE1BhmSnkauI2Or9lGBWDy+CxaMvhVbT1zBNwcysePPq8jMK8V/91zAf/dcgEalRM/4UPRu1Qy9EkLRPTYUvj4MNkRGhWU67Dufiz1nc7Hn7HUcycxH9YW1gzTeSO4UhXt6tEDfhKpTuDqd8y/yynVk5I1BhiSrqc/qaFReGNElGiO6RKO0XI9fzlzDlhNXsOXEFVzOL8Ou09ex6/R1AIDKS4FOMcHo3CIInWOC0blFMNpFBkDtzXBD8ldSXoFjWQU4dCkfhzPzcehSHs5eK7Y4dZMQ7o8hHSKQ1DESt7cKhcrLNRNleYZY3hhkSHJMp5ZcWAZfHy/c1TESd3WMhBACf+YU4ddz1/Hr+RvYdy4X2QVlOHgxDwcv5pme461U4JbIQLSPCkTrcH+0iQhA6+b+aNXMn6elyC2V6fQ4c7UIp68U4VROEU5dKcSpK0U4f60YtV3GLL6ZHxJbN0Pfm7eoYE3TF7oWph4Zl5aCnIVBhiRHar2/CoUC7aMqA8r9ia0ghMDF3FIcvJSHo5n5OJpVgCNZ+cgr0eHY5QIcu1xQ4/lAy1BftA4PQGyYL1qE+CEmRIOWoZXfRwSqOVOKXEJvEMguKMPF3JLK241SXMotwcUbJbiYW4qcwrI6/x4jg9To0iIEXVsGo0vLYHRpEYxwJ0yZdiSptS3kGAwyJDlV06+l+c9doVAgrpkf4pr5YcxtMQAqz71n5pXiSGYBzlwtwpmrRTh7tRhnrhahsKwCF3NLcTG3tNbjqbwUiArWoEWILyICNQgPUKN5YNUtPMAHzQPVaOavhhcDD1lBpzfgRkk5rhRocbVQiyuFZcgpqP5Vi6sFZbhSqEVFA1eID/FToV1EANpGBKJdRADaRQbglshARAZJo7fFGlVtCZOMHDHIkOS446cmhUKBlqF+aBnqZ7ZdCIFrReU4e7UI564V49KNUmTm3bzdKEV2QRl0elFv0DFSKoAwfx+E+PkgxFeFED8Vgn19EOKnqrp/87FgXxUCNd5QewFaPWBo4J8VSU+F3oD8Uh1ytcDJ7EKU6YFCbQUKyyqQV1KOG8U63Cgpr/y+pPL7GyXlyCvWoVBbYfXreCsVaBHqi9hQP8SG+aJlqB9iw/wQG+qLuDA/hPn7SPZDhbU4/VreGGRIcppi+nVTUSgUpp6VPq2bWTxeoTcgp1CLzBulyMorxbWiyk/QVwu1uHrz+2tFWlwvLodBANeKynGtyNZLK3jjqX1p8FN5wV/tjQC1N/zV3vBXe1X73hv+Pl7QqLyg9lZWflV5QWP8/ubXypv5NpWXEt5eCqiUlV+9lQq3/8dXF4NBQGcwoEIvUKEXKNcbUGEwoLzCgDKdAWU6Pcp0epTq9CjTGaCtuHm/XI+yCuPjVfuV6fQoLtejqKwCRdrKW2FZBYq1FSjV6W++qjfw+26by6pQAM381YgMUiMiUI2IQA0ig9RoHqRBZKAaEUEaRASqERmkkX1PH6+1JG8MMiQ5VQviybtxBQBvLyVahPiiRYhvvftV6A3ILSnH1UIt8kt1yC/RIa9Uh7wSHfJKy5FfokO+6b4O+SXlKNRW/kM0iMqfaXF55T/NK4Va59dLqTALNyovpSnweCsV1b5XQqmoDHxKReV7rlBU/uNRKhRQKoz3K38bqu9bubdATo4S3+cdhIACQggYhKisM1B131B5yrLyZ2H5VS8qg4lOb0CFQUBXYYDOIFChN0BXbbveBT1bKoVAkJ8agRpvBGgqg2ioX2XPXKif6ub3lV9D/VUI8fNBmJ8PgnxVsg8o1jK2JeyRkScGGZIsmX6ot4u3lxIRgRpEBNo2LqG8vBzrvv8Rdwy+C1q9AkU3w01xeQWKtPrK72/2BJSU66E19hjc7EnQ1uhFKDfer6jqVajtf3uFQaDCIFAGg4N+AvVRArlXmuB1aqe6GdQ0Ki/4qrygVimh8a7sufL18br5feV23+q9Wje3+6uNAcULAWoVAtTelaFF7Q0fpcBPmzdi5MhBTlv11iOwLZE1BhmSHNNgXxeXQw4UCgV8vIDwALXT/hHWPN1i/F6nN1T1cOhvPm6o7OGovp+xZwQ3e0wMQkDc/AqgWo/KzW03HzMIoEJfgcOHj6BLl85QeXtX67G52YOjxM1encpeHOP26veVysr7Vb1HN3uMlMqq72v0KjXVabSmWCzOE1QN9WWXjBwxyJD0sK1xK0qlAmqlF9QuaE10Oh2Crx7GyF6x7LGgBvHUkjy5ZplFonrIabAvEbkeB/vKG4MMSY7xeihynflCRE2rarAvo4wcMciQ5FTNWiIiajx+JpI3BhmSHNNnJjY+ROQAplNL7JCRJQYZkixPWEeGiJyPbYm8MciQ5JhOLbHtISIHqBrsyy4ZOWKQIclhY0NEzsBTS/LEIEOSw8G+ROQMDDLy5BZBJjU1Fa1atYJGo0GfPn3w66+/urpI1AR4aomIHMG4lANzjDxJPsisWrUK8+bNw4IFC/D777/jtttuQ3JyMq5ccd21Vci5TOvIsE+GiBzAdIkCdsnIkuQvUfDmm29ixowZmDZtGgBg6dKl+OGHH/Dpp5/i6aefdlm5bpSUI1cLZOaVwtvb/Hoojflbqe+59Y0dqf95Db1m7XtUVFQgpxQ4e7UY3irLX5X661n/q9b33LzSyp8ne2SIyBGMbcmVQi3OXSuuuu5WE7QxFRUVdf6vkANj/Yq0FQh10WVCJB1kysvLsX//fsyfP9+0TalUIikpCbt37671OVqtFlqt1nS/oKAAQOU1WRx5AbbXN/+J1b97Y+HvOx12TGnyxv87uMslr2wwGJx60TzjseV8YT6515H1c39NUsebn5xe23QSr2066bzXqZPc/1d4QxGdicl94x16VGt/JyQdZK5duwa9Xo/IyEiz7ZGRkThx4kStz1m8eDEWLlxosX3z5s3w8/NzWNkuZyqhUtYf5xsb9ht8fgM7NPR8KZdP4wX4XPsTGzb8aWOpbJeWlub013A1udeR9XN/zqxjS4MCQSolKkxXUQcMTns1z3Ti+DFsyD3q0GOWlJRYtZ+kg4w95s+fj3nz5pnuFxQUIDY2FsOGDUNQUJDDXmeoToe0tDQMHTpUtlfd1cm8jnKvHyD/OrJ+7q8p6jgSwAKnHLlhcn8PnVk/4xmVhkg6yISHh8PLyws5OTlm23NychAVFVXrc9RqNdRqtcV2lUrllF8iZx1XSuReR7nXD5B/HVk/9yf3OrJ+9h3TGpKeteTj44OePXsiPT3dtM1gMCA9PR2JiYkuLBkRERFJgaR7ZABg3rx5mDJlCm6//Xb07t0bb731FoqLi02zmIiIiMhzST7ITJgwAVevXsXzzz+P7OxsdOvWDRs3brQYAExERESeR/JBBgBmz56N2bNnu7oYREREJDGSHiNDREREVB8GGSIiInJbDDJERETkthhkiIiIyG0xyBAREZHbYpAhIiIit8UgQ0RERG6LQYaIiIjcFoMMERERuS23WNm3MYQQAKy/HLi1dDodSkpKUFBQINsrmsq9jnKvHyD/OrJ+7k/udWT97Gf8v238P14X2QeZwsJCAEBsbKyLS0JERES2KiwsRHBwcJ2PK0RDUcfNGQwGZGVlITAwEAqFwmHHLSgoQGxsLC5evIigoCCHHVdK5F5HudcPkH8dWT/3J/c6sn72E0KgsLAQMTExUCrrHgkj+x4ZpVKJli1bOu34QUFBsvzlrE7udZR7/QD515H1c39yryPrZ5/6emKMONiXiIiI3BaDDBEREbktBhk7qdVqLFiwAGq12tVFcRq511Hu9QPkX0fWz/3JvY6sn/PJfrAvERERyRd7ZIiIiMhtMcgQERGR22KQISIiIrfFIENERERui0HGSufPn8f06dORkJAAX19ftGnTBgsWLEB5eXm9zysrK8OsWbPQrFkzBAQEICUlBTk5OU1Uatu89NJL6NevH/z8/BASEmLVc6ZOnQqFQmF2Gz58uHML2gj21FEIgeeffx7R0dHw9fVFUlISTp065dyC2ik3NxeTJ09GUFAQQkJCMH36dBQVFdX7nEGDBlm8h3//+9+bqMQNS01NRatWraDRaNCnTx/8+uuv9e6/Zs0adOjQARqNBl26dMGGDRuaqKT2saV+y5cvt3ivNBpNE5bWNjt27MDo0aMRExMDhUKBtWvXNvicbdu2oUePHlCr1Wjbti2WL1/u9HI2hq113LZtm8V7qFAokJ2d3TQFtsHixYvRq1cvBAYGIiIiAuPGjcPJkycbfF5T/w0yyFjpxIkTMBgM+PDDD3H06FEsWbIES5cuxTPPPFPv8+bOnYv169djzZo12L59O7KysjB+/PgmKrVtysvLce+99+If//iHTc8bPnw4Ll++bLp9+eWXTiph49lTx1dffRXvvPMOli5dir1798Lf3x/JyckoKytzYkntM3nyZBw9ehRpaWn4/vvvsWPHDjz88MMNPm/GjBlm7+Grr77aBKVt2KpVqzBv3jwsWLAAv//+O2677TYkJyfjypUrte7/yy+/YNKkSZg+fToOHDiAcePGYdy4cThy5EgTl9w6ttYPqFxBtfp7lZGR0YQltk1xcTFuu+02pKamWrX/uXPncPfdd2Pw4ME4ePAg5syZg4ceegibNm1yckntZ2sdjU6ePGn2PkZERDiphPbbvn07Zs2ahT179iAtLQ06nQ7Dhg1DcXFxnc9xyd+gILu9+uqrIiEhoc7H8/LyhEqlEmvWrDFtO378uAAgdu/e3RRFtMuyZctEcHCwVftOmTJFjB071qnlcQZr62gwGERUVJR47bXXTNvy8vKEWq0WX375pRNLaLtjx44JAGLfvn2mbT/++KNQKBQiMzOzzucNHDhQPProo01QQtv17t1bzJo1y3Rfr9eLmJgYsXjx4lr3v++++8Tdd99ttq1Pnz5i5syZTi2nvWytny1/m1IDQHz77bf17vPkk0+KTp06mW2bMGGCSE5OdmLJHMeaOm7dulUAEDdu3GiSMjnSlStXBACxffv2Ovdxxd8ge2QaIT8/H2FhYXU+vn//fuh0OiQlJZm2dejQAXFxcdi9e3dTFLFJbNu2DREREWjfvj3+8Y9/4Pr1664uksOcO3cO2dnZZu9hcHAw+vTpI7n3cPfu3QgJCcHtt99u2paUlASlUom9e/fW+9wVK1YgPDwcnTt3xvz581FSUuLs4jaovLwc+/fvN/vZK5VKJCUl1fmz3717t9n+AJCcnCy59wqwr34AUFRUhPj4eMTGxmLs2LE4evRoUxS3SbjT+9dY3bp1Q3R0NIYOHYpdu3a5ujhWyc/PB4B6/++54j2U/UUjneX06dN499138frrr9e5T3Z2Nnx8fCzGYkRGRkryfKg9hg8fjvHjxyMhIQFnzpzBM888gxEjRmD37t3w8vJydfEazfg+RUZGmm2X4nuYnZ1t0T3t7e2NsLCwesv617/+FfHx8YiJicGhQ4fw1FNP4eTJk/jmm2+cXeR6Xbt2DXq9vtaf/YkTJ2p9TnZ2tlu8V4B99Wvfvj0+/fRTdO3aFfn5+Xj99dfRr18/HD161KkXx20qdb1/BQUFKC0tha+vr4tK5jjR0dFYunQpbr/9dmi1Wnz88ccYNGgQ9u7dix49eri6eHUyGAyYM2cO+vfvj86dO9e5nyv+Bj2+R+bpp5+udeBV9VvNRiUzMxPDhw/HvffeixkzZrio5Naxp362mDhxIsaMGYMuXbpg3Lhx+P7777Fv3z5s27bNcZVogLPr6GrOrt/DDz+M5ORkdOnSBZMnT8bnn3+Ob7/9FmfOnHFgLcgREhMT8cADD6Bbt24YOHAgvvnmGzRv3hwffvihq4tGVmrfvj1mzpyJnj17ol+/fvj000/Rr18/LFmyxNVFq9esWbNw5MgRfPXVV64uigWP75F57LHHMHXq1Hr3ad26ten7rKwsDB48GP369cNHH31U7/OioqJQXl6OvLw8s16ZnJwcREVFNabYVrO1fo3VunVrhIeH4/Tp07jrrrscdtz6OLOOxvcpJycH0dHRpu05OTno1q2bXce0lbX1i4qKshgkWlFRgdzcXJt+3/r06QOgstexTZs2NpfXUcLDw+Hl5WUxy6++v5+oqCib9ncle+pXk0qlQvfu3XH69GlnFLHJ1fX+BQUFyaI3pi69e/fGzz//7Opi1Gn27NmmyQMN9fy54m/Q44NM8+bN0bx5c6v2zczMxODBg9GzZ08sW7YMSmX9HVo9e/aESqVCeno6UlJSAFSOVL9w4QISExMbXXZr2FI/R7h06RKuX79u9k/f2ZxZx4SEBERFRSE9Pd0UXAoKCrB3716bZ3fZy9r6JSYmIi8vD/v370fPnj0BAFu2bIHBYDCFE2scPHgQAJr0PayNj48PevbsifT0dIwbNw5AZfd2eno6Zs+eXetzEhMTkZ6ejjlz5pi2paWlNdnfmy3sqV9Ner0ehw8fxsiRI51Y0qaTmJhoMVVXqu+fIx08eNDlf2+1EULgkUcewbfffott27YhISGhwee45G/QacOIZebSpUuibdu24q677hKXLl0Sly9fNt2q79O+fXuxd+9e07a///3vIi4uTmzZskX89ttvIjExUSQmJrqiCg3KyMgQBw4cEAsXLhQBAQHiwIED4sCBA6KwsNC0T/v27cU333wjhBCisLBQPP7442L37t3i3Llz4qeffhI9evQQ7dq1E2VlZa6qRr1sraMQQrz88ssiJCRErFu3Thw6dEiMHTtWJCQkiNLSUldUoV7Dhw8X3bt3F3v37hU///yzaNeunZg0aZLp8Zq/o6dPnxaLFi0Sv/32mzh37pxYt26daN26tbjzzjtdVQUzX331lVCr1WL58uXi2LFj4uGHHxYhISEiOztbCCHE/fffL55++mnT/rt27RLe3t7i9ddfF8ePHxcLFiwQKpVKHD582FVVqJet9Vu4cKHYtGmTOHPmjNi/f7+YOHGi0Gg04ujRo66qQr0KCwtNf2MAxJtvvikOHDggMjIyhBBCPP300+L+++837X/27Fnh5+cnnnjiCXH8+HGRmpoqvLy8xMaNG11VhQbZWsclS5aItWvXilOnTonDhw+LRx99VCiVSvHTTz+5qgp1+sc//iGCg4PFtm3bzP7nlZSUmPaRwt8gg4yVli1bJgDUejM6d+6cACC2bt1q2lZaWir++c9/itDQUOHn5yfuueces/AjJVOmTKm1ftXrA0AsW7ZMCCFESUmJGDZsmGjevLlQqVQiPj5ezJgxw9QIS5GtdRSicgr2c889JyIjI4VarRZ33XWXOHnyZNMX3grXr18XkyZNEgEBASIoKEhMmzbNLKTV/B29cOGCuPPOO0VYWJhQq9Wibdu24oknnhD5+fkuqoGld999V8TFxQkfHx/Ru3dvsWfPHtNjAwcOFFOmTDHbf/Xq1eKWW24RPj4+olOnTuKHH35o4hLbxpb6zZkzx7RvZGSkGDlypPj9999dUGrrGKca17wZ6zRlyhQxcOBAi+d069ZN+Pj4iNatW5v9LUqRrXV85ZVXRJs2bYRGoxFhYWFi0KBBYsuWLa4pfAPq+p9X/T2Rwt+g4mZhiYiIiNyOx89aIiIiIvfFIENERERui0GGiIiI3BaDDBEREbktBhkiIiJyWwwyRERE5LYYZIiIiMhtMcgQkUe6fv06IiIicP78eYce99ixY2jZsiWKi4sdelwiqh2DDBHVa+rUqbVecXv48OGuLlqjvPTSSxg7dixatWpl1f6jR4+us847d+6EQqHAoUOHcOutt6Jv37548803HVhaIqoLV/YlonpNnToVOTk5WLZsmdl2tVqN0NBQp71ueXk5fHx8nHLskpISREdHY9OmTejbt69Vz1m7di1SUlKQkZFhcQXgBx98EIcPH8a+ffsAAD/88ANmzJiBCxcuwNvb46/NS+RU7JEhogap1WpERUWZ3aqHGIVCgY8//hj33HMP/Pz80K5dO3z33Xdmxzhy5AhGjBiBgIAAREZG4v7778e1a9dMjw8aNAizZ8/GnDlzEB4ejuTkZADAd999h3bt2kGj0WDw4MH47LPPoFAokJeXh+LiYgQFBeHrr782e621a9fC398fhYWFtdZnw4YNUKvVFiGmvjKOGjUKzZs3x/Lly82eU1RUhDVr1mD69OmmbUOHDkVubi62b99u5U+YiOzFIENEDrFw4ULcd999OHToEEaOHInJkycjNzcXAJCXl4chQ4age/fu+O2337Bx40bk5OTgvvvuMzvGZ599Bh8fH+zatQtLly7FuXPn8Je//AXjxo3DH3/8gZkzZ+LZZ5817e/v74+JEyda9BYtW7YMf/nLXxAYGFhrWXfu3ImePXuabWuojN7e3njggQewfPlyVO/IXrNmDfR6PSZNmmTa5uPjg27dumHnzp12/CSJyCZOvSQlEbm9KVOmCC8vL+Hv7292e+mll0z7ABD/+te/TPeLiooEAPHjjz8KIYR48cUXxbBhw8yOe/HiRQHAdCXxgQMHiu7du5vt89RTT4nOnTubbXv22WcFAHHjxg0hhBB79+4VXl5eIisrSwghRE5OjvD29hbbtm2rs05jx44VDz74oNk2a8p4/Phxi6ulDxgwQPztb3+zeI177rlHTJ06tc4yEJFj8OQtETVo8ODB+OCDD8y2hYWFmd3v2rWr6Xt/f38EBQXhypUrAIA//vgDW7duRUBAgMWxz5w5g1tuuQUALHpJTp48iV69eplt6927t8X9Tp064bPPPsPTTz+N//73v4iPj8edd95ZZ31KS0uh0WjMtllTxg4dOqBfv3749NNPMWjQIJw+fRo7d+7EokWLLJ7j6+uLkpKSOstARI7BIENEDfL390fbtm3r3UelUpndVygUMBgMACrHkYwePRqvvPKKxfOio6PNXsceDz30EFJTU/H0009j2bJlmDZtGhQKRZ37h4eH48aNG2bbrC3j9OnT8cgjjyA1NRXLli1DmzZtMHDgQIvn5Obmok2bNnbVh4isxzEyROR0PXr0wNGjR9GqVSu0bdvW7FZfeGnfvj1+++03s23GmUHV/e1vf0NGRgbeeecdHDt2DFOmTKm3PN27d8exY8fsKuN9990HpVKJlStX4vPPP8eDDz5Ya2g6cuQIunfvXm85iKjxGGSIqEFarRbZ2dlmt+ozjhoya9Ys5ObmYtKkSdi3bx/OnDmDTZs2Ydq0adDr9XU+b+bMmThx4gSeeuop/Pnnn1i9erVp1lD18BAaGorx48fjiSeewLBhwyymR9eUnJyMo0ePmvXKWFvGgIAATJgwAfPnz8fly5cxdepUi+OfP38emZmZSEpKsvInRET2YpAhogZt3LgR0dHRZrc77rjD6ufHxMRg165d0Ov1GDZsGLp06YI5c+YgJCQESmXdzVBCQgK+/vprfPPNN+jatSs++OAD06wltVpttu/06dNRXl6OBx98sMHydOnSBT169MDq1avtKuP06dNx48YNJCcnIyYmxuL4X375JYYNG4b4+PgGy0JEjcMF8YjIrbz00ktYunQpLl68aLb9iy++wNy5c5GVlWXVQno//PADnnjiCRw5cqTeMGWr8vJytGvXDitXrkT//v0ddlwiqh0H+xKRpL3//vvo1asXmjVrhl27duG1117D7NmzTY+XlJTg8uXLePnllzFz5kyrVwO+++67cerUKWRmZiI2NtZh5b1w4QKeeeYZhhiiJsIeGSKStLlz52LVqlXIzc1FXFwc7r//fsyfP9+09P8LL7yAl156CXfeeSfWrVtX6/RpIpIvBhkiIiJyWxzsS0RERG6LQYaIiIjcFoMMERERuS0GGSIiInJbDDJERETkthhkiIiIyG0xyBAREZHbYpAhIiIit8UgQ0RERG7r/wMeEvOkPXQcIAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/examples/atomic_chain_cli/run.ipynb b/examples/atomic_chain_cli/run.ipynb
index 2b42cd0..40822f2 100644
--- a/examples/atomic_chain_cli/run.ipynb
+++ b/examples/atomic_chain_cli/run.ipynb
@@ -32,13 +32,13 @@
       "           |  '--'  ||  |      |  |\\   | |  |____ |  |__| | |  |                \n",
       "             |_______/ | _|      |__| \\__| |_______| \\______| |__|              \n",
       "--------------------------------------------------------------------------------\n",
-      "                       DPNEGF version 0.1.1.dev21+7918d32                       \n",
+      "                       DPNEGF version 0.1.1.dev97+bccd946                       \n",
       "================================================================================\n",
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO                                      Version Info                                  \n",
       "DPNEGF INFO    --------------------------------------------------------------------------------\n",
-      "DPNEGF INFO    DPNEGF               : 0.1.1.dev21+7918d32\n",
-      "DPNEGF INFO    DeePTB               : 2.1.2.dev43+b666d17\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
       "DPNEGF INFO    ================================================================================\n",
       "\n",
       "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n",
@@ -49,7 +49,15 @@
       "DPNEGF INFO    k-points: [[0 0 0]]\n",
       "DPNEGF INFO    k-points weights: [1.]\n",
       "DPNEGF INFO    --------------------------------\n",
-      "DPNEGF INFO    The AtomicData_options is {'r_max': 2.0, 'er_max': None, 'oer_max': None}\n",
+      "DPNEGF WARNING AtomicData_options is extracted from input file. This may be not consistent with the model options. Please be careful and check the cutoffs.\n",
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": 2.0,\n",
+      "                   \"er_max\": null,\n",
+      "                   \"oer_max\": null\n",
+      "               }\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732052e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
       "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
       "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
       "DPNEGF INFO    The coupling width of lead_L is 1.\n",
@@ -89,6 +97,10 @@
       "DPNEGF INFO    Reference energy E_ref: -13.638588428497314\n",
       "DPNEGF INFO    =================================================\n",
       "\n",
+      "DPNEGF INFO    Merging 400 tmp self energy files into /personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_cli/output/results/self_energy/self_energy_leadL.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Merging 400 tmp self energy files into /personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_cli/output/results/self_energy/self_energy_leadR.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
       "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
       "DPNEGF INFO    computing green's function at e = -2.000\n",
       "DPNEGF INFO    computing green's function at e = -1.599\n",
@@ -101,7 +113,8 @@
       "DPNEGF INFO    computing green's function at e = 1.208\n",
       "DPNEGF INFO    computing green's function at e = 1.609\n",
       "DPNEGF WARNING The Fermi energy of the left and right leads should be equal in nscf current calculation.\n",
-      "DPNEGF INFO    negf calculation successfully completed.\n"
+      "DPNEGF INFO    negf calculation successfully completed.\n",
+      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m"
      ]
     }
    ],
@@ -121,7 +134,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_727394/1401031374.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "/tmp/ipykernel_16472/1401031374.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
       "  negf_out = torch.load('./output/results/negf.out.pth')\n"
      ]
     }
@@ -185,7 +198,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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s2LFISEhAdnY2Fi5cCC8vL0yePBnBwcGYMWMG5s+fj7CwMAQFBeHhhx9GcnKy1VcsERERkby5NMhcunQJkydPxvXr1xEREYHbb78du3fvRkREBABg6dKlUCqVSEtLg1arRWpqKt555x1XFpmIiIgkxKVB5vPPP2/wcY1Gg/T0dKSnpzdTiYiIiMidSGqMDBEREZEtGGSIiIjIbTHIEBERkdtikCEiIiK3xSBDklVWoXd1EYjIzQkhUK5jWyJnDDIkSVuOX0Hn5zbg35tPubooROTGZn26D52e3YCs/DJXF4WchEGGJOmpLw8CAF7bdNLFJSEid7bpaC4AYO2vF11cEnIWBhmSJAUUri4CEckI2xT5YpAhIiIit8UgQ0RERG6LQYYkScFeYCJyILYp8sUgQ0RERG6LQYaIiIjcFoMMSRJ7gYnIkdimyBeDDEmSgie0iciB2KTIF4MMERERuS0GGSIiInJbDDJERCR7PF0tXwwyRERE5LYYZIiIiMhtMcgQERGR22KQIUni6WwiIrIGgwxJEoMMETkS2xT5YpAhIiIit8UgQ0RERG6LQYYkScGVUYjIgdimyBeDDBEREbktBhkiIiJyWwwyJEm8woCIHIltinwxyJAksc0hIkdimyJfDDJERETkthhkiIiIyG0xyJAkKXhCm4gciE2KfDHIEBERkdtikCEiIiK3xSBDksReYCJyJM7sK18MMiRNbHOIyIE4Rka+GGSIiIjIbTHIEBERkdtikCEiIlkSQri6CNQMGGRIkng6m4iaijnGMzDIEBGRLDHHeAYGGSIikiWeWvIMDDIkSVyigIiaqmaMYZsiXwwyJElscoioqQw1emTYpsgXgwwREckSzyx5BgYZIiIiclsMMiRJPJ1NRE1ldmqJbYpsMcgQEZEs8dSSZ2CQISIiWWKO8QwMMiRJCl5jQERNJHjVkkdgkCFJ4vlsImoqQ40uGc4jI18MMkREJE88t+QRGGSIiEiWBJOMR2CQISIiWeJVS56BQYaIiGSJ88h4BgYZIiKSpZodMuydkS8GGSIikiWGF8/AIEOSxEsliaipag72FUw1siWZIPPSSy9BoVBg7ty5pm3l5eWYPXs2WrRogYCAAKSlpSE3N9d1haRmwxhDRE1VM7swxsiXJILM3r178d5776F79+5m2+fNm4dvv/0Wa9euxbZt25CdnY0JEya4qJREROROzIIMk4xsuTzIFBcXY8qUKfjPf/6D0NBQ0/aCggJ8+OGHeOONNzB06FAkJSVh+fLl+Pnnn7F7924XlpiIiNyB2aklF5aDnMvb1QWYPXs2Ro8ejZSUFPzzn/80bd+3bx90Oh1SUlJM2zp16oT4+Hjs2rUL/fr1q/N4Wq0WWq3WdL+wsBAAoNPpoNPpHFZu47EceUypcW0dq5sdZ70+30P3x/q5P2fWsaKi+ph6vd4lP0e5v4fOrJ+1x3RpkPn888/x22+/Ye/evRaP5eTkwMfHByEhIWbbo6KikJOTU+8xlyxZgkWLFlls37RpE/z8/Jpc5toyMjIcfkypcUUdCwu9YBwps379eqe+Ft9D98f6uT9n1DFPCxj/zR09ehTr8484/DWsJff30Bn1Ky0ttWo/lwWZixcv4pFHHkFGRgY0Go3DjrtgwQLMnz/fdL+wsBBxcXEYPnw4goKCHPY6Op0OGRkZGDZsGFQqlcOOKyWurON753YBJUUAgFGjRjnlNfgeuj/Wz/05s46XbpRh0W87AACdO3fGqAGtHXp8a8j9PXRm/YxnVBrjsiCzb98+XLlyBb169TJt0+v12L59O/79739j48aNqKioQH5+vlmvTG5uLqKjo+s9rlqthlqtttiuUqmc8kvkrONKiSvqqFRWX7fk7Nfme+j+WD/354w6entXn5pQKr1c+jOU+3vojPpZezyXBZk777wThw4dMts2ffp0dOrUCU8++STi4uKgUqmQmZmJtLQ0AMCJEydw4cIFJCcnu6LI1IwUvACbiJqo5hIFXEBSvlwWZAIDA9G1a1ezbf7+/mjRooVp+4wZMzB//nyEhYUhKCgIDz/8MJKTk+sd6EtERGTEy689g8uvWmrI0qVLoVQqkZaWBq1Wi9TUVLzzzjuuLhYREbkBUc/3JC+SCjJbt241u6/RaJCeno709HTXFIiIiNxWzWUJ2CMjXy6fEI+oLlxqiYiaymC2RAGTjFwxyBARkUyxR8YTMMgQEZEsMbx4BgYZkiSeWSKipmKO8QwMMiRNHCRDRE1kNo8Mu2dki0GGiIhkifPIeAYGGSIikiWzIOO6YpCTMciQJPHEEhE1lYHzyHgEBhkiIpI9ziMjXwwyREQkS+yF8QwMMiRJNS9a4tUGRGQPwQnxPAKDDElSzTEyBjZARGQHAwf7egQGGZI89sgQkT0Er7/2CAwyJHlsfojIHqKe70leGGRI8vhBiojswQ4Zz8AgQ5KkqDHa18AWiIjsUPPUEi+/li8GGSIikiVGF8/AIEOSVPOqJXbIEJE9eGrJMzDIkOSxS5iI7GF+aonkikGGJKnmhHicR4aI7GFgj4xHYJAhyeM8MkRkD7OZfdknI1sMMiR5bH6IyC6cSMYjMMiQJClqDPcVBhcWhIjcFpco8AwMMiR57BImInuYLxrJdkSuGGRI8tj+EJE92HZ4BgYZkqYaVy2xLSIie5gNkWFDIlsMMiR5XKKAiOxh4DwyHoFBhqSJ8z8QUVOxHfEIDDIkSZz/gYiaiu2IZ2CQIUkSnP+BiJqIay15BgYZkqSabQ6XKCAie7Dt8AwMMiR57BImIntw7hjPwCBDkmS2ai3bIiKyg/nl12xI5IpBhiSJQ2SIqKnMxsi4rhjkZAwyJEk1GyADT3QTkR3Ys+sZGGRIktjmEFFTmffsslWRKwYZkiZ+kiKiJuLl156BQYYkyfzya7ZARGQ7LlHgGRhkSPLYABGRPdh2eAYGGZIk8y5hNkdEZDsO9vUMDDIkSeZrpBARNRVbErlikCFJYo8METWVgT0yHoFBhiSJVxsQUVOxHfEMDDIkSZzZl4iaynxmX7YkcsUgQ5LEQXpE1FTmay25rBjkZAwyJHmcR4aI7MG2wzMwyJDksS0iIrtw0UiPwCBDksRz20TUVGbTOLAZkS0GGZIkNkBE1FT8QOQZGGRIknjZJBE1lYGXP3oEBhmSJPP2hy0QEdmOM4R7BgYZkiRefk1ETcUZwj0DgwxJUs0mh5dQEpE9zD4QubAc5FwMMiR5bICIyB5sOzwDgwxJEwf7ElET8aIBz8AgQ5IkGrhHRGQNnlryDAwyJEk1GyADWyAisoOBg309AoMMSRIXeyOipuI0Mp6BQYYkiZdNElFTCfOpfUmmXBpk3n33XXTv3h1BQUEICgpCcnIyfvjhB9Pj5eXlmD17Nlq0aIGAgACkpaUhNzfXhSWm5sKJrIjIkTixpny5NMi0atUKL730Evbt24dff/0VQ4cOxbhx43DkyBEAwLx58/Dtt99i7dq12LZtG7KzszFhwgRXFplcgPPIEJE92HZ4Bm9XvvjYsWPN7r/44ot49913sXv3brRq1QoffvghVq1ahaFDhwIAli9fjs6dO2P37t3o16+fK4pMzUTw5DYRNREvv/YMLg0yNen1eqxduxYlJSVITk7Gvn37oNPpkJKSYtqnU6dOiI+Px65du+oNMlqtFlqt1nS/sLAQAKDT6aDT6RxWXuOxHHlMqXFlHWue29ZVVjqlDHwP3R/r5/6cWcdKvd70vd5gcMnPUe7voTPrZ+0xFcLFIykPHTqE5ORklJeXIyAgAKtWrcKoUaOwatUqTJ8+3SyUAECfPn0wZMgQvPzyy3Ue7/nnn8eiRYsstq9atQp+fn5OqQM53vP7vHCjQgEA+HtnPTqG8OMUEdnmxywFvr3gBQDoHmbAjI4GF5eIbFFaWoo///nPKCgoQFBQUL37ubxHpmPHjjhw4AAKCgrwxRdfYOrUqdi2bZvdx1uwYAHmz59vul9YWIi4uDgMHz68wR+ErXQ6HTIyMjBs2DCoVCqHHVdKXFnHl45uByrKAQC39emNge3CHf4afA/dH+vn/pxZxwvb/gAunAYAREVFY9SoHg49vjXk/h46s37GMyqNcXmQ8fHxQbt27QAASUlJ2Lt3L9566y1MnDgRFRUVyM/PR0hIiGn/3NxcREdH13s8tVoNtVptsV2lUjnll8hZx5USV9fRy8vbqa/v6vo1B7nXkfVzf86oo9LLq/qOQuHSn6Hc30Nn1M/a40luHhmDwQCtVoukpCSoVCpkZmaaHjtx4gQuXLiA5ORkF5aQmgPnkSGipjJbooDNiGy5tEdmwYIFGDlyJOLj41FUVIRVq1Zh69at2LhxI4KDgzFjxgzMnz8fYWFhCAoKwsMPP4zk5GReseRh2AARkT24vIlncGmQuXLlCu6//35cvnwZwcHB6N69OzZu3Ihhw4YBAJYuXQqlUom0tDRotVqkpqbinXfecWWRqZmYT4jH1oiIbGf+IYjtiFy5NMh8+OGHDT6u0WiQnp6O9PT0ZioRSQXnfyCipjL7QMR2RLYkN0aGCOCikUTUdFxqyTMwyJAk1WyAOM04EdnDfLAv2xG5YpAhieKikUTUNFzpxDMwyJAkcYwMETUV2xHPwCBDkiQauEdEZA3Bnl2PwCBDkse5IIjIHmw7PAODDEkSZ+QkoqbiDOGegUGGJMl8kB4bICKyHdsOz8AgQ5LEQXpE1GRsRzwCgwxJUs1uYM4jQ0T2qNl2sHdGvhhkSJLY5BBRU7Fn1zMwyJA0sQEioibiUieegUGGJI+nlojIHmw7PAODDEkSP0kRUVOZLxrJhkSuGGRIkszmkXFhOYhIHviBSL4YZEiSzHtk2AIRke34gcgzMMiQJPFqAyJqKrMlCtiOyBaDDEmS+WJvbIGIyHZsRzwDgwxJEntkiKip2I54BgYZkjy2P0RkD7YdnoFBhiSpZgPEuSCIyB4c7OsZGGRImtglTERNZH5qiQ2JXHnbsnNlZSX0ej3UarVpW25uLpYtW4aSkhLcdddduP322x1eSPI85oP0iIhsZz4hHsmVTUFm5syZ8PHxwXvvvQcAKCoqQu/evVFeXo6YmBgsXboU69atw6hRo5xSWPIcQtR3h4jIOmYfiNiMyJZNp5Z27tyJtLQ00/1PPvkEer0ep06dwu+//4758+fj1VdfdXghyfOYj5FxWTGIyI0Z2CPjEWwKMllZWWjfvr3pfmZmJtLS0hAcHAwAmDp1Ko4cOeLYEpJHMhukx49SRGQH9ux6BpuCjEajQVlZmen+7t270bdvX7PHi4uLHVc6IvCTFBHZh5PgeQabgkyPHj3w6aefAgB27NiB3NxcDB061PT4mTNnEBsb69gSkkfiqSUiaioO9vUMNg32fe655zBy5EisWbMGly9fxrRp0xATE2N6/KuvvsKAAQMcXkjyPLxskoiayvwUtQsLQk5lU5AZNGgQ9u3bh02bNiE6Ohr33HOP2eM9evRAnz59HFpAIiIie5ivGckkI1c2BRkA6Ny5Mzp37lznYw899FCTC0RUuweGn6SIyB5ca8kz2DWz79q1azFhwgR07doVXbt2xYQJE/DFF184umzkoWo3OFyigIjsYeCpJY9gU5AxGAyYOHEiJk6ciKNHj6Jdu3Zo164djhw5gokTJ2LSpEkcz0BNVvs3iL9RRGQPUc/3JC82nVp666238OOPP+Kbb77BmDFjzB775ptvMH36dLz11luYO3euI8tIHoanlojIIXjRgEewqUdm+fLlePXVVy1CDADcddddeOWVV/DRRx85rHBEAAfpEZF92HZ4BpuCzKlTp5CSklLv4ykpKTh16lSTC0WezeLUEtsiIrKDweDqElBzsCnI+Pr6Ij8/v97HCwsLodFomlom8nC1gwu7hInIHlw00jPYFGSSk5Px7rvv1vt4eno6kpOTm1wo8my1u4PZABGRPcxn9mVDIlc2DfZ95plnMHjwYFy/fh2PPfYYOnXqBCEEjh07htdffx3r1q3Dli1bnFVW8hAWPTKuKQYRuTmuGekZbAoy/fv3x+rVq/HQQw/hf//7n2m7EAJhYWH47LPPuEQBORznkSEie5gtUeDCcpBz2Tyz7913343U1FRs2rQJJ0+eBAB06NABw4cPh5+fn8MLSJ7HcoyMa8pBRO6Na7Z5BpuDjMFgwOeff44vv/wS586dg0KhQGJiIgoLC3HfffdBoVA4o5zkwdj8EJE92HZ4BpsG+wohcNddd+HBBx9EVlYWunXrhi5duuD8+fOYNm0a7r77bmeVkzyI5WBfNkdEZDsDTy15BJt6ZFasWIHt27cjMzMTQ4YMMXts8+bNGD9+PD755BPcf//9Di0keRaeWiIiRzBrO9iOyJZNPTKfffYZnn76aYsQAwBDhw7FU089hZUrVzqscOSZLNdaYgtERLZjjvEMNgWZgwcPYsSIEfU+PnLkSPz+++9NLhR5Nq61RESOYHbVEhsS2bIpyOTl5SEqKqrex6OionDjxo0mF4o8W+3mxsD2h4jsYD4hHsmVTUFGr9fD27v+YTVeXl6orKxscqHIs1lOiMcmiIhsxyUKPINNg32FEJg2bRrUanWdj2u1WocUisgMGyAisgPDi2ewKchMnTq10X14xRI1GZcoICIH4FpLnsGmILN8+XJnlYPIpHaDY+AgGSKyg9k8MmxGZMumMTJEzYGLRhKRI3DRSM/AIEOSYzGPDBsgIrIH2w6PwCBDkmMxjwxbIyKyg/lVS2xH5IpBhiSHPTJE5AgGziPjERhkSHIs11piE0REthMc7OsRGGRI8tj+EJE92HZ4BgYZkhyLy6/5UYqI7GDgPDIewaVBZsmSJejduzcCAwMRGRmJ8ePH48SJE2b7lJeXY/bs2WjRogUCAgKQlpaG3NxcF5WYmoXFqSXXFIOI3BxPLXkElwaZbdu2Yfbs2di9ezcyMjKg0+kwfPhwlJSUmPaZN28evv32W6xduxbbtm1DdnY2JkyY4MJSk7NZDPZ1SSmIyN2Jer4nebFpZl9H27Bhg9n9FStWIDIyEvv27cMdd9yBgoICfPjhh1i1ahWGDh0KoGp24c6dO2P37t3o16+fK4pNTmY52Nc15SAi92a2RAHbEdmS1BiZgoICAEBYWBgAYN++fdDpdEhJSTHt06lTJ8THx2PXrl0uKSM5X+1z2bxqiYjsYT6+ju2IXLm0R6Ymg8GAuXPnYsCAAejatSsAICcnBz4+PggJCTHbNyoqCjk5OXUeR6vVmq3CXVhYCADQ6XTQ6XQOK6/xWI48ptS4qo46XaXZfb3B4JQy8D10f6yf+3NmHWuu02YQwiU/R7m/h86sn7XHlEyQmT17Ng4fPoyffvqpScdZsmQJFi1aZLF906ZN8PPza9Kx65KRkeHwY0pNc9fxhhao+at54cJFrF9/3mmvx/fQ/bF+7s8ZdSws9AKgAABUaCuwfv16h7+GteT+HjqjfqWlpVbtJ4kgM2fOHHz33XfYvn07WrVqZdoeHR2NiooK5Ofnm/XK5ObmIjo6us5jLViwAPPnzzfdLywsRFxcHIYPH46goCCHlVmn0yEjIwPDhg2DSqVy2HGlxFV1zM4vw/O/7TDdbxXXCqNGdXX46/A9dH+sn/tzZh3f/eNnoLQYAKDy8cGoUUMcenxryP09dGb9jGdUGuPSICOEwMMPP4yvvvoKW7duRWJiotnjSUlJUKlUyMzMRFpaGgDgxIkTuHDhApKTk+s8plqthlqtttiuUqmc8kvkrONKSXPX0cu7dnei0qmvz/fQ/bF+7s8ZdRQ3e2NqvoaryP09dEb9rD2eS4PM7NmzsWrVKqxbtw6BgYGmcS/BwcHw9fVFcHAwZsyYgfnz5yMsLAxBQUF4+OGHkZyczCuWZMziqiUO0iMiO5gtGunCcpBzuTTIvPvuuwCAwYMHm21fvnw5pk2bBgBYunQplEol0tLSoNVqkZqainfeeaeZS0ouxRaIiOzAy689g8tPLTVGo9EgPT0d6enpzVAikgLLHhkiItuZXXzNJCNbkppHhgjgWktE5Bg12w62IvLFIEOSxxxDRHZh2+ERGGRIcnhqiYgcQdR7h+SEQYYkp3Z7w1NLRGQPnlryDAwyJDkWg/LYAhGRHcyvWmJDIlcMMiQ5tZsbziNDRPbgPDKegUGGJMeiQ4YtEBHZgfPIeAYGGZIgXn5NRE1nFmTYJyNbDDIkOeyRISJH4LgYz8AgQ5LHpoiI7GE+s6/LikFOxiBDkmMx2JcNEBHZwfzUEskVgwxJjuWpJTZBRGQ7A5OMR2CQIcmpPSiP7Q8R2cPs1BJbEtlikCHJYY8METkCL7/2DAwyJDmc2JeIHIMT4nkCBhmSnNpdwAa2QERkB4NZjwwbErlikCHJYwNERPZg2+EZGGRIctj2EJEjiHq+J3lhkCHJ4xIFRGQPQ41zS2xG5ItBhiSHSxQQkSOw6fAMDDIkORbzyLA1IiJ7cCoHj8AgQ5Jjefk1Gx8ish2XO/EMDDIkObXbGl5+TUT2qD2+jk2JPDHIkPSx9SEiO7AHxjMwyJDk1D6PzVNLRGQPy/F2bEvkiEGGJKd2U6PTs/EhIttV6nlqyRMwyJDk1P7QVK7Tu6YgROS2dHoDKg28AtITMMiQBJm3NgwyRGSrutoNnqaWJwYZkpzan5rKGGSIyEZ1tRvskZEnBhmSnNptTVkFgwwR2aa8wuDqIlAzYZAhyVJ7V/16luvYIBGRbYw9MsZ2hOSL7zBJjrH718/HCwBQoTegUs8wQ0TWMwYZYzsC8NSSXDHIkOQY53rw8/E2bSuvZJAhIusZT0nXbEc42FeeGGRIcoxNjVpV/evJK5eIyBbllVVthi97ZGSPQYYkx9jYKAD4qqoaIQ74JSJblN9sM4xtCMAJ8eSKQYYkx9j9q1AoTJ+m2CNDRLYwjpEx75FhlJEjBhmSnho9MpqbVxxwLhkiskWdg31dVRhyKgYZkhxjY6NQABofnloiItuV1XVqiUlGlrwb34XINRRQwFfFHhkisl15HaeWSJ7YI0OSYxrsq6j+NMVJ8YjIFsY2o+apJZ5bkicGGZKcmnM9cLAvEdmjeowM55GROwYZkpya57E1xsuvGWSIyAbGNkPDMTKyxyBDklM92FfBeWSIyC7lFbxqyVMwyJDkGOd6UADQcLAvEdmh7rWWGGXkiEGGJKfm5dfVg30ZZIjIenWeWnJVYcipGGRIsjiPDBHZq6yOU0skTwwyJD2mmX2rx8gYF4AjIrJGeWXV5decEE/+GGRIcqrXWqq5aCTnkSEi69VcNFKhqNrGy6/liUGGJKfmpybOI0NE9jCNkeGEeLLHIEOSI8wWjeQ8MkRkO1OQ8fbCzQ4Z5hiZYpAhyTE1NgoFB/sSkV1Mp5Z8vKC4eW6JY2TkiUGGJKfmPDK+nNmXiOxgbDN8VTV7ZJhk5IhBhiSL88gQkT10egMqDVWhpeZgX5InBhmSHNOEeAB8fap+RRlkiMhaNdsLjY8SCvDUkpwxyJDkmAb7KhTwVVWtXFvCMTJEZKXSm+2FUgH4eCkB0+XXJEcMMiRB1WNkQv1VAIAbJRVcJ4WIrJJXUgEACPP3gUKhqB4jwzZElhhkSHJqtjVh/j4AgEqDQGFZpYtKRETu5HpxdZCpiTlGnhhkSHJqLhqp9vZCgLrq9NL1Eq3rCkVEbsPYVhiDDAf7yhuDDEmOqLHWElDdGBm7i4mIGmJsK1r4qwGAg31lzqVBZvv27Rg7dixiY2OhUCjw9ddfmz0uhMBzzz2HmJgY+Pr6IiUlBadOnXJNYanZCNSY2hfVQeY6gwwRWaHmGBkAXGtJ5lwaZEpKSnDrrbciPT29zsdfeeUVvP3221i2bBn27NkDf39/pKamory8vJlLSq5g7A1uwR4ZIrLB9dpBxpWFIafzduWLjxw5EiNHjqzzMSEE3nzzTfzjH//AuHHjAACffPIJoqKi8PXXX2PSpEnNWVRqRtWXX1d9bRFws0emmGNkiKhxeTcH+4YHGHtkeGpJzlwaZBpy9uxZ5OTkICUlxbQtODgYffv2xa5du+oNMlqtFlpt9T+8wsJCAIBOp4NOp3NY+YzHcuQxpcZVdaysvHl1khDQ6XQI8a36Nb1aVM730EZyryPr5/6cUcdrxVW99sEaL7Pj6iod+3/AGnJ/D51ZP2uPKdkgk5OTAwCIiooy2x4VFWV6rC5LlizBokWLLLZv2rQJfn5+ji0kgIyMDIcfU2qau477rykAeOH69etYv349crOr7h8+dQ7r1//h8Nfje+j+WD/358g6XrziBUCBk4f2Q1wQqKysur916zZE+jrsZWwi9/fQGfUrLS21aj/JBhl7LViwAPPnzzfdLywsRFxcHIYPH46goCCHvY5Op0NGRgaGDRsGlUrlsONKiavqWPn7ZeDUIYSHh2PUqNug3Z+NdecPQxMcgVGjkhz2OnwP3R/r5/6cUceFB7YA0GHU0IFoHxWAf+zfjHJ9Je64YxDaRPg75DWsJff30Jn1M55RaYxkg0x0dDQAIDc3FzExMabtubm56NGjR73PU6vVUKvVFttVKpVTfomcdVwpae46enlVLRSpVCqgUqkQEVz1ESqvVMf30E5yryPr5/4cVcdKvQH5ZVWnJCJD/KBSqUyDfb28vV32c5T7e+iM+ll7PMnOI5OYmIjo6GhkZmaathUWFmLPnj1ITk52YcnI2YRpiYKq5qf6qiUO9iWiht0orQoxCgUQ6mc+2JerLcmTS3tkiouLcfr0adP9s2fP4sCBAwgLC0N8fDzmzp2Lf/7zn2jfvj0SExPx7LPPIjY2FuPHj3ddoanZKGrNI5N3c70lBafpJKJ6GKdpCPFVwUtZ1VawyZA3lwaZX3/9FUOGDDHdN45tmTp1KlasWIEnnngCJSUleOihh5Cfn4/bb78dGzZsgEajcVWRqRnUvkQyPKDqVKFOL5BXUoEWAZanDomIACCnsOqKpcjA6v8T1YtGuqBA5HQuDTKDBw9ucDVShUKBxYsXY/Hixc1YKnK16nlkqpofjcoL0UEa5BSW43xeKYMMEdXrwvUSAEBcWPVVqqZ5ZFxSInI2yY6RIc9VV2MT36KqUbpw3brL8YjIM52/2UYktLCcboM9MvLEIEOSY+ylq3laO+Hmp6vzDDJE1IDzeZZBpnqoL5OMHDHIkOQYm5qaA/SMjdKFPAYZIqqfsdc23uzUUtVX9sjIE4MMSY/54tcAgPgWVZNYXcgraf7yEJFbEEKYPuwktKg58R3XWpIzBhmSrJqXWfPUEhE15mqRFmU6PZQKoGVI9VoEvPxa3hhkSHKqJ8SrZjy1dKVIi7IKvQtKRURSZxwfExPsCx/v6n9vHCMjbwwyJDnVl19Xbwvx80Gwb9V01X9cK3ZBqYhI6s5erTr13Drc/IoljpGRNwYZkpz62prOMYEAgCPZ1i0kRkSe5XB2AQDglhjHLRBM0scgQ5JT/anJ/MR2t5bBAIDDWQXNWyAicguHbrYNXW+2FUYKDvaVNQYZkhzTGJlaA/SMjdMhBhkiqqVSb8Cxy1W9tRZBxnhqiWNkZIlBhiRH1HH5NVDdOB27XIhKvaF5C0VEknbmagnKdQb4+3gh0ezSa661JHcMMiRZtXtkElv4w9/HC+U6A85c5XwyRFTN2FPbJTYYSqV546Hg9deyxiBDkmOa2bdWn4xSqUC3VlW9MnvP5TVzqYhIyn692SbcGhdc7z7skJEnBhmSHlH3GBkAGNA2HACw8/S15iwREUmYEAI7TlW1Cf3bhVs8Xn35NaOMHDHIkOQ01NQMaF/VSP185jr0BjZKRFS1BltWfhlUXgr0TQyrdz+2GPLEIEOSU9eEeEbdWwYjUOONgjIdL8MmIgDATzd7aHvFh8LPx9vicU6IJ28MMiQ5xu7f2mNkAMDbS4nkNi0AAJnHrzRruYhImjYfq2oLbq/jtBJQsy1hkpEjBhmSnHrmwzMZ2S0aAPDdwWye8ybycPmlFdh+6ioAYETX6Dr3YY+MvDHIkOTUN4+MUUrnKPh4K/HH1RIcvczlCog82cYjOdDpBTpFB6J9VGCd+7A/Rt4YZEiy6pv7IVCjwtCOkQCAr37Las4iEZHEfLW/qg0Ye2tsvftwHhl5Y5AhyameR6Z+f0pqBQBYu+8Syir0Ti8TEUnPydwi7P4jD0oFML5ny3r348y+8sYgQ5JjzbiXIZ0iERfmi4IyHdYdYK8MkSf6ZNc5AMDwW6LRMsS30f05pk6eGGRIshrqDfZSKnBfvwQAwHvb/+DaS0Qe5kphOdb+egkAcH//hIZ3Ni0aSXLEIEOS09hgX6M/901AqJ8KZ6+VmM6TE5FnSN9yGtpKA5ISQk1TMtSHp5bkjUGGJEfAuERBw1EmQO2Nvw5qCwB4I+MkSisqnV42InK9M1eLseqXCwCAR4d1aLStMD4u2CcjSwwyJDnW9sgAwNT+rdEq1BeXC8rxVuYpp5aLiFxPCIGF645ApxcY0jGizrWVajO1JcwxssQgQ9JlRZLRqLzw/NguAIAPdpzF/gs3nFwoInKlz365iJ9OX4OPtxLP39XFqufw6mt5Y5Ahyam+/Nq61ifllijcdWss9AaBuasPIL+0wnmFIyKXOZVbhBe+OwoAeGx4ByS08Lfqeca2hB0y8sQgQ5Jjz4C8F8Z3RcsQX5y/XopZn+5DRSWvYiKSk4JSHWZ+8ivKdHoMaNcCD97exuZjcLCvPDHIkORUD/a1/jnBvip8OO02BKi9sedsHp7630HOGUEkE2UVesz4eC/OXS9FyxBfvD2pJ5RK6xsI01pL7JORJQYZkhxbBvvW1Ck6COlTesFLqcCX+7Pw9FeHoDew4SJyZ4XlOkxd/gt+PX8DQRpvfDjtNrQIUNt1LH62kScGGZIsewboDeoQgZfTukOpqBoUOHvlbyjXcQkDInd0tUiLie/txi9n8xCo9sby6b3RKTrI5uNUX35NcsQgQ5JjPCVk7WDf2v6U1ArvTOkFHy8lNhzJwT3LduHC9VJHFpGInGzvuTyM/ddPOHa5EOEBPvh8Vj8kJYTZdazqCfEYZeSIQYYkqymXTI7oGoMVD/RGiJ8Kh7IKMPrtHfjm92w2ZEQSp9Mb8O/NpzDp/d3IKSxHm3B/fPHX/ugSG2z3MXn5tbwxyJDkmMbINLHx6d82HN//30AkJYSiSFuJ//tsP6av2IuLeeydIZKi3y7cwNh//YTXNp2E3iBwd8+W+Pbh29E63LrLrOuj4FpLsubt6gIQ1Vbd2DT9Y1TLEF98/lA/pG85jXe2nMHWE1dx5xvbcH+/BDw0sJGF5oioWVwtA+auOYjvD+UAAEL9VPjH6FswoVfLRpcfsIaCq0bKGoMMSY6jz/6ovJSYm9IBY2+NxbNfH8bPZ67jg5/O4rO9F9A7TInuN8qQGKly7IsSUaMOXSrAf7afwXeHvGAQVSEmrVcrPDO6M8L8fRz+erz8Wp4YZEhy7JlHxhptIwKw8sG+2HbyKl7deAJHsgux9bIS25fuwIiu0fhL3wT0bdMCXjbMT0FEtinX6ZF57Ao+/vkcfjmXd3OrAoM6hOOJEZ2aNBamPqZTS8wxssQgQ5Jj7zwy1lAoFBjcMRJ3tI/A5mOX8eo3+3CiQIn1h3Kw/lAOooM0GNczFuNubYnOMYEO6dYm8nSVegP2nruBr/dnYf3hyygqr1qp3lupwKiu0eggLmLWvb2gUjmnZ7T6qiWnHJ5cjEGGJMe01pITM4RSqcCgDhEoucWAdkkDsHJvFr77PRs5heV4b9sfeG/bH4gN1mBo50jc2SkKyW1bQKPycl6BiGSmoEyHn09fQ8axXGw5fgU3SnWmx2KDNZjQqxX+0i8BLfy8sH79RecWhvPIyBqDDElPE+eRsVWHqED8v7u7YeHYW7Dl+BV8+VsWtp28iuyCcvx39wX8d/cFaFRKJCWEonfrMPRJDEPPuFD4+jDYEBkVluuw92wedv9xHbv/yMOR7ALUnFg72FeF1C5RuLtnK/RNDDMtMaDT6eo5ouNwHhl5Y5AhyWruszpqby+M6BqDEV1jUK7TY9eZ68g8novNx64gu6AcO09fx87T1wEAKi8FbokNRtfYIHRtGYyuscHoEB0AtTfDDclfaUUljmYX4uClAhy8lI+DWQX442qJxX5twv0xtFMkUm6Jwm0JofD2cs2MHzxDLG8MMiQ5plNLLiyDRuWFIZ0iMaRTJMQ4gVNXirHnbB72ns3DL2fzkFNYjt8v5uP3i/mm53grFWgfFYiOUQFoExGAthEBaBPhj8Rwf56WIrdUVqHHmavFOH2lGKeuFN38Woxz10pQ1zJmrVv4IbltC/RrU3WLCtI0f6HrYOqRcWkpyFkYZEhypNb7q1Ao0CEqEB2iAnFfvwQIIXDpRhkOXMzH4ewCHM0uxOGsAtwo1eHY5UIcu1xY6/lV89m0iQhAXKgvWob6omWIL1qF+iI2xBeRgRpeKUUuoTcI5BSW42JeadXtRlmN70uRW6it97nRQRp0axWM7i2D0a1VMLq1DLZ7McfmIrW2hRyDQYYkp/rya2n+c1coFIgL80NcmB/G3hoLoOrce3ZBOQ5nFeDM1WKcuVKCP64V48yVYhSWV+LSjTJculFW5/FUXgpEB2vQ8maoCQ9QIyJQjfAAH0QEVn0fEaBGmL+Py7rmyb3o9AbcKKnAlSItrhSV40qhFleKtMgtLL+5TYsrheW4WqRFZSMrxIf6qdA+MhDtogLQPjIA7SMD0SEqAJES6W2xRnVbwiQjRwwyJDnu+KlJoVCgZUhVT0tNQghcL6nAmSvFOHutBJdulCE7vwyX8suQdaMMOYXl0OkFLuaV4WJe3UGn+jWAMD8fhPipEOLngxBfFYL9VAjxNW5TIdi36rFgXxUCNd5QKwXKKwFDI/+sSHp0egPyS3W4Xg4czylCuR4oLq9EYbkO+aU63CitMH29UarDjZIK07ZibaXVr+OtVKBlqC/iw/zQKtQPcWG+iAutCurxYX5OmZiuufHya3ljkCHJaY7Lr5uLQqFAeIAa4QFq9G3TwuJxvUEgt7AcWflVAedqkRZXi7W4WqTFteKKqvtFWuSVaGEQwPWSClwvqQBgObCyft54cm8G/Hy84K/2RoDaG/5qL/j7GL+/efPxgkblBbW3EhqVFzQqJdSqWttMj1VvU3kp4O2lrPqqrPoq1d60ptIbBHR6AyoNApV6Ayr0BlTqBSoqDSiv1KNcZ0C5To8ynR5anfl94/fllXqUV9y8X6lHiVaPYm1V+Cgur6z6qq1Euc5w81W9gf27bC6rQgG08FcjKkiNyEA1IgM1iApSIyJIg6hANSKDNIgMVCMqSP6nNrnWkrwxyJDkVE+IJ+/GFQC8lArEhlSNlWmI3iCQV1IVbArKdCgoq/rknV9W9encdP/mtoLSChRpK1GirTQNyiyt0KO0Qo+rRfWPe3BkvbyVCqi8lPD2qvqqUlYFHm8vBVTKqq/eXkooFVWfmJUKBZSKqjtV2xRQKqu24+bjCoVxP6BqD4HcXCW+yz8AAQWEEDAIAYMADDd/kQxCwGCo+ipubhfG7aKq10xvEKjUC+gMVcFEpzdApxeoNBigqzRAdzO4uKJjS6UUCPZTI1CjQsDNIGrslQv1UyH0Zi9dmL+P2bYgX5XsA4q1jG0Je2TkiUGGJEumH+rt4qVUmMbL2KKiogLrvvsBtw+5E1q9AsU3w01JRSWKtXqU3vz0X6LVo7SiEtrKm70GN3sQtJXVPQfG+1pdjX0qDdDX8d9db6gKB9pKQx2lcjQlkHelGV7HkkIBqG72Qhl7qjSq6l4r35o9W95e8PVRQuNtvp+xlyxA7Y0ATfX3gRpv+CgFMjZuwKhRg502661HYFsiawwyJDmmwb4uLoccKBQK+HgB4QFqp/0jNBhE1akWw81ejJunXky9GjV7N2o9Xqmv7h0RN3tHDKLqd8DYW2LqRanRm2Lcr7JSj4OHDqF7t25QeXuZ9drU/mrsyVEoqn4uxvvGfVReSnjf7DXyMfUkGXuVqnqUavYweSsV8FI69zRac0wW5wmqh/qyS0aOGGRIetjWuBWlUgEfpQI+aP4rqnQ6HYKuHsSo3q3YY0GN4qkleeK1nCQ5chrsS0Sux8G+8sYgQ5JjXA9Frle+EFHzqh7syygjRwwyJDnVVy0RETUdPxPJG4MMSY7pMxMbHyJyANOpJXbIyBKDDEmWJ8wjQ0TOx7ZE3hhkSHJMp5bY9hCRA1QP9mWXjBwxyJDksLEhImfgqSV5YpAhyeFgXyJyBgYZeXKLIJOeno7WrVtDo9Ggb9+++OWXX1xdJGoGPLVERI5gnMqBOUaeJB9kVq9ejfnz52PhwoX47bffcOuttyI1NRVXrrhmbRVyPtM8MuyTISIHMC1RwC4ZWZL8EgVvvPEGZs6cienTpwMAli1bhu+//x4fffQRnnrqKZeV60ZpBfK0QFZ+Gby9zddDacrfSkPPbWjsSMPPa+w1696jsrISuWXAH1dL4K2y/FVpuJ4Nv2pDz80vq/p5skeGiBzB2JZcKdLi7LUSszW2nK2ysrLe/xVyYKxfsbYSoS5aJkTSQaaiogL79u3DggULTNuUSiVSUlKwa9euOp+j1Wqh1WpN9wsLCwFUrcniyAXYXtt0Emt+88ai33Y47JjS5I3/d2CnS17ZYDA4ddE847HlvDCf3OvI+rm/ZqnjzU9Or248gVc3nnDe69RL7v8rvKGIycKUfgkOPaq1vxOSDjLXrl2DXq9HVFSU2faoqCgcP368zucsWbIEixYtsti+adMm+Pn5Oaxsl7OqVsNtSFPDfqPPb2SHxp4v5fJpvACfayexfv1JG0tlu4yMDKe/hqvJvY6sn/tzZh1bGRQIUilRacDN1dYBg9NezTMdP3YU6/OOOPSYpaWlVu0n6SBjjwULFmD+/Pmm+4WFhYiLi8Pw4cMRFBTksNcZptMhIyMDw4YNk+2quzqZ11Hu9QPkX0fWz/01Rx1HAVjolCM3Tu7voTPrZzyj0hhJB5nw8HB4eXkhNzfXbHtubi6io6PrfI5arYZarbbYrlKpnPJL5KzjSonc6yj3+gHyryPr5/7kXkfWz75jWkPSVy35+PggKSkJmZmZpm0GgwGZmZlITk52YcmIiIhICiTdIwMA8+fPx9SpU3HbbbehT58+ePPNN1FSUmK6iomIiIg8l+SDzMSJE3H16lU899xzyMnJQY8ePbBhwwaLAcBERETkeSQfZABgzpw5mDNnjquLQURERBIj6TEyRERERA1hkCEiIiK3xSBDREREbotBhoiIiNwWgwwRERG5LQYZIiIiclsMMkREROS2GGSIiIjIbTHIEBERkdtyi5l9m0IIAcD65cCtpdPpUFpaisLCQtmuaCr3Osq9foD868j6uT+515H1s5/x/7bx/3h9ZB9kioqKAABxcXEuLgkRERHZqqioCMHBwfU+rhCNRR03ZzAYkJ2djcDAQCgUCocdt7CwEHFxcbh48SKCgoIcdlwpkXsd5V4/QP51ZP3cn9zryPrZTwiBoqIixMbGQqmsfySM7HtklEolWrVq5bTjBwUFyfKXsya511Hu9QPkX0fWz/3JvY6sn30a6okx4mBfIiIiclsMMkREROS2GGTspFarsXDhQqjValcXxWnkXke51w+Qfx1ZP/cn9zqyfs4n+8G+REREJF/skSEiIiK3xSBDREREbotBhoiIiNwWgwwRERG5LQYZK507dw4zZsxAYmIifH190bZtWyxcuBAVFRUNPq+8vByzZ89GixYtEBAQgLS0NOTm5jZTqW3z4osvon///vDz80NISIhVz5k2bRoUCoXZbcSIEc4taBPYU0chBJ577jnExMTA19cXKSkpOHXqlHMLaqe8vDxMmTIFQUFBCAkJwYwZM1BcXNzgcwYPHmzxHv71r39tphI3Lj09Ha1bt4ZGo0Hfvn3xyy+/NLj/2rVr0alTJ2g0GnTr1g3r169vppLax5b6rVixwuK90mg0zVha22zfvh1jx45FbGwsFAoFvv7660afs3XrVvTq1QtqtRrt2rXDihUrnF7OprC1jlu3brV4DxUKBXJycpqnwDZYsmQJevfujcDAQERGRmL8+PE4ceJEo89r7r9BBhkrHT9+HAaDAe+99x6OHDmCpUuXYtmyZXj66acbfN68efPw7bffYu3atdi2bRuys7MxYcKEZiq1bSoqKnDPPffgb3/7m03PGzFiBC5fvmy6ffbZZ04qYdPZU8dXXnkFb7/9NpYtW4Y9e/bA398fqampKC8vd2JJ7TNlyhQcOXIEGRkZ+O6777B9+3Y89NBDjT5v5syZZu/hK6+80gylbdzq1asxf/58LFy4EL/99htuvfVWpKam4sqVK3Xu//PPP2Py5MmYMWMG9u/fj/Hjx2P8+PE4fPhwM5fcOrbWD6iaQbXme3X+/PlmLLFtSkpKcOuttyI9Pd2q/c+ePYvRo0djyJAhOHDgAObOnYsHH3wQGzdudHJJ7WdrHY1OnDhh9j5GRkY6qYT227ZtG2bPno3du3cjIyMDOp0Ow4cPR0lJSb3PccnfoCC7vfLKKyIxMbHex/Pz84VKpRJr1641bTt27JgAIHbt2tUcRbTL8uXLRXBwsFX7Tp06VYwbN86p5XEGa+toMBhEdHS0ePXVV03b8vPzhVqtFp999pkTS2i7o0ePCgBi7969pm0//PCDUCgUIisrq97nDRo0SDzyyCPNUELb9enTR8yePdt0X6/Xi9jYWLFkyZI697/33nvF6NGjzbb17dtXzJo1y6nltJet9bPlb1NqAIivvvqqwX2eeOIJ0aVLF7NtEydOFKmpqU4smeNYU8ctW7YIAOLGjRvNUiZHunLligAgtm3bVu8+rvgbZI9MExQUFCAsLKzex/ft2wedToeUlBTTtk6dOiE+Ph67du1qjiI2i61btyIyMhIdO3bE3/72N1y/ft3VRXKYs2fPIicnx+w9DA4ORt++fSX3Hu7atQshISG47bbbTNtSUlKgVCqxZ8+eBp+7cuVKhIeHo2vXrliwYAFKS0udXdxGVVRUYN++fWY/e6VSiZSUlHp/9rt27TLbHwBSU1Ml914B9tUPAIqLi5GQkIC4uDiMGzcOR44caY7iNgt3ev+aqkePHoiJicGwYcOwc+dOVxfHKgUFBQDQ4P89V7yHsl800llOnz6Nf/3rX3jttdfq3ScnJwc+Pj4WYzGioqIkeT7UHiNGjMCECROQmJiIM2fO4Omnn8bIkSOxa9cueHl5ubp4TWZ8n6Kiosy2S/E9zMnJseie9vb2RlhYWINl/fOf/4yEhATExsbi4MGDePLJJ3HixAl8+eWXzi5yg65duwa9Xl/nz/748eN1PicnJ8ct3ivAvvp17NgRH330Ebp3746CggK89tpr6N+/P44cOeLUxXGbS33vX2FhIcrKyuDr6+uikjlOTEwMli1bhttuuw1arRYffPABBg8ejD179qBXr16uLl69DAYD5s6diwEDBqBr16717ueKv0GP75F56qmn6hx4VfNWu1HJysrCiBEjcM8992DmzJkuKrl17KmfLSZNmoS77roL3bp1w/jx4/Hdd99h79692Lp1q+Mq0Qhn19HVnF2/hx56CKmpqejWrRumTJmCTz75BF999RXOnDnjwFqQIyQnJ+P+++9Hjx49MGjQIHz55ZeIiIjAe++95+qikZU6duyIWbNmISkpCf3798dHH32E/v37Y+nSpa4uWoNmz56Nw4cP4/PPP3d1USx4fI/Mo48+imnTpjW4T5s2bUzfZ2dnY8iQIejfvz/ef//9Bp8XHR2NiooK5Ofnm/XK5ObmIjo6uinFtpqt9WuqNm3aIDw8HKdPn8add97psOM2xJl1NL5Pubm5iImJMW3Pzc1Fjx497DqmraytX3R0tMUg0crKSuTl5dn0+9a3b18AVb2Obdu2tbm8jhIeHg4vLy+Lq/wa+vuJjo62aX9Xsqd+talUKvTs2ROnT592RhGbXX3vX1BQkCx6Y+rTp08f/PTTT64uRr3mzJljunigsZ4/V/wNenyQiYiIQEREhFX7ZmVlYciQIUhKSsLy5cuhVDbcoZWUlASVSoXMzEykpaUBqBqpfuHCBSQnJze57NawpX6OcOnSJVy/ft3sn76zObOOiYmJiI6ORmZmpim4FBYWYs+ePTZf3WUva+uXnJyM/Px87Nu3D0lJSQCAzZs3w2AwmMKJNQ4cOAAAzfoe1sXHxwdJSUnIzMzE+PHjAVR1b2dmZmLOnDl1Pic5ORmZmZmYO3euaVtGRkaz/b3Zwp761abX63Ho0CGMGjXKiSVtPsnJyRaX6kr1/XOkAwcOuPzvrS5CCDz88MP46quvsHXrViQmJjb6HJf8DTptGLHMXLp0SbRr107ceeed4tKlS+Ly5cumW819OnbsKPbs2WPa9te//lXEx8eLzZs3i19//VUkJyeL5ORkV1ShUefPnxf79+8XixYtEgEBAWL//v1i//79oqioyLRPx44dxZdffimEEKKoqEg89thjYteuXeLs2bPixx9/FL169RLt27cX5eXlrqpGg2ytoxBCvPTSSyIkJESsW7dOHDx4UIwbN04kJiaKsrIyV1ShQSNGjBA9e/YUe/bsET/99JNo3769mDx5sunx2r+jp0+fFosXLxa//vqrOHv2rFi3bp1o06aNuOOOO1xVBTOff/65UKvVYsWKFeLo0aPioYceEiEhISInJ0cIIcR9990nnnrqKdP+O3fuFN7e3uK1114Tx44dEwsXLhQqlUocOnTIVVVokK31W7Rokdi4caM4c+aM2Ldvn5g0aZLQaDTiyJEjrqpCg4qKikx/YwDEG2+8Ifbv3y/Onz8vhBDiqaeeEvfdd59p/z/++EP4+fmJxx9/XBw7dkykp6cLLy8vsWHDBldVoVG21nHp0qXi66+/FqdOnRKHDh0SjzzyiFAqleLHH390VRXq9be//U0EBweLrVu3mv3PKy0tNe0jhb9BBhkrLV++XACo82Z09uxZAUBs2bLFtK2srEz8/e9/F6GhocLPz0/cfffdZuFHSqZOnVpn/WrWB4BYvny5EEKI0tJSMXz4cBERESFUKpVISEgQM2fONDXCUmRrHYWougT72WefFVFRUUKtVos777xTnDhxovkLb4Xr16+LyZMni4CAABEUFCSmT59uFtJq/45euHBB3HHHHSIsLEyo1WrRrl078fjjj4uCggIX1cDSv/71LxEfHy98fHxEnz59xO7du02PDRo0SEydOtVs/zVr1ogOHToIHx8f0aVLF/H99983c4ltY0v95s6da9o3KipKjBo1Svz2228uKLV1jJca174Z6zR16lQxaNAgi+f06NFD+Pj4iDZt2pj9LUqRrXV8+eWXRdu2bYVGoxFhYWFi8ODBYvPmza4pfCPq+59X8z2Rwt+g4mZhiYiIiNyOx1+1RERERO6LQYaIiIjcFoMMERERuS0GGSIiInJbDDJERETkthhkiIiIyG0xyBAREZHbYpAhIo90/fp1REZG4ty5cw497tGjR9GqVSuUlJQ49LhEVDcGGSJq0LRp0+pccXvEiBGuLlqTvPjiixg3bhxat25t1f5jx46tt847duyAQqHAwYMHccstt6Bfv3544403HFhaIqoPZ/YlogZNmzYNubm5WL58udl2tVqN0NBQp71uRUUFfHx8nHLs0tJSxMTEYOPGjejXr59Vz/n666+RlpaG8+fPW6wA/MADD+DQoUPYu3cvAOD777/HzJkzceHCBXh7e/zavEROxR4ZImqUWq1GdHS02a1miFEoFPjggw9w9913w8/PD+3bt8c333xjdozDhw9j5MiRCAgIQFRUFO677z5cu3bN9PjgwYMxZ84czJ07F+Hh4UhNTQUAfPPNN2jfvj00Gg2GDBmCjz/+GAqFAvn5+SgpKUFQUBC++OILs9f6+uuv4e/vj6Kiojrrs379eqjVaosQ01AZx4wZg4iICKxYscLsOcXFxVi7di1mzJhh2jZs2DDk5eVh27ZtVv6EicheDDJE5BCLFi3Cvffei4MHD2LUqFGYMmUK8vLyAAD5+fkYOnQoevbsiV9//RUbNmxAbm4u7r33XrNjfPzxx/Dx8cHOnTuxbNkynD17Fn/6058wfvx4/P7775g1axaeeeYZ0/7+/v6YNGmSRW/R8uXL8ac//QmBgYF1lnXHjh1ISkoy29ZYGb29vXH//fdjxYoVqNmRvXbtWuj1ekyePNm0zcfHBz169MCOHTvs+EkSkU2cuiQlEbm9qVOnCi8vL+Hv7292e/HFF037ABD/+Mc/TPeLi4sFAPHDDz8IIYR44YUXxPDhw82Oe/HiRQHAtJL4oEGDRM+ePc32efLJJ0XXrl3Ntj3zzDMCgLhx44YQQog9e/YILy8vkZ2dLYQQIjc3V3h7e4utW7fWW6dx48aJBx54wGybNWU8duyYxWrpAwcOFH/5y18sXuPuu+8W06ZNq7cMROQYPHlLRI0aMmQI3n33XbNtYWFhZve7d+9u+t7f3x9BQUG4cuUKAOD333/Hli1bEBAQYHHsM2fOoEOHDgBg0Uty4sQJ9O7d22xbnz59LO536dIFH3/8MZ566in897//RUJCAu64445661NWVgaNRmO2zZoydurUCf3798dHH32EwYMH4/Tp09ixYwcWL15s8RxfX1+UlpbWWwYicgwGGSJqlL+/P9q1a9fgPiqVyuy+QqGAwWAAUDWOZOzYsXj55ZctnhcTE2P2OvZ48MEHkZ6ejqeeegrLly/H9OnToVAo6t0/PDwcN27cMNtmbRlnzJiBhx9+GOnp6Vi+fDnatm2LQYMGWTwnLy8Pbdu2tas+RGQ9jpEhIqfr1asXjhw5gtatW6Ndu3Zmt4bCS8eOHfHrr7+abTNeGVTTX/7yF5w/fx5vv/02jh49iqlTpzZYnp49e+Lo0aN2lfHee++FUqnEqlWr8Mknn+CBBx6oMzQdPnwYPXv2bLAcRNR0DDJE1CitVoucnByzW80rjhoze/Zs5OXlYfLkydi7dy/OnDmDjRs3Yvr06dDr9fU+b9asWTh+/DiefPJJnDx5EmvWrDFdNVQzPISGhmLChAl4/PHHMXz4cIvLo2tLTU3FkSNHzHplrC1jQEAAJk6ciAULFuDy5cuYNm2axfHPnTuHrKwspKSkWPkTIiJ7McgQUaM2bNiAmJgYs9vtt99u9fNjY2Oxc+dO6PV6DB8+HN26dcPcuXMREhICpbL+ZigxMRFffPEFvvzyS3Tv3h3vvvuu6aoltVpttu+MGTNQUVGBBx54oNHydOvWDb169cKaNWvsKuOMGTNw48YNpKamIjY21uL4n332GYYPH46EhIRGy0JETcMJ8YjIrbz44otYtmwZLl68aLb9008/xbx585CdnW3VRHrff/89Hn/8cRw+fLjBMGWriooKtG/fHqtWrcKAAQMcdlwiqhsH+xKRpL3zzjvo3bs3WrRogZ07d+LVV1/FnDlzTI+Xlpbi8uXLeOmllzBr1iyrZwMePXo0Tp06haysLMTFxTmsvBcuXMDTTz/NEEPUTNgjQ0SSNm/ePKxevRp5eXmIj4/HfffdhwULFpim/n/++efx4osv4o477sC6devqvHyaiOSLQYaIiIjcFgf7EhERkdtikCEiIiK3xSBDREREbotBhoiIiNwWgwwRERG5LQYZIiIiclsMMkREROS2GGSIiIjIbTHIEBERkdv6/0cs623PmXCuAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]

From aa3c59649e348a67114ed6f5f9bdda6582c7ea4e Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 1 Sep 2025 22:24:26 +0800
Subject: [PATCH 111/152] fix(run.ipynb): update execution counts and clean up
 output messages

---
 examples/CNT/input.json |  1 -
 examples/CNT/run.ipynb  | 25 +++++++++----------------
 2 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/examples/CNT/input.json b/examples/CNT/input.json
index 1af6e7f..4b37c92 100644
--- a/examples/CNT/input.json
+++ b/examples/CNT/input.json
@@ -66,7 +66,6 @@
              "z_range": "8.6:88",
              "relative permittivity": 20            
             }
-
         },
         "sgf_solver": "Sancho-Rubio",
         "espacing": 0.05,
diff --git a/examples/CNT/run.ipynb b/examples/CNT/run.ipynb
index 9392000..c266283 100644
--- a/examples/CNT/run.ipynb
+++ b/examples/CNT/run.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "0f64a6b8",
    "metadata": {},
    "outputs": [
@@ -31,20 +31,13 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "rm: cannot remove 'output': Directory not empty\n",
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO                                      Version Info                                  \n",
       "DPNEGF INFO    --------------------------------------------------------------------------------\n",
       "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
       "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
       "DPNEGF INFO    ================================================================================\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n"
      ]
     }
@@ -72,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "830d67a4",
    "metadata": {},
    "outputs": [
@@ -178,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "db275dee",
    "metadata": {},
    "outputs": [
@@ -186,7 +179,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_17948/1724458665.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "/tmp/ipykernel_18951/1724458665.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
       "  negf_out = torch.load('./output/negf.out.pth')\n"
      ]
     }
@@ -197,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "7a29b9ff",
    "metadata": {},
    "outputs": [
@@ -207,7 +200,7 @@
        "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -218,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "8eb092f6",
    "metadata": {},
    "outputs": [
@@ -244,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "4a2fe762",
    "metadata": {},
    "outputs": [

From 9385ad9f418fa5e975737c86b15176df4fa4bdf6 Mon Sep 17 00:00:00 2001
From: Qiangqiang Gu <98570179+QG-phy@users.noreply.github.com>
Date: Thu, 4 Sep 2025 01:10:31 +0800
Subject: [PATCH 112/152] Add workflow (#25)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* fix(.gitignore): update paths from dptb to dpnegf for consistency

* feat: 添加多个问题模板和工作流配置文件

* feat: 添加单元测试工作流配置

* feat: 添加 numba 和 pyinstrument 依赖

* feat(.gitignore): 添加对 test_negf 目录下 .xyz 文件的忽略

* feat: 添加 Dockerfile 和单元测试脚本
---
 .github/ISSUE_TEMPLATE/bug-report.yml         | 49 ++++++++++++++
 .github/ISSUE_TEMPLATE/documentation.yml      | 18 +++++
 .github/ISSUE_TEMPLATE/feature-request.yml    | 32 +++++++++
 .github/ISSUE_TEMPLATE/help-wanted.yml        | 17 +++++
 .../ISSUE_TEMPLATE/software-enhancements.yml  | 26 +++++++
 .github/ISSUE_TEMPLATE/tests.yml              | 23 +++++++
 .github/dependabot.yml                        |  6 ++
 .github/release-check.md                      | 12 ++++
 .github/workflows/devcontainer.yml            | 67 +++++++++++++++++++
 .github/workflows/image.yml                   | 57 ++++++++++++++++
 .github/workflows/unit_test.yml               | 35 ++++++++++
 .gitignore                                    | 53 ++++++++-------
 Dockerfile                                    | 48 +++++++++++++
 pyproject.toml                                |  4 +-
 ut.sh                                         | 19 ++++++
 15 files changed, 439 insertions(+), 27 deletions(-)
 create mode 100755 .github/ISSUE_TEMPLATE/bug-report.yml
 create mode 100644 .github/ISSUE_TEMPLATE/documentation.yml
 create mode 100755 .github/ISSUE_TEMPLATE/feature-request.yml
 create mode 100755 .github/ISSUE_TEMPLATE/help-wanted.yml
 create mode 100755 .github/ISSUE_TEMPLATE/software-enhancements.yml
 create mode 100644 .github/ISSUE_TEMPLATE/tests.yml
 create mode 100644 .github/dependabot.yml
 create mode 100644 .github/release-check.md
 create mode 100644 .github/workflows/devcontainer.yml
 create mode 100644 .github/workflows/image.yml
 create mode 100644 .github/workflows/unit_test.yml
 create mode 100644 Dockerfile
 create mode 100644 ut.sh

diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml
new file mode 100755
index 0000000..355f81a
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -0,0 +1,49 @@
+name: Bug Report
+description: Create a report for problems with DeePTB-UniSK
+labels: [Bug]
+assignees: qqgu
+body:
+- type: textarea
+  attributes:
+    label: Describe the bug
+    description: |
+      A clear and concise description of what the bug is. The bug may results in:
+      - abnormal interruption of the program,
+      - systematic or randomized numerical error, or
+      - relatively low efficiency.
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: Expected behavior
+    description: |
+      A clear and concise description of what you expected to happen.
+
+- type: textarea
+  attributes:
+    label: To Reproduce
+    description: |
+      Steps to reproduce the behavior:
+      1. [e.g. clone the source code from ...]
+      2. [e.g. install DeePTB-UniSK ...]
+      3. [e.g. run DeePTB-UniSK with ...]
+
+      It is recommended to attach your calculation case here for the developers to reproduce the bug.
+
+- type: textarea
+  attributes:
+    label: Environment
+    description: |
+      - OS: [e.g. Ubuntu 20.04]
+      - Dependencies: [e.g. PyTorch, SciPy, NumPy, ...]
+
+- type: textarea
+  attributes:
+    label: Additional Context
+    description: |
+      Add any other context about the problem here.
+- type: markdown
+  attributes:
+    value: >
+      Thanks for contributing 🎉!
diff --git a/.github/ISSUE_TEMPLATE/documentation.yml b/.github/ISSUE_TEMPLATE/documentation.yml
new file mode 100644
index 0000000..26cf635
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/documentation.yml
@@ -0,0 +1,18 @@
+name: Docs
+description: For users or developers to report issues related to software documentation, such as missing or incomplete documentation, or documentation that is difficult to understand.
+labels: [Docs]
+assignees: Qiangqiang Gu
+body:
+- type: textarea
+  attributes:
+    label: Details
+    description: |
+      Please provide details about the documentation issue you are experiencing. Include any relevant information such as the specific section of the documentation, the information that is missing or unclear, and any suggestions for improvement. 
+    placeholder: |
+      Example: The documentation for the band structure calculations is missing. I have read the online manual, but I am still not sure how to do it. My suggestion is to add a new section to the online manual that describes this.
+  validations:
+      required: true
+- type: markdown
+  attributes:
+    value: >
+      Thank you for reporting this documentation issue. We will review and update the documentation as necessary. 📚
diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml
new file mode 100755
index 0000000..870dc16
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature-request.yml
@@ -0,0 +1,32 @@
+name: Feature Request
+description: Suggest something more for DeePTB-UniSK to do
+labels: [Feature]
+assignees: zhanghao
+
+body:
+- type: textarea
+  attributes:
+    label: Background
+    description: |
+      A clear and concise description of why this new feature is important.
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: Describe the solution you'd like
+    description: |
+      A clear and concise description of what you want to happen.
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: Additional Context
+    description: |
+      Add any other context or screenshots about the feature request here.
+
+- type: markdown
+  attributes:
+    value: >
+      Thanks for contributing 🎉!
\ No newline at end of file
diff --git a/.github/ISSUE_TEMPLATE/help-wanted.yml b/.github/ISSUE_TEMPLATE/help-wanted.yml
new file mode 100755
index 0000000..9e47c17
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/help-wanted.yml
@@ -0,0 +1,17 @@
+name: Help Wanted
+description: For general questions or assistance.
+labels: [Help wanted]
+assignees: zhanghao
+
+body:
+- type: textarea
+  attributes:
+    label: Details
+    description: |
+      Want to use DeePTB for certain system? Facing problems when installing DeePTB-UniSK? Don't know how to choose a good parameter? Feel free to reach us, and we'll do our best to help!
+  validations:
+    required: true
+- type: markdown
+  attributes:
+    value: >
+      Thanks for contributing 🎉!
diff --git a/.github/ISSUE_TEMPLATE/software-enhancements.yml b/.github/ISSUE_TEMPLATE/software-enhancements.yml
new file mode 100755
index 0000000..54c4a65
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/software-enhancements.yml
@@ -0,0 +1,26 @@
+name: Software Enhancements
+description: Suggest an idea to make DeePTB more robust and organized
+labels: [Refactor]
+assignees: zhanghao
+
+body:
+- type: textarea
+  attributes:
+    label: Describe Current Status and Possible Solution
+    description: |
+      A clear and concise description of why this enhancement is important.
+      Describing the current situation of DeePTB is preferred.
+      If you have some ideas about how to achieve it, please feel free to comment.
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: Additional Context
+    description: |
+      Add any other context or screenshots about the enhancement here.
+
+- type: markdown
+  attributes:
+    value: >
+      Thanks for contributing 🎉!
diff --git a/.github/ISSUE_TEMPLATE/tests.yml b/.github/ISSUE_TEMPLATE/tests.yml
new file mode 100644
index 0000000..1b9f913
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/tests.yml
@@ -0,0 +1,23 @@
+name: Tests
+description: Regarding tests of DeePTB-UniSK
+labels: [Test]
+assignees: zhanghao
+
+body:
+- type: textarea
+  attributes:
+    label: Describe Current Status and Possible Solution
+
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: Additional Context
+    description: |
+      Add any other context or screenshots about the enhancement here.
+
+- type: markdown
+  attributes:
+    value: >
+      Thanks for contributing 🎉!
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..5ace460
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,6 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"
diff --git a/.github/release-check.md b/.github/release-check.md
new file mode 100644
index 0000000..5963a84
--- /dev/null
+++ b/.github/release-check.md
@@ -0,0 +1,12 @@
+# Release Checklist
+
+Please review the following items before approving the release:
+
+- [ ] Check the version number is correct
+- [ ] Verify that all tests have passed
+- [ ] Review the changelog for breaking changes
+- [ ] Confirm the package builds successfully
+- [ ] Check for any outstanding issues or pull requests
+- [ ] Ensure documentation is up-to-date
+
+Once you have reviewed and are ready to publish, approve this step to continue the release process.
\ No newline at end of file
diff --git a/.github/workflows/devcontainer.yml b/.github/workflows/devcontainer.yml
new file mode 100644
index 0000000..66dc712
--- /dev/null
+++ b/.github/workflows/devcontainer.yml
@@ -0,0 +1,67 @@
+name: Container
+
+on:
+  push:
+    # 只有 main 分支的 push 会触发
+    branches:
+      - main
+    # 如果 push 只修改了 docs/ 目录下的文件，则不触发
+    paths-ignore:
+      - 'docs/**'
+      - 'examples/**'
+      - 'README.md'
+
+jobs:
+  build_and_push_container:
+    runs-on: ubuntu-latest
+    if: github.repository_owner == 'DeePTB-Lab'
+
+    # 允许 GITHUB_TOKEN 有写入软件包（Packages）的权限，这是推送到 ghcr.io 所必需的
+    permissions:
+      contents: read
+      packages: write
+
+    strategy:
+      matrix:
+        dockerfile: ["main"]
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      # ✅ 新增步骤：此 action 会创建有效、小写的 Docker 标签
+      - name: Extract Docker metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          # 提供基础镜像名，此 action 会自动处理大小写
+          images: ghcr.io/${{ github.repository_owner }}/dpnegf-${{ matrix.dockerfile }}
+          tags: |
+            type=raw,value=latest
+
+      # ✏️ 修改步骤：现在使用上面 'meta' 步骤的输出来代替手动拼接
+      - name: Build and push Docker image
+        id: build-and-push
+        uses: docker/build-push-action@v6
+        with:
+          # 指定 Dockerfile 的路径
+          file: Dockerfile
+          # 设为 true，执行推送
+          push: true
+          # 使用元数据步骤生成的、经过清理的标签和元数据
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          # [重要] 缓存引用也必须使用清理过的、正确的小写标签
+          cache-from: type=registry,ref=${{ steps.meta.outputs.tags }}
+          # 推荐使用 gha 作为缓存后端，性能更好
+          cache-to: type=gha
diff --git a/.github/workflows/image.yml b/.github/workflows/image.yml
new file mode 100644
index 0000000..1a76d4c
--- /dev/null
+++ b/.github/workflows/image.yml
@@ -0,0 +1,57 @@
+name: Build and Push Release Image
+
+on:
+  # 允许在 GitHub UI 上手动触发
+  workflow_dispatch:
+  # 当一个以 "v" 开头的 tag (e.g., v1.0, v2.3.4) 被推送到仓库时触发
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  build_and_push_release:
+    runs-on: ubuntu-latest
+    if: github.repository_owner == 'DeePTB-Lab'
+    
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - name: Checkout repository with full history
+        uses: actions/checkout@v4
+        with:
+          # ✅ 关键修改：获取所有历史记录和标签，以便动态版本控制能够工作
+          fetch-depth: 0
+
+      - name: Extract Docker metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          # metadata-action 会自动处理组织名的大小写问题
+          images: |
+            ghcr.io/${{ github.repository_owner }}/dpnegf
+          tags: |
+            # 从 Git 标签中提取语义化版本 (e.g., v1.2.3 -> 1.2.3, 1.2, 1)
+            type=semver,pattern={{version}}
+            # 只有在手动触发时，才添加 'latest' 标签
+            type=raw,value=latest,enable=${{ github.event_name == 'workflow_dispatch' }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v6
+        with:
+          # 使用上面步骤生成的动态标签和元数据
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          file: Dockerfile
+          push: true
\ No newline at end of file
diff --git a/.github/workflows/unit_test.yml b/.github/workflows/unit_test.yml
new file mode 100644
index 0000000..74033e9
--- /dev/null
+++ b/.github/workflows/unit_test.yml
@@ -0,0 +1,35 @@
+name: DeePTB-UniSK Tests
+
+on:
+  pull_request:
+    paths-ignore:
+      - 'docs/**'
+  push:
+    branches:
+      - main
+    paths-ignore:
+      - 'docs/**'
+      - 'examples/**'
+  workflow_dispatch: 
+  
+jobs: 
+  run-tests:
+    runs-on: ubuntu-latest
+    if: github.repository_owner == 'DeePTB-Lab'
+    # 使用由 'Container' 工作流构建的开发镜像
+    container: ghcr.io/deeptb-lab/dpnegf-main:latest
+    steps: 
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Add git safe directory
+        # 当在容器内操作由外部挂载的工作区时，需要这个命令来避免 git 权限问题
+        run: |
+          git config --global --add safe.directory ${GITHUB_WORKSPACE}
+          
+      - name: Run Unit Tests
+        # 这个脚本应该包含所有测试命令
+        run: |
+          bash ut.sh
diff --git a/.gitignore b/.gitignore
index ba4135a..1c5c5fd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,37 +1,38 @@
 test*.ipynb
 **/processed*/*
-dptb/tests/**/*.yaml
-dptb/tests/**/pre_*.pt
-dptb/tests/**/*.pth
-dptb/tests/**/*.npy
-dptb/tests/**/*.traj
-dptb/tests/**/out*/*
-dptb/tests/**/out*/*
-dptb/tests/**/*lmdb
-dptb/tests/**/*h5
+dpnegf/tests/data/test_negf/**/*.xyz
+dpnegf/tests/**/*.yaml
+dpnegf/tests/**/pre_*.pt
+dpnegf/tests/**/*.pth
+dpnegf/tests/**/*.npy
+dpnegf/tests/**/*.traj
+dpnegf/tests/**/out*/*
+dpnegf/tests/**/out*/*
+dpnegf/tests/**/*lmdb
+dpnegf/tests/**/*h5
 examples/_*
 playground/*
 *.dat
 *log*
-dptb/tests/data/**/out*/config_*.json
+dpnegf/tests/data/**/out*/config_*.json
 bandstructure.npy
-dptb/tests/data/hBN/data/set.0/xdat2.traj
-dptb/tests/data/postrun/run_config.json
-dptb/tests/data/test_all/test_config.json
-dptb/tests/data/test_all/checkpoint/best_nnsk_b5.000_c6.615_w0.265.json
-dptb/tests/data/test_all/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json
-dptb/tests/data/test_all/fancy_ones/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json
-dptb/tests/data/test_negf/test_negf_run/out_negf/run_config.json
-dptb/data/try_test.ipynb
-dptb/negf/check.ipynb
-dptb/tests/data/test_negf/show.ipynb
-dptb/tests/data/test_tbtrans/show.ipynb
+dpnegf/tests/data/hBN/data/set.0/xdat2.traj
+dpnegf/tests/data/postrun/run_config.json
+dpnegf/tests/data/test_all/test_config.json
+dpnegf/tests/data/test_all/checkpoint/best_nnsk_b5.000_c6.615_w0.265.json
+dpnegf/tests/data/test_all/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json
+dpnegf/tests/data/test_all/fancy_ones/checkpoint/best_nnsk_b4.000_c4.000_w0.300.json
+dpnegf/tests/data/test_negf/test_negf_run/out_negf/run_config.json
+dpnegf/data/try_test.ipynb
+dpnegf/negf/check.ipynb
+dpnegf/tests/data/test_negf/show.ipynb
+dpnegf/tests/data/test_tbtrans/show.ipynb
 run_config.json
-dptb/nnet/__pycache__/
-dptb/sktb/__pycache__/
-dptb/negf/__pycache__/
-dptb/softsort/__pycache__/
-dptb/__pycache__/
+dpnegf/nnet/__pycache__/
+dpnegf/sktb/__pycache__/
+dpnegf/negf/__pycache__/
+dpnegf/softsort/__pycache__/
+dpnegf/__pycache__/
 .VSCodeCounter
 train_config.json
 results
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..ecfb7ac
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,48 @@
+FROM ubuntu:20.04
+SHELL ["/bin/bash", "-c"]
+
+ARG MINIFORGE_NAME=Miniforge3
+ARG MINIFORGE_VERSION=23.11.0-0
+ARG TARGETPLATFORM
+
+ENV CONDA_DIR=/opt/conda
+ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
+ENV PATH=${CONDA_DIR}/bin:${PATH}
+
+RUN apt-get update > /dev/null && \
+    apt-get install --no-install-recommends --yes \
+        wget bzip2 ca-certificates \
+        git \
+        tini \
+        g++ \
+        > /dev/null && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/* && \
+    wget --no-hsts --quiet https://github.com/conda-forge/miniforge/releases/download/${MINIFORGE_VERSION}/${MINIFORGE_NAME}-${MINIFORGE_VERSION}-Linux-$(uname -m).sh -O /tmp/miniforge.sh && \
+    /bin/bash /tmp/miniforge.sh -b -p ${CONDA_DIR} && \
+    rm /tmp/miniforge.sh && \
+    conda clean --tarballs --index-cache --packages --yes && \
+    find ${CONDA_DIR} -follow -type f -name '*.a' -delete && \
+    find ${CONDA_DIR} -follow -type f -name '*.pyc' -delete && \
+    conda clean --force-pkgs-dirs --all --yes  && \
+    echo ". ${CONDA_DIR}/etc/profile.d/conda.sh && conda activate base" >> /etc/skel/.bashrc && \
+    echo ". ${CONDA_DIR}/etc/profile.d/conda.sh && conda activate base" >> ~/.bashrc
+
+WORKDIR /app
+COPY . .
+
+# 2. 创建环境并安装所有依赖
+RUN \
+    sed -i 's/build-backend = "poetry_dynamic_versioning.backend"/build-backend = "poetry.core.masonry.api"/' pyproject.toml && \
+    conda create -n dpnegf python=3.10 -c conda-forge -y && \
+    git clone https://github.com/deepmodeling/DeePTB.git && \
+    conda run -n dpnegf pip install --upgrade pip setuptools wheel && \
+    conda run -n dpnegf pip install torch==2.1.1 && \
+    conda run -n dpnegf pip install torch-scatter -f https://data.pyg.org/whl/torch-2.1.1+cpu.html && \
+    conda run -n dpnegf pip install ./DeePTB && \
+     conda run -n dpnegf pip install ./ && \
+    conda clean --all -y && \
+    rm -rf /root/.cache/pip
+
+# 3. 设置默认启动环境
+RUN echo "conda activate dpnegf" >> ~/.bashrc
diff --git a/pyproject.toml b/pyproject.toml
index d3863e1..2d17b18 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,6 +31,8 @@ h5py = ">=3.7.0,<=3.11.0,!=3.10.0"
 lmdb = "1.4.1"
 pyfiglet = "1.0.2"
 tensorboard = "*"
+numba = "*"
+pyinstrument = "*"
 
 [tool.poetry.group.dev.dependencies]
 pytest = ">=7.2.0"
@@ -56,7 +58,7 @@ pyfiglet = "1.0.2"
 tensorboard = "*"
 numba = "*"
 joblib = "*"
-
+pyinstrument = "*"
 
 [tool.poetry.group.pybinding]
 optional = true
diff --git a/ut.sh b/ut.sh
new file mode 100644
index 0000000..ba53ffe
--- /dev/null
+++ b/ut.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+# This command ensures that the script will exit immediately if any command fails.
+set -e
+
+echo "--- Installing/updating package from PR in editable mode ---"
+
+# We use 'conda run' to execute the commands within the 'dpusk' environment.
+# 1. `pip install -e .`: The '-e' (editable) flag is crucial. It installs the
+#    package from the current directory (the PR's code) in a way that links
+#    back to the source files. This ensures that the tests run against the
+#    very latest code from the pull request, not the version baked into the
+#    Docker image.
+# 2. `pytest ./tests/`: After the package is installed, we run the tests.
+
+conda run -n dpnegf bash -c "pip install -e . && pytest dpnegf/tests/"
+
+echo "--- Unit Tests Passed Successfully ---"
+

From af295b9661e4594a1a2b4391c14ecb1c3beb6564 Mon Sep 17 00:00:00 2001
From: Qiangqiang Gu <98570179+QG-phy@users.noreply.github.com>
Date: Thu, 4 Sep 2025 01:48:35 +0800
Subject: [PATCH 113/152] Rename workflow from DeePTB-UniSK to DPNEGF Tests

---
 .github/workflows/unit_test.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/unit_test.yml b/.github/workflows/unit_test.yml
index 74033e9..44d9529 100644
--- a/.github/workflows/unit_test.yml
+++ b/.github/workflows/unit_test.yml
@@ -1,4 +1,4 @@
-name: DeePTB-UniSK Tests
+name: DPNEGF Tests
 
 on:
   pull_request:

From 17a122a3a4f6d8fa880d9dbf4597ce3a25acf951 Mon Sep 17 00:00:00 2001
From: Qiangqiang Gu <98570179+QG-phy@users.noreply.github.com>
Date: Thu, 4 Sep 2025 01:52:05 +0800
Subject: [PATCH 114/152] Add joblib dependency to pyproject.toml

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 2d17b18..3882e8d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,7 @@ lmdb = "1.4.1"
 pyfiglet = "1.0.2"
 tensorboard = "*"
 numba = "*"
+joblib = "*"
 pyinstrument = "*"
 
 [tool.poetry.group.dev.dependencies]

From 889702b0a61ee6622ec6fa9dee757f6fc857a2e7 Mon Sep 17 00:00:00 2001
From: Qiangqiang Gu <98570179+QG-phy@users.noreply.github.com>
Date: Thu, 4 Sep 2025 02:03:41 +0800
Subject: [PATCH 115/152] Add docs  (#26)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* fix(.gitignore): update paths from dptb to dpnegf for consistency

* feat: 添加多个问题模板和工作流配置文件

* feat: 添加单元测试工作流配置

* feat: 添加 numba 和 pyinstrument 依赖

* feat(.gitignore): 添加对 test_negf 目录下 .xyz 文件的忽略

* feat: 添加 Dockerfile 和单元测试脚本

* Add documentation for input parameters and training options

- Created `set_info.rst` to document the parameters related to setting information for trajectories, including `nframes`, `natoms`, `pos_type`, `pbc`, and `bandinfo`.
- Created `train_options.rst` to detail the training options for DeePTB, covering parameters such as `num_epoch`, `batch_size`, `optimizer`, `lr_scheduler`, and various loss options for training, validation, and reference data.
- Added `requirements.txt` to specify dependencies, including `urllib3`, `linkify-it-py`, `myst-nb`, `sphinx_rtd_theme`, `sphinx-book-theme`, and `jupyter`.
---
 .github/workflows/add_pages_doc.yml   |   35 +
 docs/CITATIONS.md                     |   44 +
 docs/CONTRIBUTING.md                  |  148 +++
 docs/DevelopingTeam.md                |   17 +
 docs/Makefile                         |   20 +
 docs/conf.py                          |   96 ++
 docs/easy_install.md                  |   78 ++
 docs/hands_on/index.rst               |    6 +
 docs/hands_on/tutorial1_base_sk.ipynb | 1284 +++++++++++++++++++++++
 docs/index.rst                        |   67 ++
 docs/input_params/common_options.rst  |   53 +
 docs/input_params/data_options.rst    |  251 +++++
 docs/input_params/index.rst           |   13 +
 docs/input_params/model_options.rst   | 1376 ++++++++++++++++++++++++
 docs/input_params/run_options.rst     | 1382 +++++++++++++++++++++++++
 docs/input_params/set_info.rst        |   82 ++
 docs/input_params/train_options.rst   |  718 +++++++++++++
 docs/requirements.txt                 |    7 +
 18 files changed, 5677 insertions(+)
 create mode 100644 .github/workflows/add_pages_doc.yml
 create mode 100644 docs/CITATIONS.md
 create mode 100644 docs/CONTRIBUTING.md
 create mode 100644 docs/DevelopingTeam.md
 create mode 100644 docs/Makefile
 create mode 100644 docs/conf.py
 create mode 100644 docs/easy_install.md
 create mode 100644 docs/hands_on/index.rst
 create mode 100755 docs/hands_on/tutorial1_base_sk.ipynb
 create mode 100644 docs/index.rst
 create mode 100644 docs/input_params/common_options.rst
 create mode 100644 docs/input_params/data_options.rst
 create mode 100644 docs/input_params/index.rst
 create mode 100644 docs/input_params/model_options.rst
 create mode 100644 docs/input_params/run_options.rst
 create mode 100644 docs/input_params/set_info.rst
 create mode 100644 docs/input_params/train_options.rst
 create mode 100644 docs/requirements.txt

diff --git a/.github/workflows/add_pages_doc.yml b/.github/workflows/add_pages_doc.yml
new file mode 100644
index 0000000..578ef29
--- /dev/null
+++ b/.github/workflows/add_pages_doc.yml
@@ -0,0 +1,35 @@
+name: Build and Deploy Docs
+
+on:
+  push:
+    branches: [ main ]  # 或者您的默认分支名
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+    if: github.repository == 'DeePTB-Lab/dpnegf'
+    runs-on: ubuntu-22.04
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.11'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install sphinx sphinx_book_theme linkify-it-py
+        pip install myst-nb jupyter
+        if [ -f docs/requirements.txt ]; then pip install -r docs/requirements.txt; fi
+    - name: Build docs
+      run: |
+        cd docs
+        python conf.py
+        sphinx-build -b html . _build/html
+    - name: Deploy
+      uses: peaceiris/actions-gh-pages@v4
+      if: github.event_name == 'push' && github.ref == 'refs/heads/main'
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        publish_dir: ./docs/_build/html
diff --git a/docs/CITATIONS.md b/docs/CITATIONS.md
new file mode 100644
index 0000000..cae01d4
--- /dev/null
+++ b/docs/CITATIONS.md
@@ -0,0 +1,44 @@
+# How to Cite
+
+The following references are required to be cited when using DeePTB. Specifically:
+
+- **For DeePTB-SK:**
+  
+    Q. Gu, Z. Zhouyin, S. K. Pandey, P. Zhang, L. Zhang, and W. E, Deep Learning Tight-Binding Approach for Large-Scale Electronic Simulations at Finite Temperatures with Ab Initio Accuracy, Nat Commun 15, 6772 (2024).
+    ```latex
+    @article{guDeep2024,
+      title = {Deep Learning Tight-Binding Approach for Large-Scale Electronic Simulations at Finite Temperatures with  Ab Initio Accuracy},
+      author = {Gu, Qiangqiang and Zhouyin, Zhanghao and Pandey, Shishir Kumar and Zhang, Peng and Zhang, Linfeng and E,    Weinan},
+      year = {2024},
+      month = aug,
+      journal = {Nature Communications},
+      volume = {15},
+      number = {1},
+      pages = {6772},
+      publisher = {Nature Publishing Group},
+      issn = {2041-1723},
+      doi = {10.1038/s41467-024-51006-4},
+      copyright = {2024 The Author(s)},
+      keywords = {Computational methods,Electronic properties and materials,Electronic structure}
+    }
+    ```
+- **For DeePTB-E3:**
+  
+    Z. Zhouyin, Z. Gan, S. K. Pandey, L. Zhang, and Q. Gu, Learning Local Equivariant Representations for Quantum Operators, arXiv:2407.06053.
+    
+
+    ```latex
+    @misc{zhouyinLearning2024,
+      title = {Learning Local Equivariant Representations for Quantum Operators},
+      author = {Zhouyin, Zhanghao and Gan, Zixi and Pandey, Shishir Kumar and Zhang, Linfeng and Gu, Qiangqiang},
+      year = {2024},
+      month = jul,
+      number = {arXiv:2407.06053},
+      eprint = {2407.06053},
+      primaryclass = {cond-mat, physics:quant-ph},
+      publisher = {arXiv},
+      doi = {10.48550/arXiv.2407.06053},
+      archiveprefix = {arXiv},
+      keywords = {Computer Science - Machine Learning,Condensed Matter - Materials Science,Quantum Physics},
+    }
+    ```
diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
new file mode 100644
index 0000000..06ddfcc
--- /dev/null
+++ b/docs/CONTRIBUTING.md
@@ -0,0 +1,148 @@
+# Contributing to DeePTB
+
+We heartily welcome contributions to the DeePTB project. This guide provides technical and non-technical guidelines to help you contribute effectively.
+
+## Table of Contents
+
+- [Got a question?](#got-a-question)
+- [Project Structure](#project-structure)
+- [Submitting an Issue](#submitting-an-issue)
+- [Comment Style for Documentation](#comment-style-for-documentation)
+- [Code Formatting Style](#code-formatting-style)
+- [Adding a Unit Test](#adding-a-unit-test)
+- [Running Unit Tests](#running-unit-tests)
+- [Submitting a Pull Request](#submitting-a-pull-request)
+- [After Your Pull Request is Merged](#after-your-pull-request-is-merged)
+- [Commit Message Guidelines](#commit-message-guidelines)
+
+## Got a question?
+
+For questions and discussions about DeePTB, use our [GitHub Discussions](https://github.com/deepmodeling/DeePTB/discussions). If you find a bug or want to propose a new feature, please use the [issue tracker](https://github.com/deepmodeling/DeePTB/issues/new/choose).
+
+## Project Structure
+
+DeePTB is organized into several modules. Here's a brief overview:
+
+- `data`: Data processing module.
+- `entrypoints`: Entry points for the command-line interface.
+- `negf`: Nonequilibrium Green's Function (NEGF) module.
+- `nn`: Neural network model module.
+- `nnops`: Neural network operations module.
+- `plugins`: Plugins for various functionalities.
+- `postprocess`: Post-processing module.
+- `tests`: Unit tests for DeePTB.
+- `utils`: Utility module with tools and constants.
+
+## Submitting an Issue
+
+Before you submit an issue, please search the issue tracker, and maybe your problem has been discussed and fixed. You can [submit new issues](https://github.com/deepmodeling/DeePTB/issues/new/choose) by filling our issue forms.
+To help us reproduce and confirm a bug, please provide a test case and building environment in your issue.
+
+
+## Comment Style for Documentation
+
+We encourage you to add comments to your code, especially for complex logic or decisions. Use clear, concise language and reference any related issues or discussions.
+
+## Code Formatting Style
+
+Adhere to the [PEP 8](https://www.python.org/dev/peps/pep-0008/) style guide for Python code. For other languages, follow their respective community standards.
+
+## Adding a Unit Test
+
+When contributing a new feature or fixing a bug, it's important to include tests to ensure the code works as expected and to prevent future regressions.
+
+1. Locate the appropriate test file in the `tests` directory, using `pytest` for Python tests.
+2. Write your test case, following the existing structure and style.
+
+## Running Unit Tests
+
+To run all unit tests, use the following command in the project root directory:
+
+```bash
+pytest ./dptb/tests
+```
+
+To run a specific test, use:
+
+```bash
+pytest ./dptb/tests/test_file.py
+```
+
+## Submitting a Pull Request
+
+1. [Fork](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) the [DeePTB repository](https://github.com/deepmodeling/DeePTB). If you already had an existing fork, [sync](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork) the fork to keep your modification up-to-date.
+2. Create a new branch for your changes.
+     ```shell
+     git checkout -b my-fix-branch
+     ```
+3. Make your changes, including tests and documentation updates.
+4. Commit your changes with a [proper commit message](#commit-message-guidelines).
+5. Push your branch to your fork on GitHub.
+6. Submit a pull request (PR) with `deepmodeling/DeePTB:main` as the base repository. It is required to document your PR following [our guidelines](#commit-message-guidelines).
+
+### After Your Pull Request is Merged
+
+- Delete the remote branch on GitHub either [through the GitHub web UI](https://docs.github.com/en/repositories/configuring-branches-and-merges-in-your-repository/managing-branches-in-your-repository/deleting-and-restoring-branches-in-a-pull-request#deleting-a-branch-used-for-a-pull-request) or your local shell as follows:
+
+    ```shell
+    git push origin --delete my-fix-branch
+    ```
+
+- Check out the master branch:
+
+    ```shell
+    git checkout develop -f
+    ```
+
+- Delete the local branch:
+
+    ```shell
+    git branch -D my-fix-branch
+    ```
+
+- Update your master with the latest upstream version:
+
+    ```shell
+    git pull --ff upstream develop
+    ```
+
+## Commit Message Guidelines
+A well-formatted commit message leads a more readable history when we look through some changes, and helps us generate change log.
+
+We follow the [Conventional Commits](https://www.conventionalcommits.org) specification for commit messages:
+
+```text
+<type>[optional scope]: <description>
+
+[optional body]
+
+[optional footer(s)]
+```
+
+Example:
+
+```text
+Fix(data): handle missing data gracefully
+
+When a required data file is missing, the program now displays a user-friendly error message and exits.
+
+Fixes #123
+```
+
+- Header
+  - type: The general intention of this commit
+    - `Feature`: A new feature
+    - `Fix`: A bug fix
+    - `Docs`: Only documentation changes
+    - `Style`: Changes that do not affect the meaning of the code
+    - `Refactor`: A code change that neither fixes a bug nor adds a feature
+    - `Perf`: A code change that improves performance
+    - `Test`: Adding missing tests or correcting existing tests
+    - `Build`: Changes that affect the build system or external dependencies
+    - `CI`: Changes to our CI configuration files and scripts
+    - `Revert`: Reverting commits
+  - scope: optional, could be the module which this commit changes; for example, `orbital`
+  - description: A short summary of the code changes: tell others what you did in one sentence.
+- Body: optional, providing detailed, additional, or contextual information about the code changes, e.g. the motivation of this commit, referenced materials, the coding implementation, and so on.
+- Footer: optional, reference GitHub issues or PRs that this commit closes or is related to. [Use a keyword](https://docs.github.com/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) to close an issue, e.g. "Fix #753".
+
diff --git a/docs/DevelopingTeam.md b/docs/DevelopingTeam.md
new file mode 100644
index 0000000..d69febb
--- /dev/null
+++ b/docs/DevelopingTeam.md
@@ -0,0 +1,17 @@
+# Development Team
+
+## Current Members
+The current development team consists of the following research members / groups:
+- Jijie Zou
+- Zhanghao Zhouyin
+- Yike Huang
+- Qiangqiang Gu
+- ...
+
+## Join Us!
+We welcome contributions from the community! If you're interested in contributing to this project, here's how you can get involved:
+
+- Browse our [open issues](https://github.com/DeePTB-Lab/dpnegf/issues)
+
+Whether you're a developer, researcher, or enthusiast, there's a place for you in our community. Don't hesitate to reach out if you have any questions or ideas!
+
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 0000000..d7b9e87
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = $(PWD)
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 0000000..925e70b
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,96 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+
+# -- Project information -----------------------------------------------------
+
+project = 'DPNEGF'
+copyright = '2025, DPNEGF'
+author = 'DPNEGF'
+
+# The full version, including alpha/beta/rc tags
+# release = '2.3.5'
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+        'myst_nb',
+]
+nb_execute_notebooks = "off"  # 不执行notebooks
+
+myst_enable_extensions = [
+    "amsmath",
+    "colon_fence",
+    "deflist",
+    "dollarmath",
+    "fieldlist",
+    "html_admonition",
+    "html_image",
+    "linkify",
+    "replacements",
+    "smartquotes",
+    "strikethrough",
+    "substitution",
+    "tasklist",
+]
+myst_heading_anchors = 4
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_book_theme'
+html_logo = 'deeptb-logo.svg'
+
+
+# Changes for compatibility with Read the Docs
+import os
+
+# Define the canonical URL if you are using a custom domain on Read the Docs
+html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "")
+
+# Tell Jinja2 templates the build is running on Read the Docs
+if os.environ.get("READTHEDOCS", "") == "True":
+    if "html_context" not in globals():
+        html_context = {}
+    html_context["READTHEDOCS"] = True
+
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+latex_engine = 'xelatex'
+mathjax_path = 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.2.0/es5/tex-mml-chtml.min.js'
+#deepmodeling_current_site = 'Tutorials'
+latex_elements = {
+    'extraclassoptions':'openany,oneside'
+}
diff --git a/docs/easy_install.md b/docs/easy_install.md
new file mode 100644
index 0000000..e81382e
--- /dev/null
+++ b/docs/easy_install.md
@@ -0,0 +1,78 @@
+# Installation Guide
+
+This guide will help you install DeePTB, a Python package that utilizes deep learning to construct electronic tight-binding Hamiltonians.
+
+## Prerequisites
+
+Before installing DeePTB, ensure you have the following prerequisites:
+  - Git
+  - Python 3.9 to 3.12.
+  - Torch 2.0.0 to 2.5.1 ([PyTorch Installation](https://pytorch.org/get-started/locally)).
+  - ifermi (optional, for 3D fermi-surface plotting).
+  - TBPLaS (optional).
+
+## Installation Methods
+
+
+
+### From Source
+  
+Highly recommended to install DeePTB from source to get the latest features and bug fixes.
+1. **Setup Python environment**:
+    
+    Using conda (recommended, python >=3.9, <=3.12 ), e.g.,
+    ```bash
+    conda create -n dptb_venv python=3.10
+    conda activate dptb_venv
+    ```
+    or using venv (make sure python >=3.9,<=3.12)
+    ```bash
+    python -m venv dptb_venv
+    source dptb_venv/bin/activate
+    ```
+2. **Clone DeePTB and  Navigate to the root directory**:
+    ```bash
+    git clone https://github.com/deepmodeling/DeePTB.git
+    cd DeePTB
+    ```
+3. **Install `torch`**:
+    ```bash
+    pip install "torch>=2.0.0,<=2.5.0"
+    ```
+4. **Install `torch-scatter`** (two ways):
+    - **Recommended**: Install torch and torch-scatter using the following commands:
+        ```bash
+         python docs/auto_install_torch_scatter.py
+        ```
+    - **Manual**: Install torch and torch-scatter manually:
+        ```bash
+        pip install torch-scatter -f https://data.pyg.org/whl/torch-${version}+${CUDA}.html
+        ```
+        where `${version}` is the version of torch, e.g., 2.5.0, and `${CUDA}` is the CUDA version, e.g., cpu, cu118, cu121, cu124. See [torch_scatter doc](https://github.com/rusty1s/pytorch_scatter) for more details.   
+
+5. **Install DeePTB**:   
+    ```bash
+    pip install .
+    ```
+    
+### From PyPi
+
+1. Install PyTorch first by following the instructions on [PyTorch: Get Started](https://pytorch.org/get-started/locally).
+2. Install DeePTB using pip:
+   ```bash
+   pip install dptb
+   ```
+
+### Additional Tips
+
+- Keep your DeePTB installation up-to-date by pulling the latest changes from the repository and re-installing.
+- If you encounter any issues during installation, consult the [DeePTB documentation](https://deeptb.readthedocs.io/en/latest/) or seek help from the community.
+
+## Contributing
+
+We welcome contributions to DeePTB. If you are interested in contributing, please read our [contributing guidelines](https://deeptb.readthedocs.io/en/latest/community/contribution_guide.html).
+
+## License
+
+DeePTB is open-source software released under the [LGPL-3.0](https://github.com/deepmodeling/DeePTB/blob/main/LICENSE) provided in the repository.
+
diff --git a/docs/hands_on/index.rst b/docs/hands_on/index.rst
new file mode 100644
index 0000000..5dfe0e3
--- /dev/null
+++ b/docs/hands_on/index.rst
@@ -0,0 +1,6 @@
+=================================================
+A quick Example
+=================================================
+
+.. toctree::
+   tutorial1_base_sk
diff --git a/docs/hands_on/tutorial1_base_sk.ipynb b/docs/hands_on/tutorial1_base_sk.ipynb
new file mode 100755
index 0000000..e9b6f9f
--- /dev/null
+++ b/docs/hands_on/tutorial1_base_sk.ipynb
@@ -0,0 +1,1284 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "c7fed360",
+      "metadata": {},
+      "source": [
+        "# Tutorial 1: deeptb-sk baseline model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8eaf407f",
+      "metadata": {},
+      "source": [
+        "## Introduction\n",
+        "\n",
+        "**DeePTB** is a method that uses deep learning to accelerate first-principles electronic structure simulations.\n",
+        "\n",
+        "### Version Features\n",
+        "- **v1**: Constructed tight-binding (TB) models with first-principles accuracy (DeePTB-SK)\n",
+        "- **v2**: Added E3 equivariant networks to represent single-electron operators (Hamiltonian, density matrix, and overlap matrix) (DeePTB-E3)\n",
+        "- **v2.2**: Incorporated built-in SK empirical parameters covering commonly used elements across the periodic table\n",
+        "\n",
+        "Through these capabilities, DeePTB provides multiple approaches to accelerate electronic structure simulations of materials.\n",
+        "\n",
+        "### Learning Objectives\n",
+        "\n",
+        "In this tutorial, you will:\n",
+        "1. Learn how to use built-in base model to plot band structure for given crystal structure\n",
+        "2. Learn how to generate a empirical sk model in deeptb-sk format for target system"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0c5db933",
+      "metadata": {},
+      "source": [
+        "## 1. Calculating Band Structure for a Given Structure\n",
+        "\n",
+        "The deeptb-sk module now [since v2.2] has built-in empirical SK parameter models covering elements across the periodic table. \n",
+        "\n",
+        "These can be directly used to obtain empirical SKTB models for given structures. It also supports directly plotting band structures for a given structure."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "a980d847",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[01;34m./\u001b[0m\n",
+            "\u251c\u2500\u2500 \u001b[00mgaas.vasp\u001b[0m\n",
+            "\u251c\u2500\u2500 \u001b[00mhBN.vasp\u001b[0m\n",
+            "\u2514\u2500\u2500 \u001b[00msilicon.vasp\u001b[0m\n",
+            "\n",
+            "0 directories, 3 files\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "workdir='/root/soft/DeePTB/examples/base_model/'\n",
+        "os.chdir(f\"{workdir}/structures\")\n",
+        "!tree -L 1 ./"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c6d703e8",
+      "metadata": {},
+      "source": [
+        "Run the band structure plotting command.\n",
+        "**Note** that the selection of high-symmetry paths in the Brillouin zone is based on the seekpath.get_path_orig_cell function, which has the following characteristics to be aware of:\n",
+        "1. It does not support 2D materials and will treat 2D materials as 3D materials\n",
+        "   \n",
+        "2. If the input cell is a non-standard primitive unit cell, the returned k path is equivalent to the k path for the standard cell. For example, the band structure calculated along the k path for the standard and non-standard unit cells will be the same up to numerical errors.\n",
+        "   \n",
+        "3. If the input cell is a supercell of a smaller primitive cell, the returned k path is that of the associated primitive cell, in the basis of supercell reciprocal lattice. In this case, the k points are not the high-symmetry points of the first Brillouin zone of the given supercell, but the high-symmetry points of the Brillouin zone of the associated primitive cell.\n",
+        "\n",
+        "The command for plotting the band structure is as follows: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "55ca0ab2",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+            " \n",
+            " \n",
+            "#################################################################################\n",
+            "#                                                                               #\n",
+            "#                                                                               #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
+            "#                                                                               #\n",
+            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
+            "#                                                                               #\n",
+            "#################################################################################\n",
+            " \n",
+            " \n",
+            "/root/dptb_venv/lib/python3.10/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead\n",
+            "  conv_lattice = dataset[\"std_lattice\"]\n",
+            "DEEPTB INFO    The structure space group is: Fd-3m (No. 227)\n",
+            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
+            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
+            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
+            "/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+            "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
+            "Figure(640x560)\n",
+            "DEEPTB INFO    band calculation successfully completed.\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 1. Silicon\n",
+        "!dptb run band -i poly4 -stu silicon.vasp -o band_si\n",
+        "\n",
+        "# display the band plot:\n",
+        "from IPython.display import Image, display\n",
+        "import os\n",
+        "image_path = f'./band_si/results/band.png'\n",
+        "display(Image(filename=image_path))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9e8fd859",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+            " \n",
+            " \n",
+            "#################################################################################\n",
+            "#                                                                               #\n",
+            "#                                                                               #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
+            "#                                                                               #\n",
+            "#                         Version: 2.0.4.dev77+45eaa8c                          #\n",
+            "#                                                                               #\n",
+            "#################################################################################\n",
+            " \n",
+            " \n",
+            "/root/dptb_venv/lib/python3.10/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead\n",
+            "  conv_lattice = dataset[\"std_lattice\"]\n",
+            "DEEPTB INFO    The structure space group is: F-43m (No. 216)\n",
+            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
+            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
+            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
+            "/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+            "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
+            "Figure(640x560)\n",
+            "DEEPTB INFO    band calculation successfully completed.\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 2. GaAs\n",
+        "!dptb run band -i poly4 -stu gaas.vasp -o band_gaas\n",
+        "\n",
+        "# display the band plot:\n",
+        "from IPython.display import Image, display\n",
+        "import os\n",
+        "image_path = f'./band_gaas/results/band.png'\n",
+        "display(Image(filename=image_path))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "bdce4498",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+            " \n",
+            " \n",
+            "#################################################################################\n",
+            "#                                                                               #\n",
+            "#                                                                               #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
+            "#                                                                               #\n",
+            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
+            "#                                                                               #\n",
+            "#################################################################################\n",
+            " \n",
+            " \n",
+            "/root/dptb_venv/lib/python3.10/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead\n",
+            "  conv_lattice = dataset[\"std_lattice\"]\n",
+            "DEEPTB INFO    The structure space group is: P-6m2 (No. 187)\n",
+            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
+            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
+            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
+            "/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+            "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
+            "Figure(640x560)\n",
+            "DEEPTB INFO    band calculation successfully completed.\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 3. hBN 2D \n",
+        "!dptb run band -i poly4 -stu hBN.vasp -o band_hBN\n",
+        "\n",
+        "# display the band plot:\n",
+        "from IPython.display import Image, display\n",
+        "import os\n",
+        "image_path = f'./band_hBN/results/band.png'\n",
+        "display(Image(filename=image_path))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "16a7eee3",
+      "metadata": {},
+      "source": [
+        "## 2. Extracting SK Parameter Files for a Given System\n",
+        "\n",
+        "Since there is a built-in baseline model covering the periodic table, for the target research system, you can extract the empirical parameter model for your target system from this built-in baseline model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "f4d144f5",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[01;34m./\u001b[0m\n",
+            "\u251c\u2500\u2500 \u001b[00mgaas.json\u001b[0m\n",
+            "\u251c\u2500\u2500 \u001b[00mhbn_sp.json\u001b[0m\n",
+            "\u251c\u2500\u2500 \u001b[00mhbn_spd.json\u001b[0m\n",
+            "\u2514\u2500\u2500 \u001b[00msilicon.json\u001b[0m\n",
+            "\n",
+            "0 directories, 4 files\n"
+          ]
+        }
+      ],
+      "source": [
+        "os.chdir(f\"{workdir}/confs\")\n",
+        "!tree -L 1 ./"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "10376e0d",
+      "metadata": {},
+      "source": [
+        "For the target system, we first need to define the basis set configuration and save it in a JSON file. Below is the configuration we use for hBN."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "57b4a974",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "    \"common_options\": {\n",
+            "        \"basis\": {\n",
+            "            \"B\": [\"s\",\"p\",\"d\"],\n",
+            "            \"N\": [\"s\",\"p\",\"d\"]\n",
+            "        }\n",
+            "    }\n",
+            "}"
+          ]
+        }
+      ],
+      "source": [
+        "!cat hbn_spd.json"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "81f0228f",
+      "metadata": {},
+      "source": [
+        "Run the following command to extract the empirical model settings and parameters for the target system from the built-in empirical model covering the periodic table, and save them in the sktb.json file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "df8715cf",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+            " \n",
+            " \n",
+            "#################################################################################\n",
+            "#                                                                               #\n",
+            "#                                                                               #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
+            "#                                                                               #\n",
+            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
+            "#                                                                               #\n",
+            "#################################################################################\n",
+            " \n",
+            " \n",
+            "DEEPTB INFO    Extracting empirical SK parameters for BN\n",
+            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
+            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
+            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
+            "DEEPTB INFO    Empirical SK parameters are saved in hbn_spd_model/sktb.json\n",
+            "DEEPTB INFO    If you want to further train the model, please use `dptb config` command to generate input template.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!dptb esk hbn_spd.json -m poly4 -o hbn_spd_model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7946902a",
+      "metadata": {},
+      "source": [
+        "The above command will create an hbn_spd_model folder and save the sktb.json model file in it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "bc7b427d",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{\n",
+            "    \"version\": 2,\n",
+            "    \"unit\": \"eV\",\n",
+            "    \"model_options\": {\n",
+            "        \"nnsk\": {\n",
+            "            \"onsite\": {\n",
+            "                \"method\": \"uniform_noref\"\n",
+            "            },\n",
+            "            \"hopping\": {\n",
+            "                \"method\": \"poly4pow\",\n",
+            "                \"rs\": {\n",
+            "                    \"B-B\": 4.22,\n",
+            "                    \"B-N\": 4.04,\n",
+            "                    \"N-B\": 4.04,\n",
+            "                    \"N-N\": 3.85\n",
+            "                },\n",
+            "                \"w\": 0.2\n",
+            "            },\n",
+            "            \"soc\": {},\n",
+            "            \"freeze\": false,\n",
+            "            \"push\": false,\n",
+            "            \"std\": 0.01,\n",
+            "            \"atomic_radius\": \"cov\"\n",
+            "        }\n",
+            "    },\n",
+            "    \"common_options\": {\n",
+            "        \"basis\": {\n",
+            "            \"B\": [\n",
+            "                \"s\",\n",
+            "                \"p\",\n",
+            "                \"d\"\n",
+            "            ],\n",
+            "            \"N\": [\n",
+            "                \"s\",\n",
+            "                \"p\",\n",
+            "                \"d\"\n",
+            "            ]\n",
+            "        },\n",
+            "        \"dtype\": \"float32\",\n",
+            "        \"device\": \"cuda\",\n",
+            "        \"overlap\": true\n",
+            "    },\n",
+            "    \"model_params\": {\n",
+            "        \"onsite\": {\n",
+            "            \"B-s-0\": [\n",
+            "                -9.436724662780762\n",
+            "            ],\n",
+            "            \"B-p-0\": [\n",
+            "                -3.6036229133605957\n",
+            "            ],\n",
+            "            \"B-d-0\": [\n",
+            "                0.0\n",
+            "            ],\n",
+            "            \"N-s-0\": [\n",
+            "                -18.575620651245117\n",
+            "            ],\n",
+            "            \"N-p-0\": [\n",
+            "                -7.092767238616943\n",
+            "            ],\n",
+            "            \"N-d-0\": [\n",
+            "                0.0\n",
+            "            ]\n",
+            "        },\n",
+            "        \"hopping\": {\n",
+            "            \"B-B-s-s-0\": [\n",
+            "                -4.163856506347656,\n",
+            "                3.1430838108062744,\n",
+            "                3.5406551361083984,\n",
+            "                -13.840292930603027,\n",
+            "                4.28679084777832,\n",
+            "                0.0003413402009755373\n",
+            "            ],\n",
+            "            \"B-B-s-p-0\": [\n",
+            "                -4.2632155418396,\n",
+            "                1.855499505996704,\n",
+            "                5.719208240509033,\n",
+            "                -12.824378967285156,\n",
+            "                2.3826582431793213,\n",
+            "                0.00027443747967481613\n",
+            "            ],\n",
+            "            \"B-B-s-d-0\": [\n",
+            "                0.00014130362251307815,\n",
+            "                -0.00012631429126486182,\n",
+            "                0.0001882282958831638,\n",
+            "                -0.0005035149515606463,\n",
+            "                0.00022772896045353264,\n",
+            "                -0.40316706895828247\n",
+            "            ],\n",
+            "            \"B-B-p-p-0\": [\n",
+            "                4.029307842254639,\n",
+            "                -0.4571390151977539,\n",
+            "                -5.698226451873779,\n",
+            "                5.24634313583374,\n",
+            "                1.8383373022079468,\n",
+            "                0.00098962162155658\n",
+            "            ],\n",
+            "            \"B-B-p-p-1\": [\n",
+            "                -1.7277371883392334,\n",
+            "                1.7380834817886353,\n",
+            "                0.2498142123222351,\n",
+            "                -4.095367431640625,\n",
+            "                1.5244688987731934,\n",
+            "                0.6229994893074036\n",
+            "            ],\n",
+            "            \"B-B-p-d-0\": [\n",
+            "                -3.374056541360915e-05,\n",
+            "                0.00013169155863579363,\n",
+            "                -0.00022330175852403045,\n",
+            "                -8.510611951351166e-05,\n",
+            "                -6.722987745888531e-05,\n",
+            "                -0.3973250985145569\n",
+            "            ],\n",
+            "            \"B-B-p-d-1\": [\n",
+            "                -7.808039663359523e-05,\n",
+            "                3.817861215793528e-05,\n",
+            "                -5.0371396355330944e-05,\n",
+            "                -0.000102539241197519,\n",
+            "                -1.8067279597744346e-05,\n",
+            "                -0.3949469327926636\n",
+            "            ],\n",
+            "            \"B-B-d-d-0\": [\n",
+            "                -6.081238097976893e-05,\n",
+            "                9.681181109044701e-05,\n",
+            "                -9.043539466802031e-05,\n",
+            "                5.160560249350965e-05,\n",
+            "                -0.0002392987225903198,\n",
+            "                0.3956696689128876\n",
+            "            ],\n",
+            "            \"B-B-d-d-1\": [\n",
+            "                0.00015222658112179488,\n",
+            "                6.237948400666937e-05,\n",
+            "                0.0002462207048665732,\n",
+            "                -0.0003655400942079723,\n",
+            "                0.0004969655419699848,\n",
+            "                -0.39649420976638794\n",
+            "            ],\n",
+            "            \"B-B-d-d-2\": [\n",
+            "                -6.792173371650279e-05,\n",
+            "                0.00012900785077363253,\n",
+            "                1.0697433026507497e-05,\n",
+            "                -0.00010661676060408354,\n",
+            "                -9.13233234314248e-05,\n",
+            "                -0.3952234387397766\n",
+            "            ],\n",
+            "            \"N-B-s-s-0\": [\n",
+            "                -5.198118686676025,\n",
+            "                4.442707061767578,\n",
+            "                4.672472953796387,\n",
+            "                -22.44223403930664,\n",
+            "                8.299448013305664,\n",
+            "                -0.27995532751083374\n",
+            "            ],\n",
+            "            \"N-B-s-p-0\": [\n",
+            "                -6.1210198402404785,\n",
+            "                4.5004191398620605,\n",
+            "                6.178765296936035,\n",
+            "                -23.72483253479004,\n",
+            "                7.701132774353027,\n",
+            "                -0.17325514554977417\n",
+            "            ],\n",
+            "            \"N-B-p-s-0\": [\n",
+            "                -4.536301136016846,\n",
+            "                2.089078426361084,\n",
+            "                7.104267597198486,\n",
+            "                -18.932785034179688,\n",
+            "                5.050920486450195,\n",
+            "                -0.08721348643302917\n",
+            "            ],\n",
+            "            \"N-B-s-d-0\": [\n",
+            "                -7.312803063541651e-05,\n",
+            "                0.00010872803977690637,\n",
+            "                0.00023205213074106723,\n",
+            "                -0.0005027204751968384,\n",
+            "                0.0001486523833591491,\n",
+            "                -0.43798455595970154\n",
+            "            ],\n",
+            "            \"N-B-d-s-0\": [\n",
+            "                0.00018420522974338382,\n",
+            "                -0.00017437210772186518,\n",
+            "                -0.00016033451538532972,\n",
+            "                0.0007334152469411492,\n",
+            "                -8.776571485213935e-05,\n",
+            "                0.4466051459312439\n",
+            "            ],\n",
+            "            \"N-B-p-p-0\": [\n",
+            "                4.69775390625,\n",
+            "                -0.6529765129089355,\n",
+            "                -6.943027973175049,\n",
+            "                8.185653686523438,\n",
+            "                1.3665649890899658,\n",
+            "                -0.2770684063434601\n",
+            "            ],\n",
+            "            \"N-B-p-p-1\": [\n",
+            "                -1.963960886001587,\n",
+            "                1.9514763355255127,\n",
+            "                0.5242166519165039,\n",
+            "                -5.668179988861084,\n",
+            "                2.2336957454681396,\n",
+            "                -0.8325421810150146\n",
+            "            ],\n",
+            "            \"N-B-p-d-0\": [\n",
+            "                -6.0136051615700126e-05,\n",
+            "                8.034883649088442e-05,\n",
+            "                -0.0002142499142792076,\n",
+            "                0.00013417207810562104,\n",
+            "                3.860515425913036e-05,\n",
+            "                -0.44638267159461975\n",
+            "            ],\n",
+            "            \"N-B-d-p-0\": [\n",
+            "                -0.00015385006554424763,\n",
+            "                9.536759898765013e-05,\n",
+            "                -0.00010434392606839538,\n",
+            "                2.4020744604058564e-05,\n",
+            "                -0.00022013107081875205,\n",
+            "                0.44291141629219055\n",
+            "            ],\n",
+            "            \"N-B-p-d-1\": [\n",
+            "                -4.726650513475761e-05,\n",
+            "                8.19785927888006e-05,\n",
+            "                0.00011910804460057989,\n",
+            "                -0.0003437807899899781,\n",
+            "                0.00014456789358519018,\n",
+            "                0.4415556490421295\n",
+            "            ],\n",
+            "            \"N-B-d-p-1\": [\n",
+            "                9.281534585170448e-05,\n",
+            "                -0.000124435915495269,\n",
+            "                0.00014234631089493632,\n",
+            "                -0.0006778020178899169,\n",
+            "                0.00030078168492764235,\n",
+            "                -0.4489174485206604\n",
+            "            ],\n",
+            "            \"N-B-d-d-0\": [\n",
+            "                -0.0001388048694934696,\n",
+            "                0.00010272378858644515,\n",
+            "                -0.00019081367645412683,\n",
+            "                3.532186383381486e-07,\n",
+            "                -0.00017708863015286624,\n",
+            "                0.45609885454177856\n",
+            "            ],\n",
+            "            \"N-B-d-d-1\": [\n",
+            "                -0.0001369681558571756,\n",
+            "                0.0001792228576960042,\n",
+            "                -0.00011874250776600093,\n",
+            "                -0.00022725776943843812,\n",
+            "                -5.964709271211177e-05,\n",
+            "                -0.45647943019866943\n",
+            "            ],\n",
+            "            \"N-B-d-d-2\": [\n",
+            "                0.00014195215771906078,\n",
+            "                -7.780492160236463e-05,\n",
+            "                8.625858754385263e-05,\n",
+            "                0.0001418764004483819,\n",
+            "                3.1707189918961376e-05,\n",
+            "                -0.45413458347320557\n",
+            "            ],\n",
+            "            \"N-N-s-s-0\": [\n",
+            "                -6.06509256362915,\n",
+            "                4.933748245239258,\n",
+            "                7.251394271850586,\n",
+            "                -31.393178939819336,\n",
+            "                11.564867973327637,\n",
+            "                0.6752002239227295\n",
+            "            ],\n",
+            "            \"N-N-s-p-0\": [\n",
+            "                -6.407330513000488,\n",
+            "                4.119426727294922,\n",
+            "                8.804641723632812,\n",
+            "                -30.208786010742188,\n",
+            "                9.762639999389648,\n",
+            "                0.4003536105155945\n",
+            "            ],\n",
+            "            \"N-N-s-d-0\": [\n",
+            "                0.00011224352056160569,\n",
+            "                0.00017669328371994197,\n",
+            "                2.3582368157804012e-05,\n",
+            "                -0.0004224574367981404,\n",
+            "                0.000269725191174075,\n",
+            "                0.5320302248001099\n",
+            "            ],\n",
+            "            \"N-N-p-p-0\": [\n",
+            "                5.50653076171875,\n",
+            "                -1.4403566122055054,\n",
+            "                -8.006013870239258,\n",
+            "                13.777935981750488,\n",
+            "                -0.6363162994384766,\n",
+            "                0.36886876821517944\n",
+            "            ],\n",
+            "            \"N-N-p-p-1\": [\n",
+            "                -2.2638466358184814,\n",
+            "                2.1415014266967773,\n",
+            "                1.0750678777694702,\n",
+            "                -7.8354573249816895,\n",
+            "                3.0920794010162354,\n",
+            "                -1.063719391822815\n",
+            "            ],\n",
+            "            \"N-N-p-d-0\": [\n",
+            "                7.20867101335898e-05,\n",
+            "                0.00036340532824397087,\n",
+            "                0.00024355569621548057,\n",
+            "                -0.0004975462798029184,\n",
+            "                0.0004125885898247361,\n",
+            "                0.5261558294296265\n",
+            "            ],\n",
+            "            \"N-N-p-d-1\": [\n",
+            "                0.00015687875566072762,\n",
+            "                4.641729174181819e-05,\n",
+            "                1.3238663086667657e-05,\n",
+            "                0.00046566742821596563,\n",
+            "                -0.00020128212054260075,\n",
+            "                -0.533370852470398\n",
+            "            ],\n",
+            "            \"N-N-d-d-0\": [\n",
+            "                8.857469947542995e-05,\n",
+            "                0.00029952620388939977,\n",
+            "                0.00024031809880398214,\n",
+            "                -0.00025129984715022147,\n",
+            "                0.0003825896419584751,\n",
+            "                0.5308742523193359\n",
+            "            ],\n",
+            "            \"N-N-d-d-1\": [\n",
+            "                3.192495569237508e-05,\n",
+            "                -0.0001347306970274076,\n",
+            "                -0.00010394358105259016,\n",
+            "                -0.00015763661940582097,\n",
+            "                -0.0002910229086410254,\n",
+            "                0.527771532535553\n",
+            "            ],\n",
+            "            \"N-N-d-d-2\": [\n",
+            "                4.771947715198621e-05,\n",
+            "                7.819013262633234e-05,\n",
+            "                0.00018762303807307035,\n",
+            "                0.00027266336837783456,\n",
+            "                0.0002987241605296731,\n",
+            "                -0.5279330611228943\n",
+            "            ]\n",
+            "        },\n",
+            "        \"overlap\": {\n",
+            "            \"B-B-s-s-0\": [\n",
+            "                0.20907151699066162,\n",
+            "                -0.23900975286960602,\n",
+            "                -0.06029646843671799,\n",
+            "                0.805722713470459,\n",
+            "                -0.34798508882522583,\n",
+            "                -0.07375287264585495\n",
+            "            ],\n",
+            "            \"B-B-s-p-0\": [\n",
+            "                0.25513431429862976,\n",
+            "                -0.22145505249500275,\n",
+            "                -0.26228660345077515,\n",
+            "                1.159956932067871,\n",
+            "                -0.4085614085197449,\n",
+            "                -0.0003635674365796149\n",
+            "            ],\n",
+            "            \"B-B-s-d-0\": [\n",
+            "                -0.00010135513730347157,\n",
+            "                0.00012300396338105202,\n",
+            "                -0.00016775091353338212,\n",
+            "                1.0182542609982193e-05,\n",
+            "                -0.0003550578549038619,\n",
+            "                -0.3522164523601532\n",
+            "            ],\n",
+            "            \"B-B-p-p-0\": [\n",
+            "                -0.2968706786632538,\n",
+            "                0.1348806917667389,\n",
+            "                0.5835362076759338,\n",
+            "                -1.4401586055755615,\n",
+            "                0.34514784812927246,\n",
+            "                0.00011754724982893094\n",
+            "            ],\n",
+            "            \"B-B-p-p-1\": [\n",
+            "                0.0973825603723526,\n",
+            "                -0.14244243502616882,\n",
+            "                0.023147646337747574,\n",
+            "                0.47471076250076294,\n",
+            "                -0.2704032361507416,\n",
+            "                -0.667027473449707\n",
+            "            ],\n",
+            "            \"B-B-p-d-0\": [\n",
+            "                -0.00011588518100325018,\n",
+            "                0.00018029639613814652,\n",
+            "                -9.216874605044723e-05,\n",
+            "                -0.00048209051601588726,\n",
+            "                4.212089697830379e-05,\n",
+            "                0.3581341505050659\n",
+            "            ],\n",
+            "            \"B-B-p-d-1\": [\n",
+            "                -0.00011588512279558927,\n",
+            "                0.00018029661441687495,\n",
+            "                -9.216976468451321e-05,\n",
+            "                -0.000482094066683203,\n",
+            "                4.2125670006498694e-05,\n",
+            "                0.35813406109809875\n",
+            "            ],\n",
+            "            \"B-B-d-d-0\": [\n",
+            "                -0.0001013551518553868,\n",
+            "                0.0001230038469657302,\n",
+            "                -0.00016775041876826435,\n",
+            "                1.018380862660706e-05,\n",
+            "                -0.0003550600085873157,\n",
+            "                -0.3522164225578308\n",
+            "            ],\n",
+            "            \"B-B-d-d-1\": [\n",
+            "                0.00010135525371879339,\n",
+            "                -0.0001230034977197647,\n",
+            "                0.0001677492109593004,\n",
+            "                -1.018853799905628e-05,\n",
+            "                0.0003550658584572375,\n",
+            "                -0.35221636295318604\n",
+            "            ],\n",
+            "            \"B-B-d-d-2\": [\n",
+            "                9.815972589422017e-05,\n",
+            "                -0.00016340948059223592,\n",
+            "                0.0001705800968920812,\n",
+            "                0.0002916558878496289,\n",
+            "                -6.5286149038001895e-06,\n",
+            "                -0.3533816933631897\n",
+            "            ],\n",
+            "            \"N-B-s-s-0\": [\n",
+            "                0.1997109055519104,\n",
+            "                -0.2244972437620163,\n",
+            "                -0.11343343555927277,\n",
+            "                0.9763136506080627,\n",
+            "                -0.4251488149166107,\n",
+            "                -0.2723999619483948\n",
+            "            ],\n",
+            "            \"N-B-s-p-0\": [\n",
+            "                0.2620687186717987,\n",
+            "                -0.2704774737358093,\n",
+            "                -0.23450201749801636,\n",
+            "                1.4295225143432617,\n",
+            "                -0.593266487121582,\n",
+            "                -0.0280486810952425\n",
+            "            ],\n",
+            "            \"N-B-p-s-0\": [\n",
+            "                0.2336491048336029,\n",
+            "                -0.18477198481559753,\n",
+            "                -0.32471197843551636,\n",
+            "                1.258323073387146,\n",
+            "                -0.4374508261680603,\n",
+            "                -3.666881821118295e-05\n",
+            "            ],\n",
+            "            \"N-B-s-d-0\": [\n",
+            "                -2.9759947210550308e-05,\n",
+            "                9.752172627486289e-05,\n",
+            "                -0.0001534644834464416,\n",
+            "                0.00047143836854957044,\n",
+            "                1.3361132005229592e-05,\n",
+            "                0.40016892552375793\n",
+            "            ],\n",
+            "            \"N-B-d-s-0\": [\n",
+            "                -0.00019644841086119413,\n",
+            "                0.0001292879751417786,\n",
+            "                -0.00011083389108534902,\n",
+            "                -0.00012051903468091041,\n",
+            "                -0.00022029990213923156,\n",
+            "                -0.4031250476837158\n",
+            "            ],\n",
+            "            \"N-B-p-p-0\": [\n",
+            "                -0.280412495136261,\n",
+            "                0.11416203528642654,\n",
+            "                0.6316858530044556,\n",
+            "                -1.5773518085479736,\n",
+            "                0.4028981328010559,\n",
+            "                -0.00018129641830455512\n",
+            "            ],\n",
+            "            \"N-B-p-p-1\": [\n",
+            "                0.09812135994434357,\n",
+            "                -0.14181673526763916,\n",
+            "                0.005639200564473867,\n",
+            "                0.5506149530410767,\n",
+            "                -0.3099479675292969,\n",
+            "                0.6836565732955933\n",
+            "            ],\n",
+            "            \"N-B-p-d-0\": [\n",
+            "                8.620692824479192e-05,\n",
+            "                7.003633072599769e-05,\n",
+            "                0.00014002059469930828,\n",
+            "                1.5960773453116417e-05,\n",
+            "                0.0003343636344652623,\n",
+            "                0.39526382088661194\n",
+            "            ],\n",
+            "            \"N-B-d-p-0\": [\n",
+            "                -0.00017928905435837805,\n",
+            "                0.00014779999037273228,\n",
+            "                -6.557474262081087e-05,\n",
+            "                0.00033531803637742996,\n",
+            "                -1.5598576283082366e-05,\n",
+            "                -0.4068489074707031\n",
+            "            ],\n",
+            "            \"N-B-p-d-1\": [\n",
+            "                3.969529643654823e-05,\n",
+            "                -0.0001192440977320075,\n",
+            "                0.00016035677981562912,\n",
+            "                -0.0005162757006473839,\n",
+            "                -1.6432837583124638e-05,\n",
+            "                -0.3948792815208435\n",
+            "            ],\n",
+            "            \"N-B-d-p-1\": [\n",
+            "                0.0002542896254453808,\n",
+            "                -4.0774408262223005e-05,\n",
+            "                0.00016710262570995837,\n",
+            "                0.0006886226474307477,\n",
+            "                7.42142292438075e-05,\n",
+            "                0.4113677740097046\n",
+            "            ],\n",
+            "            \"N-B-d-d-0\": [\n",
+            "                -0.0001716944680083543,\n",
+            "                2.248838427476585e-05,\n",
+            "                -0.00012059728032909334,\n",
+            "                0.0001464982924517244,\n",
+            "                -6.761669646948576e-05,\n",
+            "                -0.4281955659389496\n",
+            "            ],\n",
+            "            \"N-B-d-d-1\": [\n",
+            "                -0.0001716944680083543,\n",
+            "                2.248838427476585e-05,\n",
+            "                -0.00012059728032909334,\n",
+            "                0.0001464982924517244,\n",
+            "                -6.761669646948576e-05,\n",
+            "                0.4281955659389496\n",
+            "            ],\n",
+            "            \"N-B-d-d-2\": [\n",
+            "                -9.511156531516463e-05,\n",
+            "                0.0001717804989311844,\n",
+            "                -1.6953305021161214e-05,\n",
+            "                0.0001179392565973103,\n",
+            "                9.918229625327513e-05,\n",
+            "                0.4204893112182617\n",
+            "            ],\n",
+            "            \"N-N-s-s-0\": [\n",
+            "                0.1918010711669922,\n",
+            "                -0.2127472460269928,\n",
+            "                -0.1606387048959732,\n",
+            "                1.155442476272583,\n",
+            "                -0.5151748657226562,\n",
+            "                0.48609858751296997\n",
+            "            ],\n",
+            "            \"N-N-s-p-0\": [\n",
+            "                0.24681918323040009,\n",
+            "                -0.23861254751682281,\n",
+            "                -0.31103551387786865,\n",
+            "                1.6039279699325562,\n",
+            "                -0.660601019859314,\n",
+            "                -0.07287845760583878\n",
+            "            ],\n",
+            "            \"N-N-s-d-0\": [\n",
+            "                -6.115203723311424e-05,\n",
+            "                -0.0002562953741289675,\n",
+            "                -0.00012758496450260282,\n",
+            "                0.0004416516749188304,\n",
+            "                -0.00025469131651334465,\n",
+            "                0.488688588142395\n",
+            "            ],\n",
+            "            \"N-N-p-p-0\": [\n",
+            "                -0.2732810080051422,\n",
+            "                0.10842154920101166,\n",
+            "                0.699445903301239,\n",
+            "                -1.881728172302246,\n",
+            "                0.5495500564575195,\n",
+            "                0.00020055289496667683\n",
+            "            ],\n",
+            "            \"N-N-p-p-1\": [\n",
+            "                0.10150457173585892,\n",
+            "                -0.1420295238494873,\n",
+            "                -0.021784797310829163,\n",
+            "                0.6614340543746948,\n",
+            "                -0.36609184741973877,\n",
+            "                -0.7560092806816101\n",
+            "            ],\n",
+            "            \"N-N-p-d-0\": [\n",
+            "                -1.3360753655433655e-05,\n",
+            "                -0.00014160110731609166,\n",
+            "                0.00010747101623564959,\n",
+            "                -0.00040430951048620045,\n",
+            "                0.0002254340797662735,\n",
+            "                -0.4823491871356964\n",
+            "            ],\n",
+            "            \"N-N-p-d-1\": [\n",
+            "                5.713030986953527e-05,\n",
+            "                0.00020219954603817314,\n",
+            "                -1.679777051322162e-05,\n",
+            "                -0.00015270523726940155,\n",
+            "                0.0001418725005351007,\n",
+            "                -0.4920305609703064\n",
+            "            ],\n",
+            "            \"N-N-d-d-0\": [\n",
+            "                2.117294025083538e-05,\n",
+            "                -0.00019494653679430485,\n",
+            "                9.384578152094036e-05,\n",
+            "                -0.00038998579839244485,\n",
+            "                -7.752966484986246e-05,\n",
+            "                0.487053245306015\n",
+            "            ],\n",
+            "            \"N-N-d-d-1\": [\n",
+            "                -3.1081654014997184e-06,\n",
+            "                -0.00021633837604895234,\n",
+            "                3.2148207537829876e-05,\n",
+            "                -3.418952110223472e-05,\n",
+            "                -0.00027515843976289034,\n",
+            "                0.4870029091835022\n",
+            "            ],\n",
+            "            \"N-N-d-d-2\": [\n",
+            "                -8.452979091089219e-05,\n",
+            "                -0.00021817802917212248,\n",
+            "                -5.2256466005928814e-05,\n",
+            "                -0.0005801816005259752,\n",
+            "                -6.465519254561514e-05,\n",
+            "                -0.4823436141014099\n",
+            "            ]\n",
+            "        }\n",
+            "    }\n",
+            "}"
+          ]
+        }
+      ],
+      "source": [
+        "!cat hbn_spd_model/sktb.json"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "db1704e2",
+      "metadata": {},
+      "source": [
+        "We can also load the generated sktb.json model file to plot the band structure:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "f3b9ee35",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+            " \n",
+            " \n",
+            "#################################################################################\n",
+            "#                                                                               #\n",
+            "#                                                                               #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
+            "#                                                                               #\n",
+            "#                         Version: 2.1.2.dev50+d4f488d                          #\n",
+            "#                                                                               #\n",
+            "#################################################################################\n",
+            " \n",
+            " \n",
+            "DEEPTB INFO    The structure space group is: P-6m2 (No. 187)\n",
+            "DEEPTB WARNING CUDA is not available. The model will be loaded on CPU.\n",
+            "/Users/aisiqg/Software/venv/pydptb/lib/python3.9/site-packages/torch/nested/__init__.py:58: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/NestedTensorImpl.cpp:180.)\n",
+            "  return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)\n",
+            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
+            "DEEPTB INFO    band calculation successfully completed.\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "!dptb run band -i ./hbn_spd_model/sktb.json -stu ../structures/hBN.vasp -o band_hBN\n",
+        "\n",
+        "# display the band plot:\n",
+        "from IPython.display import Image, display\n",
+        "import os\n",
+        "image_path = f'./band_hBN/results/band.png'\n",
+        "display(Image(filename=image_path))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "20aca3a2",
+      "metadata": {},
+      "source": [
+        "We can see that the band structure is the same as before. \n",
+        "\n",
+        "Here we can choose different basis settings, e.g. `hbn_sp.json` as the input config:\n",
+        "```json\n",
+        "{\n",
+        "    \"common_options\": {\n",
+        "        \"basis\": {\n",
+        "            \"B\": [\"s\",\"p\"],\n",
+        "            \"N\": [\"s\",\"p\"]\n",
+        "        }\n",
+        "    }\n",
+        "}\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "5847351c",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+            " \n",
+            " \n",
+            "#################################################################################\n",
+            "#                                                                               #\n",
+            "#                                                                               #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
+            "#                                                                               #\n",
+            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
+            "#                                                                               #\n",
+            "#################################################################################\n",
+            " \n",
+            " \n",
+            "DEEPTB INFO    Extracting empirical SK parameters for BN\n",
+            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
+            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
+            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
+            "DEEPTB INFO    Empirical SK parameters are saved in hbn_sp_model/sktb.json\n",
+            "DEEPTB INFO    If you want to further train the model, please use `dptb config` command to generate input template.\n"
+          ]
+        }
+      ],
+      "source": [
+        "!dptb esk hbn_sp.json -m poly4 -o hbn_sp_model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "cf264978",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+            " \n",
+            " \n",
+            "#################################################################################\n",
+            "#                                                                               #\n",
+            "#                                                                               #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
+            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
+            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
+            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
+            "#                                                                               #\n",
+            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
+            "#                                                                               #\n",
+            "#################################################################################\n",
+            " \n",
+            " \n",
+            "/root/dptb_venv/lib/python3.10/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead\n",
+            "  conv_lattice = dataset[\"std_lattice\"]\n",
+            "DEEPTB INFO    The structure space group is: P-6m2 (No. 187)\n",
+            "/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+            "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
+            "Figure(640x560)\n",
+            "DEEPTB INFO    band calculation successfully completed.\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "!dptb run band -i ./hbn_sp_model/sktb.json -stu ../structures/hBN.vasp -o band_hBN\n",
+        "\n",
+        "# display the band plot:\n",
+        "from IPython.display import Image, display\n",
+        "import os\n",
+        "image_path = f'./band_hBN/results/band.png'\n",
+        "display(Image(filename=image_path))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5363a34e",
+      "metadata": {},
+      "source": [
+        "It can be clearly seen that the bands near 0 eV are missing. This is because for the hBN system, our built-in empirical model parameters only include sp orbitals. The d orbital parameters are all set to 0 just to maintain a consistent format."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f0b4e111",
+      "metadata": {},
+      "source": [
+        "Similarly, we can obtain the corresponding model parameters for individual Si and GaAs systems. Readers are invited to explore this themselves."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b81e0745",
+      "metadata": {},
+      "source": [
+        "<div style=\"color:black; background-color:#FFF3E9; border: 1px solid #FFE0C3; border-radius: 10px; margin-bottom:1rem\">\n",
+        "    <p style=\"margin:1rem; padding-left: 1rem; line-height: 2.5;\">\n",
+        "        Author: <a style=\"font-weight:normal\" href=\"mailto:guqq@ustc.edu.cn\">Gu, Qiangqiang : guqq@ustc.edu.cn</a>\n",
+        "    </p>\n",
+        "    <p style=\"margin:1rem; padding-left: 1rem; line-height: 2.5;\">\n",
+        "        Thank you for reading!\n",
+        "    </p>\n",
+        "</div>"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.6"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000..55fdf69
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,67 @@
+.. DeePTB documentation master file.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+=================================================
+DPNEGF Documentation
+=================================================
+
+**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green’s Function (**NEGF**) method, establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy. 
+
+--------------
+Key Features:
+--------------
+
+DeePTB contains two main components: 
+
+1. **DeePTB-SK**: deep learning based local environment dependent Slater-Koster TB.
+
+   - Customizable Slater-Koster parameterization with neural network corrections.
+   - Flexible basis and exchange-correlation functional choices.
+   - Handle systems with strong spin-orbit coupling (SOC) effects.
+
+2. **DeePTB-E3**: E3-equivariant neural networks for representing quantum operators.
+
+   - Construct DFT Hamiltonians/density and overlap matrices under full LCAO basis.
+   - Utilize (**S**\ trictly) **L**\ ocalized **E**\ quivariant **M**\ essage-passing (**(S)LEM**) model for high data-efficiency and accuracy.
+   - Employs SO(2) convolution for efficient handling of higher-order orbitals in LCAO basis.
+
+
+For more details, see our papers:
+
+* `DeePTB-NEGF: arXiv:2411.08800 <https://arxiv.org/abs/2411.08800v2>`_
+* `DeePTB-SK: Nat Commun 15, 6772 (2024) <https://doi.org/10.1038/s41467-024-51006-4>`_
+* `DeePTB-E3: ICLR 2025 Spotlight <https://openreview.net/forum?id=kpq3IIjUD3>`_
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Quick Start
+
+   easy_install
+   hands_on/index
+
+.. toctree::
+   :maxdepth: 2
+   :caption: INPUT TAG
+   
+   input_params/index
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Citing DeePTB-NEGF
+
+   CITATIONS
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Developing Team
+
+   DevelopingTeam
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Community
+
+   CONTRIBUTING
+
diff --git a/docs/input_params/common_options.rst b/docs/input_params/common_options.rst
new file mode 100644
index 0000000..ff26ce5
--- /dev/null
+++ b/docs/input_params/common_options.rst
@@ -0,0 +1,53 @@
+========================================
+Common Options
+========================================
+.. _`common_options`: 
+
+common_options: 
+    | type: ``dict``
+    | argument path: ``common_options``
+
+    .. _`common_options/basis`: 
+
+    basis: 
+        | type: ``dict``
+        | argument path: ``common_options/basis``
+
+        The atomic orbitals used to construct the basis. e.p. {'A':['2s','2p','s*'],'B':'[3s','3p']}
+
+    .. _`common_options/overlap`: 
+
+    overlap: 
+        | type: ``bool``, optional, default: ``False``
+        | argument path: ``common_options/overlap``
+
+        Whether to calculate the overlap matrix. Default: False
+
+    .. _`common_options/device`: 
+
+    device: 
+        | type: ``str``, optional, default: ``cpu``
+        | argument path: ``common_options/device``
+
+        The device to run the calculation, choose among `cpu` and `cuda[:int]`, Default: `cpu`
+
+    .. _`common_options/dtype`: 
+
+    dtype: 
+        | type: ``str``, optional, default: ``float32``
+        | argument path: ``common_options/dtype``
+
+        The digital number's precison, choose among: 
+                            Default: `float32`
+                                - `float32`: indicating torch.float32
+                                - `float64`: indicating torch.float64
+                
+
+    .. _`common_options/seed`: 
+
+    seed: 
+        | type: ``int``, optional, default: ``3982377700``
+        | argument path: ``common_options/seed``
+
+        The random seed used to initialize the parameters and determine the shuffling order of datasets. Default: `3982377700`
+
diff --git a/docs/input_params/data_options.rst b/docs/input_params/data_options.rst
new file mode 100644
index 0000000..95443cb
--- /dev/null
+++ b/docs/input_params/data_options.rst
@@ -0,0 +1,251 @@
+========================================
+Data Options
+========================================
+.. _`data_options`: 
+
+data_options: 
+    | type: ``dict``
+    | argument path: ``data_options``
+
+    The options for dataset settings in training.
+
+    .. _`data_options/r_max`: 
+
+    r_max: 
+        | type: ``str`` | ``float`` | ``int``, optional, default: ``5.0``
+        | argument path: ``data_options/r_max``
+
+        r_max
+
+    .. _`data_options/oer_max`: 
+
+    oer_max: 
+        | type: ``str`` | ``float`` | ``int``, optional, default: ``5.0``
+        | argument path: ``data_options/oer_max``
+
+        oer_max
+
+    .. _`data_options/er_max`: 
+
+    er_max: 
+        | type: ``str`` | ``float`` | ``int``, optional, default: ``5.0``
+        | argument path: ``data_options/er_max``
+
+        er_max
+
+    .. _`data_options/train`: 
+
+    train: 
+        | type: ``dict``
+        | argument path: ``data_options/train``
+
+        The dataset settings for training.
+
+        .. _`data_options/train/type`: 
+
+        type: 
+            | type: ``str``, optional, default: ``DefaultDataset``
+            | argument path: ``data_options/train/type``
+
+            The type of dataset.
+
+        .. _`data_options/train/root`: 
+
+        root: 
+            | type: ``str``
+            | argument path: ``data_options/train/root``
+
+            This is where the dataset stores data files.
+
+        .. _`data_options/train/prefix`: 
+
+        prefix: 
+            | type: ``str`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``data_options/train/prefix``
+
+            The prefix of the folders under root, which will be loaded in dataset.
+
+        .. _`data_options/train/separator`: 
+
+        separator: 
+            | type: ``str``, optional, default: ``.``
+            | argument path: ``data_options/train/separator``
+
+            the sepatator used to separate the prefix and suffix in the dataset directory. Default: '.'
+
+        .. _`data_options/train/get_Hamiltonian`: 
+
+        get_Hamiltonian: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/train/get_Hamiltonian``
+
+            Choose whether the Hamiltonian blocks (and overlap blocks, if provided) are loaded when building dataset.
+
+        .. _`data_options/train/get_overlap`: 
+
+        get_overlap: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/train/get_overlap``
+
+            Choose whether the overlap blocks are loaded when building dataset.
+
+        .. _`data_options/train/get_DM`: 
+
+        get_DM: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/train/get_DM``
+
+            Choose whether the density matrix is loaded when building dataset.
+
+        .. _`data_options/train/get_eigenvalues`: 
+
+        get_eigenvalues: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/train/get_eigenvalues``
+
+            Choose whether the eigenvalues and k-points are loaded when building dataset.
+
+    .. _`data_options/validation`: 
+
+    validation: 
+        | type: ``dict``, optional
+        | argument path: ``data_options/validation``
+
+        The dataset settings for validation.
+
+        .. _`data_options/validation/type`: 
+
+        type: 
+            | type: ``str``, optional, default: ``DefaultDataset``
+            | argument path: ``data_options/validation/type``
+
+            The type of dataset.
+
+        .. _`data_options/validation/root`: 
+
+        root: 
+            | type: ``str``
+            | argument path: ``data_options/validation/root``
+
+            This is where the dataset stores data files.
+
+        .. _`data_options/validation/prefix`: 
+
+        prefix: 
+            | type: ``str`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``data_options/validation/prefix``
+
+            The prefix of the folders under root, which will be loaded in dataset.
+
+        .. _`data_options/validation/separator`: 
+
+        separator: 
+            | type: ``str``, optional, default: ``.``
+            | argument path: ``data_options/validation/separator``
+
+            the sepatator used to separate the prefix and suffix in the dataset directory. Default: '.'
+
+        .. _`data_options/validation/get_Hamiltonian`: 
+
+        get_Hamiltonian: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/validation/get_Hamiltonian``
+
+            Choose whether the Hamiltonian blocks (and overlap blocks, if provided) are loaded when building dataset.
+
+        .. _`data_options/validation/get_overlap`: 
+
+        get_overlap: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/validation/get_overlap``
+
+            Choose whether the overlap blocks are loaded when building dataset.
+
+        .. _`data_options/validation/get_DM`: 
+
+        get_DM: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/validation/get_DM``
+
+            Choose whether the density matrix is loaded when building dataset.
+
+        .. _`data_options/validation/get_eigenvalues`: 
+
+        get_eigenvalues: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/validation/get_eigenvalues``
+
+            Choose whether the eigenvalues and k-points are loaded when building dataset.
+
+    .. _`data_options/reference`: 
+
+    reference: 
+        | type: ``dict``, optional
+        | argument path: ``data_options/reference``
+
+        The dataset settings for reference.
+
+        .. _`data_options/reference/type`: 
+
+        type: 
+            | type: ``str``, optional, default: ``DefaultDataset``
+            | argument path: ``data_options/reference/type``
+
+            The type of dataset.
+
+        .. _`data_options/reference/root`: 
+
+        root: 
+            | type: ``str``
+            | argument path: ``data_options/reference/root``
+
+            This is where the dataset stores data files.
+
+        .. _`data_options/reference/prefix`: 
+
+        prefix: 
+            | type: ``str`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``data_options/reference/prefix``
+
+            The prefix of the folders under root, which will be loaded in dataset.
+
+        .. _`data_options/reference/separator`: 
+
+        separator: 
+            | type: ``str``, optional, default: ``.``
+            | argument path: ``data_options/reference/separator``
+
+            the sepatator used to separate the prefix and suffix in the dataset directory. Default: '.'
+
+        .. _`data_options/reference/get_Hamiltonian`: 
+
+        get_Hamiltonian: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/reference/get_Hamiltonian``
+
+            Choose whether the Hamiltonian blocks (and overlap blocks, if provided) are loaded when building dataset.
+
+        .. _`data_options/reference/get_overlap`: 
+
+        get_overlap: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/reference/get_overlap``
+
+            Choose whether the overlap blocks are loaded when building dataset.
+
+        .. _`data_options/reference/get_DM`: 
+
+        get_DM: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/reference/get_DM``
+
+            Choose whether the density matrix is loaded when building dataset.
+
+        .. _`data_options/reference/get_eigenvalues`: 
+
+        get_eigenvalues: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``data_options/reference/get_eigenvalues``
+
+            Choose whether the eigenvalues and k-points are loaded when building dataset.
+
diff --git a/docs/input_params/index.rst b/docs/input_params/index.rst
new file mode 100644
index 0000000..6716871
--- /dev/null
+++ b/docs/input_params/index.rst
@@ -0,0 +1,13 @@
+========================================
+Full Input Parameters
+========================================
+
+.. toctree::
+   :maxdepth: 2
+
+   common_options
+   train_options
+   model_options
+   data_options
+   run_options
+   set_info
diff --git a/docs/input_params/model_options.rst b/docs/input_params/model_options.rst
new file mode 100644
index 0000000..d966280
--- /dev/null
+++ b/docs/input_params/model_options.rst
@@ -0,0 +1,1376 @@
+========================================
+Model Options
+========================================
+.. _`model_options`: 
+
+model_options: 
+    | type: ``dict``, optional
+    | argument path: ``model_options``
+
+    The parameters to define the `nnsk`,`mix` and `dptb` model.
+
+    .. _`model_options/embedding`: 
+
+    embedding: 
+        | type: ``dict``, optional
+        | argument path: ``model_options/embedding``
+
+        The parameters to define the embedding model.
+
+
+        Depending on the value of *method*, different sub args are accepted. 
+
+        .. _`model_options/embedding/method`: 
+
+        method:
+            | type: ``str`` (flag key), default: ``se2``
+            | argument path: ``model_options/embedding/method`` 
+            | possible choices: |code:model_options/embedding[se2]|_, |code:model_options/embedding[baseline]|_, |code:model_options/embedding[deeph-e3]|_, |code:model_options/embedding[e3baseline_5]|_, |code:model_options/embedding[e3baseline_6]|_, |code:model_options/embedding[slem]|_, |code:model_options/embedding[lem]|_, |code:model_options/embedding[e3baseline_nonlocal]|_
+
+            The parameters to define the embedding model.
+
+            .. |code:model_options/embedding[se2]| replace:: ``se2``
+            .. _`code:model_options/embedding[se2]`: `model_options/embedding[se2]`_
+            .. |code:model_options/embedding[baseline]| replace:: ``baseline``
+            .. _`code:model_options/embedding[baseline]`: `model_options/embedding[baseline]`_
+            .. |code:model_options/embedding[deeph-e3]| replace:: ``deeph-e3``
+            .. _`code:model_options/embedding[deeph-e3]`: `model_options/embedding[deeph-e3]`_
+            .. |code:model_options/embedding[e3baseline_5]| replace:: ``e3baseline_5``
+            .. _`code:model_options/embedding[e3baseline_5]`: `model_options/embedding[e3baseline_5]`_
+            .. |code:model_options/embedding[e3baseline_6]| replace:: ``e3baseline_6``
+            .. _`code:model_options/embedding[e3baseline_6]`: `model_options/embedding[e3baseline_6]`_
+            .. |code:model_options/embedding[slem]| replace:: ``slem``
+            .. _`code:model_options/embedding[slem]`: `model_options/embedding[slem]`_
+            .. |code:model_options/embedding[lem]| replace:: ``lem``
+            .. _`code:model_options/embedding[lem]`: `model_options/embedding[lem]`_
+            .. |code:model_options/embedding[e3baseline_nonlocal]| replace:: ``e3baseline_nonlocal``
+            .. _`code:model_options/embedding[e3baseline_nonlocal]`: `model_options/embedding[e3baseline_nonlocal]`_
+
+        .. |flag:model_options/embedding/method| replace:: *method*
+        .. _`flag:model_options/embedding/method`: `model_options/embedding/method`_
+
+
+        .. _`model_options/embedding[se2]`: 
+
+        When |flag:model_options/embedding/method|_ is set to ``se2``: 
+
+        .. _`model_options/embedding[se2]/rs`: 
+
+        rs: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[se2]/rs``
+
+            The soft cutoff where the smooth function starts.
+
+        .. _`model_options/embedding[se2]/rc`: 
+
+        rc: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[se2]/rc``
+
+            The hard cutoff where the smooth function value ~0.0
+
+        .. _`model_options/embedding[se2]/radial_net`: 
+
+        radial_net: 
+            | type: ``dict``
+            | argument path: ``model_options/embedding[se2]/radial_net``
+
+            network to build the descriptors.
+
+            .. _`model_options/embedding[se2]/radial_net/neurons`: 
+
+            neurons: 
+                | type: ``list``
+                | argument path: ``model_options/embedding[se2]/radial_net/neurons``
+
+                the size of nn for descriptor
+
+            .. _`model_options/embedding[se2]/radial_net/activation`: 
+
+            activation: 
+                | type: ``str``, optional, default: ``tanh``
+                | argument path: ``model_options/embedding[se2]/radial_net/activation``
+
+                activation
+
+            .. _`model_options/embedding[se2]/radial_net/if_batch_normalized`: 
+
+            if_batch_normalized: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``model_options/embedding[se2]/radial_net/if_batch_normalized``
+
+                whether to turn on the batch normalization.
+
+        .. _`model_options/embedding[se2]/n_axis`: 
+
+        n_axis: 
+            | type: ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``model_options/embedding[se2]/n_axis``
+
+            the out axis shape of the deepmd-se2 descriptor.
+
+
+        .. _`model_options/embedding[baseline]`: 
+
+        When |flag:model_options/embedding/method|_ is set to ``baseline``: 
+
+        .. _`model_options/embedding[baseline]/p`: 
+
+        p: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[baseline]/p``
+
+        .. _`model_options/embedding[baseline]/rc`: 
+
+        rc: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[baseline]/rc``
+
+        .. _`model_options/embedding[baseline]/n_basis`: 
+
+        n_basis: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[baseline]/n_basis``
+
+        .. _`model_options/embedding[baseline]/n_radial`: 
+
+        n_radial: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[baseline]/n_radial``
+
+        .. _`model_options/embedding[baseline]/n_sqrt_radial`: 
+
+        n_sqrt_radial: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[baseline]/n_sqrt_radial``
+
+        .. _`model_options/embedding[baseline]/n_layer`: 
+
+        n_layer: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[baseline]/n_layer``
+
+        .. _`model_options/embedding[baseline]/radial_net`: 
+
+        radial_net: 
+            | type: ``dict``
+            | argument path: ``model_options/embedding[baseline]/radial_net``
+
+            .. _`model_options/embedding[baseline]/radial_net/neurons`: 
+
+            neurons: 
+                | type: ``list``
+                | argument path: ``model_options/embedding[baseline]/radial_net/neurons``
+
+            .. _`model_options/embedding[baseline]/radial_net/activation`: 
+
+            activation: 
+                | type: ``str``, optional, default: ``tanh``
+                | argument path: ``model_options/embedding[baseline]/radial_net/activation``
+
+            .. _`model_options/embedding[baseline]/radial_net/if_batch_normalized`: 
+
+            if_batch_normalized: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``model_options/embedding[baseline]/radial_net/if_batch_normalized``
+
+        .. _`model_options/embedding[baseline]/hidden_net`: 
+
+        hidden_net: 
+            | type: ``dict``
+            | argument path: ``model_options/embedding[baseline]/hidden_net``
+
+            .. _`model_options/embedding[baseline]/hidden_net/neurons`: 
+
+            neurons: 
+                | type: ``list``
+                | argument path: ``model_options/embedding[baseline]/hidden_net/neurons``
+
+            .. _`model_options/embedding[baseline]/hidden_net/activation`: 
+
+            activation: 
+                | type: ``str``, optional, default: ``tanh``
+                | argument path: ``model_options/embedding[baseline]/hidden_net/activation``
+
+            .. _`model_options/embedding[baseline]/hidden_net/if_batch_normalized`: 
+
+            if_batch_normalized: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``model_options/embedding[baseline]/hidden_net/if_batch_normalized``
+
+        .. _`model_options/embedding[baseline]/n_axis`: 
+
+        n_axis: 
+            | type: ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``model_options/embedding[baseline]/n_axis``
+
+
+        .. _`model_options/embedding[deeph-e3]`: 
+
+        When |flag:model_options/embedding/method|_ is set to ``deeph-e3``: 
+
+        .. _`model_options/embedding[deeph-e3]/irreps_embed`: 
+
+        irreps_embed: 
+            | type: ``str``, optional, default: ``64x0e``
+            | argument path: ``model_options/embedding[deeph-e3]/irreps_embed``
+
+        .. _`model_options/embedding[deeph-e3]/irreps_mid`: 
+
+        irreps_mid: 
+            | type: ``str``, optional, default: ``64x0e+32x1o+16x2e+8x3o+8x4e+4x5o``
+            | argument path: ``model_options/embedding[deeph-e3]/irreps_mid``
+
+        .. _`model_options/embedding[deeph-e3]/lmax`: 
+
+        lmax: 
+            | type: ``int``, optional, default: ``3``
+            | argument path: ``model_options/embedding[deeph-e3]/lmax``
+
+        .. _`model_options/embedding[deeph-e3]/n_basis`: 
+
+        n_basis: 
+            | type: ``int``, optional, default: ``128``
+            | argument path: ``model_options/embedding[deeph-e3]/n_basis``
+
+        .. _`model_options/embedding[deeph-e3]/rc`: 
+
+        rc: 
+            | type: ``float``
+            | argument path: ``model_options/embedding[deeph-e3]/rc``
+
+        .. _`model_options/embedding[deeph-e3]/n_layer`: 
+
+        n_layer: 
+            | type: ``int``, optional, default: ``3``
+            | argument path: ``model_options/embedding[deeph-e3]/n_layer``
+
+
+        .. _`model_options/embedding[e3baseline_5]`: 
+
+        When |flag:model_options/embedding/method|_ is set to ``e3baseline_5``: 
+
+        .. _`model_options/embedding[e3baseline_5]/irreps_hidden`: 
+
+        irreps_hidden: 
+            | type: ``str``
+            | argument path: ``model_options/embedding[e3baseline_5]/irreps_hidden``
+
+        .. _`model_options/embedding[e3baseline_5]/lmax`: 
+
+        lmax: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[e3baseline_5]/lmax``
+
+        .. _`model_options/embedding[e3baseline_5]/avg_num_neighbors`: 
+
+        avg_num_neighbors: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[e3baseline_5]/avg_num_neighbors``
+
+        .. _`model_options/embedding[e3baseline_5]/r_max`: 
+
+        r_max: 
+            | type: ``dict`` | ``float`` | ``int``
+            | argument path: ``model_options/embedding[e3baseline_5]/r_max``
+
+        .. _`model_options/embedding[e3baseline_5]/n_layers`: 
+
+        n_layers: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[e3baseline_5]/n_layers``
+
+        .. _`model_options/embedding[e3baseline_5]/n_radial_basis`: 
+
+        n_radial_basis: 
+            | type: ``int``, optional, default: ``10``
+            | argument path: ``model_options/embedding[e3baseline_5]/n_radial_basis``
+
+        .. _`model_options/embedding[e3baseline_5]/PolynomialCutoff_p`: 
+
+        PolynomialCutoff_p: 
+            | type: ``int``, optional, default: ``6``
+            | argument path: ``model_options/embedding[e3baseline_5]/PolynomialCutoff_p``
+
+            The order of polynomial cutoff function. Default: 6
+
+        .. _`model_options/embedding[e3baseline_5]/cutoff_type`: 
+
+        cutoff_type: 
+            | type: ``str``, optional, default: ``polynomial``
+            | argument path: ``model_options/embedding[e3baseline_5]/cutoff_type``
+
+            The type of cutoff function. Default: polynomial
+
+        .. _`model_options/embedding[e3baseline_5]/env_embed_multiplicity`: 
+
+        env_embed_multiplicity: 
+            | type: ``int``, optional, default: ``1``
+            | argument path: ``model_options/embedding[e3baseline_5]/env_embed_multiplicity``
+
+        .. _`model_options/embedding[e3baseline_5]/tp_radial_emb`: 
+
+        tp_radial_emb: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[e3baseline_5]/tp_radial_emb``
+
+            Whether to use tensor product radial embedding.
+
+        .. _`model_options/embedding[e3baseline_5]/tp_radial_channels`: 
+
+        tp_radial_channels: 
+            | type: ``list``, optional, default: ``[128, 128]``
+            | argument path: ``model_options/embedding[e3baseline_5]/tp_radial_channels``
+
+            The number of channels in tensor product radial embedding.
+
+        .. _`model_options/embedding[e3baseline_5]/latent_channels`: 
+
+        latent_channels: 
+            | type: ``list``, optional, default: ``[128, 128]``
+            | argument path: ``model_options/embedding[e3baseline_5]/latent_channels``
+
+            The number of channels in latent embedding.
+
+        .. _`model_options/embedding[e3baseline_5]/latent_dim`: 
+
+        latent_dim: 
+            | type: ``int``, optional, default: ``256``
+            | argument path: ``model_options/embedding[e3baseline_5]/latent_dim``
+
+            The dimension of latent embedding.
+
+        .. _`model_options/embedding[e3baseline_5]/res_update`: 
+
+        res_update: 
+            | type: ``bool``, optional, default: ``True``
+            | argument path: ``model_options/embedding[e3baseline_5]/res_update``
+
+            Whether to use residual update.
+
+        .. _`model_options/embedding[e3baseline_5]/res_update_ratios`: 
+
+        res_update_ratios: 
+            | type: ``float``, optional, default: ``0.5``
+            | argument path: ``model_options/embedding[e3baseline_5]/res_update_ratios``
+
+            The ratios of residual update, should in (0,1).
+
+        .. _`model_options/embedding[e3baseline_5]/res_update_ratios_learnable`: 
+
+        res_update_ratios_learnable: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[e3baseline_5]/res_update_ratios_learnable``
+
+            Whether to make the ratios of residual update learnable.
+
+
+        .. _`model_options/embedding[e3baseline_6]`: 
+
+        When |flag:model_options/embedding/method|_ is set to ``e3baseline_6``: 
+
+        .. _`model_options/embedding[e3baseline_6]/irreps_hidden`: 
+
+        irreps_hidden: 
+            | type: ``str``
+            | argument path: ``model_options/embedding[e3baseline_6]/irreps_hidden``
+
+        .. _`model_options/embedding[e3baseline_6]/lmax`: 
+
+        lmax: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[e3baseline_6]/lmax``
+
+        .. _`model_options/embedding[e3baseline_6]/avg_num_neighbors`: 
+
+        avg_num_neighbors: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[e3baseline_6]/avg_num_neighbors``
+
+        .. _`model_options/embedding[e3baseline_6]/r_max`: 
+
+        r_max: 
+            | type: ``dict`` | ``float`` | ``int``
+            | argument path: ``model_options/embedding[e3baseline_6]/r_max``
+
+        .. _`model_options/embedding[e3baseline_6]/n_layers`: 
+
+        n_layers: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[e3baseline_6]/n_layers``
+
+        .. _`model_options/embedding[e3baseline_6]/n_radial_basis`: 
+
+        n_radial_basis: 
+            | type: ``int``, optional, default: ``10``
+            | argument path: ``model_options/embedding[e3baseline_6]/n_radial_basis``
+
+        .. _`model_options/embedding[e3baseline_6]/PolynomialCutoff_p`: 
+
+        PolynomialCutoff_p: 
+            | type: ``int``, optional, default: ``6``
+            | argument path: ``model_options/embedding[e3baseline_6]/PolynomialCutoff_p``
+
+            The order of polynomial cutoff function. Default: 6
+
+        .. _`model_options/embedding[e3baseline_6]/cutoff_type`: 
+
+        cutoff_type: 
+            | type: ``str``, optional, default: ``polynomial``
+            | argument path: ``model_options/embedding[e3baseline_6]/cutoff_type``
+
+            The type of cutoff function. Default: polynomial
+
+        .. _`model_options/embedding[e3baseline_6]/env_embed_multiplicity`: 
+
+        env_embed_multiplicity: 
+            | type: ``int``, optional, default: ``1``
+            | argument path: ``model_options/embedding[e3baseline_6]/env_embed_multiplicity``
+
+        .. _`model_options/embedding[e3baseline_6]/tp_radial_emb`: 
+
+        tp_radial_emb: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[e3baseline_6]/tp_radial_emb``
+
+            Whether to use tensor product radial embedding.
+
+        .. _`model_options/embedding[e3baseline_6]/tp_radial_channels`: 
+
+        tp_radial_channels: 
+            | type: ``list``, optional, default: ``[128, 128]``
+            | argument path: ``model_options/embedding[e3baseline_6]/tp_radial_channels``
+
+            The number of channels in tensor product radial embedding.
+
+        .. _`model_options/embedding[e3baseline_6]/latent_channels`: 
+
+        latent_channels: 
+            | type: ``list``, optional, default: ``[128, 128]``
+            | argument path: ``model_options/embedding[e3baseline_6]/latent_channels``
+
+            The number of channels in latent embedding.
+
+        .. _`model_options/embedding[e3baseline_6]/latent_dim`: 
+
+        latent_dim: 
+            | type: ``int``, optional, default: ``256``
+            | argument path: ``model_options/embedding[e3baseline_6]/latent_dim``
+
+            The dimension of latent embedding.
+
+        .. _`model_options/embedding[e3baseline_6]/res_update`: 
+
+        res_update: 
+            | type: ``bool``, optional, default: ``True``
+            | argument path: ``model_options/embedding[e3baseline_6]/res_update``
+
+            Whether to use residual update.
+
+        .. _`model_options/embedding[e3baseline_6]/res_update_ratios`: 
+
+        res_update_ratios: 
+            | type: ``float``, optional, default: ``0.5``
+            | argument path: ``model_options/embedding[e3baseline_6]/res_update_ratios``
+
+            The ratios of residual update, should in (0,1).
+
+        .. _`model_options/embedding[e3baseline_6]/res_update_ratios_learnable`: 
+
+        res_update_ratios_learnable: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[e3baseline_6]/res_update_ratios_learnable``
+
+            Whether to make the ratios of residual update learnable.
+
+
+        .. _`model_options/embedding[slem]`: 
+
+        When |flag:model_options/embedding/method|_ is set to ``slem``: 
+
+        .. _`model_options/embedding[slem]/irreps_hidden`: 
+
+        irreps_hidden: 
+            | type: ``str``
+            | argument path: ``model_options/embedding[slem]/irreps_hidden``
+
+        .. _`model_options/embedding[slem]/avg_num_neighbors`: 
+
+        avg_num_neighbors: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[slem]/avg_num_neighbors``
+
+        .. _`model_options/embedding[slem]/r_max`: 
+
+        r_max: 
+            | type: ``dict`` | ``float`` | ``int``
+            | argument path: ``model_options/embedding[slem]/r_max``
+
+        .. _`model_options/embedding[slem]/n_layers`: 
+
+        n_layers: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[slem]/n_layers``
+
+        .. _`model_options/embedding[slem]/n_radial_basis`: 
+
+        n_radial_basis: 
+            | type: ``int``, optional, default: ``10``
+            | argument path: ``model_options/embedding[slem]/n_radial_basis``
+
+        .. _`model_options/embedding[slem]/PolynomialCutoff_p`: 
+
+        PolynomialCutoff_p: 
+            | type: ``int``, optional, default: ``6``
+            | argument path: ``model_options/embedding[slem]/PolynomialCutoff_p``
+
+            The order of polynomial cutoff function. Default: 6
+
+        .. _`model_options/embedding[slem]/cutoff_type`: 
+
+        cutoff_type: 
+            | type: ``str``, optional, default: ``polynomial``
+            | argument path: ``model_options/embedding[slem]/cutoff_type``
+
+            The type of cutoff function. Default: polynomial
+
+        .. _`model_options/embedding[slem]/env_embed_multiplicity`: 
+
+        env_embed_multiplicity: 
+            | type: ``int``, optional, default: ``10``
+            | argument path: ``model_options/embedding[slem]/env_embed_multiplicity``
+
+        .. _`model_options/embedding[slem]/tp_radial_emb`: 
+
+        tp_radial_emb: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[slem]/tp_radial_emb``
+
+            Whether to use tensor product radial embedding.
+
+        .. _`model_options/embedding[slem]/tp_radial_channels`: 
+
+        tp_radial_channels: 
+            | type: ``list``, optional, default: ``[32]``
+            | argument path: ``model_options/embedding[slem]/tp_radial_channels``
+
+            The number of channels in tensor product radial embedding.
+
+        .. _`model_options/embedding[slem]/latent_channels`: 
+
+        latent_channels: 
+            | type: ``list``, optional, default: ``[32]``
+            | argument path: ``model_options/embedding[slem]/latent_channels``
+
+            The number of channels in latent embedding.
+
+        .. _`model_options/embedding[slem]/latent_dim`: 
+
+        latent_dim: 
+            | type: ``int``, optional, default: ``64``
+            | argument path: ``model_options/embedding[slem]/latent_dim``
+
+            The dimension of latent embedding.
+
+        .. _`model_options/embedding[slem]/res_update`: 
+
+        res_update: 
+            | type: ``bool``, optional, default: ``True``
+            | argument path: ``model_options/embedding[slem]/res_update``
+
+            Whether to use residual update.
+
+        .. _`model_options/embedding[slem]/res_update_ratios`: 
+
+        res_update_ratios: 
+            | type: ``float``, optional, default: ``0.5``
+            | argument path: ``model_options/embedding[slem]/res_update_ratios``
+
+            The ratios of residual update, should in (0,1).
+
+        .. _`model_options/embedding[slem]/res_update_ratios_learnable`: 
+
+        res_update_ratios_learnable: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[slem]/res_update_ratios_learnable``
+
+            Whether to make the ratios of residual update learnable.
+
+
+        .. _`model_options/embedding[lem]`: 
+
+        When |flag:model_options/embedding/method|_ is set to ``lem``: 
+
+        .. _`model_options/embedding[lem]/irreps_hidden`: 
+
+        irreps_hidden: 
+            | type: ``str``
+            | argument path: ``model_options/embedding[lem]/irreps_hidden``
+
+        .. _`model_options/embedding[lem]/avg_num_neighbors`: 
+
+        avg_num_neighbors: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[lem]/avg_num_neighbors``
+
+        .. _`model_options/embedding[lem]/r_max`: 
+
+        r_max: 
+            | type: ``dict`` | ``float`` | ``int``
+            | argument path: ``model_options/embedding[lem]/r_max``
+
+        .. _`model_options/embedding[lem]/n_layers`: 
+
+        n_layers: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[lem]/n_layers``
+
+        .. _`model_options/embedding[lem]/n_radial_basis`: 
+
+        n_radial_basis: 
+            | type: ``int``, optional, default: ``10``
+            | argument path: ``model_options/embedding[lem]/n_radial_basis``
+
+        .. _`model_options/embedding[lem]/PolynomialCutoff_p`: 
+
+        PolynomialCutoff_p: 
+            | type: ``int``, optional, default: ``6``
+            | argument path: ``model_options/embedding[lem]/PolynomialCutoff_p``
+
+            The order of polynomial cutoff function. Default: 6
+
+        .. _`model_options/embedding[lem]/cutoff_type`: 
+
+        cutoff_type: 
+            | type: ``str``, optional, default: ``polynomial``
+            | argument path: ``model_options/embedding[lem]/cutoff_type``
+
+            The type of cutoff function. Default: polynomial
+
+        .. _`model_options/embedding[lem]/env_embed_multiplicity`: 
+
+        env_embed_multiplicity: 
+            | type: ``int``, optional, default: ``10``
+            | argument path: ``model_options/embedding[lem]/env_embed_multiplicity``
+
+        .. _`model_options/embedding[lem]/tp_radial_emb`: 
+
+        tp_radial_emb: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[lem]/tp_radial_emb``
+
+            Whether to use tensor product radial embedding.
+
+        .. _`model_options/embedding[lem]/tp_radial_channels`: 
+
+        tp_radial_channels: 
+            | type: ``list``, optional, default: ``[32]``
+            | argument path: ``model_options/embedding[lem]/tp_radial_channels``
+
+            The number of channels in tensor product radial embedding.
+
+        .. _`model_options/embedding[lem]/latent_channels`: 
+
+        latent_channels: 
+            | type: ``list``, optional, default: ``[32]``
+            | argument path: ``model_options/embedding[lem]/latent_channels``
+
+            The number of channels in latent embedding.
+
+        .. _`model_options/embedding[lem]/latent_dim`: 
+
+        latent_dim: 
+            | type: ``int``, optional, default: ``64``
+            | argument path: ``model_options/embedding[lem]/latent_dim``
+
+            The dimension of latent embedding.
+
+        .. _`model_options/embedding[lem]/res_update`: 
+
+        res_update: 
+            | type: ``bool``, optional, default: ``True``
+            | argument path: ``model_options/embedding[lem]/res_update``
+
+            Whether to use residual update.
+
+        .. _`model_options/embedding[lem]/res_update_ratios`: 
+
+        res_update_ratios: 
+            | type: ``float``, optional, default: ``0.5``
+            | argument path: ``model_options/embedding[lem]/res_update_ratios``
+
+            The ratios of residual update, should in (0,1).
+
+        .. _`model_options/embedding[lem]/res_update_ratios_learnable`: 
+
+        res_update_ratios_learnable: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[lem]/res_update_ratios_learnable``
+
+            Whether to make the ratios of residual update learnable.
+
+
+        .. _`model_options/embedding[e3baseline_nonlocal]`: 
+
+        When |flag:model_options/embedding/method|_ is set to ``e3baseline_nonlocal``: 
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/irreps_hidden`: 
+
+        irreps_hidden: 
+            | type: ``str``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/irreps_hidden``
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/lmax`: 
+
+        lmax: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/lmax``
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/avg_num_neighbors`: 
+
+        avg_num_neighbors: 
+            | type: ``float`` | ``int``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/avg_num_neighbors``
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/r_max`: 
+
+        r_max: 
+            | type: ``dict`` | ``float`` | ``int``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/r_max``
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/n_layers`: 
+
+        n_layers: 
+            | type: ``int``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/n_layers``
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/n_radial_basis`: 
+
+        n_radial_basis: 
+            | type: ``int``, optional, default: ``10``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/n_radial_basis``
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/PolynomialCutoff_p`: 
+
+        PolynomialCutoff_p: 
+            | type: ``int``, optional, default: ``6``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/PolynomialCutoff_p``
+
+            The order of polynomial cutoff function. Default: 6
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/cutoff_type`: 
+
+        cutoff_type: 
+            | type: ``str``, optional, default: ``polynomial``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/cutoff_type``
+
+            The type of cutoff function. Default: polynomial
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/env_embed_multiplicity`: 
+
+        env_embed_multiplicity: 
+            | type: ``int``, optional, default: ``1``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/env_embed_multiplicity``
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/tp_radial_emb`: 
+
+        tp_radial_emb: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/tp_radial_emb``
+
+            Whether to use tensor product radial embedding.
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/tp_radial_channels`: 
+
+        tp_radial_channels: 
+            | type: ``list``, optional, default: ``[128, 128]``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/tp_radial_channels``
+
+            The number of channels in tensor product radial embedding.
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/latent_channels`: 
+
+        latent_channels: 
+            | type: ``list``, optional, default: ``[128, 128]``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/latent_channels``
+
+            The number of channels in latent embedding.
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/latent_dim`: 
+
+        latent_dim: 
+            | type: ``int``, optional, default: ``256``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/latent_dim``
+
+            The dimension of latent embedding.
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/res_update`: 
+
+        res_update: 
+            | type: ``bool``, optional, default: ``True``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/res_update``
+
+            Whether to use residual update.
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/res_update_ratios`: 
+
+        res_update_ratios: 
+            | type: ``float``, optional, default: ``0.5``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/res_update_ratios``
+
+            The ratios of residual update, should in (0,1).
+
+        .. _`model_options/embedding[e3baseline_nonlocal]/res_update_ratios_learnable`: 
+
+        res_update_ratios_learnable: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/embedding[e3baseline_nonlocal]/res_update_ratios_learnable``
+
+            Whether to make the ratios of residual update learnable.
+
+    .. _`model_options/prediction`: 
+
+    prediction: 
+        | type: ``dict``, optional
+        | argument path: ``model_options/prediction``
+
+        The parameters to define the prediction model
+
+
+        Depending on the value of *method*, different sub args are accepted. 
+
+        .. _`model_options/prediction/method`: 
+
+        method:
+            | type: ``str`` (flag key)
+            | argument path: ``model_options/prediction/method`` 
+            | possible choices: |code:model_options/prediction[sktb]|_, |code:model_options/prediction[e3tb]|_
+
+            The options to indicate the prediction model. Can be sktb or e3tb.
+
+            .. |code:model_options/prediction[sktb]| replace:: ``sktb``
+            .. _`code:model_options/prediction[sktb]`: `model_options/prediction[sktb]`_
+            .. |code:model_options/prediction[e3tb]| replace:: ``e3tb``
+            .. _`code:model_options/prediction[e3tb]`: `model_options/prediction[e3tb]`_
+
+        .. |flag:model_options/prediction/method| replace:: *method*
+        .. _`flag:model_options/prediction/method`: `model_options/prediction/method`_
+
+
+        .. _`model_options/prediction[sktb]`: 
+
+        When |flag:model_options/prediction/method|_ is set to ``sktb``: 
+
+        neural network options for prediction model.
+
+        .. _`model_options/prediction[sktb]/neurons`: 
+
+        neurons: 
+            | type: ``list``
+            | argument path: ``model_options/prediction[sktb]/neurons``
+
+            neurons in the neural network.
+
+        .. _`model_options/prediction[sktb]/activation`: 
+
+        activation: 
+            | type: ``str``, optional, default: ``tanh``
+            | argument path: ``model_options/prediction[sktb]/activation``
+
+            activation function.
+
+        .. _`model_options/prediction[sktb]/if_batch_normalized`: 
+
+        if_batch_normalized: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/prediction[sktb]/if_batch_normalized``
+
+            if to turn on batch normalization
+
+
+        .. _`model_options/prediction[e3tb]`: 
+
+        When |flag:model_options/prediction/method|_ is set to ``e3tb``: 
+
+        neural network options for prediction model.
+
+        .. _`model_options/prediction[e3tb]/scales_trainable`: 
+
+        scales_trainable: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/prediction[e3tb]/scales_trainable``
+
+            whether to scale the trianing target.
+
+        .. _`model_options/prediction[e3tb]/shifts_trainable`: 
+
+        shifts_trainable: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/prediction[e3tb]/shifts_trainable``
+
+            whether to shift the training target.
+
+        .. _`model_options/prediction[e3tb]/neurons`: 
+
+        neurons: 
+            | type: ``list`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``model_options/prediction[e3tb]/neurons``
+
+            neurons in the neural network.
+
+        .. _`model_options/prediction[e3tb]/activation`: 
+
+        activation: 
+            | type: ``str``, optional, default: ``tanh``
+            | argument path: ``model_options/prediction[e3tb]/activation``
+
+            activation function.
+
+        .. _`model_options/prediction[e3tb]/if_batch_normalized`: 
+
+        if_batch_normalized: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``model_options/prediction[e3tb]/if_batch_normalized``
+
+            if to turn on batch normalization
+
+    .. _`model_options/nnsk`: 
+
+    nnsk: 
+        | type: ``dict``, optional
+        | argument path: ``model_options/nnsk``
+
+        The parameters to define the nnsk model.
+
+        .. _`model_options/nnsk/onsite`: 
+
+        onsite: 
+            | type: ``dict``
+            | argument path: ``model_options/nnsk/onsite``
+
+            The onsite options to define the onsite of nnsk model.
+
+
+            Depending on the value of *method*, different sub args are accepted. 
+
+            .. _`model_options/nnsk/onsite/method`: 
+
+            method:
+                | type: ``str`` (flag key)
+                | argument path: ``model_options/nnsk/onsite/method`` 
+                | possible choices: |code:model_options/nnsk/onsite[strain]|_, |code:model_options/nnsk/onsite[uniform]|_, |code:model_options/nnsk/onsite[NRL]|_, |code:model_options/nnsk/onsite[none]|_
+
+                The onsite correction mode, the onsite energy is expressed as the energy of isolated atoms plus the model correction, the correction mode are:
+                                    Default: `none`: use the database onsite energy value.
+                                    - `strain`: The strain mode correct the onsite matrix densly by $$H_{i,i}^{lm,l^\prime m^\prime} = \epsilon_l^0 \delta_{ll^\prime}\delta_{mm^\prime} + \sum_p \sum_{\zeta} \Big[ \mathcal{U}_{\zeta}(\hat{r}_{ip}) \ \epsilon_{ll^\prime \zeta} \Big]_{mm^\prime}$$ which is also parameterized as a set of Slater-Koster like integrals.
+
+                                    - `uniform`: The correction is a energy shift respect of orbital of each atom. Which is formally written as: 
+                                                $$H_{i,i}^{lm,l^\prime m^\prime} = (\epsilon_l^0+\epsilon_l^\prime) \delta_{ll^\prime}\delta_{mm^\prime}$$ Where $\epsilon_l^0$ is the isolated energy level from the DeePTB onsite database, and $\epsilon_l^\prime$ is the parameters to fit.
+                                    - `NRL`: use the NRL-TB formula.
+                
+
+                .. |code:model_options/nnsk/onsite[strain]| replace:: ``strain``
+                .. _`code:model_options/nnsk/onsite[strain]`: `model_options/nnsk/onsite[strain]`_
+                .. |code:model_options/nnsk/onsite[uniform]| replace:: ``uniform``
+                .. _`code:model_options/nnsk/onsite[uniform]`: `model_options/nnsk/onsite[uniform]`_
+                .. |code:model_options/nnsk/onsite[NRL]| replace:: ``NRL``
+                .. _`code:model_options/nnsk/onsite[NRL]`: `model_options/nnsk/onsite[NRL]`_
+                .. |code:model_options/nnsk/onsite[none]| replace:: ``none``
+                .. _`code:model_options/nnsk/onsite[none]`: `model_options/nnsk/onsite[none]`_
+
+            .. |flag:model_options/nnsk/onsite/method| replace:: *method*
+            .. _`flag:model_options/nnsk/onsite/method`: `model_options/nnsk/onsite/method`_
+
+
+            .. _`model_options/nnsk/onsite[strain]`: 
+
+            When |flag:model_options/nnsk/onsite/method|_ is set to ``strain``: 
+
+            .. _`model_options/nnsk/onsite[strain]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/onsite[strain]/rs``
+
+                The smooth cutoff `fc` for strain model. rs is where fc = 0.5
+
+            .. _`model_options/nnsk/onsite[strain]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/onsite[strain]/w``
+
+                The decay factor of `fc` for strain and nrl model.
+
+
+            .. _`model_options/nnsk/onsite[uniform]`: 
+
+            When |flag:model_options/nnsk/onsite/method|_ is set to ``uniform``: 
+
+
+
+            .. _`model_options/nnsk/onsite[NRL]`: 
+
+            When |flag:model_options/nnsk/onsite/method|_ is set to ``NRL``: 
+
+            .. _`model_options/nnsk/onsite[NRL]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/onsite[NRL]/rs``
+
+                The smooth cutoff of `fc` for nrl model, rc is where fc ~ 0.0
+
+            .. _`model_options/nnsk/onsite[NRL]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/onsite[NRL]/w``
+
+                The decay factor of `fc` for strain and nrl model.
+
+            .. _`model_options/nnsk/onsite[NRL]/lda`: 
+
+            lda: 
+                | type: ``float``, optional, default: ``1.0``
+                | argument path: ``model_options/nnsk/onsite[NRL]/lda``
+
+                The lambda type encoding value in nrl model. now only support elementary substance
+
+
+            .. _`model_options/nnsk/onsite[none]`: 
+
+            When |flag:model_options/nnsk/onsite/method|_ is set to ``none``: 
+
+
+        .. _`model_options/nnsk/hopping`: 
+
+        hopping: 
+            | type: ``dict``
+            | argument path: ``model_options/nnsk/hopping``
+
+            The hopping options to define the hopping of nnsk model.
+
+
+            Depending on the value of *method*, different sub args are accepted. 
+
+            .. _`model_options/nnsk/hopping/method`: 
+
+            method:
+                | type: ``str`` (flag key)
+                | argument path: ``model_options/nnsk/hopping/method`` 
+                | possible choices: |code:model_options/nnsk/hopping[powerlaw]|_, |code:model_options/nnsk/hopping[poly1pow]|_, |code:model_options/nnsk/hopping[poly2pow]|_, |code:model_options/nnsk/hopping[poly3pow]|_, |code:model_options/nnsk/hopping[poly2exp]|_, |code:model_options/nnsk/hopping[varTang96]|_, |code:model_options/nnsk/hopping[NRL0]|_, |code:model_options/nnsk/hopping[NRL1]|_, |code:model_options/nnsk/hopping[custom]|_
+
+                The hopping formula. 
+                                    -  `powerlaw`: the powerlaw formula for bond length dependence for sk integrals.
+                                    -  `varTang96`: a variational formula based on Tang96 formula.
+                                    -  `NRL0`: the old version of NRL formula for overlap, we set overlap and hopping share same options.
+                                    -  `NRL1`: the new version of NRL formula for overlap. 
+                    
+
+                .. |code:model_options/nnsk/hopping[powerlaw]| replace:: ``powerlaw``
+                .. _`code:model_options/nnsk/hopping[powerlaw]`: `model_options/nnsk/hopping[powerlaw]`_
+                .. |code:model_options/nnsk/hopping[poly1pow]| replace:: ``poly1pow``
+                .. _`code:model_options/nnsk/hopping[poly1pow]`: `model_options/nnsk/hopping[poly1pow]`_
+                .. |code:model_options/nnsk/hopping[poly2pow]| replace:: ``poly2pow``
+                .. _`code:model_options/nnsk/hopping[poly2pow]`: `model_options/nnsk/hopping[poly2pow]`_
+                .. |code:model_options/nnsk/hopping[poly3pow]| replace:: ``poly3pow``
+                .. _`code:model_options/nnsk/hopping[poly3pow]`: `model_options/nnsk/hopping[poly3pow]`_
+                .. |code:model_options/nnsk/hopping[poly2exp]| replace:: ``poly2exp``
+                .. _`code:model_options/nnsk/hopping[poly2exp]`: `model_options/nnsk/hopping[poly2exp]`_
+                .. |code:model_options/nnsk/hopping[varTang96]| replace:: ``varTang96``
+                .. _`code:model_options/nnsk/hopping[varTang96]`: `model_options/nnsk/hopping[varTang96]`_
+                .. |code:model_options/nnsk/hopping[NRL0]| replace:: ``NRL0``
+                .. _`code:model_options/nnsk/hopping[NRL0]`: `model_options/nnsk/hopping[NRL0]`_
+                .. |code:model_options/nnsk/hopping[NRL1]| replace:: ``NRL1``
+                .. _`code:model_options/nnsk/hopping[NRL1]`: `model_options/nnsk/hopping[NRL1]`_
+                .. |code:model_options/nnsk/hopping[custom]| replace:: ``custom``
+                .. _`code:model_options/nnsk/hopping[custom]`: `model_options/nnsk/hopping[custom]`_
+
+            .. |flag:model_options/nnsk/hopping/method| replace:: *method*
+            .. _`flag:model_options/nnsk/hopping/method`: `model_options/nnsk/hopping/method`_
+
+
+            .. _`model_options/nnsk/hopping[powerlaw]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``powerlaw``: 
+
+            .. _`model_options/nnsk/hopping[powerlaw]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/hopping[powerlaw]/rs``
+
+                The cut-off for smooth function fc for powerlaw and varTang96, fc(rs)=0.5
+
+            .. _`model_options/nnsk/hopping[powerlaw]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/hopping[powerlaw]/w``
+
+                 The decay w in fc
+
+
+            .. _`model_options/nnsk/hopping[poly1pow]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``poly1pow``: 
+
+            .. _`model_options/nnsk/hopping[poly1pow]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/hopping[poly1pow]/rs``
+
+                The cut-off for smooth function fc for powerlaw and varTang96, fc(rs)=0.5
+
+            .. _`model_options/nnsk/hopping[poly1pow]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/hopping[poly1pow]/w``
+
+                 The decay w in fc
+
+
+            .. _`model_options/nnsk/hopping[poly2pow]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``poly2pow``: 
+
+            .. _`model_options/nnsk/hopping[poly2pow]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/hopping[poly2pow]/rs``
+
+                The cut-off for smooth function fc for powerlaw and varTang96, fc(rs)=0.5
+
+            .. _`model_options/nnsk/hopping[poly2pow]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/hopping[poly2pow]/w``
+
+                 The decay w in fc
+
+
+            .. _`model_options/nnsk/hopping[poly3pow]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``poly3pow``: 
+
+            .. _`model_options/nnsk/hopping[poly3pow]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/hopping[poly3pow]/rs``
+
+                The cut-off for smooth function fc for powerlaw and varTang96, fc(rs)=0.5
+
+            .. _`model_options/nnsk/hopping[poly3pow]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/hopping[poly3pow]/w``
+
+                 The decay w in fc
+
+
+            .. _`model_options/nnsk/hopping[poly2exp]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``poly2exp``: 
+
+            .. _`model_options/nnsk/hopping[poly2exp]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/hopping[poly2exp]/rs``
+
+                The cut-off for smooth function fc for powerlaw and varTang96, fc(rs)=0.5
+
+            .. _`model_options/nnsk/hopping[poly2exp]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/hopping[poly2exp]/w``
+
+                 The decay w in fc
+
+
+            .. _`model_options/nnsk/hopping[varTang96]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``varTang96``: 
+
+            .. _`model_options/nnsk/hopping[varTang96]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/hopping[varTang96]/rs``
+
+                The cut-off for smooth function fc for powerlaw and varTang96, fc(rs)=0.5
+
+            .. _`model_options/nnsk/hopping[varTang96]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/hopping[varTang96]/w``
+
+                 The decay w in fc
+
+
+            .. _`model_options/nnsk/hopping[NRL0]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``NRL0``: 
+
+            .. _`model_options/nnsk/hopping[NRL0]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/hopping[NRL0]/rs``
+
+                The cut-off for smooth function fc for NRL, fc(rc) = 0.
+
+            .. _`model_options/nnsk/hopping[NRL0]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/hopping[NRL0]/w``
+
+                 The decay w in fc
+
+
+            .. _`model_options/nnsk/hopping[NRL1]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``NRL1``: 
+
+            .. _`model_options/nnsk/hopping[NRL1]/rs`: 
+
+            rs: 
+                | type: ``float``, optional, default: ``6.0``
+                | argument path: ``model_options/nnsk/hopping[NRL1]/rs``
+
+                The cut-off for smooth function fc for NRL, fc(rc) = 0.
+
+            .. _`model_options/nnsk/hopping[NRL1]/w`: 
+
+            w: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``model_options/nnsk/hopping[NRL1]/w``
+
+                 The decay w in fc
+
+
+            .. _`model_options/nnsk/hopping[custom]`: 
+
+            When |flag:model_options/nnsk/hopping/method|_ is set to ``custom``: 
+
+
+        .. _`model_options/nnsk/soc`: 
+
+        soc: 
+            | type: ``dict``, optional, default: ``{}``
+            | argument path: ``model_options/nnsk/soc``
+
+            The soc options to define the soc of nnsk model,
+                            Default: {} # empty dict
+
+                            - {'method':'none'} : use database soc value. 
+                            - {'method':uniform} : set lambda_il; assign a soc lambda value for each orbital -l on each atomtype i; l=0,1,2 for s p d.
+
+        .. _`model_options/nnsk/freeze`: 
+
+        freeze: 
+            | type: ``str`` | ``bool`` | ``list``, optional, default: ``False``
+            | argument path: ``model_options/nnsk/freeze``
+
+            The parameters to define the freeze of nnsk model can be bool and string and list.
+
+                                Default: False
+
+                                 - True: freeze all the nnsk parameters
+
+                                 - False: train all the nnsk parameters
+ 
+                                 - 'hopping','onsite','overlap' and 'soc' to freeze the corresponding parameters.
+                                 - list of the strings e.g. ['overlap','soc'] to freeze both overlap and soc parameters.
+
+        .. _`model_options/nnsk/std`: 
+
+        std: 
+            | type: ``float``, optional, default: ``0.01``
+            | argument path: ``model_options/nnsk/std``
+
+            The std value to initialize the nnsk parameters. Default: 0.01
+
+        .. _`model_options/nnsk/push`: 
+
+        push: 
+            | type: ``dict`` | ``bool``, optional, default: ``False``
+            | argument path: ``model_options/nnsk/push``
+
+            The parameters to define the push the soft cutoff of nnsk model.
+
+            .. _`model_options/nnsk/push/rs_thr`: 
+
+            rs_thr: 
+                | type: ``float`` | ``int``, optional, default: ``0.0``
+                | argument path: ``model_options/nnsk/push/rs_thr``
+
+                The step size for cutoff value for smooth function in the nnsk anlytical formula.
+
+            .. _`model_options/nnsk/push/rc_thr`: 
+
+            rc_thr: 
+                | type: ``float`` | ``int``, optional, default: ``0.0``
+                | argument path: ``model_options/nnsk/push/rc_thr``
+
+                The step size for cutoff value for smooth function in the nnsk anlytical formula.
+
+            .. _`model_options/nnsk/push/w_thr`: 
+
+            w_thr: 
+                | type: ``float`` | ``int``, optional, default: ``0.0``
+                | argument path: ``model_options/nnsk/push/w_thr``
+
+                The step size for decay factor w.
+
+            .. _`model_options/nnsk/push/ovp_thr`: 
+
+            ovp_thr: 
+                | type: ``float`` | ``int``, optional, default: ``0.0``
+                | argument path: ``model_options/nnsk/push/ovp_thr``
+
+                The step size for overlap reduction
+
+            .. _`model_options/nnsk/push/period`: 
+
+            period: 
+                | type: ``int``, optional, default: ``100``
+                | argument path: ``model_options/nnsk/push/period``
+
+                the interval of iterations to modify the rs w values.
+
+    .. _`model_options/dftbsk`: 
+
+    dftbsk: 
+        | type: ``dict``, optional
+        | argument path: ``model_options/dftbsk``
+
+        The parameters to define the dftb sk model.
+
+        .. _`model_options/dftbsk/skdata`: 
+
+        skdata: 
+            | type: ``str``
+            | argument path: ``model_options/dftbsk/skdata``
+
+            The path to the skfile or sk database.
+
+        .. _`model_options/dftbsk/r_max`: 
+
+        r_max: 
+            | type: ``float``
+            | argument path: ``model_options/dftbsk/r_max``
+
+            the cutoff values to use sk files.
+
diff --git a/docs/input_params/run_options.rst b/docs/input_params/run_options.rst
new file mode 100644
index 0000000..f7d1d8a
--- /dev/null
+++ b/docs/input_params/run_options.rst
@@ -0,0 +1,1382 @@
+========================================
+Run Options
+========================================
+.. _`run_op`: 
+
+run_op: 
+    | type: ``dict``
+    | argument path: ``run_op``
+
+    .. _`run_op/task_options`: 
+
+    task_options: 
+        | type: ``dict``, optional
+        | argument path: ``run_op/task_options``
+
+        the task to run, includes: band, dos, pdos, FS2D, FS3D, ifermi
+
+
+        Depending on the value of *task*, different sub args are accepted. 
+
+        .. _`run_op/task_options/task`: 
+
+        task:
+            | type: ``str`` (flag key)
+            | argument path: ``run_op/task_options/task`` 
+            | possible choices: |code:run_op/task_options[band]|_, |code:run_op/task_options[dos]|_, |code:run_op/task_options[pdos]|_, |code:run_op/task_options[FS2D]|_, |code:run_op/task_options[FS3D]|_, |code:run_op/task_options[write_sk]|_, |code:run_op/task_options[ifermi]|_, |code:run_op/task_options[negf]|_, |code:run_op/task_options[tbtrans_negf]|_, |code:run_op/task_options[write_block]|_
+
+            The string define the task DeePTB conduct, includes: 
+                                - `band`: for band structure plotting. 
+                                - `dos`: for density of states plotting.
+                                - `pdos`: for projected density of states plotting.
+                                - `FS2D`: for 2D fermi-surface plotting.
+                                - `FS3D`: for 3D fermi-surface plotting.
+                                - `write_sk`: for transcript the nnsk model to standard sk parameter table
+                                - `ifermi`: for fermi surface plotting.
+                                - `negf`: for non-equilibrium green function calculation.
+                                - `tbtrans_negf`: for non-equilibrium green function calculation with tbtrans.
+                
+
+            .. |code:run_op/task_options[band]| replace:: ``band``
+            .. _`code:run_op/task_options[band]`: `run_op/task_options[band]`_
+            .. |code:run_op/task_options[dos]| replace:: ``dos``
+            .. _`code:run_op/task_options[dos]`: `run_op/task_options[dos]`_
+            .. |code:run_op/task_options[pdos]| replace:: ``pdos``
+            .. _`code:run_op/task_options[pdos]`: `run_op/task_options[pdos]`_
+            .. |code:run_op/task_options[FS2D]| replace:: ``FS2D``
+            .. _`code:run_op/task_options[FS2D]`: `run_op/task_options[FS2D]`_
+            .. |code:run_op/task_options[FS3D]| replace:: ``FS3D``
+            .. _`code:run_op/task_options[FS3D]`: `run_op/task_options[FS3D]`_
+            .. |code:run_op/task_options[write_sk]| replace:: ``write_sk``
+            .. _`code:run_op/task_options[write_sk]`: `run_op/task_options[write_sk]`_
+            .. |code:run_op/task_options[ifermi]| replace:: ``ifermi``
+            .. _`code:run_op/task_options[ifermi]`: `run_op/task_options[ifermi]`_
+            .. |code:run_op/task_options[negf]| replace:: ``negf``
+            .. _`code:run_op/task_options[negf]`: `run_op/task_options[negf]`_
+            .. |code:run_op/task_options[tbtrans_negf]| replace:: ``tbtrans_negf``
+            .. _`code:run_op/task_options[tbtrans_negf]`: `run_op/task_options[tbtrans_negf]`_
+            .. |code:run_op/task_options[write_block]| replace:: ``write_block``
+            .. _`code:run_op/task_options[write_block]`: `run_op/task_options[write_block]`_
+
+        .. |flag:run_op/task_options/task| replace:: *task*
+        .. _`flag:run_op/task_options/task`: `run_op/task_options/task`_
+
+
+        .. _`run_op/task_options[band]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``band``: 
+
+        .. _`run_op/task_options[band]/kline_type`: 
+
+        kline_type: 
+            | type: ``str``
+            | argument path: ``run_op/task_options[band]/kline_type``
+
+            The different type to build kpath line mode.
+                                - "abacus" : the abacus format 
+                                - "vasp" : the vasp format
+                                - "ase" : the ase format
+                    
+
+        .. _`run_op/task_options[band]/kpath`: 
+
+        kpath: 
+            | type: ``str`` | ``list``
+            | argument path: ``run_op/task_options[band]/kpath``
+
+            for abacus, this is list, for vasp it is a string to specifc the kpath.
+
+        .. _`run_op/task_options[band]/klabels`: 
+
+        klabels: 
+            | type: ``list``, optional, default: ``['']``
+            | argument path: ``run_op/task_options[band]/klabels``
+
+            the labels for high symmetry kpoint
+
+        .. _`run_op/task_options[band]/E_fermi`: 
+
+        E_fermi: 
+            | type: ``float`` | ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/task_options[band]/E_fermi``
+
+            the fermi level used to plot band
+
+        .. _`run_op/task_options[band]/emin`: 
+
+        emin: 
+            | type: ``float`` | ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/task_options[band]/emin``
+
+            the min energy to show the band plot
+
+        .. _`run_op/task_options[band]/emax`: 
+
+        emax: 
+            | type: ``float`` | ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/task_options[band]/emax``
+
+            the max energy to show the band plot
+
+        .. _`run_op/task_options[band]/nkpoints`: 
+
+        nkpoints: 
+            | type: ``int``, optional, default: ``0``
+            | argument path: ``run_op/task_options[band]/nkpoints``
+
+            the max energy to show the band plot
+
+        .. _`run_op/task_options[band]/ref_band`: 
+
+        ref_band: 
+            | type: ``str`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/task_options[band]/ref_band``
+
+            the reference band structure to be ploted together with dptb bands.
+
+        .. _`run_op/task_options[band]/nel_atom`: 
+
+        nel_atom: 
+            | type: ``dict`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/task_options[band]/nel_atom``
+
+            the valence electron number of each type of atom.
+
+
+        .. _`run_op/task_options[dos]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``dos``: 
+
+        .. _`run_op/task_options[dos]/mesh_grid`: 
+
+        mesh_grid: 
+            | type: ``list``
+            | argument path: ``run_op/task_options[dos]/mesh_grid``
+
+        .. _`run_op/task_options[dos]/sigma`: 
+
+        sigma: 
+            | type: ``float``
+            | argument path: ``run_op/task_options[dos]/sigma``
+
+        .. _`run_op/task_options[dos]/npoints`: 
+
+        npoints: 
+            | type: ``int``
+            | argument path: ``run_op/task_options[dos]/npoints``
+
+        .. _`run_op/task_options[dos]/width`: 
+
+        width: 
+            | type: ``list``
+            | argument path: ``run_op/task_options[dos]/width``
+
+        .. _`run_op/task_options[dos]/E_fermi`: 
+
+        E_fermi: 
+            | type: ``float`` | ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/task_options[dos]/E_fermi``
+
+        .. _`run_op/task_options[dos]/gamma_center`: 
+
+        gamma_center: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[dos]/gamma_center``
+
+
+        .. _`run_op/task_options[pdos]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``pdos``: 
+
+        .. _`run_op/task_options[pdos]/mesh_grid`: 
+
+        mesh_grid: 
+            | type: ``list``
+            | argument path: ``run_op/task_options[pdos]/mesh_grid``
+
+        .. _`run_op/task_options[pdos]/sigma`: 
+
+        sigma: 
+            | type: ``float``
+            | argument path: ``run_op/task_options[pdos]/sigma``
+
+        .. _`run_op/task_options[pdos]/npoints`: 
+
+        npoints: 
+            | type: ``int``
+            | argument path: ``run_op/task_options[pdos]/npoints``
+
+        .. _`run_op/task_options[pdos]/width`: 
+
+        width: 
+            | type: ``list``
+            | argument path: ``run_op/task_options[pdos]/width``
+
+        .. _`run_op/task_options[pdos]/E_fermi`: 
+
+        E_fermi: 
+            | type: ``float`` | ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/task_options[pdos]/E_fermi``
+
+        .. _`run_op/task_options[pdos]/atom_index`: 
+
+        atom_index: 
+            | type: ``list``
+            | argument path: ``run_op/task_options[pdos]/atom_index``
+
+        .. _`run_op/task_options[pdos]/orbital_index`: 
+
+        orbital_index: 
+            | type: ``list``
+            | argument path: ``run_op/task_options[pdos]/orbital_index``
+
+        .. _`run_op/task_options[pdos]/gamma_center`: 
+
+        gamma_center: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[pdos]/gamma_center``
+
+
+        .. _`run_op/task_options[FS2D]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``FS2D``: 
+
+        .. _`run_op/task_options[FS2D]/mesh_grid`: 
+
+        mesh_grid: 
+            | type: ``list``
+            | argument path: ``run_op/task_options[FS2D]/mesh_grid``
+
+        .. _`run_op/task_options[FS2D]/sigma`: 
+
+        sigma: 
+            | type: ``float``
+            | argument path: ``run_op/task_options[FS2D]/sigma``
+
+        .. _`run_op/task_options[FS2D]/E0`: 
+
+        E0: 
+            | type: ``int``
+            | argument path: ``run_op/task_options[FS2D]/E0``
+
+        .. _`run_op/task_options[FS2D]/intpfactor`: 
+
+        intpfactor: 
+            | type: ``int``
+            | argument path: ``run_op/task_options[FS2D]/intpfactor``
+
+
+        .. _`run_op/task_options[FS3D]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``FS3D``: 
+
+        .. _`run_op/task_options[FS3D]/mesh_grid`: 
+
+        mesh_grid: 
+            | type: ``list``
+            | argument path: ``run_op/task_options[FS3D]/mesh_grid``
+
+        .. _`run_op/task_options[FS3D]/sigma`: 
+
+        sigma: 
+            | type: ``float``
+            | argument path: ``run_op/task_options[FS3D]/sigma``
+
+        .. _`run_op/task_options[FS3D]/E0`: 
+
+        E0: 
+            | type: ``int``
+            | argument path: ``run_op/task_options[FS3D]/E0``
+
+        .. _`run_op/task_options[FS3D]/intpfactor`: 
+
+        intpfactor: 
+            | type: ``int``
+            | argument path: ``run_op/task_options[FS3D]/intpfactor``
+
+
+        .. _`run_op/task_options[write_sk]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``write_sk``: 
+
+        .. _`run_op/task_options[write_sk]/format`: 
+
+        format: 
+            | type: ``str``, optional, default: ``sktable``
+            | argument path: ``run_op/task_options[write_sk]/format``
+
+        .. _`run_op/task_options[write_sk]/thr`: 
+
+        thr: 
+            | type: ``float``, optional, default: ``0.001``
+            | argument path: ``run_op/task_options[write_sk]/thr``
+
+
+        .. _`run_op/task_options[ifermi]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``ifermi``: 
+
+        .. _`run_op/task_options[ifermi]/fermisurface`: 
+
+        fermisurface: 
+            | type: ``dict``
+            | argument path: ``run_op/task_options[ifermi]/fermisurface``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/mesh_grid`: 
+
+            mesh_grid: 
+                | type: ``list``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/mesh_grid``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/mu`: 
+
+            mu: 
+                | type: ``float`` | ``int``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/mu``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/sigma`: 
+
+            sigma: 
+                | type: ``float``, optional, default: ``0.1``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/sigma``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/intpfactor`: 
+
+            intpfactor: 
+                | type: ``int``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/intpfactor``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/wigner_seitz`: 
+
+            wigner_seitz: 
+                | type: ``bool``, optional, default: ``True``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/wigner_seitz``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/nworkers`: 
+
+            nworkers: 
+                | type: ``int``, optional, default: ``-1``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/nworkers``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/plot_type`: 
+
+            plot_type: 
+                | type: ``str``, optional, default: ``plotly``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_type``
+
+                plot_type: Method used for plotting. Valid options are: matplotlib, plotly, mayavi, crystal_toolkit.
+
+            .. _`run_op/task_options[ifermi]/fermisurface/use_gui`: 
+
+            use_gui: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/use_gui``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/plot_fs_bands`: 
+
+            plot_fs_bands: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_fs_bands``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/fs_plane`: 
+
+            fs_plane: 
+                | type: ``list``, optional, default: ``[0, 0, 1]``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/fs_plane``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/fs_distance`: 
+
+            fs_distance: 
+                | type: ``float`` | ``int``, optional, default: ``0``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/fs_distance``
+
+            .. _`run_op/task_options[ifermi]/fermisurface/plot_options`: 
+
+            plot_options: 
+                | type: ``dict``, optional, default: ``{}``
+                | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options``
+
+                .. _`run_op/task_options[ifermi]/fermisurface/plot_options/colors`: 
+
+                colors: 
+                    | type: ``str`` | ``dict`` | ``NoneType`` | ``list``, optional, default: ``None``
+                    | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options/colors``
+
+                    The color specification for the iso-surfaces. Valid options are:
+                                    - A single color to use for all Fermi surfaces, specified as a tuple of
+                                      rgb values from 0 to 1. E.g., red would be ``(1, 0, 0)``.
+                                    - A list of colors, specified as above.
+                                    - A dictionary of ``{Spin.up: color1, Spin.down: color2}``, where the
+                                      colors are specified as above.
+                                    - A string specifying which matplotlib colormap to use. See
+                                      https://matplotlib.org/tutorials/colors/colormaps.html for more
+                                      information.
+                                    - ``None``, in which case the default colors will be used.
+                
+
+                .. _`run_op/task_options[ifermi]/fermisurface/plot_options/projection_axis`: 
+
+                projection_axis: 
+                    | type: ``list`` | ``NoneType``, optional, default: ``None``
+                    | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options/projection_axis``
+
+                    projection_axis: Projection axis that can be used to calculate the color of
+                                    vector properties. If None, the norm of the properties will be used,
+                                    otherwise the color will be determined by the dot product of the
+                                    properties with the projection axis. Only has an effect when used with
+                                    the ``vector_properties`` option.
+
+                .. _`run_op/task_options[ifermi]/fermisurface/plot_options/hide_surface`: 
+
+                hide_surface: 
+                    | type: ``bool``, optional, default: ``False``
+                    | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options/hide_surface``
+
+                    hide_surface: Whether to hide the Fermi surface. Only recommended in combination with the ``vector_properties`` option.
+
+                .. _`run_op/task_options[ifermi]/fermisurface/plot_options/hide_labels`: 
+
+                hide_labels: 
+                    | type: ``bool``, optional, default: ``False``
+                    | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options/hide_labels``
+
+                    hide_labels: Whether to show the high-symmetry k-point labels.
+
+                .. _`run_op/task_options[ifermi]/fermisurface/plot_options/hide_cell`: 
+
+                hide_cell: 
+                    | type: ``bool``, optional, default: ``False``
+                    | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options/hide_cell``
+
+                    hide_cell: Whether to show the reciprocal cell boundary.
+
+                .. _`run_op/task_options[ifermi]/fermisurface/plot_options/vector_spacing`: 
+
+                vector_spacing: 
+                    | type: ``float``, optional, default: ``0.2``
+                    | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options/vector_spacing``
+
+                    vector_spacing: The rough spacing between arrows. Uses a custom algorithm
+                                    for resampling the Fermi surface to ensure that arrows are not too close
+                                    together. Only has an effect when used with the ``vector_properties``
+                                    option.
+
+                .. _`run_op/task_options[ifermi]/fermisurface/plot_options/azimuth`: 
+
+                azimuth: 
+                    | type: ``float``, optional, default: ``45.0``
+                    | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options/azimuth``
+
+                    azimuth: The azimuth of the viewpoint in degrees. i.e. the angle subtended by the position vector on a sphere projected on to the x-y plane.
+
+                .. _`run_op/task_options[ifermi]/fermisurface/plot_options/elevation`: 
+
+                elevation: 
+                    | type: ``float``, optional, default: ``35.0``
+                    | argument path: ``run_op/task_options[ifermi]/fermisurface/plot_options/elevation``
+
+                    The zenith angle of the viewpoint in degrees, i.e. the angle subtended by the position vector and the z-axis.
+
+        .. _`run_op/task_options[ifermi]/property`: 
+
+        property: 
+            | type: ``dict``, optional, default: ``{}``
+            | argument path: ``run_op/task_options[ifermi]/property``
+
+            .. _`run_op/task_options[ifermi]/property/velocity`: 
+
+            velocity: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``run_op/task_options[ifermi]/property/velocity``
+
+            .. _`run_op/task_options[ifermi]/property/color_properties`: 
+
+            color_properties: 
+                | type: ``str`` | ``bool``, optional, default: ``False``
+                | argument path: ``run_op/task_options[ifermi]/property/color_properties``
+
+                color_properties: Whether to use the properties to color the Fermi surface.
+                                If the properties is a vector then the norm of the properties will be
+                                used. Note, this will only take effect if the Fermi surface has
+                                properties. If set to True, the viridis colormap will be used.
+                                Alternative colormaps can be selected by setting ``color_properties``
+                                to a matplotlib colormap name. This setting will override the ``colors``
+                                option. For vector properties, the arrows are colored according to the
+                                norm of the properties by default. If used in combination with the
+                                ``projection_axis`` option, the color will be determined by the dot
+                                product of the properties with the projection axis.
+
+            .. _`run_op/task_options[ifermi]/property/colormap`: 
+
+            colormap: 
+                | type: ``str``, optional, default: ``viridis``
+                | argument path: ``run_op/task_options[ifermi]/property/colormap``
+
+            .. _`run_op/task_options[ifermi]/property/prop_plane`: 
+
+            prop_plane: 
+                | type: ``list``, optional, default: ``[0, 0, 1]``
+                | argument path: ``run_op/task_options[ifermi]/property/prop_plane``
+
+            .. _`run_op/task_options[ifermi]/property/prop_distance`: 
+
+            prop_distance: 
+                | type: ``float`` | ``int``, optional, default: ``0``
+                | argument path: ``run_op/task_options[ifermi]/property/prop_distance``
+
+            .. _`run_op/task_options[ifermi]/property/plot_options`: 
+
+            plot_options: 
+                | type: ``dict``, optional, default: ``{}``
+                | argument path: ``run_op/task_options[ifermi]/property/plot_options``
+
+                .. _`run_op/task_options[ifermi]/property/plot_options/colors`: 
+
+                colors: 
+                    | type: ``str`` | ``dict`` | ``NoneType`` | ``list``, optional, default: ``None``
+                    | argument path: ``run_op/task_options[ifermi]/property/plot_options/colors``
+
+                    The color specification for the iso-surfaces. Valid options are:
+                                    - A single color to use for all Fermi surfaces, specified as a tuple of
+                                      rgb values from 0 to 1. E.g., red would be ``(1, 0, 0)``.
+                                    - A list of colors, specified as above.
+                                    - A dictionary of ``{Spin.up: color1, Spin.down: color2}``, where the
+                                      colors are specified as above.
+                                    - A string specifying which matplotlib colormap to use. See
+                                      https://matplotlib.org/tutorials/colors/colormaps.html for more
+                                      information.
+                                    - ``None``, in which case the default colors will be used.
+                
+
+                .. _`run_op/task_options[ifermi]/property/plot_options/projection_axis`: 
+
+                projection_axis: 
+                    | type: ``list`` | ``NoneType``, optional, default: ``None``
+                    | argument path: ``run_op/task_options[ifermi]/property/plot_options/projection_axis``
+
+                    projection_axis: Projection axis that can be used to calculate the color of
+                                    vector properties. If None, the norm of the properties will be used,
+                                    otherwise the color will be determined by the dot product of the
+                                    properties with the projection axis. Only has an effect when used with
+                                    the ``vector_properties`` option.
+
+                .. _`run_op/task_options[ifermi]/property/plot_options/hide_surface`: 
+
+                hide_surface: 
+                    | type: ``bool``, optional, default: ``False``
+                    | argument path: ``run_op/task_options[ifermi]/property/plot_options/hide_surface``
+
+                    hide_surface: Whether to hide the Fermi surface. Only recommended in combination with the ``vector_properties`` option.
+
+                .. _`run_op/task_options[ifermi]/property/plot_options/hide_labels`: 
+
+                hide_labels: 
+                    | type: ``bool``, optional, default: ``False``
+                    | argument path: ``run_op/task_options[ifermi]/property/plot_options/hide_labels``
+
+                    hide_labels: Whether to show the high-symmetry k-point labels.
+
+                .. _`run_op/task_options[ifermi]/property/plot_options/hide_cell`: 
+
+                hide_cell: 
+                    | type: ``bool``, optional, default: ``False``
+                    | argument path: ``run_op/task_options[ifermi]/property/plot_options/hide_cell``
+
+                    hide_cell: Whether to show the reciprocal cell boundary.
+
+                .. _`run_op/task_options[ifermi]/property/plot_options/vector_spacing`: 
+
+                vector_spacing: 
+                    | type: ``float``, optional, default: ``0.2``
+                    | argument path: ``run_op/task_options[ifermi]/property/plot_options/vector_spacing``
+
+                    vector_spacing: The rough spacing between arrows. Uses a custom algorithm
+                                    for resampling the Fermi surface to ensure that arrows are not too close
+                                    together. Only has an effect when used with the ``vector_properties``
+                                    option.
+
+                .. _`run_op/task_options[ifermi]/property/plot_options/azimuth`: 
+
+                azimuth: 
+                    | type: ``float``, optional, default: ``45.0``
+                    | argument path: ``run_op/task_options[ifermi]/property/plot_options/azimuth``
+
+                    azimuth: The azimuth of the viewpoint in degrees. i.e. the angle subtended by the position vector on a sphere projected on to the x-y plane.
+
+                .. _`run_op/task_options[ifermi]/property/plot_options/elevation`: 
+
+                elevation: 
+                    | type: ``float``, optional, default: ``35.0``
+                    | argument path: ``run_op/task_options[ifermi]/property/plot_options/elevation``
+
+                    The zenith angle of the viewpoint in degrees, i.e. the angle subtended by the position vector and the z-axis.
+
+
+        .. _`run_op/task_options[negf]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``negf``: 
+
+        .. _`run_op/task_options[negf]/scf`: 
+
+        scf: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/scf``
+
+        .. _`run_op/task_options[negf]/block_tridiagonal`: 
+
+        block_tridiagonal: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/block_tridiagonal``
+
+        .. _`run_op/task_options[negf]/ele_T`: 
+
+        ele_T: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[negf]/ele_T``
+
+        .. _`run_op/task_options[negf]/unit`: 
+
+        unit: 
+            | type: ``str``, optional, default: ``Hartree``
+            | argument path: ``run_op/task_options[negf]/unit``
+
+        .. _`run_op/task_options[negf]/scf_options`: 
+
+        scf_options: 
+            | type: ``dict``, optional, default: ``{}``
+            | argument path: ``run_op/task_options[negf]/scf_options``
+
+
+            Depending on the value of *mode*, different sub args are accepted. 
+
+            .. _`run_op/task_options[negf]/scf_options/mode`: 
+
+            mode:
+                | type: ``str`` (flag key), default: ``PDIIS``
+                | argument path: ``run_op/task_options[negf]/scf_options/mode`` 
+                | possible choices: |code:run_op/task_options[negf]/scf_options[PDIIS]|_
+
+                .. |code:run_op/task_options[negf]/scf_options[PDIIS]| replace:: ``PDIIS``
+                .. _`code:run_op/task_options[negf]/scf_options[PDIIS]`: `run_op/task_options[negf]/scf_options[PDIIS]`_
+
+            .. |flag:run_op/task_options[negf]/scf_options/mode| replace:: *mode*
+            .. _`flag:run_op/task_options[negf]/scf_options/mode`: `run_op/task_options[negf]/scf_options/mode`_
+
+
+            .. _`run_op/task_options[negf]/scf_options[PDIIS]`: 
+
+            When |flag:run_op/task_options[negf]/scf_options/mode|_ is set to ``PDIIS``: 
+
+            .. _`run_op/task_options[negf]/scf_options[PDIIS]/mixing_period`: 
+
+            mixing_period: 
+                | type: ``int``, optional, default: ``3``
+                | argument path: ``run_op/task_options[negf]/scf_options[PDIIS]/mixing_period``
+
+            .. _`run_op/task_options[negf]/scf_options[PDIIS]/step_size`: 
+
+            step_size: 
+                | type: ``float`` | ``int``, optional, default: ``0.05``
+                | argument path: ``run_op/task_options[negf]/scf_options[PDIIS]/step_size``
+
+            .. _`run_op/task_options[negf]/scf_options[PDIIS]/n_history`: 
+
+            n_history: 
+                | type: ``int``, optional, default: ``6``
+                | argument path: ``run_op/task_options[negf]/scf_options[PDIIS]/n_history``
+
+            .. _`run_op/task_options[negf]/scf_options[PDIIS]/abs_err`: 
+
+            abs_err: 
+                | type: ``float`` | ``int``, optional, default: ``1e-06``
+                | argument path: ``run_op/task_options[negf]/scf_options[PDIIS]/abs_err``
+
+            .. _`run_op/task_options[negf]/scf_options[PDIIS]/rel_err`: 
+
+            rel_err: 
+                | type: ``float`` | ``int``, optional, default: ``0.0001``
+                | argument path: ``run_op/task_options[negf]/scf_options[PDIIS]/rel_err``
+
+            .. _`run_op/task_options[negf]/scf_options[PDIIS]/max_iter`: 
+
+            max_iter: 
+                | type: ``int``, optional, default: ``100``
+                | argument path: ``run_op/task_options[negf]/scf_options[PDIIS]/max_iter``
+
+        .. _`run_op/task_options[negf]/stru_options`: 
+
+        stru_options: 
+            | type: ``dict``
+            | argument path: ``run_op/task_options[negf]/stru_options``
+
+            .. _`run_op/task_options[negf]/stru_options/device`: 
+
+            device: 
+                | type: ``dict``
+                | argument path: ``run_op/task_options[negf]/stru_options/device``
+
+                .. _`run_op/task_options[negf]/stru_options/device/id`: 
+
+                id: 
+                    | type: ``str``
+                    | argument path: ``run_op/task_options[negf]/stru_options/device/id``
+
+                .. _`run_op/task_options[negf]/stru_options/device/sort`: 
+
+                sort: 
+                    | type: ``bool``, optional, default: ``True``
+                    | argument path: ``run_op/task_options[negf]/stru_options/device/sort``
+
+            .. _`run_op/task_options[negf]/stru_options/lead_L`: 
+
+            lead_L: 
+                | type: ``dict``
+                | argument path: ``run_op/task_options[negf]/stru_options/lead_L``
+
+                .. _`run_op/task_options[negf]/stru_options/lead_L/id`: 
+
+                id: 
+                    | type: ``str``
+                    | argument path: ``run_op/task_options[negf]/stru_options/lead_L/id``
+
+                .. _`run_op/task_options[negf]/stru_options/lead_L/voltage`: 
+
+                voltage: 
+                    | type: ``float`` | ``int``
+                    | argument path: ``run_op/task_options[negf]/stru_options/lead_L/voltage``
+
+            .. _`run_op/task_options[negf]/stru_options/lead_R`: 
+
+            lead_R: 
+                | type: ``dict``
+                | argument path: ``run_op/task_options[negf]/stru_options/lead_R``
+
+                .. _`run_op/task_options[negf]/stru_options/lead_R/id`: 
+
+                id: 
+                    | type: ``str``
+                    | argument path: ``run_op/task_options[negf]/stru_options/lead_R/id``
+
+                .. _`run_op/task_options[negf]/stru_options/lead_R/voltage`: 
+
+                voltage: 
+                    | type: ``float`` | ``int``
+                    | argument path: ``run_op/task_options[negf]/stru_options/lead_R/voltage``
+
+            .. _`run_op/task_options[negf]/stru_options/kmesh`: 
+
+            kmesh: 
+                | type: ``list``, optional, default: ``[1, 1, 1]``
+                | argument path: ``run_op/task_options[negf]/stru_options/kmesh``
+
+            .. _`run_op/task_options[negf]/stru_options/pbc`: 
+
+            pbc: 
+                | type: ``list``, optional, default: ``[False, False, False]``
+                | argument path: ``run_op/task_options[negf]/stru_options/pbc``
+
+            .. _`run_op/task_options[negf]/stru_options/gamma_center`: 
+
+            gamma_center: 
+                | type: ``list`` | ``bool``, optional, default: ``True``
+                | argument path: ``run_op/task_options[negf]/stru_options/gamma_center``
+
+            .. _`run_op/task_options[negf]/stru_options/time_reversal_symmetry`: 
+
+            time_reversal_symmetry: 
+                | type: ``list`` | ``bool``, optional, default: ``True``
+                | argument path: ``run_op/task_options[negf]/stru_options/time_reversal_symmetry``
+
+        .. _`run_op/task_options[negf]/poisson_options`: 
+
+        poisson_options: 
+            | type: ``dict``, optional, default: ``{}``
+            | argument path: ``run_op/task_options[negf]/poisson_options``
+
+
+            Depending on the value of *solver*, different sub args are accepted. 
+
+            .. _`run_op/task_options[negf]/poisson_options/solver`: 
+
+            solver:
+                | type: ``str`` (flag key), default: ``fmm``
+                | argument path: ``run_op/task_options[negf]/poisson_options/solver`` 
+                | possible choices: |code:run_op/task_options[negf]/poisson_options[fmm]|_
+
+                .. |code:run_op/task_options[negf]/poisson_options[fmm]| replace:: ``fmm``
+                .. _`code:run_op/task_options[negf]/poisson_options[fmm]`: `run_op/task_options[negf]/poisson_options[fmm]`_
+
+            .. |flag:run_op/task_options[negf]/poisson_options/solver| replace:: *solver*
+            .. _`flag:run_op/task_options[negf]/poisson_options/solver`: `run_op/task_options[negf]/poisson_options/solver`_
+
+
+            .. _`run_op/task_options[negf]/poisson_options[fmm]`: 
+
+            When |flag:run_op/task_options[negf]/poisson_options/solver|_ is set to ``fmm``: 
+
+            .. _`run_op/task_options[negf]/poisson_options[fmm]/err`: 
+
+            err: 
+                | type: ``float`` | ``int``, optional, default: ``1e-05``
+                | argument path: ``run_op/task_options[negf]/poisson_options[fmm]/err``
+
+        .. _`run_op/task_options[negf]/sgf_solver`: 
+
+        sgf_solver: 
+            | type: ``str``, optional, default: ``Sancho-Rubio``
+            | argument path: ``run_op/task_options[negf]/sgf_solver``
+
+        .. _`run_op/task_options[negf]/espacing`: 
+
+        espacing: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[negf]/espacing``
+
+        .. _`run_op/task_options[negf]/emin`: 
+
+        emin: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[negf]/emin``
+
+        .. _`run_op/task_options[negf]/emax`: 
+
+        emax: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[negf]/emax``
+
+        .. _`run_op/task_options[negf]/e_fermi`: 
+
+        e_fermi: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[negf]/e_fermi``
+
+        .. _`run_op/task_options[negf]/density_options`: 
+
+        density_options: 
+            | type: ``dict``, optional, default: ``{}``
+            | argument path: ``run_op/task_options[negf]/density_options``
+
+
+            Depending on the value of *method*, different sub args are accepted. 
+
+            .. _`run_op/task_options[negf]/density_options/method`: 
+
+            method:
+                | type: ``str`` (flag key), default: ``Ozaki``
+                | argument path: ``run_op/task_options[negf]/density_options/method`` 
+                | possible choices: |code:run_op/task_options[negf]/density_options[Ozaki]|_
+
+                .. |code:run_op/task_options[negf]/density_options[Ozaki]| replace:: ``Ozaki``
+                .. _`code:run_op/task_options[negf]/density_options[Ozaki]`: `run_op/task_options[negf]/density_options[Ozaki]`_
+
+            .. |flag:run_op/task_options[negf]/density_options/method| replace:: *method*
+            .. _`flag:run_op/task_options[negf]/density_options/method`: `run_op/task_options[negf]/density_options/method`_
+
+
+            .. _`run_op/task_options[negf]/density_options[Ozaki]`: 
+
+            When |flag:run_op/task_options[negf]/density_options/method|_ is set to ``Ozaki``: 
+
+            .. _`run_op/task_options[negf]/density_options[Ozaki]/R`: 
+
+            R: 
+                | type: ``float`` | ``int``, optional, default: ``1000000.0``
+                | argument path: ``run_op/task_options[negf]/density_options[Ozaki]/R``
+
+            .. _`run_op/task_options[negf]/density_options[Ozaki]/M_cut`: 
+
+            M_cut: 
+                | type: ``int``, optional, default: ``30``
+                | argument path: ``run_op/task_options[negf]/density_options[Ozaki]/M_cut``
+
+            .. _`run_op/task_options[negf]/density_options[Ozaki]/n_gauss`: 
+
+            n_gauss: 
+                | type: ``int``, optional, default: ``10``
+                | argument path: ``run_op/task_options[negf]/density_options[Ozaki]/n_gauss``
+
+        .. _`run_op/task_options[negf]/eta_lead`: 
+
+        eta_lead: 
+            | type: ``float`` | ``int``, optional, default: ``1e-05``
+            | argument path: ``run_op/task_options[negf]/eta_lead``
+
+        .. _`run_op/task_options[negf]/eta_device`: 
+
+        eta_device: 
+            | type: ``float`` | ``int``, optional, default: ``0.0``
+            | argument path: ``run_op/task_options[negf]/eta_device``
+
+        .. _`run_op/task_options[negf]/out_dos`: 
+
+        out_dos: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/out_dos``
+
+        .. _`run_op/task_options[negf]/out_tc`: 
+
+        out_tc: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/out_tc``
+
+        .. _`run_op/task_options[negf]/out_density`: 
+
+        out_density: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/out_density``
+
+        .. _`run_op/task_options[negf]/out_potential`: 
+
+        out_potential: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/out_potential``
+
+        .. _`run_op/task_options[negf]/out_current`: 
+
+        out_current: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/out_current``
+
+        .. _`run_op/task_options[negf]/out_current_nscf`: 
+
+        out_current_nscf: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/out_current_nscf``
+
+        .. _`run_op/task_options[negf]/out_ldos`: 
+
+        out_ldos: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/out_ldos``
+
+        .. _`run_op/task_options[negf]/out_lcurrent`: 
+
+        out_lcurrent: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[negf]/out_lcurrent``
+
+
+        .. _`run_op/task_options[tbtrans_negf]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``tbtrans_negf``: 
+
+        .. _`run_op/task_options[tbtrans_negf]/scf`: 
+
+        scf: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/scf``
+
+        .. _`run_op/task_options[tbtrans_negf]/block_tridiagonal`: 
+
+        block_tridiagonal: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/block_tridiagonal``
+
+        .. _`run_op/task_options[tbtrans_negf]/ele_T`: 
+
+        ele_T: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[tbtrans_negf]/ele_T``
+
+        .. _`run_op/task_options[tbtrans_negf]/unit`: 
+
+        unit: 
+            | type: ``str``, optional, default: ``Hartree``
+            | argument path: ``run_op/task_options[tbtrans_negf]/unit``
+
+        .. _`run_op/task_options[tbtrans_negf]/scf_options`: 
+
+        scf_options: 
+            | type: ``dict``, optional, default: ``{}``
+            | argument path: ``run_op/task_options[tbtrans_negf]/scf_options``
+
+
+            Depending on the value of *mode*, different sub args are accepted. 
+
+            .. _`run_op/task_options[tbtrans_negf]/scf_options/mode`: 
+
+            mode:
+                | type: ``str`` (flag key), default: ``PDIIS``
+                | argument path: ``run_op/task_options[tbtrans_negf]/scf_options/mode`` 
+                | possible choices: |code:run_op/task_options[tbtrans_negf]/scf_options[PDIIS]|_
+
+                .. |code:run_op/task_options[tbtrans_negf]/scf_options[PDIIS]| replace:: ``PDIIS``
+                .. _`code:run_op/task_options[tbtrans_negf]/scf_options[PDIIS]`: `run_op/task_options[tbtrans_negf]/scf_options[PDIIS]`_
+
+            .. |flag:run_op/task_options[tbtrans_negf]/scf_options/mode| replace:: *mode*
+            .. _`flag:run_op/task_options[tbtrans_negf]/scf_options/mode`: `run_op/task_options[tbtrans_negf]/scf_options/mode`_
+
+
+            .. _`run_op/task_options[tbtrans_negf]/scf_options[PDIIS]`: 
+
+            When |flag:run_op/task_options[tbtrans_negf]/scf_options/mode|_ is set to ``PDIIS``: 
+
+            .. _`run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/mixing_period`: 
+
+            mixing_period: 
+                | type: ``int``, optional, default: ``3``
+                | argument path: ``run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/mixing_period``
+
+            .. _`run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/step_size`: 
+
+            step_size: 
+                | type: ``float`` | ``int``, optional, default: ``0.05``
+                | argument path: ``run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/step_size``
+
+            .. _`run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/n_history`: 
+
+            n_history: 
+                | type: ``int``, optional, default: ``6``
+                | argument path: ``run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/n_history``
+
+            .. _`run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/abs_err`: 
+
+            abs_err: 
+                | type: ``float`` | ``int``, optional, default: ``1e-06``
+                | argument path: ``run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/abs_err``
+
+            .. _`run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/rel_err`: 
+
+            rel_err: 
+                | type: ``float`` | ``int``, optional, default: ``0.0001``
+                | argument path: ``run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/rel_err``
+
+            .. _`run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/max_iter`: 
+
+            max_iter: 
+                | type: ``int``, optional, default: ``100``
+                | argument path: ``run_op/task_options[tbtrans_negf]/scf_options[PDIIS]/max_iter``
+
+        .. _`run_op/task_options[tbtrans_negf]/stru_options`: 
+
+        stru_options: 
+            | type: ``dict``
+            | argument path: ``run_op/task_options[tbtrans_negf]/stru_options``
+
+            .. _`run_op/task_options[tbtrans_negf]/stru_options/device`: 
+
+            device: 
+                | type: ``dict``
+                | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/device``
+
+                .. _`run_op/task_options[tbtrans_negf]/stru_options/device/id`: 
+
+                id: 
+                    | type: ``str``
+                    | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/device/id``
+
+                .. _`run_op/task_options[tbtrans_negf]/stru_options/device/sort`: 
+
+                sort: 
+                    | type: ``bool``, optional, default: ``True``
+                    | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/device/sort``
+
+            .. _`run_op/task_options[tbtrans_negf]/stru_options/lead_L`: 
+
+            lead_L: 
+                | type: ``dict``
+                | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/lead_L``
+
+                .. _`run_op/task_options[tbtrans_negf]/stru_options/lead_L/id`: 
+
+                id: 
+                    | type: ``str``
+                    | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/lead_L/id``
+
+                .. _`run_op/task_options[tbtrans_negf]/stru_options/lead_L/voltage`: 
+
+                voltage: 
+                    | type: ``float`` | ``int``
+                    | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/lead_L/voltage``
+
+            .. _`run_op/task_options[tbtrans_negf]/stru_options/lead_R`: 
+
+            lead_R: 
+                | type: ``dict``
+                | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/lead_R``
+
+                .. _`run_op/task_options[tbtrans_negf]/stru_options/lead_R/id`: 
+
+                id: 
+                    | type: ``str``
+                    | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/lead_R/id``
+
+                .. _`run_op/task_options[tbtrans_negf]/stru_options/lead_R/voltage`: 
+
+                voltage: 
+                    | type: ``float`` | ``int``
+                    | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/lead_R/voltage``
+
+            .. _`run_op/task_options[tbtrans_negf]/stru_options/kmesh`: 
+
+            kmesh: 
+                | type: ``list``, optional, default: ``[1, 1, 1]``
+                | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/kmesh``
+
+            .. _`run_op/task_options[tbtrans_negf]/stru_options/pbc`: 
+
+            pbc: 
+                | type: ``list``, optional, default: ``[False, False, False]``
+                | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/pbc``
+
+            .. _`run_op/task_options[tbtrans_negf]/stru_options/gamma_center`: 
+
+            gamma_center: 
+                | type: ``list`` | ``bool``, optional, default: ``True``
+                | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/gamma_center``
+
+            .. _`run_op/task_options[tbtrans_negf]/stru_options/time_reversal_symmetry`: 
+
+            time_reversal_symmetry: 
+                | type: ``list`` | ``bool``, optional, default: ``True``
+                | argument path: ``run_op/task_options[tbtrans_negf]/stru_options/time_reversal_symmetry``
+
+        .. _`run_op/task_options[tbtrans_negf]/poisson_options`: 
+
+        poisson_options: 
+            | type: ``dict``, optional, default: ``{}``
+            | argument path: ``run_op/task_options[tbtrans_negf]/poisson_options``
+
+
+            Depending on the value of *solver*, different sub args are accepted. 
+
+            .. _`run_op/task_options[tbtrans_negf]/poisson_options/solver`: 
+
+            solver:
+                | type: ``str`` (flag key), default: ``fmm``
+                | argument path: ``run_op/task_options[tbtrans_negf]/poisson_options/solver`` 
+                | possible choices: |code:run_op/task_options[tbtrans_negf]/poisson_options[fmm]|_
+
+                .. |code:run_op/task_options[tbtrans_negf]/poisson_options[fmm]| replace:: ``fmm``
+                .. _`code:run_op/task_options[tbtrans_negf]/poisson_options[fmm]`: `run_op/task_options[tbtrans_negf]/poisson_options[fmm]`_
+
+            .. |flag:run_op/task_options[tbtrans_negf]/poisson_options/solver| replace:: *solver*
+            .. _`flag:run_op/task_options[tbtrans_negf]/poisson_options/solver`: `run_op/task_options[tbtrans_negf]/poisson_options/solver`_
+
+
+            .. _`run_op/task_options[tbtrans_negf]/poisson_options[fmm]`: 
+
+            When |flag:run_op/task_options[tbtrans_negf]/poisson_options/solver|_ is set to ``fmm``: 
+
+            .. _`run_op/task_options[tbtrans_negf]/poisson_options[fmm]/err`: 
+
+            err: 
+                | type: ``float`` | ``int``, optional, default: ``1e-05``
+                | argument path: ``run_op/task_options[tbtrans_negf]/poisson_options[fmm]/err``
+
+        .. _`run_op/task_options[tbtrans_negf]/sgf_solver`: 
+
+        sgf_solver: 
+            | type: ``str``, optional, default: ``Sancho-Rubio``
+            | argument path: ``run_op/task_options[tbtrans_negf]/sgf_solver``
+
+        .. _`run_op/task_options[tbtrans_negf]/espacing`: 
+
+        espacing: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[tbtrans_negf]/espacing``
+
+        .. _`run_op/task_options[tbtrans_negf]/emin`: 
+
+        emin: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[tbtrans_negf]/emin``
+
+        .. _`run_op/task_options[tbtrans_negf]/emax`: 
+
+        emax: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[tbtrans_negf]/emax``
+
+        .. _`run_op/task_options[tbtrans_negf]/e_fermi`: 
+
+        e_fermi: 
+            | type: ``float`` | ``int``
+            | argument path: ``run_op/task_options[tbtrans_negf]/e_fermi``
+
+        .. _`run_op/task_options[tbtrans_negf]/density_options`: 
+
+        density_options: 
+            | type: ``dict``, optional, default: ``{}``
+            | argument path: ``run_op/task_options[tbtrans_negf]/density_options``
+
+
+            Depending on the value of *method*, different sub args are accepted. 
+
+            .. _`run_op/task_options[tbtrans_negf]/density_options/method`: 
+
+            method:
+                | type: ``str`` (flag key), default: ``Ozaki``
+                | argument path: ``run_op/task_options[tbtrans_negf]/density_options/method`` 
+                | possible choices: |code:run_op/task_options[tbtrans_negf]/density_options[Ozaki]|_
+
+                .. |code:run_op/task_options[tbtrans_negf]/density_options[Ozaki]| replace:: ``Ozaki``
+                .. _`code:run_op/task_options[tbtrans_negf]/density_options[Ozaki]`: `run_op/task_options[tbtrans_negf]/density_options[Ozaki]`_
+
+            .. |flag:run_op/task_options[tbtrans_negf]/density_options/method| replace:: *method*
+            .. _`flag:run_op/task_options[tbtrans_negf]/density_options/method`: `run_op/task_options[tbtrans_negf]/density_options/method`_
+
+
+            .. _`run_op/task_options[tbtrans_negf]/density_options[Ozaki]`: 
+
+            When |flag:run_op/task_options[tbtrans_negf]/density_options/method|_ is set to ``Ozaki``: 
+
+            .. _`run_op/task_options[tbtrans_negf]/density_options[Ozaki]/R`: 
+
+            R: 
+                | type: ``float`` | ``int``, optional, default: ``1000000.0``
+                | argument path: ``run_op/task_options[tbtrans_negf]/density_options[Ozaki]/R``
+
+            .. _`run_op/task_options[tbtrans_negf]/density_options[Ozaki]/M_cut`: 
+
+            M_cut: 
+                | type: ``int``, optional, default: ``30``
+                | argument path: ``run_op/task_options[tbtrans_negf]/density_options[Ozaki]/M_cut``
+
+            .. _`run_op/task_options[tbtrans_negf]/density_options[Ozaki]/n_gauss`: 
+
+            n_gauss: 
+                | type: ``int``, optional, default: ``10``
+                | argument path: ``run_op/task_options[tbtrans_negf]/density_options[Ozaki]/n_gauss``
+
+        .. _`run_op/task_options[tbtrans_negf]/eta_lead`: 
+
+        eta_lead: 
+            | type: ``float`` | ``int``, optional, default: ``1e-05``
+            | argument path: ``run_op/task_options[tbtrans_negf]/eta_lead``
+
+        .. _`run_op/task_options[tbtrans_negf]/eta_device`: 
+
+        eta_device: 
+            | type: ``float`` | ``int``, optional, default: ``0.0``
+            | argument path: ``run_op/task_options[tbtrans_negf]/eta_device``
+
+        .. _`run_op/task_options[tbtrans_negf]/out_dos`: 
+
+        out_dos: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/out_dos``
+
+        .. _`run_op/task_options[tbtrans_negf]/out_tc`: 
+
+        out_tc: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/out_tc``
+
+        .. _`run_op/task_options[tbtrans_negf]/out_density`: 
+
+        out_density: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/out_density``
+
+        .. _`run_op/task_options[tbtrans_negf]/out_potential`: 
+
+        out_potential: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/out_potential``
+
+        .. _`run_op/task_options[tbtrans_negf]/out_current`: 
+
+        out_current: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/out_current``
+
+        .. _`run_op/task_options[tbtrans_negf]/out_current_nscf`: 
+
+        out_current_nscf: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/out_current_nscf``
+
+        .. _`run_op/task_options[tbtrans_negf]/out_ldos`: 
+
+        out_ldos: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/out_ldos``
+
+        .. _`run_op/task_options[tbtrans_negf]/out_lcurrent`: 
+
+        out_lcurrent: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``run_op/task_options[tbtrans_negf]/out_lcurrent``
+
+
+        .. _`run_op/task_options[write_block]`: 
+
+        When |flag:run_op/task_options/task|_ is set to ``write_block``: 
+
+
+    .. _`run_op/structure`: 
+
+    structure: 
+        | type: ``str`` | ``NoneType``, optional, default: ``None``
+        | argument path: ``run_op/structure``
+
+        the structure to run the task
+
+    .. _`run_op/use_gui`: 
+
+    use_gui: 
+        | type: ``bool``, optional, default: ``False``
+        | argument path: ``run_op/use_gui``
+
+        To use the GUI or not
+
+    .. _`run_op/device`: 
+
+    device: 
+        | type: ``str`` | ``NoneType``, optional, default: ``None``
+        | argument path: ``run_op/device``
+
+        The device to run the calculation, choose among `cpu` and `cuda[:int]`, Default: None. default None means to use the device seeting in the model ckpt file.
+
+    .. _`run_op/dtype`: 
+
+    dtype: 
+        | type: ``str`` | ``NoneType``, optional, default: ``None``
+        | argument path: ``run_op/dtype``
+
+        The digital number's precison, choose among: 
+                            Default: None,
+                                - `float32`: indicating torch.float32
+                                - `float64`: indicating torch.float64
+                            default None means to use the device seeting in the model ckpt file.
+                
+
+    .. _`run_op/AtomicData_options`: 
+
+    AtomicData_options: 
+        | type: ``dict``
+        | argument path: ``run_op/AtomicData_options``
+
+        .. _`run_op/AtomicData_options/r_max`: 
+
+        r_max: 
+            | type: ``dict`` | ``float`` | ``int``
+            | argument path: ``run_op/AtomicData_options/r_max``
+
+            the cutoff value for bond considering in TB model.
+
+        .. _`run_op/AtomicData_options/er_max`: 
+
+        er_max: 
+            | type: ``dict`` | ``float`` | ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/AtomicData_options/er_max``
+
+            The cutoff value for environment for each site for env correction model. should set for nnsk+env correction model.
+
+        .. _`run_op/AtomicData_options/oer_max`: 
+
+        oer_max: 
+            | type: ``dict`` | ``float`` | ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``run_op/AtomicData_options/oer_max``
+
+            The cutoff value for onsite environment for nnsk model, for now only need to set in strain and NRL mode.
+
+        .. _`run_op/AtomicData_options/pbc`: 
+
+        pbc: 
+            | type: ``bool``
+            | argument path: ``run_op/AtomicData_options/pbc``
+
+            The periodic condition for the structure, can bool or list of bool to specific x,y,z direction.
+
diff --git a/docs/input_params/set_info.rst b/docs/input_params/set_info.rst
new file mode 100644
index 0000000..386737f
--- /dev/null
+++ b/docs/input_params/set_info.rst
@@ -0,0 +1,82 @@
+========================================
+Set Info
+========================================
+.. _`setinfo`: 
+
+setinfo: 
+    | type: ``dict``
+    | argument path: ``setinfo``
+
+    .. _`setinfo/nframes`: 
+
+    nframes: 
+        | type: ``int``
+        | argument path: ``setinfo/nframes``
+
+        Number of frames in this trajectory.
+
+    .. _`setinfo/natoms`: 
+
+    natoms: 
+        | type: ``int``, optional, default: ``-1``
+        | argument path: ``setinfo/natoms``
+
+        Number of atoms in each frame.
+
+    .. _`setinfo/pos_type`: 
+
+    pos_type: 
+        | type: ``str``
+        | argument path: ``setinfo/pos_type``
+
+        Type of atomic position input. Can be frac / cart / ase.
+
+    .. _`setinfo/pbc`: 
+
+    pbc: 
+        | type: ``list`` | ``bool``
+        | argument path: ``setinfo/pbc``
+
+        The periodic condition for the structure, can bool or list of bool to specific x,y,z direction.
+
+    .. _`setinfo/bandinfo`: 
+
+    bandinfo: 
+        | type: ``dict``, optional
+        | argument path: ``setinfo/bandinfo``
+
+        .. _`setinfo/bandinfo/band_min`: 
+
+        band_min: 
+            | type: ``int``, optional, default: ``0``
+            | argument path: ``setinfo/bandinfo/band_min``
+
+            the minum band index for the training band window with respected to the correctly selected DFT bands.
+                               `important`: before setting this tag you should make sure you have already  exclude all the irrelevant in your training data.
+                                            This logic for band_min and max is based on the simple fact the total number TB bands > the bands you care.   
+                   
+
+        .. _`setinfo/bandinfo/band_max`: 
+
+        band_max: 
+            | type: ``int`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``setinfo/bandinfo/band_max``
+
+            The maxmum band index for training band window
+
+        .. _`setinfo/bandinfo/emin`: 
+
+        emin: 
+            | type: ``float`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``setinfo/bandinfo/emin``
+
+            the minmum energy window, 0 meand the min value of the band at index band_min
+
+        .. _`setinfo/bandinfo/emax`: 
+
+        emax: 
+            | type: ``float`` | ``NoneType``, optional, default: ``None``
+            | argument path: ``setinfo/bandinfo/emax``
+
+            the max energy window, emax value is respect to the min value of the band at index band_min
+
diff --git a/docs/input_params/train_options.rst b/docs/input_params/train_options.rst
new file mode 100644
index 0000000..c119cce
--- /dev/null
+++ b/docs/input_params/train_options.rst
@@ -0,0 +1,718 @@
+========================================
+Train Options
+========================================
+.. _`train_options`: 
+
+train_options: 
+    | type: ``dict``, optional
+    | argument path: ``train_options``
+
+    Options that defines the training behaviour of DeePTB.
+
+    .. _`train_options/num_epoch`: 
+
+    num_epoch: 
+        | type: ``int``
+        | argument path: ``train_options/num_epoch``
+
+        Total number of training epochs. It is worth noted, if the model is reloaded with `-r` or `--restart` option, epoch which have been trained will counted from the time that the checkpoint is saved.
+
+    .. _`train_options/batch_size`: 
+
+    batch_size: 
+        | type: ``int``, optional, default: ``1``
+        | argument path: ``train_options/batch_size``
+
+        The batch size used in training, Default: 1
+
+    .. _`train_options/ref_batch_size`: 
+
+    ref_batch_size: 
+        | type: ``int``, optional, default: ``1``
+        | argument path: ``train_options/ref_batch_size``
+
+        The batch size used in reference data, Default: 1
+
+    .. _`train_options/val_batch_size`: 
+
+    val_batch_size: 
+        | type: ``int``, optional, default: ``1``
+        | argument path: ``train_options/val_batch_size``
+
+        The batch size used in validation data, Default: 1
+
+    .. _`train_options/optimizer`: 
+
+    optimizer: 
+        | type: ``dict``, optional, default: ``{}``
+        | argument path: ``train_options/optimizer``
+
+                The optimizer setting for selecting the gradient optimizer of model training. Optimizer supported includes `Adam`, `SGD` and `LBFGS` 
+
+                For more information about these optmization algorithm, we refer to:
+
+                - `Adam`: [Adam: A Method for Stochastic Optimization.](https://arxiv.org/abs/1412.6980)
+
+                - `SGD`: [Stochastic Gradient Descent.](https://pytorch.org/docs/stable/generated/torch.optim.SGD.html)
+
+                - `LBFGS`: [On the limited memory BFGS method for large scale optimization.](http://users.iems.northwestern.edu/~nocedal/PDFfiles/limited-memory.pdf) 
+
+    
+
+
+        Depending on the value of *type*, different sub args are accepted. 
+
+        .. _`train_options/optimizer/type`: 
+
+        type:
+            | type: ``str`` (flag key), default: ``Adam``
+            | argument path: ``train_options/optimizer/type`` 
+            | possible choices: |code:train_options/optimizer[Adam]|_, |code:train_options/optimizer[SGD]|_
+
+            select type of optimizer, support type includes: `Adam`, `SGD` and `LBFGS`. Default: `Adam`
+
+            .. |code:train_options/optimizer[Adam]| replace:: ``Adam``
+            .. _`code:train_options/optimizer[Adam]`: `train_options/optimizer[Adam]`_
+            .. |code:train_options/optimizer[SGD]| replace:: ``SGD``
+            .. _`code:train_options/optimizer[SGD]`: `train_options/optimizer[SGD]`_
+
+        .. |flag:train_options/optimizer/type| replace:: *type*
+        .. _`flag:train_options/optimizer/type`: `train_options/optimizer/type`_
+
+
+        .. _`train_options/optimizer[Adam]`: 
+
+        When |flag:train_options/optimizer/type|_ is set to ``Adam``: 
+
+        .. _`train_options/optimizer[Adam]/lr`: 
+
+        lr: 
+            | type: ``float``, optional, default: ``0.001``
+            | argument path: ``train_options/optimizer[Adam]/lr``
+
+            learning rate. Default: 1e-3
+
+        .. _`train_options/optimizer[Adam]/betas`: 
+
+        betas: 
+            | type: ``list``, optional, default: ``[0.9, 0.999]``
+            | argument path: ``train_options/optimizer[Adam]/betas``
+
+            coefficients used for computing running averages of gradient and its square Default: (0.9, 0.999)
+
+        .. _`train_options/optimizer[Adam]/eps`: 
+
+        eps: 
+            | type: ``float``, optional, default: ``1e-08``
+            | argument path: ``train_options/optimizer[Adam]/eps``
+
+            term added to the denominator to improve numerical stability, Default: 1e-8
+
+        .. _`train_options/optimizer[Adam]/weight_decay`: 
+
+        weight_decay: 
+            | type: ``float``, optional, default: ``0``
+            | argument path: ``train_options/optimizer[Adam]/weight_decay``
+
+            weight decay (L2 penalty), Default: 0
+
+        .. _`train_options/optimizer[Adam]/amsgrad`: 
+
+        amsgrad: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``train_options/optimizer[Adam]/amsgrad``
+
+            whether to use the AMSGrad variant of this algorithm from the paper On the [Convergence of Adam and Beyond](https://openreview.net/forum?id=ryQu7f-RZ) ,Default: False
+
+
+        .. _`train_options/optimizer[SGD]`: 
+
+        When |flag:train_options/optimizer/type|_ is set to ``SGD``: 
+
+        .. _`train_options/optimizer[SGD]/lr`: 
+
+        lr: 
+            | type: ``float``, optional, default: ``0.001``
+            | argument path: ``train_options/optimizer[SGD]/lr``
+
+            learning rate. Default: 1e-3
+
+        .. _`train_options/optimizer[SGD]/momentum`: 
+
+        momentum: 
+            | type: ``float``, optional, default: ``0.0``
+            | argument path: ``train_options/optimizer[SGD]/momentum``
+
+            momentum factor Default: 0
+
+        .. _`train_options/optimizer[SGD]/weight_decay`: 
+
+        weight_decay: 
+            | type: ``float``, optional, default: ``0.0``
+            | argument path: ``train_options/optimizer[SGD]/weight_decay``
+
+            weight decay (L2 penalty), Default: 0
+
+        .. _`train_options/optimizer[SGD]/dampening`: 
+
+        dampening: 
+            | type: ``float``, optional, default: ``0.0``
+            | argument path: ``train_options/optimizer[SGD]/dampening``
+
+            dampening for momentum, Default: 0
+
+        .. _`train_options/optimizer[SGD]/nesterov`: 
+
+        nesterov: 
+            | type: ``bool``, optional, default: ``False``
+            | argument path: ``train_options/optimizer[SGD]/nesterov``
+
+            enables Nesterov momentum, Default: False
+
+    .. _`train_options/lr_scheduler`: 
+
+    lr_scheduler: 
+        | type: ``dict``, optional, default: ``{}``
+        | argument path: ``train_options/lr_scheduler``
+
+        The learning rate scheduler tools settings, the lr scheduler is used to scales down the learning rate during the training process. Proper setting can make the training more stable and efficient. The supported lr schedular includes: `Exponential Decaying (exp)`, `Linear multiplication (linear)`
+
+
+        Depending on the value of *type*, different sub args are accepted. 
+
+        .. _`train_options/lr_scheduler/type`: 
+
+        type:
+            | type: ``str`` (flag key), default: ``exp``
+            | argument path: ``train_options/lr_scheduler/type`` 
+            | possible choices: |code:train_options/lr_scheduler[exp]|_, |code:train_options/lr_scheduler[linear]|_, |code:train_options/lr_scheduler[rop]|_
+
+            select type of lr_scheduler, support type includes `exp`, `linear`
+
+            .. |code:train_options/lr_scheduler[exp]| replace:: ``exp``
+            .. _`code:train_options/lr_scheduler[exp]`: `train_options/lr_scheduler[exp]`_
+            .. |code:train_options/lr_scheduler[linear]| replace:: ``linear``
+            .. _`code:train_options/lr_scheduler[linear]`: `train_options/lr_scheduler[linear]`_
+            .. |code:train_options/lr_scheduler[rop]| replace:: ``rop``
+            .. _`code:train_options/lr_scheduler[rop]`: `train_options/lr_scheduler[rop]`_
+
+        .. |flag:train_options/lr_scheduler/type| replace:: *type*
+        .. _`flag:train_options/lr_scheduler/type`: `train_options/lr_scheduler/type`_
+
+
+        .. _`train_options/lr_scheduler[exp]`: 
+
+        When |flag:train_options/lr_scheduler/type|_ is set to ``exp``: 
+
+        .. _`train_options/lr_scheduler[exp]/gamma`: 
+
+        gamma: 
+            | type: ``float``, optional, default: ``0.999``
+            | argument path: ``train_options/lr_scheduler[exp]/gamma``
+
+            Multiplicative factor of learning rate decay.
+
+
+        .. _`train_options/lr_scheduler[linear]`: 
+
+        When |flag:train_options/lr_scheduler/type|_ is set to ``linear``: 
+
+        .. _`train_options/lr_scheduler[linear]/start_factor`: 
+
+        start_factor: 
+            | type: ``float``, optional, default: ``0.3333333``
+            | argument path: ``train_options/lr_scheduler[linear]/start_factor``
+
+            The number we multiply learning rate in the first epoch.         The multiplication factor changes towards end_factor in the following epochs. Default: 1./3.
+
+        .. _`train_options/lr_scheduler[linear]/end_factor`: 
+
+        end_factor: 
+            | type: ``float``, optional, default: ``0.3333333``
+            | argument path: ``train_options/lr_scheduler[linear]/end_factor``
+
+            The number we multiply learning rate in the first epoch.     The multiplication factor changes towards end_factor in the following epochs. Default: 1./3.
+
+        .. _`train_options/lr_scheduler[linear]/total_iters`: 
+
+        total_iters: 
+            | type: ``int``, optional, default: ``5``
+            | argument path: ``train_options/lr_scheduler[linear]/total_iters``
+
+            The number of iterations that multiplicative factor reaches to 1. Default: 5.
+
+
+        .. _`train_options/lr_scheduler[rop]`: 
+
+        When |flag:train_options/lr_scheduler/type|_ is set to ``rop``: 
+
+        rop: reduce on plateau
+
+        .. _`train_options/lr_scheduler[rop]/mode`: 
+
+        mode: 
+            | type: ``str``, optional, default: ``min``
+            | argument path: ``train_options/lr_scheduler[rop]/mode``
+
+            One of min, max. In min mode, lr will be reduced when the quantity monitored has stopped decreasing;         in max mode it will be reduced when the quantity monitored has stopped increasing. Default: 'min'.
+
+        .. _`train_options/lr_scheduler[rop]/factor`: 
+
+        factor: 
+            | type: ``float``, optional, default: ``0.1``
+            | argument path: ``train_options/lr_scheduler[rop]/factor``
+
+            Factor by which the learning rate will be reduced. new_lr = lr * factor. Default: 0.1.
+
+        .. _`train_options/lr_scheduler[rop]/patience`: 
+
+        patience: 
+            | type: ``int``, optional, default: ``10``
+            | argument path: ``train_options/lr_scheduler[rop]/patience``
+
+            Number of epochs with no improvement after which learning rate will be reduced. For example,         if patience = 2, then we will ignore the first 2 epochs with no improvement,         and will only decrease the LR after the 3rd epoch if the loss still hasn't improved then. Default: 10.
+
+        .. _`train_options/lr_scheduler[rop]/threshold`: 
+
+        threshold: 
+            | type: ``float``, optional, default: ``0.0001``
+            | argument path: ``train_options/lr_scheduler[rop]/threshold``
+
+            Threshold for measuring the new optimum, to only focus on significant changes. Default: 1e-4.
+
+        .. _`train_options/lr_scheduler[rop]/threshold_mode`: 
+
+        threshold_mode: 
+            | type: ``str``, optional, default: ``rel``
+            | argument path: ``train_options/lr_scheduler[rop]/threshold_mode``
+
+            One of rel, abs. In rel mode, dynamic_threshold = best * ( 1 + threshold ) in 'max' mode or         best * ( 1 - threshold ) in min mode. In abs mode,         dynamic_threshold = best + threshold in max mode or best - threshold in min mode. Default: 'rel'.
+
+        .. _`train_options/lr_scheduler[rop]/cooldown`: 
+
+        cooldown: 
+            | type: ``int``, optional, default: ``0``
+            | argument path: ``train_options/lr_scheduler[rop]/cooldown``
+
+            Number of epochs to wait before resuming normal operation after lr has been reduced. Default: 0.
+
+        .. _`train_options/lr_scheduler[rop]/min_lr`: 
+
+        min_lr: 
+            | type: ``list`` | ``float``, optional, default: ``0``
+            | argument path: ``train_options/lr_scheduler[rop]/min_lr``
+
+            A scalar or a list of scalars.         A lower bound on the learning rate of all param groups or each group respectively. Default: 0.
+
+        .. _`train_options/lr_scheduler[rop]/eps`: 
+
+        eps: 
+            | type: ``float``, optional, default: ``1e-08``
+            | argument path: ``train_options/lr_scheduler[rop]/eps``
+
+            Minimal decay applied to lr.         If the difference between new and old lr is smaller than eps, the update is ignored. Default: 1e-8.
+
+    .. _`train_options/save_freq`: 
+
+    save_freq: 
+        | type: ``int``, optional, default: ``10``
+        | argument path: ``train_options/save_freq``
+
+        Frequency, or every how many iteration to saved the current model into checkpoints, The name of checkpoint is formulated as `latest|best_dptb|nnsk_b<bond_cutoff>_c<sk_cutoff>_w<sk_decay_w>`. Default: `10`
+
+    .. _`train_options/validation_freq`: 
+
+    validation_freq: 
+        | type: ``int``, optional, default: ``10``
+        | argument path: ``train_options/validation_freq``
+
+        Frequency or every how many iteration to do model validation on validation datasets. Default: `10`
+
+    .. _`train_options/display_freq`: 
+
+    display_freq: 
+        | type: ``int``, optional, default: ``1``
+        | argument path: ``train_options/display_freq``
+
+        Frequency, or every how many iteration to display the training log to screem. Default: `1`
+
+    .. _`train_options/max_ckpt`: 
+
+    max_ckpt: 
+        | type: ``int``, optional, default: ``4``
+        | argument path: ``train_options/max_ckpt``
+
+        The maximum number of saved checkpoints, Default: 4
+
+    .. _`train_options/loss_options`: 
+
+    loss_options: 
+        | type: ``dict``
+        | argument path: ``train_options/loss_options``
+
+        .. _`train_options/loss_options/train`: 
+
+        train: 
+            | type: ``dict``
+            | argument path: ``train_options/loss_options/train``
+
+            Loss options for training.
+
+
+            Depending on the value of *method*, different sub args are accepted. 
+
+            .. _`train_options/loss_options/train/method`: 
+
+            method:
+                | type: ``str`` (flag key)
+                | argument path: ``train_options/loss_options/train/method`` 
+                | possible choices: |code:train_options/loss_options/train[eigvals]|_, |code:train_options/loss_options/train[skints]|_, |code:train_options/loss_options/train[hamil_abs]|_, |code:train_options/loss_options/train[hamil_blas]|_
+
+                The loss function type, defined by a string like `<fitting target>_<loss type>`, Default: `eigs_l2dsf`. supported loss functions includes:
+
+                                    - `eigvals`: The mse loss predicted and labeled eigenvalues and Delta eigenvalues between different k.
+                                    - `hamil`: 
+                                    - `hamil_abs`:
+                                    - `hamil_blas`:
+                
+
+                .. |code:train_options/loss_options/train[eigvals]| replace:: ``eigvals``
+                .. _`code:train_options/loss_options/train[eigvals]`: `train_options/loss_options/train[eigvals]`_
+                .. |code:train_options/loss_options/train[skints]| replace:: ``skints``
+                .. _`code:train_options/loss_options/train[skints]`: `train_options/loss_options/train[skints]`_
+                .. |code:train_options/loss_options/train[hamil_abs]| replace:: ``hamil_abs``
+                .. _`code:train_options/loss_options/train[hamil_abs]`: `train_options/loss_options/train[hamil_abs]`_
+                .. |code:train_options/loss_options/train[hamil_blas]| replace:: ``hamil_blas``
+                .. _`code:train_options/loss_options/train[hamil_blas]`: `train_options/loss_options/train[hamil_blas]`_
+
+            .. |flag:train_options/loss_options/train/method| replace:: *method*
+            .. _`flag:train_options/loss_options/train/method`: `train_options/loss_options/train/method`_
+
+
+            .. _`train_options/loss_options/train[eigvals]`: 
+
+            When |flag:train_options/loss_options/train/method|_ is set to ``eigvals``: 
+
+            .. _`train_options/loss_options/train[eigvals]/diff_on`: 
+
+            diff_on: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/train[eigvals]/diff_on``
+
+                Whether to use random differences in loss function. Default: False
+
+            .. _`train_options/loss_options/train[eigvals]/eout_weight`: 
+
+            eout_weight: 
+                | type: ``float``, optional, default: ``0.01``
+                | argument path: ``train_options/loss_options/train[eigvals]/eout_weight``
+
+                The weight of eigenvalue out of range. Default: 0.01
+
+            .. _`train_options/loss_options/train[eigvals]/diff_weight`: 
+
+            diff_weight: 
+                | type: ``float``, optional, default: ``0.01``
+                | argument path: ``train_options/loss_options/train[eigvals]/diff_weight``
+
+                The weight of eigenvalue difference. Default: 0.01
+
+            .. _`train_options/loss_options/train[eigvals]/diff_valence`: 
+
+            diff_valence: 
+                | type: ``dict`` | ``NoneType``, optional, default: ``None``
+                | argument path: ``train_options/loss_options/train[eigvals]/diff_valence``
+
+                set the difference of the number of valence electrons in DFT and TB. eg {'A':6,'B':7}, Default: None, which means no difference
+
+            .. _`train_options/loss_options/train[eigvals]/spin_deg`: 
+
+            spin_deg: 
+                | type: ``int``, optional, default: ``2``
+                | argument path: ``train_options/loss_options/train[eigvals]/spin_deg``
+
+                The spin degeneracy of band structure. Default: 2
+
+
+            .. _`train_options/loss_options/train[skints]`: 
+
+            When |flag:train_options/loss_options/train/method|_ is set to ``skints``: 
+
+            .. _`train_options/loss_options/train[skints]/skdata`: 
+
+            skdata: 
+                | type: ``str``
+                | argument path: ``train_options/loss_options/train[skints]/skdata``
+
+                The path to the skfile or sk database.
+
+
+            .. _`train_options/loss_options/train[hamil_abs]`: 
+
+            When |flag:train_options/loss_options/train/method|_ is set to ``hamil_abs``: 
+
+            .. _`train_options/loss_options/train[hamil_abs]/onsite_shift`: 
+
+            onsite_shift: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/train[hamil_abs]/onsite_shift``
+
+                Whether to use onsite shift in loss function. Default: False
+
+
+            .. _`train_options/loss_options/train[hamil_blas]`: 
+
+            When |flag:train_options/loss_options/train/method|_ is set to ``hamil_blas``: 
+
+            .. _`train_options/loss_options/train[hamil_blas]/onsite_shift`: 
+
+            onsite_shift: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/train[hamil_blas]/onsite_shift``
+
+                Whether to use onsite shift in loss function. Default: False
+
+        .. _`train_options/loss_options/validation`: 
+
+        validation: 
+            | type: ``dict``, optional
+            | argument path: ``train_options/loss_options/validation``
+
+            Loss options for validation.
+
+
+            Depending on the value of *method*, different sub args are accepted. 
+
+            .. _`train_options/loss_options/validation/method`: 
+
+            method:
+                | type: ``str`` (flag key)
+                | argument path: ``train_options/loss_options/validation/method`` 
+                | possible choices: |code:train_options/loss_options/validation[eigvals]|_, |code:train_options/loss_options/validation[skints]|_, |code:train_options/loss_options/validation[hamil_abs]|_, |code:train_options/loss_options/validation[hamil_blas]|_
+
+                The loss function type, defined by a string like `<fitting target>_<loss type>`, Default: `eigs_l2dsf`. supported loss functions includes:
+
+                                    - `eigvals`: The mse loss predicted and labeled eigenvalues and Delta eigenvalues between different k.
+                                    - `hamil`: 
+                                    - `hamil_abs`:
+                                    - `hamil_blas`:
+                
+
+                .. |code:train_options/loss_options/validation[eigvals]| replace:: ``eigvals``
+                .. _`code:train_options/loss_options/validation[eigvals]`: `train_options/loss_options/validation[eigvals]`_
+                .. |code:train_options/loss_options/validation[skints]| replace:: ``skints``
+                .. _`code:train_options/loss_options/validation[skints]`: `train_options/loss_options/validation[skints]`_
+                .. |code:train_options/loss_options/validation[hamil_abs]| replace:: ``hamil_abs``
+                .. _`code:train_options/loss_options/validation[hamil_abs]`: `train_options/loss_options/validation[hamil_abs]`_
+                .. |code:train_options/loss_options/validation[hamil_blas]| replace:: ``hamil_blas``
+                .. _`code:train_options/loss_options/validation[hamil_blas]`: `train_options/loss_options/validation[hamil_blas]`_
+
+            .. |flag:train_options/loss_options/validation/method| replace:: *method*
+            .. _`flag:train_options/loss_options/validation/method`: `train_options/loss_options/validation/method`_
+
+
+            .. _`train_options/loss_options/validation[eigvals]`: 
+
+            When |flag:train_options/loss_options/validation/method|_ is set to ``eigvals``: 
+
+            .. _`train_options/loss_options/validation[eigvals]/diff_on`: 
+
+            diff_on: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/validation[eigvals]/diff_on``
+
+                Whether to use random differences in loss function. Default: False
+
+            .. _`train_options/loss_options/validation[eigvals]/eout_weight`: 
+
+            eout_weight: 
+                | type: ``float``, optional, default: ``0.01``
+                | argument path: ``train_options/loss_options/validation[eigvals]/eout_weight``
+
+                The weight of eigenvalue out of range. Default: 0.01
+
+            .. _`train_options/loss_options/validation[eigvals]/diff_weight`: 
+
+            diff_weight: 
+                | type: ``float``, optional, default: ``0.01``
+                | argument path: ``train_options/loss_options/validation[eigvals]/diff_weight``
+
+                The weight of eigenvalue difference. Default: 0.01
+
+            .. _`train_options/loss_options/validation[eigvals]/diff_valence`: 
+
+            diff_valence: 
+                | type: ``dict`` | ``NoneType``, optional, default: ``None``
+                | argument path: ``train_options/loss_options/validation[eigvals]/diff_valence``
+
+                set the difference of the number of valence electrons in DFT and TB. eg {'A':6,'B':7}, Default: None, which means no difference
+
+            .. _`train_options/loss_options/validation[eigvals]/spin_deg`: 
+
+            spin_deg: 
+                | type: ``int``, optional, default: ``2``
+                | argument path: ``train_options/loss_options/validation[eigvals]/spin_deg``
+
+                The spin degeneracy of band structure. Default: 2
+
+
+            .. _`train_options/loss_options/validation[skints]`: 
+
+            When |flag:train_options/loss_options/validation/method|_ is set to ``skints``: 
+
+            .. _`train_options/loss_options/validation[skints]/skdata`: 
+
+            skdata: 
+                | type: ``str``
+                | argument path: ``train_options/loss_options/validation[skints]/skdata``
+
+                The path to the skfile or sk database.
+
+
+            .. _`train_options/loss_options/validation[hamil_abs]`: 
+
+            When |flag:train_options/loss_options/validation/method|_ is set to ``hamil_abs``: 
+
+            .. _`train_options/loss_options/validation[hamil_abs]/onsite_shift`: 
+
+            onsite_shift: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/validation[hamil_abs]/onsite_shift``
+
+                Whether to use onsite shift in loss function. Default: False
+
+
+            .. _`train_options/loss_options/validation[hamil_blas]`: 
+
+            When |flag:train_options/loss_options/validation/method|_ is set to ``hamil_blas``: 
+
+            .. _`train_options/loss_options/validation[hamil_blas]/onsite_shift`: 
+
+            onsite_shift: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/validation[hamil_blas]/onsite_shift``
+
+                Whether to use onsite shift in loss function. Default: False
+
+        .. _`train_options/loss_options/reference`: 
+
+        reference: 
+            | type: ``dict``, optional
+            | argument path: ``train_options/loss_options/reference``
+
+            Loss options for reference data in training.
+
+
+            Depending on the value of *method*, different sub args are accepted. 
+
+            .. _`train_options/loss_options/reference/method`: 
+
+            method:
+                | type: ``str`` (flag key)
+                | argument path: ``train_options/loss_options/reference/method`` 
+                | possible choices: |code:train_options/loss_options/reference[eigvals]|_, |code:train_options/loss_options/reference[skints]|_, |code:train_options/loss_options/reference[hamil_abs]|_, |code:train_options/loss_options/reference[hamil_blas]|_
+
+                The loss function type, defined by a string like `<fitting target>_<loss type>`, Default: `eigs_l2dsf`. supported loss functions includes:
+
+                                    - `eigvals`: The mse loss predicted and labeled eigenvalues and Delta eigenvalues between different k.
+                                    - `hamil`: 
+                                    - `hamil_abs`:
+                                    - `hamil_blas`:
+                
+
+                .. |code:train_options/loss_options/reference[eigvals]| replace:: ``eigvals``
+                .. _`code:train_options/loss_options/reference[eigvals]`: `train_options/loss_options/reference[eigvals]`_
+                .. |code:train_options/loss_options/reference[skints]| replace:: ``skints``
+                .. _`code:train_options/loss_options/reference[skints]`: `train_options/loss_options/reference[skints]`_
+                .. |code:train_options/loss_options/reference[hamil_abs]| replace:: ``hamil_abs``
+                .. _`code:train_options/loss_options/reference[hamil_abs]`: `train_options/loss_options/reference[hamil_abs]`_
+                .. |code:train_options/loss_options/reference[hamil_blas]| replace:: ``hamil_blas``
+                .. _`code:train_options/loss_options/reference[hamil_blas]`: `train_options/loss_options/reference[hamil_blas]`_
+
+            .. |flag:train_options/loss_options/reference/method| replace:: *method*
+            .. _`flag:train_options/loss_options/reference/method`: `train_options/loss_options/reference/method`_
+
+
+            .. _`train_options/loss_options/reference[eigvals]`: 
+
+            When |flag:train_options/loss_options/reference/method|_ is set to ``eigvals``: 
+
+            .. _`train_options/loss_options/reference[eigvals]/diff_on`: 
+
+            diff_on: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/reference[eigvals]/diff_on``
+
+                Whether to use random differences in loss function. Default: False
+
+            .. _`train_options/loss_options/reference[eigvals]/eout_weight`: 
+
+            eout_weight: 
+                | type: ``float``, optional, default: ``0.01``
+                | argument path: ``train_options/loss_options/reference[eigvals]/eout_weight``
+
+                The weight of eigenvalue out of range. Default: 0.01
+
+            .. _`train_options/loss_options/reference[eigvals]/diff_weight`: 
+
+            diff_weight: 
+                | type: ``float``, optional, default: ``0.01``
+                | argument path: ``train_options/loss_options/reference[eigvals]/diff_weight``
+
+                The weight of eigenvalue difference. Default: 0.01
+
+            .. _`train_options/loss_options/reference[eigvals]/diff_valence`: 
+
+            diff_valence: 
+                | type: ``dict`` | ``NoneType``, optional, default: ``None``
+                | argument path: ``train_options/loss_options/reference[eigvals]/diff_valence``
+
+                set the difference of the number of valence electrons in DFT and TB. eg {'A':6,'B':7}, Default: None, which means no difference
+
+            .. _`train_options/loss_options/reference[eigvals]/spin_deg`: 
+
+            spin_deg: 
+                | type: ``int``, optional, default: ``2``
+                | argument path: ``train_options/loss_options/reference[eigvals]/spin_deg``
+
+                The spin degeneracy of band structure. Default: 2
+
+
+            .. _`train_options/loss_options/reference[skints]`: 
+
+            When |flag:train_options/loss_options/reference/method|_ is set to ``skints``: 
+
+            .. _`train_options/loss_options/reference[skints]/skdata`: 
+
+            skdata: 
+                | type: ``str``
+                | argument path: ``train_options/loss_options/reference[skints]/skdata``
+
+                The path to the skfile or sk database.
+
+
+            .. _`train_options/loss_options/reference[hamil_abs]`: 
+
+            When |flag:train_options/loss_options/reference/method|_ is set to ``hamil_abs``: 
+
+            .. _`train_options/loss_options/reference[hamil_abs]/onsite_shift`: 
+
+            onsite_shift: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/reference[hamil_abs]/onsite_shift``
+
+                Whether to use onsite shift in loss function. Default: False
+
+
+            .. _`train_options/loss_options/reference[hamil_blas]`: 
+
+            When |flag:train_options/loss_options/reference/method|_ is set to ``hamil_blas``: 
+
+            .. _`train_options/loss_options/reference[hamil_blas]/onsite_shift`: 
+
+            onsite_shift: 
+                | type: ``bool``, optional, default: ``False``
+                | argument path: ``train_options/loss_options/reference[hamil_blas]/onsite_shift``
+
+                Whether to use onsite shift in loss function. Default: False
+
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000..05dac10
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,7 @@
+urllib3 == 1.26.15
+# myst_parser[linkify]  # 注释掉，因为myst-nb已包含myst_parser功能
+linkify-it-py
+myst-nb[linkify]
+sphinx_rtd_theme
+sphinx-book-theme
+jupyter  # 添加jupyter支持

From 767400c31d4c6d4520e6f7e844a69c31690d9e4f Mon Sep 17 00:00:00 2001
From: Qiangqiang Gu <98570179+QG-phy@users.noreply.github.com>
Date: Thu, 4 Sep 2025 02:19:06 +0800
Subject: [PATCH 116/152] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E5=B7=A5=E4=BD=9C?=
 =?UTF-8?q?=E6=B5=81=E5=90=8D=E7=A7=B0=E5=B9=B6=E6=B7=BB=E5=8A=A0=20DeepTB?=
 =?UTF-8?q?=20=E6=A0=87=E5=BF=97=E6=96=87=E4=BB=B6=20(#27)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .github/workflows/add_pages_doc.yml |   9 ++--
 .github/workflows/devcontainer.yml  |   2 +-
 .github/workflows/image.yml         |   2 +-
 docs/deeptb-logo.png                | Bin 0 -> 266740 bytes
 docs/deeptb-logo.svg                |  70 ++++++++++++++++++++++++++++
 5 files changed, 77 insertions(+), 6 deletions(-)
 create mode 100644 docs/deeptb-logo.png
 create mode 100644 docs/deeptb-logo.svg

diff --git a/.github/workflows/add_pages_doc.yml b/.github/workflows/add_pages_doc.yml
index 578ef29..60a2268 100644
--- a/.github/workflows/add_pages_doc.yml
+++ b/.github/workflows/add_pages_doc.yml
@@ -1,11 +1,12 @@
-name: Build and Deploy Docs
+name: Publishing Docs
 
 on:
   push:
     branches: [ main ]  # 或者您的默认分支名
-  pull_request:
-    branches: [ main ]
-
+    paths:
+      - '.github/**'
+      - 'docs/**'
+      - 'examples/**'
 jobs:
   build:
     if: github.repository == 'DeePTB-Lab/dpnegf'
diff --git a/.github/workflows/devcontainer.yml b/.github/workflows/devcontainer.yml
index 66dc712..83e53b1 100644
--- a/.github/workflows/devcontainer.yml
+++ b/.github/workflows/devcontainer.yml
@@ -1,4 +1,4 @@
-name: Container
+name: Build Container
 
 on:
   push:
diff --git a/.github/workflows/image.yml b/.github/workflows/image.yml
index 1a76d4c..330fc8a 100644
--- a/.github/workflows/image.yml
+++ b/.github/workflows/image.yml
@@ -1,4 +1,4 @@
-name: Build and Push Release Image
+name: Build Release Image
 
 on:
   # 允许在 GitHub UI 上手动触发
diff --git a/docs/deeptb-logo.png b/docs/deeptb-logo.png
new file mode 100644
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@@ -0,0 +1,70 @@
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+<svg xmlns="http://www.w3.org/2000/svg" version="1.1" viewBox="0 0 301.74 89.29">
+  <defs>
+    <style>
+      .cls-1 {
+        font-family: Arial-BoldItalicMT, Arial;
+        font-weight: 700;
+      }
+
+      .cls-1, .cls-2, .cls-3, .cls-4 {
+        fill: #fff;
+      }
+
+      .cls-1, .cls-3 {
+        font-size: 72px;
+        font-style: italic;
+      }
+
+      .cls-5 {
+        stroke-width: 1.5px;
+      }
+
+      .cls-5, .cls-2 {
+        stroke: #fff;
+        stroke-miterlimit: 10;
+      }
+
+      .cls-5, .cls-6 {
+        fill: #26115a;
+      }
+
+      .cls-2 {
+        stroke-width: 2px;
+      }
+
+      .cls-3 {
+        font-family: Arial-ItalicMT, Arial;
+      }
+
+      .cls-7 {
+        fill: #440cdc;
+      }
+    </style>
+  </defs>
+  <!-- Generator: Adobe Illustrator 28.6.0, SVG Export Plug-In . SVG Version: 1.2.0 Build 709)  -->
+  <g>
+    <g id="_图层_1" data-name="图层_1">
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+      <g>
+        <text class="cls-3" transform="translate(7.43 72.45)"><tspan x="0" y="0" xml:space="preserve">D    P</tspan></text>
+        <line class="cls-2" x1="31.52" y1="34.29" x2="41.58" y2="46.43"/>
+        <line class="cls-2" x1="26.83" y1="57.03" x2="42.07" y2="47.01"/>
+        <line class="cls-2" x1="26.83" y1="57.03" x2="32.01" y2="34.87"/>
+        <text class="cls-3" transform="translate(57.95 68.19)"><tspan x="0" y="0">ee</tspan></text>
+        <rect class="cls-4" x="51.77" y="46.23" width="103.95" height="4.89"/>
+        <circle class="cls-5" cx="26.83" cy="57.03" r="3.69"/>
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+      <text class="cls-1" transform="translate(192.09 65.95)"><tspan x="0" y="0">TB</tspan></text>
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From 6e3d4fb37644870c7e06c7c3227cbed4fc677829 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 5 Sep 2025 10:44:29 +0800
Subject: [PATCH 117/152] update doc index.rst

---
 docs/index.rst | 19 +++++--------------
 1 file changed, 5 insertions(+), 14 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index 55fdf69..8a2f3dd 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -6,25 +6,16 @@
 DPNEGF Documentation
 =================================================
 
-**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green’s Function (**NEGF**) method, establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy. 
+**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green's Function (**NEGF**) method, 
+establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy. 
 
 --------------
 Key Features:
 --------------
 
-DeePTB contains two main components: 
-
-1. **DeePTB-SK**: deep learning based local environment dependent Slater-Koster TB.
-
-   - Customizable Slater-Koster parameterization with neural network corrections.
-   - Flexible basis and exchange-correlation functional choices.
-   - Handle systems with strong spin-orbit coupling (SOC) effects.
-
-2. **DeePTB-E3**: E3-equivariant neural networks for representing quantum operators.
-
-   - Construct DFT Hamiltonians/density and overlap matrices under full LCAO basis.
-   - Utilize (**S**\ trictly) **L**\ ocalized **E**\ quivariant **M**\ essage-passing (**(S)LEM**) model for high data-efficiency and accuracy.
-   - Employs SO(2) convolution for efficient handling of higher-order orbitals in LCAO basis.
+By using DeePTB-SK or DeePTB-E3—both available within the DeePTB package—DeePTB-NEGF can compute quantum transport 
+properties in open-boundary systems with either environment-corrected Slater-Koster(SK) TB Hamiltonian 
+or linear combination of atomic orbitals (LCAO) Kohn-Sham Hamiltonian.
 
 
 For more details, see our papers:

From 63df22894265c78de5fd11c831b5a59290c2cfa2 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Fri, 5 Sep 2025 10:44:42 +0800
Subject: [PATCH 118/152] update install doc

---
 docs/easy_install.md | 65 ++++++++++----------------------------------
 1 file changed, 14 insertions(+), 51 deletions(-)

diff --git a/docs/easy_install.md b/docs/easy_install.md
index e81382e..8752f99 100644
--- a/docs/easy_install.md
+++ b/docs/easy_install.md
@@ -1,78 +1,41 @@
 # Installation Guide
 
-This guide will help you install DeePTB, a Python package that utilizes deep learning to construct electronic tight-binding Hamiltonians.
+This guide will help you install DPNEGF, a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green's Function (**NEGF**) method.
 
 ## Prerequisites
 
-Before installing DeePTB, ensure you have the following prerequisites:
+Before installing DPNEGF, ensure you have the following prerequisites:
   - Git
   - Python 3.9 to 3.12.
   - Torch 2.0.0 to 2.5.1 ([PyTorch Installation](https://pytorch.org/get-started/locally)).
-  - ifermi (optional, for 3D fermi-surface plotting).
-  - TBPLaS (optional).
+  - [DeePTB](https://github.com/deepmodeling/DeePTB) ≥ 2.1.1
 
 ## Installation Methods
 
-
-
 ### From Source
-  
-Highly recommended to install DeePTB from source to get the latest features and bug fixes.
-1. **Setup Python environment**:
-    
-    Using conda (recommended, python >=3.9, <=3.12 ), e.g.,
-    ```bash
-    conda create -n dptb_venv python=3.10
-    conda activate dptb_venv
-    ```
-    or using venv (make sure python >=3.9,<=3.12)
-    ```bash
-    python -m venv dptb_venv
-    source dptb_venv/bin/activate
-    ```
-2. **Clone DeePTB and  Navigate to the root directory**:
-    ```bash
-    git clone https://github.com/deepmodeling/DeePTB.git
-    cd DeePTB
-    ```
-3. **Install `torch`**:
+We recommend installing **DPNEGF** within a dedicated virtual environment to manage dependencies effectively. Ensure that both DPNEGF and DeePTB are installed in the same environment for compatibility.
+
+1. Clone the repository:
     ```bash
-    pip install "torch>=2.0.0,<=2.5.0"
+    git clone https://github.com/DeePTB-Lab/dpnegf.git
     ```
-4. **Install `torch-scatter`** (two ways):
-    - **Recommended**: Install torch and torch-scatter using the following commands:
-        ```bash
-         python docs/auto_install_torch_scatter.py
-        ```
-    - **Manual**: Install torch and torch-scatter manually:
-        ```bash
-        pip install torch-scatter -f https://data.pyg.org/whl/torch-${version}+${CUDA}.html
-        ```
-        where `${version}` is the version of torch, e.g., 2.5.0, and `${CUDA}` is the CUDA version, e.g., cpu, cu118, cu121, cu124. See [torch_scatter doc](https://github.com/rusty1s/pytorch_scatter) for more details.   
-
-5. **Install DeePTB**:   
+2. Navigate to the root directory and install DPNEGF:
     ```bash
+    cd dpnegf
     pip install .
     ```
-    
-### From PyPi
 
-1. Install PyTorch first by following the instructions on [PyTorch: Get Started](https://pytorch.org/get-started/locally).
-2. Install DeePTB using pip:
-   ```bash
-   pip install dptb
-   ```
 
 ### Additional Tips
 
-- Keep your DeePTB installation up-to-date by pulling the latest changes from the repository and re-installing.
-- If you encounter any issues during installation, consult the [DeePTB documentation](https://deeptb.readthedocs.io/en/latest/) or seek help from the community.
+- Keep your DPNEGF installation up-to-date by pulling the latest changes from the repository and re-installing.
+- If you encounter any issues during installation, consult the [DPNEGF documentation](https://deeptb-lab.github.io/dpnegf/) or seek help from the community.
 
 ## Contributing
 
-We welcome contributions to DeePTB. If you are interested in contributing, please read our [contributing guidelines](https://deeptb.readthedocs.io/en/latest/community/contribution_guide.html).
+We welcome contributions to DeePTB. If you are interested in contributing, please read our [contributing guidelines](https://deeptb-lab.github.io/dpnegf/CONTRIBUTING.html).
 
-## License
+<!-- ## License
 
-DeePTB is open-source software released under the [LGPL-3.0](https://github.com/deepmodeling/DeePTB/blob/main/LICENSE) provided in the repository.
+DPNEGF is open-source software released under the [LGPL-3.0](https://github.com/deepmodeling/DeePTB/blob/main/LICENSE) provided in the repository. -->
 

From 598e9ecd904c7751fd3cc44b3eddd6cd6db08d69 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 8 Sep 2025 11:19:54 +0800
Subject: [PATCH 119/152] add license

---
 docs/easy_install.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/easy_install.md b/docs/easy_install.md
index 8752f99..0b9e7f6 100644
--- a/docs/easy_install.md
+++ b/docs/easy_install.md
@@ -35,7 +35,7 @@ We recommend installing **DPNEGF** within a dedicated virtual environment to man
 
 We welcome contributions to DeePTB. If you are interested in contributing, please read our [contributing guidelines](https://deeptb-lab.github.io/dpnegf/CONTRIBUTING.html).
 
-<!-- ## License
+## License
 
-DPNEGF is open-source software released under the [LGPL-3.0](https://github.com/deepmodeling/DeePTB/blob/main/LICENSE) provided in the repository. -->
+DPNEGF is open-source software released under the [LGPL-3.0](https://github.com/DeePTB-Lab/dpnegf/blob/main/LICENSE) provided in the repository.
 

From da71df72e368f566c92828d878e518961afbe58f Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 8 Sep 2025 11:24:54 +0800
Subject: [PATCH 120/152] add tutorial1

---
 docs/hands_on/tutorial1_c_chain.ipynb | 287 ++++++++++++++++++++++++++
 1 file changed, 287 insertions(+)
 create mode 100644 docs/hands_on/tutorial1_c_chain.ipynb

diff --git a/docs/hands_on/tutorial1_c_chain.ipynb b/docs/hands_on/tutorial1_c_chain.ipynb
new file mode 100644
index 0000000..28c1470
--- /dev/null
+++ b/docs/hands_on/tutorial1_c_chain.ipynb
@@ -0,0 +1,287 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8c5c64a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "\n",
+    "from dpnegf.runner.NEGF import NEGF\n",
+    "from dptb.nn.build import build_model\n",
+    "import json\n",
+    "\n",
+    "from dpnegf.utils.loggers import set_log_handles\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b639b288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "workdir='/root/soft/DPNEGF/examples/base_model/'\n",
+    "os.chdir(f\"{workdir}/structures\")\n",
+    "!tree -L 1 ./"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0f64a6b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO                                      Version Info                                  \n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "\n",
+      "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n"
+     ]
+    }
+   ],
+   "source": [
+    "INPUT_file =  \"./input_files/negf_chain_new.json\" \n",
+    "model =  \"./input_files/nnsk_C_new.json\"\n",
+    "structure =  \"./input_files/chain.vasp\" \n",
+    "output = \"output\"  \n",
+    "\n",
+    "if os.path.exists(output):\n",
+    "    os.system('rm -rf %s' % output)\n",
+    "\n",
+    "\n",
+    "negf_json = json.load(open(INPUT_file))\n",
+    "model_json = json.load(open(model))\n",
+    "\n",
+    "log_path = output+'/log'\n",
+    "log_level = logging.INFO\n",
+    "set_log_handles(log_level, Path(log_path) if log_path else None)\n",
+    "\n",
+    "model = build_model(model,model_options= model_json['model_options'],\n",
+    "                    common_options=model_json['common_options'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "830d67a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    ------ k-point for NEGF -----\n",
+      "DPNEGF INFO    Gamma Center: True\n",
+      "DPNEGF INFO    Time Reversal: True\n",
+      "DPNEGF INFO    k-points Num: 1\n",
+      "DPNEGF INFO    k-points: [[0 0 0]]\n",
+      "DPNEGF INFO    k-points weights: [1.]\n",
+      "DPNEGF INFO    --------------------------------\n",
+      "DPNEGF WARNING AtomicData_options is extracted from input file. This may be not consistent with the model options. Please be careful and check the cutoffs.\n",
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": 2.0\n",
+      "               }\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732052e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
+      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+      "DPNEGF INFO    The coupling width of lead_L is 1.\n",
+      "DPNEGF INFO    The coupling width of lead_R is 1.\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
+      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0\n",
+      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 1.0}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 1.0}\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
+      "DPNEGF INFO    q_cal: 3.999994485346824, total_electrons: 4.0, diff q: 5.514653175886508e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.638588428497314 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
+      "DPNEGF INFO    q_cal: 4.000002782981872, total_electrons: 4.0, diff q: 2.7829818716185173e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.638587474822998 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
+      "DPNEGF INFO    -------------------------------------------------\n",
+      "DPNEGF INFO    Zero bias case detected.\n",
+      "DPNEGF INFO    Fermi level for lead_L: -13.638588428497314\n",
+      "DPNEGF INFO    Fermi level for lead_R: -13.638587474822998\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -13.638588428497314\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -13.638587474822998\n",
+      "DPNEGF INFO    Reference energy E_ref: -13.638588428497314\n",
+      "DPNEGF INFO    =================================================\n",
+      "\n",
+      "DPNEGF INFO    Merging 400 tmp self energy files into output/self_energy/self_energy_leadL.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Merging 400 tmp self energy files into output/self_energy/self_energy_leadR.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -2.000\n",
+      "DPNEGF INFO    computing green's function at e = -1.599\n",
+      "DPNEGF INFO    computing green's function at e = -1.198\n",
+      "DPNEGF INFO    computing green's function at e = -0.797\n",
+      "DPNEGF INFO    computing green's function at e = -0.396\n",
+      "DPNEGF INFO    computing green's function at e = 0.005\n",
+      "DPNEGF INFO    computing green's function at e = 0.406\n",
+      "DPNEGF INFO    computing green's function at e = 0.807\n",
+      "DPNEGF INFO    computing green's function at e = 1.208\n",
+      "DPNEGF INFO    computing green's function at e = 1.609\n",
+      "DPNEGF WARNING The Fermi energy of the left and right leads should be equal in nscf current calculation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "negf = NEGF(\n",
+    "    model=model,\n",
+    "    AtomicData_options=negf_json['AtomicData_options'],\n",
+    "    structure=structure,\n",
+    "    results_path=output,  \n",
+    "    **negf_json['task_options']\n",
+    ")\n",
+    "   \n",
+    "negf.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "db275dee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_16165/1724458665.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  negf_out = torch.load('./output/negf.out.pth')\n"
+     ]
+    }
+   ],
+   "source": [
+    "negf_out = torch.load('./output/negf.out.pth')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7a29b9ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negf_out.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8eb092f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['DOS'][str(negf_out['k'][0])])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('DOS')\n",
+    "plt.title('DOS vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4a2fe762",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('Transmission')\n",
+    "plt.title('Transmission vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dpnegf-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From f7fddece01354ad6d31594c1e31a22238908a367 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 8 Sep 2025 11:33:26 +0800
Subject: [PATCH 121/152] add citation

---
 docs/CITATIONS.md | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/docs/CITATIONS.md b/docs/CITATIONS.md
index cae01d4..37a6445 100644
--- a/docs/CITATIONS.md
+++ b/docs/CITATIONS.md
@@ -2,6 +2,21 @@
 
 The following references are required to be cited when using DeePTB. Specifically:
 
+- **For DPNEGF:**
+  
+    Zou J, Zhouyin Z, Lin D, et al. Deep learning accelerated quantum transport simulations in nanoelectronics: From break junctions to field-effect transistors[J]. arXiv preprint arXiv:2411.08800, 2024.
+    ```latex
+    @article{zou2025deep,
+      title={Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistors}, 
+      author={Jijie Zou and Zhanghao Zhouyin and Dongying Lin and Yike Huang and Linfeng Zhang and Shimin Hou and Qiangqiang Gu},
+      year={2025},
+      eprint={2411.08800},
+      archivePrefix={arXiv},
+      primaryClass={cond-mat.mes-hall},
+      url={https://arxiv.org/abs/2411.08800}}     
+    ```
+
+
 - **For DeePTB-SK:**
   
     Q. Gu, Z. Zhouyin, S. K. Pandey, P. Zhang, L. Zhang, and W. E, Deep Learning Tight-Binding Approach for Large-Scale Electronic Simulations at Finite Temperatures with Ab Initio Accuracy, Nat Commun 15, 6772 (2024).

From 7c2e5e501e2391acb4e288a8acf0e3fb926223b5 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 8 Sep 2025 15:05:28 +0800
Subject: [PATCH 122/152] update citations

---
 docs/CITATIONS.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/CITATIONS.md b/docs/CITATIONS.md
index 37a6445..cd26de6 100644
--- a/docs/CITATIONS.md
+++ b/docs/CITATIONS.md
@@ -1,6 +1,6 @@
 # How to Cite
 
-The following references are required to be cited when using DeePTB. Specifically:
+The following references are required to be cited when using DPNEGF. 
 
 - **For DPNEGF:**
   
@@ -16,6 +16,7 @@ The following references are required to be cited when using DeePTB. Specificall
       url={https://arxiv.org/abs/2411.08800}}     
     ```
 
+DPNEGF is compatible with both modeling strategies available in DeePTB: **DeePTB-SK** and **DeePTB-E3**. Specifically:
 
 - **For DeePTB-SK:**
   

From e376ca592ec45ef4d4703d5180684589a6bf0754 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 8 Sep 2025 15:14:19 +0800
Subject: [PATCH 123/152] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E8=B4=A1=E7=8C=AE?=
 =?UTF-8?q?=E6=8C=87=E5=8D=97=E4=B8=AD=E7=9A=84=E9=A1=B9=E7=9B=AE=E5=90=8D?=
 =?UTF-8?q?=E7=A7=B0=E4=B8=BADPNEGF=EF=BC=8C=E5=B9=B6=E8=B0=83=E6=95=B4?=
 =?UTF-8?q?=E7=9B=AE=E5=BD=95=E7=BB=93=E6=9E=84=E4=BB=A5=E6=8F=90=E9=AB=98?=
 =?UTF-8?q?=E5=8F=AF=E8=AF=BB=E6=80=A7?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 docs/CONTRIBUTING.md | 30 +++++++++++++++++-------------
 1 file changed, 17 insertions(+), 13 deletions(-)

diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
index 06ddfcc..ab4c02a 100644
--- a/docs/CONTRIBUTING.md
+++ b/docs/CONTRIBUTING.md
@@ -1,23 +1,27 @@
-# Contributing to DeePTB
+# Contributing to DPNEGF
+
+We heartily welcome contributions to the DPNEGF project. This guide provides technical and non-technical guidelines to help you contribute effectively.
+
 
-We heartily welcome contributions to the DeePTB project. This guide provides technical and non-technical guidelines to help you contribute effectively.
 
 ## Table of Contents
 
-- [Got a question?](#got-a-question)
-- [Project Structure](#project-structure)
-- [Submitting an Issue](#submitting-an-issue)
-- [Comment Style for Documentation](#comment-style-for-documentation)
-- [Code Formatting Style](#code-formatting-style)
-- [Adding a Unit Test](#adding-a-unit-test)
-- [Running Unit Tests](#running-unit-tests)
-- [Submitting a Pull Request](#submitting-a-pull-request)
-- [After Your Pull Request is Merged](#after-your-pull-request-is-merged)
-- [Commit Message Guidelines](#commit-message-guidelines)
+- [Contributing to DPNEGF](#contributing-to-dpnegf)
+  - [Table of Contents](#table-of-contents)
+  - [Got a question?](#got-a-question)
+  - [Project Structure](#project-structure)
+  - [Submitting an Issue](#submitting-an-issue)
+  - [Comment Style for Documentation](#comment-style-for-documentation)
+  - [Code Formatting Style](#code-formatting-style)
+  - [Adding a Unit Test](#adding-a-unit-test)
+  - [Running Unit Tests](#running-unit-tests)
+  - [Submitting a Pull Request](#submitting-a-pull-request)
+    - [After Your Pull Request is Merged](#after-your-pull-request-is-merged)
+  - [Commit Message Guidelines](#commit-message-guidelines)
 
 ## Got a question?
 
-For questions and discussions about DeePTB, use our [GitHub Discussions](https://github.com/deepmodeling/DeePTB/discussions). If you find a bug or want to propose a new feature, please use the [issue tracker](https://github.com/deepmodeling/DeePTB/issues/new/choose).
+For questions and discussions about DPNEGF, use our [GitHub Discussions](https://github.com/DeePTB-Lab/dpnegf/discussions). If you find a bug or want to propose a new feature, please use the [issue tracker](https://github.com/DeePTB-Lab/dpnegf/issues).
 
 ## Project Structure
 

From 81d08e195b1150b08ca15350cbecab1b5b596ebb Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 8 Sep 2025 15:39:03 +0800
Subject: [PATCH 124/152] update contributing

---
 docs/CONTRIBUTING.md | 28 ++++++++++++----------------
 1 file changed, 12 insertions(+), 16 deletions(-)

diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
index ab4c02a..5c356fc 100644
--- a/docs/CONTRIBUTING.md
+++ b/docs/CONTRIBUTING.md
@@ -4,10 +4,9 @@ We heartily welcome contributions to the DPNEGF project. This guide provides tec
 
 
 
-## Table of Contents
-
+## Table of Content
 - [Contributing to DPNEGF](#contributing-to-dpnegf)
-  - [Table of Contents](#table-of-contents)
+  - [Table of Content](#table-of-content)
   - [Got a question?](#got-a-question)
   - [Project Structure](#project-structure)
   - [Submitting an Issue](#submitting-an-issue)
@@ -25,21 +24,18 @@ For questions and discussions about DPNEGF, use our [GitHub Discussions](https:/
 
 ## Project Structure
 
-DeePTB is organized into several modules. Here's a brief overview:
+DPNEGF is organized into several modules. Here's a brief overview:
 
 - `data`: Data processing module.
 - `entrypoints`: Entry points for the command-line interface.
-- `negf`: Nonequilibrium Green's Function (NEGF) module.
-- `nn`: Neural network model module.
-- `nnops`: Neural network operations module.
-- `plugins`: Plugins for various functionalities.
-- `postprocess`: Post-processing module.
-- `tests`: Unit tests for DeePTB.
-- `utils`: Utility module with tools and constants.
+- `negf`: Nonequilibrium Green's Function (NEGF) methods.
+- `runner`: Main entry point for executing NEGF calculations.
+- `tests`: Unit tests for the DPNEGF project.
+- `utils`: Utility functions, tools, and constants used throughout the codebase.
 
 ## Submitting an Issue
 
-Before you submit an issue, please search the issue tracker, and maybe your problem has been discussed and fixed. You can [submit new issues](https://github.com/deepmodeling/DeePTB/issues/new/choose) by filling our issue forms.
+Before you submit an issue, please search the issue tracker, and maybe your problem has been discussed and fixed. You can [submit new issues](https://github.com/DeePTB-Lab/dpnegf/issues) by filling our issue forms.
 To help us reproduce and confirm a bug, please provide a test case and building environment in your issue.
 
 
@@ -63,18 +59,18 @@ When contributing a new feature or fixing a bug, it's important to include tests
 To run all unit tests, use the following command in the project root directory:
 
 ```bash
-pytest ./dptb/tests
+pytest ./dpnegf/tests
 ```
 
 To run a specific test, use:
 
 ```bash
-pytest ./dptb/tests/test_file.py
+pytest ./dpnegf/tests/test_file.py
 ```
 
 ## Submitting a Pull Request
 
-1. [Fork](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) the [DeePTB repository](https://github.com/deepmodeling/DeePTB). If you already had an existing fork, [sync](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork) the fork to keep your modification up-to-date.
+1. [Fork](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) the [DPNEGF  repository](https://github.com/DeePTB-Lab/dpnegf). If you already had an existing fork, [sync](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork) the fork to keep your modification up-to-date.
 2. Create a new branch for your changes.
      ```shell
      git checkout -b my-fix-branch
@@ -82,7 +78,7 @@ pytest ./dptb/tests/test_file.py
 3. Make your changes, including tests and documentation updates.
 4. Commit your changes with a [proper commit message](#commit-message-guidelines).
 5. Push your branch to your fork on GitHub.
-6. Submit a pull request (PR) with `deepmodeling/DeePTB:main` as the base repository. It is required to document your PR following [our guidelines](#commit-message-guidelines).
+6. Submit a pull request (PR) with `DeePTB-Lab/dpnegf:main` as the base repository. It is required to document your PR following [our guidelines](#commit-message-guidelines).
 
 ### After Your Pull Request is Merged
 

From 0016370ed46e476892c75540cc833fab5989ae65 Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Sun, 14 Sep 2025 15:19:54 +0800
Subject: [PATCH 125/152] add license (#30)

---
 docs/easy_install.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/easy_install.md b/docs/easy_install.md
index 8752f99..0b9e7f6 100644
--- a/docs/easy_install.md
+++ b/docs/easy_install.md
@@ -35,7 +35,7 @@ We recommend installing **DPNEGF** within a dedicated virtual environment to man
 
 We welcome contributions to DeePTB. If you are interested in contributing, please read our [contributing guidelines](https://deeptb-lab.github.io/dpnegf/CONTRIBUTING.html).
 
-<!-- ## License
+## License
 
-DPNEGF is open-source software released under the [LGPL-3.0](https://github.com/deepmodeling/DeePTB/blob/main/LICENSE) provided in the repository. -->
+DPNEGF is open-source software released under the [LGPL-3.0](https://github.com/DeePTB-Lab/dpnegf/blob/main/LICENSE) provided in the repository.
 

From e9926e175df9da7c07345b9e347803b7ebfcb772 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 14 Sep 2025 15:20:10 +0800
Subject: [PATCH 126/152] build(deps): bump actions/checkout from 4 to 5 (#28)

Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 5.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v4...v5)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-version: '5'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/devcontainer.yml | 2 +-
 .github/workflows/image.yml        | 2 +-
 .github/workflows/unit_test.yml    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/devcontainer.yml b/.github/workflows/devcontainer.yml
index 83e53b1..4634c08 100644
--- a/.github/workflows/devcontainer.yml
+++ b/.github/workflows/devcontainer.yml
@@ -27,7 +27,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
diff --git a/.github/workflows/image.yml b/.github/workflows/image.yml
index 330fc8a..9dfab72 100644
--- a/.github/workflows/image.yml
+++ b/.github/workflows/image.yml
@@ -19,7 +19,7 @@ jobs:
 
     steps:
       - name: Checkout repository with full history
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
         with:
           # ✅ 关键修改：获取所有历史记录和标签，以便动态版本控制能够工作
           fetch-depth: 0
diff --git a/.github/workflows/unit_test.yml b/.github/workflows/unit_test.yml
index 44d9529..203ced8 100644
--- a/.github/workflows/unit_test.yml
+++ b/.github/workflows/unit_test.yml
@@ -20,7 +20,7 @@ jobs:
     container: ghcr.io/deeptb-lab/dpnegf-main:latest
     steps: 
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
         with:
           fetch-depth: 0
 

From db00bab03ba8d864e623b595c5341bf52cb2a71a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 14 Sep 2025 15:20:19 +0800
Subject: [PATCH 127/152] build(deps): bump actions/setup-python from 5 to 6
 (#32)

Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5 to 6.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v5...v6)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-version: '6'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/add_pages_doc.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/add_pages_doc.yml b/.github/workflows/add_pages_doc.yml
index 60a2268..1bcea00 100644
--- a/.github/workflows/add_pages_doc.yml
+++ b/.github/workflows/add_pages_doc.yml
@@ -14,7 +14,7 @@ jobs:
     steps:
     - uses: actions/checkout@v4
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: '3.11'
     - name: Install dependencies

From 6acecd71f3218704d7b50c4dee7d385eff3187e2 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 15 Sep 2025 22:24:55 +0800
Subject: [PATCH 128/152] update tutorial

---
 docs/hands_on/tutorial1_c_chain.ipynb         | 685 ++++++++++++++++--
 .../input_files/negf_chain_new.json           |  10 +-
 2 files changed, 626 insertions(+), 69 deletions(-)

diff --git a/docs/hands_on/tutorial1_c_chain.ipynb b/docs/hands_on/tutorial1_c_chain.ipynb
index 28c1470..06b7638 100644
--- a/docs/hands_on/tutorial1_c_chain.ipynb
+++ b/docs/hands_on/tutorial1_c_chain.ipynb
@@ -1,43 +1,187 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "8c5c64a9",
+   "cell_type": "markdown",
+   "id": "f132d6ec",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "import os\n",
-    "import torch\n",
+    "# Tutorial 1:  Quantum Transport in a One-Dimensional Chain\n",
     "\n",
-    "from dpnegf.runner.NEGF import NEGF\n",
-    "from dptb.nn.build import build_model\n",
-    "import json\n",
+    "A quick start for DeePTB-NEGF."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7025b7fe",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
     "\n",
-    "from dpnegf.utils.loggers import set_log_handles\n",
-    "import logging\n",
-    "from pathlib import Path\n",
+    "**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green's Function (**NEGF**) method, \n",
+    "establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy. \n",
+    "\n",
+    "Based on the accurate electronic structure prediction in large-scale and complex systems, DPNEGF implements the\n",
+    "high-efficiency algorithm for high-throughput and large-scale quantum transport simulations in nanoelectronics.\n",
+    "\n",
+    "\n",
+    "### Learning Objectives\n",
     "\n",
-    "import matplotlib.pyplot as plt"
+    "In this tutorial, you will learn \n",
+    "1. how to load DeePTB model and plot band structure\n",
+    "2. how to calculate the tranmission spectrum\n",
+    "   \n",
+    "For demonstration, we use a one-dimensional chain as an example.\n",
+    "\n",
+    "### Requirement\n",
+    "\n",
+    "DeePTB and DPNEGF installed. Detailed installation instructions can be found in README.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "959c7fa0",
+   "metadata": {},
+   "source": [
+    "### *WARM UP*: A short introduction to NEGF\n",
+    "\n",
+    "The **Non-Equilibrium Green’s Function (NEGF)** method is a widely used theoretical framework \n",
+    "for studying quantum transport in nanoscale devices (molecular junctions, nanowires, CNT-FETs, etc.).  \n",
+    "It provides a rigorous way to compute current, density of states (DOS), and transmission by combining \n",
+    "quantum mechanics with open boundary conditions.\n",
+    "\n",
+    "\n",
+    "#### 1. Partitioning the system\n",
+    "We divide the full system into three regions:\n",
+    "- **Left electrode (L)**: semi-infinite periodic lead\n",
+    "- **Device region (D)**: finite scattering region of interest\n",
+    "- **Right electrode (R)**: semi-infinite periodic lead\n",
+    "\n",
+    "The Hamiltonian of the total system can be written schematically as:\n",
+    "\n",
+    "$$\n",
+    "H = \\begin{bmatrix}\n",
+    "H_L & V_{LD} & 0 \\\\\n",
+    "V_{DL} & H_D & V_{DR} \\\\\n",
+    "0 & V_{RD} & H_R\n",
+    "\\end{bmatrix}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "\n",
+    "#### 2. Green's function of the device\n",
+    "The central object is the **retarded Green’s function** of the device region:\n",
+    "\n",
+    "$$\n",
+    "G^r(E) = \\Big[ E S - H_D - \\Sigma_L^r(E) - \\Sigma_R^r(E) \\Big]^{-1},\n",
+    "$$\n",
+    "\n",
+    "where:\n",
+    "- $H_D$: device Hamiltonian,\n",
+    "- $\\Sigma_{L/R}^r(E)$: electrode retarded self-energies, describing the coupling between device and semi-infinite electrodes.\n",
+    "\n",
+    "The **self-energy** contains information about level broadening induced by the leads.\n",
+    "\n",
+    "\n",
+    "\n",
+    "#### 3. Transmission function\n",
+    "The **transmission probability** at energy \\(E\\) is given by:\n",
+    "\n",
+    "$$\n",
+    "T(E) = \\mathrm{Tr} \\big[ \\Gamma_L(E) \\, G^r(E) \\, \\Gamma_R(E) \\, G^a(E) \\big],\n",
+    "$$\n",
+    "\n",
+    "with:\n",
+    "- $\\Gamma_{L/R}(E) = i \\big[ \\Sigma_{L/R}^r(E) - \\Sigma_{L/R}^a(E) \\big]$\n",
+    "  (level broadening matrices),\n",
+    "- $G^a(E) = \\big(G^r(E)\\big)^\\dagger$.\n",
+    "\n",
+    "\n",
+    "\n",
+    "#### 4. Current (Landauer–Büttiker formula)\n",
+    "The current under bias voltage $V$ is obtained by integrating the transmission over energy:\n",
+    "\n",
+    "$$\n",
+    "I(V) = \\frac{2e}{h} \\int dE \\, T(E, V) \\, \\big[ f_L(E) - f_R(E) \\big],\n",
+    "$$\n",
+    "\n",
+    "where $f_{L/R}(E)$ are Fermi–Dirac distributions of the electrodes (shifted by bias).\n",
+    "\n",
+    "\n",
+    "\n",
+    "#### 5. Key physical quantities\n",
+    "- **DOS**: density of states, related to the spectral function $A(E) = i(G^r - G^a)$.\n",
+    "- **LDOS**: local density of states, giving spatially resolved information inside the device.\n",
+    "- **Transmission spectrum**: energy-resolved measure of electron transport capability.\n",
+    "\n",
+    "\n",
+    " \n",
+    "NEGF connects *microscopic Hamiltonians* (from first principles or tight-binding) with *observable transport quantities* (DOS,  current), incorporating the quantum effects naturally.\n",
+    "This makes it an essential tool for nanoelectronics and quantum device simulations.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc8d4d3a",
+   "metadata": {},
+   "source": [
+    "## 1. Model loading and band plotting\n",
+    "\n",
+    "In this section we will:\n",
+    "- Load a pretrained DeePTB model for a linear atomic chain,\n",
+    "- Plot the band structure (expected: a cosine-like dispersion for a single orbital 1D chain).\n",
+    "\n",
+    "\n",
+    "For demonstration, here we prepare a Slater-Koster Tight-Binding model with one single orbital at each atomic site.\n",
+    "\n",
+    "Since there is a built-in baseline model covering the periodic table, for any target system of your interest, you can extract the corresponding model from this built-in baseline model. For more details about DeePTB and the built-in baseline model, please see [DeePTB-Tutorial 1: DeePTB-SK Baseline Model](https://deeptb.readthedocs.io/en/latest/quick_start/hands_on/tutorial1_base_sk.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "390a872a",
+   "metadata": {},
+   "source": [
+    "Switch to the tutorial input directory. The examples and input files used in this tutorial are stored under `examples/atomic_chain_api/input_files`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b639b288",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[01;34m./\u001b[00m\n",
+      "├── chain.vasp\n",
+      "├── negf_chain_new.json\n",
+      "└── nnsk_C_new.json\n",
+      "\n",
+      "0 directories, 3 files\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
-    "workdir='/root/soft/DPNEGF/examples/base_model/'\n",
-    "os.chdir(f\"{workdir}/structures\")\n",
+    "workdir='../../examples/atomic_chain_api/input_files'\n",
+    "os.chdir(workdir)\n",
     "!tree -L 1 ./"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7ee1872c",
+   "metadata": {},
+   "source": [
+    "The logging settings."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "0f64a6b8",
+   "id": "463ca65d",
    "metadata": {},
    "outputs": [
     {
@@ -50,35 +194,308 @@
       "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
       "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
       "DPNEGF INFO    ================================================================================\n",
-      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dpnegf.utils.loggers import set_log_handles\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "results_path = '../band_plot'\n",
+    "log_path = results_path+'/log'\n",
+    "log_level = logging.INFO\n",
+    "set_log_handles(log_level, Path(log_path) if log_path else None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52a9cbcf",
+   "metadata": {},
+   "source": [
+    "Load model from file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "04d77b4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n"
      ]
     }
    ],
    "source": [
-    "INPUT_file =  \"./input_files/negf_chain_new.json\" \n",
-    "model =  \"./input_files/nnsk_C_new.json\"\n",
-    "structure =  \"./input_files/chain.vasp\" \n",
-    "output = \"output\"  \n",
+    "from dptb.nn.build import build_model\n",
+    "import json\n",
+    "model =  \"nnsk_C_new.json\" # the model for demonsration\n",
+    "model_json = json.load(open(model))\n",
+    "model = build_model(model,\n",
+    "                    model_options= model_json['model_options'],\n",
+    "                    common_options=model_json['common_options'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "146abc51",
+   "metadata": {},
+   "source": [
+    "After the model is loaded, bands for specific structures can be ploted. \n",
     "\n",
-    "if os.path.exists(output):\n",
-    "    os.system('rm -rf %s' % output)\n",
+    "Here we load the full system and split it into unit cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "99775e30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Atoms(symbols='C12', pbc=True, cell=[10.0, 10.0, 19.2])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from ase.io import read\n",
+    "structure =  \"chain.vasp\" \n",
+    "atoms = read(structure)\n",
+    "atoms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "609ac0a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Atoms(symbols='C', pbc=True, cell=[10.0, 10.0, 1.5999999999999999])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "uni_cell_atoms = atoms[0:1]\n",
+    "uni_cell_atoms.cell[2][2] = atoms.cell[2][2]/len(atoms)\n",
+    "uni_cell_atoms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b944bbf9",
+   "metadata": {},
+   "source": [
+    "Because we only consider one orbital per atomic site, each atom contributes a single valence electron in the model.\n",
     "\n",
+    "As visible in the band diagram, the characteristic cosine-like dispersion of a one-dimensional chain appears."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a4bf51df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF ERROR   TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+      "rm: cannot remove '../band_plot': Directory not empty\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 3.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+      "DPNEGF WARNING eig_solver is not set, using default 'torch'.\n",
+      "DPNEGF INFO    KPOINTS  klist: 101 kpoints\n",
+      "DPNEGF INFO    The eigenvalues are already in data. will use them.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF INFO    Fermi energy converged after 18 iterations.\n",
+      "DPNEGF INFO    q_cal: 0.9999982678381729, total_electrons: 1.0, diff q: 1.7321618270838002e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.65731141812973 based on the valence electrons setting nel_atom : {'C': 1} .\n",
+      "DPNEGF INFO    No Fermi energy provided, using estimated value: -13.6573 eV\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x560 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from dptb.postprocess.bandstructure.band import Band\n",
+    " \n",
+    "task_options = {\n",
+    "        \"task\": \"band\",\n",
+    "        \"kline_type\":\"abacus\",\n",
+    "        \"kpath\":[[0.0,   0.0,   0.5,   50],   \n",
+    "                 [0.0,   0.0,   0.0,   50],               \n",
+    "                 [0.0,   0.0,  -0.5,   1]\n",
+    "                ],\n",
+    "        \"klabels\":[\"X\",\"G\",\"X\"],\n",
+    "        \"emin\":-1.5,\n",
+    "        \"emax\": 1.5,\n",
+    "        \"nel_atom\":{\"C\": 1} \n",
     "\n",
-    "negf_json = json.load(open(INPUT_file))\n",
-    "model_json = json.load(open(model))\n",
+    "       }\n",
     "\n",
-    "log_path = output+'/log'\n",
-    "log_level = logging.INFO\n",
-    "set_log_handles(log_level, Path(log_path) if log_path else None)\n",
+    "if os.path.exists(results_path):\n",
+    "    os.system('rm -r %s' % results_path)\n",
+    "\n",
+    "\n",
+    "band = Band(model, results_path)\n",
+    "AtomicData_options = {\"r_max\": 3.0, \"pbc\": True}\n",
+    "band.get_bands(data = uni_cell_atoms, \n",
+    "               kpath_kwargs = task_options,\n",
+    "               AtomicData_options = AtomicData_options)\n",
+    "band.band_plot(emin = task_options['emin'],\n",
+    "               emax = task_options['emax'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fdc2558",
+   "metadata": {},
+   "source": [
+    "## 2. NEGF calculation\n",
+    "\n",
+    "After the model is loaded, we can calculate the transmission spectrum for the one-dimension chain.\n",
     "\n",
-    "model = build_model(model,model_options= model_json['model_options'],\n",
-    "                    common_options=model_json['common_options'])\n"
+    "A sample input file `negf_chain_new.json` is provided and can be loaded directly."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
+   "id": "0f64a6b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    Numba is available and JIT functions are compiled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    from dpnegf.runner.NEGF import NEGF\n",
+    "except ImportError as e:\n",
+    "    raise ImportError(\"dpnegf not found. Please install firstly.\") from e\n",
+    "\n",
+    "negf_input_file =  \"negf_chain_new.json\" \n",
+    "structure =  \"chain.vasp\" \n",
+    "output = \"../negf_output\"  \n",
+    "if os.path.exists(output):\n",
+    "    os.system('rm -rf %s' % output)\n",
+    "os.makedirs(output)\n",
+    "negf_json = json.load(open(negf_input_file))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21e1c96d",
+   "metadata": {},
+   "source": [
+    "This input file contains:\n",
+    "- Energy range and step for the transmission calculation,\n",
+    "- Structural information that determines how to divide the system into device and electrode regions (left and right),\n",
+    "- Other important  parameters such as the number of valence electrons per element (the `nel_atom` field), which affects charge counting and Fermi level.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3e0f05b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.02, -1.5, 1.5)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Energy step and range for transmission calculation\n",
+    "negf_json['task_options']['espacing'], negf_json['task_options']['emin'], negf_json['task_options']['emax']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "14801354",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'gamma_center': True,\n",
+       " 'time_reversal_symmetry': True,\n",
+       " 'nel_atom': {'C': 1.0},\n",
+       " 'kmesh': [1, 1, 1],\n",
+       " 'pbc': [False, False, False],\n",
+       " 'device': {'id': '4-8', 'sort': True},\n",
+       " 'lead_L': {'id': '0-4',\n",
+       "  'voltage': 0.0,\n",
+       "  'kmesh_lead_Ef': [1, 1, 20],\n",
+       "  'useBloch': False},\n",
+       " 'lead_R': {'id': '8-12',\n",
+       "  'voltage': 0.0,\n",
+       "  'kmesh_lead_Ef': [1, 1, 20],\n",
+       "  'useBloch': False}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Structural information for device and electrodes\n",
+    "negf_json['task_options'][\"stru_options\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2f0aa7b",
+   "metadata": {},
+   "source": [
+    "### Running NEGF from API\n",
+    "\n",
+    "Note that the calculation of self-energy files may take some time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "id": "830d67a4",
    "metadata": {},
    "outputs": [
@@ -89,19 +506,23 @@
       "DPNEGF INFO    ------ k-point for NEGF -----\n",
       "DPNEGF INFO    Gamma Center: True\n",
       "DPNEGF INFO    Time Reversal: True\n",
-      "DPNEGF INFO    k-points Num: 1\n",
+      "DPNEGF INFO    k-points Num: 1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "DPNEGF INFO    k-points: [[0 0 0]]\n",
       "DPNEGF INFO    k-points weights: [1.]\n",
       "DPNEGF INFO    --------------------------------\n",
       "DPNEGF WARNING AtomicData_options is extracted from input file. This may be not consistent with the model options. Please be careful and check the cutoffs.\n",
       "DPNEGF INFO    The AtomicData_options is:\n",
       "               {\n",
-      "                   \"r_max\": 2.0\n",
+      "                   \"r_max\": 3.0\n",
       "               }\n",
       "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732052e-10 (threshold: 1.000000e-05)\n",
       "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
-      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
-      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
       "DPNEGF INFO    The coupling width of lead_L is 1.\n",
       "DPNEGF INFO    The coupling width of lead_R is 1.\n",
       "DPNEGF INFO    --------------------------------------------------------------------------------\n",
@@ -114,7 +535,7 @@
       "DPNEGF INFO    Number of electrons in lead_R: {'C': 1.0}\n",
       "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
       "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
-      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 3.0\n",
       "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
       "DPNEGF INFO    Getting eigenvalues from the model.\n",
       "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
@@ -123,7 +544,7 @@
       "DPNEGF INFO    Estimated E_fermi: -13.638588428497314 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
       "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
       "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
-      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 2.0\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 3.0\n",
       "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
       "DPNEGF INFO    Getting eigenvalues from the model.\n",
       "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
@@ -139,26 +560,29 @@
       "DPNEGF INFO    Reference energy E_ref: -13.638588428497314\n",
       "DPNEGF INFO    =================================================\n",
       "\n",
-      "DPNEGF INFO    Merging 400 tmp self energy files into output/self_energy/self_energy_leadL.h5\n",
+      "DPNEGF INFO    Merging 150 tmp self energy files into ../negf_output/self_energy/self_energy_leadL.h5\n",
       "DPNEGF INFO    Merge complete.\n",
-      "DPNEGF INFO    Merging 400 tmp self energy files into output/self_energy/self_energy_leadR.h5\n",
+      "DPNEGF INFO    Merging 150 tmp self energy files into ../negf_output/self_energy/self_energy_leadR.h5\n",
       "DPNEGF INFO    Merge complete.\n",
       "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -2.000\n",
-      "DPNEGF INFO    computing green's function at e = -1.599\n",
+      "DPNEGF INFO    computing green's function at e = -1.500\n",
       "DPNEGF INFO    computing green's function at e = -1.198\n",
-      "DPNEGF INFO    computing green's function at e = -0.797\n",
-      "DPNEGF INFO    computing green's function at e = -0.396\n",
-      "DPNEGF INFO    computing green's function at e = 0.005\n",
-      "DPNEGF INFO    computing green's function at e = 0.406\n",
-      "DPNEGF INFO    computing green's function at e = 0.807\n",
-      "DPNEGF INFO    computing green's function at e = 1.208\n",
-      "DPNEGF INFO    computing green's function at e = 1.609\n",
-      "DPNEGF WARNING The Fermi energy of the left and right leads should be equal in nscf current calculation.\n"
+      "DPNEGF INFO    computing green's function at e = -0.896\n",
+      "DPNEGF INFO    computing green's function at e = -0.594\n",
+      "DPNEGF INFO    computing green's function at e = -0.292\n",
+      "DPNEGF INFO    computing green's function at e = 0.010\n",
+      "DPNEGF INFO    computing green's function at e = 0.312\n",
+      "DPNEGF INFO    computing green's function at e = 0.614\n",
+      "DPNEGF INFO    computing green's function at e = 0.916\n",
+      "DPNEGF INFO    computing green's function at e = 1.218\n"
      ]
     }
    ],
    "source": [
+    "if os.path.exists(output):\n",
+    "    os.system('rm -r %s' % output)\n",
+    "os.makedirs(output)\n",
+    "\n",
     "negf = NEGF(\n",
     "    model=model,\n",
     "    AtomicData_options=negf_json['AtomicData_options'],\n",
@@ -170,38 +594,171 @@
     "negf.compute()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9c84a6c9",
+   "metadata": {},
+   "source": [
+    "### Running NEGF from the Command Line\n",
+    "\n",
+    "The NEGF calculation can also be executed via the command-line interface (CLI). This allows batch runs and straightforward integration with job scripts on HPC systems. \n",
+    "\n",
+    "See the example CLI commands below.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "db275dee",
+   "execution_count": 11,
+   "id": "524faa1e",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_16165/1724458665.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
-      "  negf_out = torch.load('./output/negf.out.pth')\n"
+      "/personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_api/input_files\n",
+      "sisl is not installed.Thus the input for TBtrans can not be generated, please install it first!\n",
+      "tbtrans is not in the Environment PATH. Thus the input for TBtrans can be generated but not run.\n",
+      "================================================================================\n",
+      "            _______  .______   .__   __.  _______   _______  _______            \n",
+      "           |       \\ |   _  \\  |  \\ |  | |   ____| /  _____||   ____|           \n",
+      "           |  .--.  ||  |_)  | |   \\|  | |  |__   |  |  __  |  |__              \n",
+      "           |  |  |  ||   ___/  |  . `  | |   __|  |  | |_ | |   __|             \n",
+      "           |  '--'  ||  |      |  |\\   | |  |____ |  |__| | |  |                \n",
+      "             |_______/ | _|      |__| \\__| |_______| \\______| |__|              \n",
+      "--------------------------------------------------------------------------------\n",
+      "                       DPNEGF version 0.1.1.dev97+bccd946                       \n",
+      "================================================================================\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO                                      Version Info                                  \n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "\n",
+      "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n",
+      "DPNEGF INFO    ------ k-point for NEGF -----\n",
+      "DPNEGF INFO    Gamma Center: True\n",
+      "DPNEGF INFO    Time Reversal: True\n",
+      "DPNEGF INFO    k-points Num: 1\n",
+      "DPNEGF INFO    k-points: [[0 0 0]]\n",
+      "DPNEGF INFO    k-points weights: [1.]\n",
+      "DPNEGF INFO    --------------------------------\n",
+      "DPNEGF WARNING AtomicData_options is extracted from input file. This may be not consistent with the model options. Please be careful and check the cutoffs.\n",
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": 3.0,\n",
+      "                   \"er_max\": null,\n",
+      "                   \"oer_max\": null\n",
+      "               }\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732052e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
+      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+      "DPNEGF INFO    The coupling width of lead_L is 1.\n",
+      "DPNEGF INFO    The coupling width of lead_R is 1.\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
+      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0.0\n",
+      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0.0\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 1.0}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 1.0}\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 3.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
+      "DPNEGF INFO    q_cal: 3.999994485346824, total_electrons: 4.0, diff q: 5.514653175886508e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.638588428497314 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
+      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 3.0\n",
+      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
+      "DPNEGF INFO    q_cal: 4.000002782981872, total_electrons: 4.0, diff q: 2.7829818716185173e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -13.638587474822998 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
+      "DPNEGF INFO    -------------------------------------------------\n",
+      "DPNEGF INFO    Zero bias case detected.\n",
+      "DPNEGF INFO    Fermi level for lead_L: -13.638588428497314\n",
+      "DPNEGF INFO    Fermi level for lead_R: -13.638587474822998\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -13.638588428497314\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -13.638587474822998\n",
+      "DPNEGF INFO    Reference energy E_ref: -13.638588428497314\n",
+      "DPNEGF INFO    =================================================\n",
+      "\n",
+      "DPNEGF INFO    Merging 150 tmp self energy files into /personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_api/input_files/../negf_output_cli/results/self_energy/self_energy_leadL.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Merging 150 tmp self energy files into /personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_api/input_files/../negf_output_cli/results/self_energy/self_energy_leadR.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -1.500\n",
+      "DPNEGF INFO    computing green's function at e = -1.198\n",
+      "DPNEGF INFO    computing green's function at e = -0.896\n",
+      "DPNEGF INFO    computing green's function at e = -0.594\n",
+      "DPNEGF INFO    computing green's function at e = -0.292\n",
+      "DPNEGF INFO    computing green's function at e = 0.010\n",
+      "DPNEGF INFO    computing green's function at e = 0.312\n",
+      "DPNEGF INFO    computing green's function at e = 0.614\n",
+      "DPNEGF INFO    computing green's function at e = 0.916\n",
+      "DPNEGF INFO    computing green's function at e = 1.218\n",
+      "DPNEGF INFO    negf calculation successfully completed.\n",
+      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m"
      ]
     }
    ],
    "source": [
-    "negf_out = torch.load('./output/negf.out.pth')"
+    "# Command line for DPNEGF\n",
+    "! pwd\n",
+    "! [ -d \"../negf_output_cli\" ] && rm -r ../negf_output_cli\n",
+    "! dpnegf run negf_chain_new.json  -i nnsk_C_new.json -stu chain.vasp -o ../negf_output_cli"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fd1a663",
+   "metadata": {},
+   "source": [
+    "We can inspect the outputs of the NEGF run by loading the results file (`negf.out.pth`). The output  contains:\n",
+    "- `T_avg`: the total transmission as a function of energy,\n",
+    "- `T_k`: k-point resolved transmission (if k-sampling is used),\n",
+    "- `DOS`: density of states on the energy grid,\n",
+    "- `LDOS`: local density of states defined for each atomic site.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
+   "id": "db275dee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "results_path = os.path.join(output, 'negf.out.pth')\n",
+    "if os.path.exists(results_path) is False:\n",
+    "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
+    "negf_out = torch.load(results_path,weights_only=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "id": "7a29b9ff",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
+       "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg'])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -212,13 +769,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "id": "8eb092f6",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -238,13 +795,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "id": "4a2fe762",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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yJXx9fREbG4sWLVqoWDWRKYYbIiIiEgrn3BAREZFQGG6IiIhIKBVuQnFBQQEuX76MKlWqFLkoFREREdkeSZKQmZmJ2rVrP/TeaBUu3Fy+fFnR+88QERFR+blw4YLJXe3/rcKFG8OS8xcuXFD0PjQAkJubi23btiEkJASOjo6KHtsWiN4+QPw2sn3aJ3ob2T7ts1YbMzIy4O3tXapbx1S4cGM4FeXm5maVcOPi4gI3Nzchf2lFbx8gfhvZPu0TvY1sn/ZZu42lmVLCCcVEREQkFIYbIiIiEgrDDREREQmF4YaIiIiEwnBDREREQmG4ISIiIqEw3BAREZFQGG6IiIhIKAw3REREJBSGGyIiIhKKquHml19+Qe/evVG7dm3odDps2LDhoc9JTExEmzZtoNfr0ahRI8TGxlq9TiIiItIOVcPN3bt30bp1ayxdurRU+ycnJ6NXr17o0qULDh06hMmTJ+P555/H1q1brVwpERERaYWqN87s0aMHevToUer9Y2JiUL9+fbzzzjsAgMcffxy//vor3n33XYSGhlqrTLIRufkFuJqZXap9JUkq02vk5eXhZjZwKf0eHBxyy3QMW8b2mSrNTfhsSV5uLm5mA5fT78HBMU/tchRXHu3TO9jBw1VvlWOTbdDUXcGTkpIQHBxstC00NBSTJ08u9jnZ2dnIzi78g5iRkQHg/l1Lc3OV7dwNx1P6uLZCzfblF0jo8f4uJN/IKodXc0D0gZ3l8DpqYfu0T/Q2Wr99Uf9tiucCHrXqaxRF9L8TgPXaaM7xNBVuUlNT4enpabTN09MTGRkZuHfvHipVqmTynPnz5yM6Otpk+7Zt2+Di4mKVOuPj461yXFuhRvvu5gLJN+7/ujroJGjrszYR2Yp8CSiADpv3HEf1G0dVq0P0vxOA8m3Myir9h1tNhZuyiIyMREREhPw4IyMD3t7eCAkJgZubm6KvlZubi/j4eHTr1g2Ojo6KHtsWqNm+m3dzgH2JAIDj0SFWO5XA91DbRG8fIH4brd2+z349h4VbT6NOnTro2bOl4sd/GNHfP8B6bTSceSkNTYUbLy8vpKWlGW1LS0uDm5tbkaM2AKDX66HXm55bdXR0tNovljWPbQvUaJ+DQ4HR61t7ngTfQ20TvX2A+G20Vvvs7e9fR6PT2an68xP9/QOUb6M5x9LUOjeBgYFISEgw2hYfH4/AwECVKqLy8uD0YK1NACUi26H756R22S45IK1QNdzcuXMHhw4dwqFDhwDcv9T70KFDSElJAXD/lNKIESPk/V944QX89ddfeO2113Dy5El8+OGHWLt2LaZMmaJG+VSOynjxExGREcNno7JeUUnaoGq42bdvH/z8/ODn5wcAiIiIgJ+fH2bPng0AuHLlihx0AKB+/frYtGkT4uPj0bp1a7zzzjv47LPPeBl4BSD98zmLgzZEpARGG7GpOuemc+fOJabnolYf7ty5Mw4ePGjFqsgm/fNrwmxDRJYwnNbmwI3YNDXnhojzbYjIEuxBKgaGG9IEw4csdkxEZAl5zo26ZZCVMdyQJnAImYiUxAnFYmO4IU3ghGIiUoKhC2G0ERvDDWmCJE8oZrohorLT8bxUhcBwQ5og90PMNkRkgcJsw3QjMoYb0gTD+XFmGyKyhHxaitlGaAw3pAnyaSmmGyKyBNe5qRAYbkhTOOeGiCzBHqRiYLghIqIKh3NuxMZwQ5rA01JEpITCG2eqWwdZF8MNaYK8zo3KdRCRthlObTPbiI3hhjShcOSG8YaIyo4jNxUDww1pAu8tRURKKOxDmG5ExnBDmiAVLlFMRFRmHLmpGBhuSFOYbYjIElxOomJguCFN4IcsIlIS+xSxMdyQJnBCMREpQj4txXgjMoYb0oh/LgVntiEiC8j3llK1CrI2hhvSBM4nJiIl6HhvqQqB4YY0Qb4UnEM3RGQBjtxUDAw3pAkcuSEiJeg456ZCYLghTeHADRFZgn1IxcBwQ5rAO/gSEVFpMdyQJhSOIPNjFxGVnXzjTH5eEhrDDWlC4To36tZBRNomz7nhaLDQGG5IEwwdEbMNESmBIzdiY7ghTeDIDREpgevcVAwMN6QpvOkdEVmicJ0bphuRMdyQpnDkhogswT6kYmC4IU3gEDIRKYl9itgYbkgTOKGYiJQgXwquch1kXQw3pAmFE4oZb4io7HS8uVSFwHBDmsB+iIiUwAnFFQPDDWmC4SZ3HLghIksU3jhT3TrIuhhuSBMM/RDDDRFZhnNuKgKGG9IEec4NpxQTkQUKR24Yb0TGcENERERCYbghjeCcGyKyHC+WqhgYbkgTCk9LERGVHe8tVTEw3JAmFE4oZrwhorLjyE3FwHBDmsCRGyJSQuEifow3ImO4IU2QmG6ISAHy1VLqlkFWxnBDmiCfllK1CiLSOvneUkw3QmO4IU3hnBsisgi7kAqB4YY0gZ+yiEhJvLeU2BhuSBMMHRE/dBGRJTifuGJguCFtMMwnZrohIgtwnZuKgeGGNKFwQjHTDRGVHde5qRgYbkgTJI7cEJECeOPMioHhhjSBk/+ISAkc/a0YGG5IU3gpOBFZgl1IxcBwQ5rAEWQiUhL7FLEx3JAmcIViIlJC4YRiphuRMdyQJhgm/3FImYgsIk8oVrcMsi6GG9IEeeSG4YaILCDfW0rlOsi6VA83S5cuhY+PD5ydnREQEIC9e/eWuP+SJUvQpEkTVKpUCd7e3pgyZQr+/vvvcqqWVCPfFJzphojKjpeCVwyqhpu4uDhEREQgKioKBw4cQOvWrREaGoqrV68Wuf/q1asxffp0REVF4cSJE/j8888RFxeHGTNmlHPlVN7k2y8w2xCRBbiIX8WgarhZvHgxxo4di7CwMDRr1gwxMTFwcXHBsmXLitz/t99+w5NPPomhQ4fCx8cHISEhGDJkyENHe0j75EX81C2DiDROXk6C6UZoDmq9cE5ODvbv34/IyEh5m52dHYKDg5GUlFTkczp06ICVK1di7969aNeuHf766y/8+OOPGD58eLGvk52djezsbPlxRkYGACA3Nxe5ubkKtQbyMR/8r2jUbF9efj6A+0PJ1nx9vofaJnr7APHbaO325eXlAbB+X1Ic0d8/wHptNOd4OkmlE4+XL19GnTp18NtvvyEwMFDe/tprr2HHjh3Ys2dPkc97//338eqrr0KSJOTl5eGFF17ARx99VOzrvPHGG4iOjjbZvnr1ari4uFjeECoXR2/q8Okpe9RzlRDRMl/tcohIo85mAO8fc0ANZwkz/diXaElWVhaGDh2K27dvw83NrcR9VRu5KYvExET873//w4cffoiAgACcOXMGL7/8MubOnYtZs2YV+ZzIyEhERETIjzMyMuDt7Y2QkJCH/nDMlZubi/j4eHTr1g2Ojo6KHtsWqNk+/Ymr+PTUIVSrVg09ewZY7XX4Hmqb6O0DxG+jtdu3//wtvH/sd7i4VEbPnk8pfvyHEf39A6zXRsOZl9JQLdx4eHjA3t4eaWlpRtvT0tLg5eVV5HNmzZqF4cOH4/nnnwcAtGzZEnfv3sW4cePw+uuvw87OdAqRXq+HXq832e7o6Gi1XyxrHtsWqNE+O3v7+/+105XLa/M91DbR2weI30Zrtc/R8Z8/ezqo+vMT/f0DlG+jOcdSbUKxk5MT/P39kZCQIG8rKChAQkKC0WmqB2VlZZkEGPt//ujxsj6xcYViIlLGP+vc8E+G0FQ9LRUREYGRI0eibdu2aNeuHZYsWYK7d+8iLCwMADBixAjUqVMH8+fPBwD07t0bixcvhp+fn3xaatasWejdu7ccckhM8tVSvBaciCxQeLEU043IVA03gwYNwrVr1zB79mykpqbC19cXW7ZsgaenJwAgJSXFaKRm5syZ0Ol0mDlzJi5duoQaNWqgd+/emDdvnlpNoHLzzzo3KldBRNomr3PDbCM01ScUh4eHIzw8vMjvJSYmGj12cHBAVFQUoqKiyqEyIiIi0iLVb79AVBqFp6XUrYOItM1wapsjN2JjuCFNKJxQzHRDRGXHHqRiYLghTZB4uRQRKYA3zqwYGG5IEyROKCYiBRhGfxltxMZwQ5rAOTdEpITCkRt16yDrYrghTeCcGyJSEte5ERvDDREREQmF4YY0wTD5j6eliMgSPC1VMTDckKYw3BCRJTihuGJguCFNkCcUc84NEVmAIzcVA8MNaYJ8KTizDRFZoLAPYboRGcMNaQI/ZRGREuTTUuxThMZwQ5pQuM4Nh26IqOzk01LqlkFWxnBDREREQmG4IU3graWISAmGPoT3lhIbww1pAte5ISIl8LRUxcBwQ5rAkRsiUgYnFFcEDDekDZxQTEQKKFznhulGZAw3pAnyOjcq10FE2ibPuVG1CrI2hhvShMJLwdWtg4i0TcdJNxUCww1pAvshIlIS+xSxMdyQxnDohojKjj1IxcBwQ5rA01JEpAROKK4YGG5IEzihmIiUIN9bSuU6yLoYbkgTOHJDREooHLlRtw6yLoYb0oTCRfyYbojIchLHboTGcEPawNsvEJECOHJTMTDckCbIIzcMN0RkAcM6N8w2YmO4ISIiIqEw3JAmyBOKOeeGiCwg9yAcuhEaww1pglSYboiIyqzw7gtMNyJjuCFNKLxaioio7OR1bphthMZwQ5pQuM4N4w0RlR3vm1kxMNyQJnDkhoiUYOhDePsFsTHckCZIXOeGiJTAkZsKgeGGiIiIhMJwQ5rCgRsisgQnFFcMDDekCZxQTERKYBdSMTDckCYY1qRgv0RElniwD+GkYnEx3JAmSLxciogU8ODoL7ONuBhuSBMKsw3TDRGVndHIjWpVkLUx3JAmFM65UbcOItK2B/sQnpYSF8MNaQLvA0NESmOvIi6GG9IUDtwQkSV4artiYLghTeBpKSJShNFpKfXKIOtiuCFN4acuIrKE0ZwbnpgSFsMNaQLvLUVESjBe50a1MsjKGG5IE3haioiUwFXOKwaGG9KEwg9Y7JiIqOw4clMxMNyQJrATIiKlcc6NuBhuSFM4okxElmAfUjE4lOVJ6enp2Lt3L65evYqCggKj740YMUKRwogexBtnEpESHrzikiPC4jI73Hz//fcYNmwY7ty5Azc3N6PJWTqdjuGGrIITiolICcaXgpOozD4t9corr2D06NG4c+cO0tPTcevWLfnr5s2b1qiRiDfOJCLF8d5S4jI73Fy6dAmTJk2Ci4uLNeohKhrXuSEiBXDkpmIwO9yEhoZi37591qiFqFiFIzdERGXHOTcVg9nhplevXpg6dSreeOMNfPPNN/juu++Mvsy1dOlS+Pj4wNnZGQEBAdi7d2+J+6enp2PixImoVasW9Ho9HnvsMfz4449mvy5pCzshIlIc+xVhmT2heOzYsQCAOXPmmHxPp9MhPz+/1MeKi4tDREQEYmJiEBAQgCVLliA0NBSnTp1CzZo1TfbPyclBt27dULNmTXz99deoU6cOzp8/j2rVqpnbDNIori5KRJZgF1IxmB1u/n3ptyUWL16MsWPHIiwsDAAQExODTZs2YdmyZZg+fbrJ/suWLcPNmzfx22+/wdHREQDg4+OjWD1ku7jYFhEpwWiFYvYrwirTOjdKyMnJwf79+xEZGSlvs7OzQ3BwMJKSkop8znfffYfAwEBMnDgRGzduRI0aNTB06FBMmzYN9vb2RT4nOzsb2dnZ8uOMjAwAQG5uLnJzcxVsEeTjKX1cW6Fm+/Lz74dqSSqw6uvzPdQ20dsHiN9Ga7cvv6Aw0OTk5iI3t3yHckR//wDrtdGc4+mkMlwLt2PHDrz99ts4ceIEAKBZs2aYOnUqOnbsWOpjXL58GXXq1MFvv/2GwMBAeftrr72GHTt2YM+ePSbPadq0Kc6dO4dhw4bhxRdfxJkzZ/Diiy9i0qRJiIqKKvJ13njjDURHR5tsX716Na/40pDvz9vhp8t2CKpVgKd9lBs9JKKKpUACpuy+/7l+Xts8uDqqXBCVWlZWFoYOHYrbt2/Dzc2txH3NHrlZuXIlwsLC8PTTT2PSpEkAgF27dqFr166IjY3F0KFDy1Z1KRQUFKBmzZr45JNPYG9vD39/f1y6dAlvvfVWseEmMjISERER8uOMjAx4e3sjJCTkoT8cc+Xm5iI+Ph7dunWTT5uJRM32Hdt2Grh8Dg3q10fPHk2s9jp8D7VN9PYB4rfR2u2TJAlTdscDALoGB+ORyk6Kv0ZJRH//AOu10XDmpTTMDjfz5s3DokWLMGXKFHnbpEmTsHjxYsydO7fU4cbDwwP29vZIS0sz2p6WlgYvL68in1OrVi04OjoanYJ6/PHHkZqaipycHDg5mf6S6vV66PV6k+2Ojo5W+8Wy5rFtgRrt09ndv7DP3s6uXF6b76G2id4+QPw2lkf7HBwcVPsZiv7+Acq30ZxjmX0p+F9//YXevXubbO/Tpw+Sk5NLfRwnJyf4+/sjISFB3lZQUICEhASj01QPevLJJ3HmzBmjSc2nT59GrVq1igw2JBDO+yMihXGJCXGZHW68vb2NAonBTz/9BG9vb7OOFRERgU8//RRffPEFTpw4gQkTJuDu3bvy1VMjRowwmnA8YcIE3Lx5Ey+//DJOnz6NTZs24X//+x8mTpxobjNIo3gZJxERPYzZp6VeeeUVTJo0CYcOHUKHDh0A3J9zExsbi/fee8+sYw0aNAjXrl3D7NmzkZqaCl9fX2zZsgWenp4AgJSUFNjZFeYvb29vbN26FVOmTEGrVq1Qp04dvPzyy5g2bZq5zSCNkVcoZrohIgvpdPdHbXgpuLjMDjcTJkyAl5cX3nnnHaxduxbA/XkvcXFx6Nu3r9kFhIeHIzw8vMjvJSYmmmwLDAzE7t27zX4d0jbDRX2MNkRkKR3++cDEbCOsMq1z079/f/Tv31/pWoiKJfHmUkSkEN0/QzfMNuIye84NkRoKsw3TDRFZxtCLcEKxuEo1clO9enWcPn0aHh4ecHd3L3Hew82bNxUrjsjA0Alxyg0RWcrQj3DsRlylCjfvvvsuqlSpIv8/J3VSeWMnRERK48iNuEoVbkaOHCn//6hRo6xVC1Gx5JEbdcsgIgHo/plSzGwjLrPn3Bw4cABHjhyRH2/cuBH9+vXDjBkzkJOTo2hxRP/GQUMishj7EeGZHW7Gjx+P06dPA7i/WvGgQYPg4uKCdevW4bXXXlO8QKIHcUIxEVmqcEIxx25EZXa4OX36NHx9fQEA69atQ1BQEFavXo3Y2Fh88803StdHBOCBdW6YbYjIQvKEYmYbYZkdbiRJku/t9NNPP6Fnz54A7q8efP36dWWrI/oHl7khIqVwBFh8Zoebtm3b4s0338SKFSuwY8cO9OrVCwCQnJws3zaBSGmFi/ixUyIiy3DkRnxmh5slS5bgwIEDCA8Px+uvv45GjRoBAL7++mv5XlNESuN1DUSkNPYr4jL79gutWrUyulrK4K233oK9vb0iRRH9Gy8FJyKlcIVi8Zk9cnPhwgVcvHhRfrx3715MnjwZX375JRwdHRUtjujfeFaKiCzFhWjFZ3a4GTp0KLZv3w4ASE1NRbdu3bB37168/vrrmDNnjuIFEgG8txQRKUceuVG1CrIms8PN0aNH0a5dOwDA2rVr0aJFC/z2229YtWoVYmNjla6PCADvLUVECpInFDPeiMrscJObmwu9Xg/g/qXgffr0AQA0bdoUV65cUbY6Itk/69yoXAURaR9HbsRndrhp3rw5YmJisHPnTsTHx6N79+4AgMuXL+ORRx5RvEAigCM3RKQcw5wbDtyIy+xws3DhQnz88cfo3LkzhgwZgtatWwMAvvvuO/l0FZHSCsMN0w0RWaawG2G6EZXZl4J37twZ169fR0ZGBtzd3eXt48aNg4uLi6LFERlwPQoiUhpHbsRldrgBAHt7e6NgAwA+Pj5K1ENERGRVHP8VX6nCTZs2bZCQkAB3d3f4+fmVeGrgwIEDihVHZMA5N0SkFHnOjcp1kPWUKtz07dtXvkKqX79+1qyHqEhc54aIlMIVisVXqnATFRVV5P8TlReO3BCRUuQbZ3LsRlhlmnNjcOfOHRQUFBhtc3Nzs6ggoqJIXOeGiBTDS8FFZ/al4MnJyejVqxcqV66MqlWrwt3dHe7u7qhWrZrJJGMixXDkhogUIo/cMNwIy+yRm+eeew6SJGHZsmXw9PTkuiNULtgHEZHSeFpKXGaHm8OHD2P//v1o0qSJNeohKpLhHjCcUExEluKEYvGZfVrqiSeewIULF6xRC9FDcaCQiCzFfkR8Zo/cfPbZZ3jhhRdw6dIltGjRAo6Ojkbfb9WqlWLFERnwAxYRKYUjwOIzO9xcu3YNZ8+eRVhYmLxNp9NBkiTodDrk5+crWiARwHtLEZFyOKFYfGaHm9GjR8PPzw9fffUVJxRTuSlcxI+IyDLynBuOCQvL7HBz/vx5fPfdd2jUqJE16iEqkjyhmOmGiCwk336B2UZYZk8o/s9//oPDhw9boxaiYrEPIiKlsV8Rl9kjN71798aUKVNw5MgRtGzZ0mRCcZ8+fRQrjkhmmHOjbhVEJBCJQzfCMjvcvPDCCwCAOXPmmHyPE4rJ2jjHi4gsxW5EfGaHm3/fS4qoPMj3lmKnREQWKrxxJonK7Dk3RUlPT1fiMETFknhaiogUouONM4VndrhZuHAh4uLi5McDBw5E9erVUadOHU40JquROyEO3RCRhQq7EaYbUZkdbmJiYuDt7Q0AiI+Px08//YQtW7agR48emDp1quIFEgEPnJZSuQ4i0j7eW0p8Zs+5SU1NlcPNDz/8gGeffRYhISHw8fFBQECA4gUSAeyEiEh57FbEZfbIjbu7u3zjzC1btiA4OBjA/UvqeKUUWQvPShGRUriIn/jMHrl5+umnMXToUDRu3Bg3btxAjx49AAAHDx7kqsVkdbzhHRFZir2I+MwON++++y58fHxw4cIFLFq0CK6urgCAK1eu4MUXX1S8QCLgwRtnqlsHEQlAvnEmh25EZXa4cXR0xKuvvmqyfcqUKYoURFQ0TigmImUU3jiTRGV2uAGAP//8E9u3b8fVq1dNFvWbPXu2IoURPYgjN0SkFM65EZ/Z4ebTTz/FhAkT4OHhAS8vL6Pl8HU6HcMNWYU8oZhjN0RkocKRG6YbUZkdbt58803MmzcP06ZNs0Y9REXiuXEiUhy7FWGZfSn4rVu3MHDgQGvUQlQsuQ/iwA0RWYj3lhKf2eFm4MCB2LZtmzVqIXooZhsishRPb4vP7NNSjRo1wqxZs7B79260bNkSjo6ORt+fNGmSYsURGRROKGanRESWkUduOHQjLLPDzSeffAJXV1fs2LEDO3bsMPqeTqdjuCGrKJxQTESkDE4oFpfZ4SY5OdkadRCVyDChmAM3RGQpXgouPrPn3BCpieGGiCzFRfzEV6ZF/C5evIjvvvsOKSkpyMnJMfre4sWLFSmM6EH8hEVESuMSE+IyO9wkJCSgT58+aNCgAU6ePIkWLVrg3LlzkCQJbdq0sUaNRPK5cV7lQESW4qXg4jP7tFRkZCReffVVHDlyBM7Ozvjmm29w4cIFBAUFcf0bshrefoGIlKLjeSnhmR1uTpw4gREjRgAAHBwccO/ePbi6umLOnDlYuHBhmYpYunQpfHx84OzsjICAAOzdu7dUz1uzZg10Oh369etXptclIqKKhyPA4jM73FSuXFmeZ1OrVi2cPXtW/t7169fNLiAuLg4RERGIiorCgQMH0Lp1a4SGhuLq1aslPu/cuXN49dVX0bFjR7Nfk7SH69wQkVIKT0tx6EZUZoeb9u3b49dffwUA9OzZE6+88grmzZuH0aNHo3379mYXsHjxYowdOxZhYWFo1qwZYmJi4OLigmXLlhX7nPz8fAwbNgzR0dFo0KCB2a9J2lM454aIyDLyWSlmG2GZHW4WL16MgIAAAEB0dDS6du2KuLg4+Pj44PPPPzfrWDk5Odi/fz+Cg4MLC7KzQ3BwMJKSkop93pw5c1CzZk2MGTPG3PJJozjnhogUw3VuhGfW1VL5+fm4ePEiWrVqBeD+KaqYmJgyv/j169eRn58PT09Po+2enp44efJkkc/59ddf8fnnn+PQoUOleo3s7GxkZ2fLjzMyMgAAubm5yM3NLVvhxTAcT+nj2go121fwTy+Un5dv1dfne6htorcPEL+N5dE+SSoAAOTl5ZX7z1H09w+wXhvNOZ5Z4cbe3h4hISE4ceIEqlWrZm5dFsvMzMTw4cPx6aefwsPDo1TPmT9/PqKjo022b9u2DS4uLkqXCACIj4+3ynFthRrtu3nDHoAOBw8dBC5Y/+MW30NtE719gPhttGb7bqff70/27d+P7GR1hm9Ef/8A5duYlZVV6n3NXuemRYsW+Ouvv1C/fn1zn2rCw8MD9vb2SEtLM9qelpYGLy8vk/3Pnj2Lc+fOoXfv3vK2goL7CdzBwQGnTp1Cw4YNjZ4TGRmJiIgI+XFGRga8vb0REhICNzc3i9vwoNzcXMTHx6Nbt24mNxQVgZrtW3F5L5CZjjZ+fujRwvR3Qyl8D7VN9PYB4rexPNq3/OIenL9zG23a+KNbs5pWeY3iiP7+AdZro+HMS2mYHW7efPNNvPrqq5g7dy78/f1RuXJlo++bExicnJzg7++PhIQE+XLugoICJCQkIDw83GT/pk2b4siRI0bbZs6ciczMTLz33nvw9vY2eY5er4derzfZ7ujoaLVfLGse2xao0T7DVVKODg7l8tp8D7VN9PYB4rfRmu2z+6c/sXewV+1nKPr7ByjfRnOOVepwM2fOHLzyyivo2bMnAKBPnz5Gl+VKkgSdTof8/HwzSgUiIiIwcuRItG3bFu3atcOSJUtw9+5dhIWFAQBGjBiBOnXqYP78+XB2dkaLFi2Mnm84Pfbv7SQWTigmIqXwxpniK3W4iY6OxgsvvIDt27crWsCgQYNw7do1zJ49G6mpqfD19cWWLVvkScYpKSmws+P9PSu6wj6I6YaILPPAx3IVqyBrKnW4MdxgLCgoSPEiwsPDizwNBQCJiYklPjc2Nlbxesj2GH7/OHJDRJaSF/FjthGWWUMiXB2W1GLog/gbSESWMtx+gdlGXGZNKH7sscceGnBu3rxpUUFEReEnLCJSGvsVcZkVbqKjo1G1alVr1UJULHnkhqOHRGQp3ltKeGaFm8GDB6NmzfJdE4DoQYw2RGQp9iPiK/WcG35iJlVxQjERKYQTisVX6nAj8beAVFR4WkrVMohIAJxQLL5Sn5Yy3OaASA3yIn4cUCYiCxWO3DDeiIqr45EmyBP/mG2IyEIcARYfww1pAj9gEZHS2K+Ii+GGNEHiwA0RKaRwzg3TjagYbkgTuM4NESmFV0uJj+GGNIXRhoiIHobhhjSBN84kIqUYRoA5ciMuhhvSFF4KTkSWMvQizDbiYrghTZAnFDPbEJGFuM6N+BhuSBN4VQMRKY29irgYbkgTeCk4ESlF7keYboTFcEOaIPdBTDdEZCF5QjHTjbAYbkhTOKGYiCzFXkR8DDekCbwUnIiUwkX8xMdwQ5ogr1CsahVEJAbDaSkSFcMNaYN8KTjjDRFZhiM34mO4IU1gH0RESuOEYnEx3JAmcM4NESlFXqGY2UZYDDekCZxzQ0RKkU9LqVsGWRHDDWkKR26IyFJcUkJ8DDekCRJX8SMiheh4Xkp4DDekCYaJfxy5ISJL8bSU+BhuSBN4bykiUorhtBQHbsTFcEOawE6IiJQmsWMRFsMNaQoX8SMii/G0lPAYbkgT5HVuVK6DiLSP84nFx3BDmiCvc8N0Q0QWMowAM9uIi+GGNIXrUxCRpdiLiI/hhjRBvlqKvRIRWajwxpkcuxEVww1pAm9wR0RK4Wck8THckCbwAxYRKY39irgYbkgTOKGYiJRSOKGY6UZUDDekCYUrFDPdEJFleCm4+BhuSCN4bykiUggX8RMeww1pCsMNEVmKI8DiY7ghTeBpKSJSSuGl4OrWQdbDcEOawAnFRKQUec4NT0wJi+GGNIGLbRGR0titiIvhhjRBHrlRtQoiEgFHgMXHcEOawNsvEJFSDHP3OCIsLoYb0oTCTojphogswwnF4mO4IU3hyA0RWYr9iPgYbkgTOG5DRMox3H6BRMVwQ9ogz7lhvCEiy/C0lPgYbkgT2AcRkdK4zo24GG5IEwwTijluQ0SW4o0zxcdwQ5rAFYqJSCk63jhTeAw3pAm8txQRKUXuRzh0IyyGG9IUjtwQkaXYj4iP4YY0gRP/iEgphTfOJFHZRLhZunQpfHx84OzsjICAAOzdu7fYfT/99FN07NgR7u7ucHd3R3BwcIn7kxh4+wUiUophSQmelRKX6uEmLi4OERERiIqKwoEDB9C6dWuEhobi6tWrRe6fmJiIIUOGYPv27UhKSoK3tzdCQkJw6dKlcq6cylPhhGKmGyJSBkeExaV6uFm8eDHGjh2LsLAwNGvWDDExMXBxccGyZcuK3H/VqlV48cUX4evri6ZNm+Kzzz5DQUEBEhISyrlyKlfsg4hIYRy5EZeq4SYnJwf79+9HcHCwvM3Ozg7BwcFISkoq1TGysrKQm5uL6tWrW6tMsgGGT1gctyEiS/FScPE5qPni169fR35+Pjw9PY22e3p64uTJk6U6xrRp01C7dm2jgPSg7OxsZGdny48zMjIAALm5ucjNzS1j5UUzHE/p49oKNdtn+ISVl5dn1dfne6htorcPEL+N5dE+qaAAAJCfn1/uP0fR3z/Aem0053iqhhtLLViwAGvWrEFiYiKcnZ2L3Gf+/PmIjo422b5t2za4uLhYpa74+HirHNdWqNG+ggJ7ADps//lnVNNb//X4Hmqb6O0DxG+jNdt37pwdADucPfsXfvzxjNVepySiv3+A8m3Mysoq9b6qhhsPDw/Y29sjLS3NaHtaWhq8vLxKfO7bb7+NBQsW4KeffkKrVq2K3S8yMhIRERHy44yMDHkSspubm2UN+Jfc3FzEx8ejW7ducHR0VPTYtkDN9kXsiQcKJHTt+h94uhUdZJXA91DbRG8fIH4by6N9hzefQuKV82jQoAF6hj5mldcojujvH2C9NhrOvJSGquHGyckJ/v7+SEhIQL9+/QBAnhwcHh5e7PMWLVqEefPmYevWrWjbtm2Jr6HX66HXm37Ud3R0tNovljWPbQvUaJ/h3lJO5fTafA+1TfT2AeK30Zrts7e/P93Uzs5OtZ+h6O8foHwbzTmW6qelIiIiMHLkSLRt2xbt2rXDkiVLcPfuXYSFhQEARowYgTp16mD+/PkAgIULF2L27NlYvXo1fHx8kJqaCgBwdXWFq6urau0g65In/nFGMRFZSF7nRuU6yHpUDzeDBg3CtWvXMHv2bKSmpsLX1xdbtmyRJxmnpKTAzq7woq6PPvoIOTk5eOaZZ4yOExUVhTfeeKM8S6dyxEs2iUhpEjsWYakebgAgPDy82NNQiYmJRo/PnTtn/YLIZvHGmURkKfn2C8w2wlJ9ET+ih3nw0xUXKCYii3GdG+Ex3JDNe/DTFbMNEVnKMALMkRtxMdyQpvDeUkRkKXYj4mO4IZv34Icr9klEZCl5zg1PTAmL4YZsHufcEJGS5HtLMdsIi+GGbB77HyIiMgfDDdk84wnFHLohIssUTijmRydRMdyQzTM6L85sQ0QW0vFScOEx3JDNMxq5YbghIgtxET/xMdyQpjDbEJHF+ClJeAw3pClc54aILMVLwcXHcEM2jysUE5GSeCm4+BhuyObx0xURWQN7FnEx3JDN44RiIlIS7y0lPoYbsnnGt19guiEiyxR+SGK6ERXDDdk83n6BiJTES8HFx3BDNo/9DxEpiROKxcdwQ5rCkRsishSXlBAfww3ZPN5bioisgVdiiovhhmwf+x8isgKelhIXww3ZvAc/XXE0mYgsxRtnio/hhmweVygmIiVxnRvxMdyQzTNa54ZDN0RkocKRG6YbUTHckM0zWudGxTqISAxcw098DDekKRy4ISJLsR8RH8MN2TyeliIiJclzblSug6yH4YZsHif9EZE1SOxchMVwQzaPk/6ISEm8FFx8DDdk+/7pgXhGioiUxIEbcTHckM0z9D/MNkSkBMPcPWYbcTHckM2T5JEbxhsispyhJ+GcG3Ex3JBmMNoQkRL4OUl8DDdk8wwTitkhEZES5JEbVasga2K4IZvHkWMisgr2LcJiuCGbVzihmEM3RGS5wgnFTDeiYrghmydP+mO2ISIFyOvcMNsIi+GGbB6zDREpqfBqKVXLICtiuCHN4IRiIlIET0sJj+GGNINzbohICexJxMdwQzZP4u0XiEhBnHMjPoYbsnnyOjcq10FEYjCMAjPbiIvhhmweP10RkTWwbxEXww3ZPHmdG56XIiIFFHYlTDeiYrghm2dY54bRhoiUwEvBxcdwQzZP7n+YbohIAfKEYnXLICtiuCGbx0X8iEhJ8oRiDt0Ii+GGNINzbohIEexKhMdwQxrwz5wbdkhEpAB5zo2qVZA1MdyQzePIMRFZA/sWcTHckM2TLwVXtQoiEoVOx0X8RMdwQzav8PYLjDdEZLnCS8EZb0TFcEM2j7dfICIl8XOS+BhuyObxxplEpCTeOFN8DDekIUw3RGQ5HfsS4THckM3jyA0RKalwhWIO3YiK4YZsHjsgIrIGnpYSF8MN2TzefoGIrIHhRlw2EW6WLl0KHx8fODs7IyAgAHv37i1x/3Xr1qFp06ZwdnZGy5Yt8eOPP5ZTpaQmnpYiIiUUrnPDdCMq1cNNXFwcIiIiEBUVhQMHDqB169YIDQ3F1atXi9z/t99+w5AhQzBmzBgcPHgQ/fr1Q79+/XD06NFyrpzKS+HIDdMNEVmucJ0bVcsgK3JQu4DFixdj7NixCAsLAwDExMRg06ZNWLZsGaZPn26y/3vvvYfu3btj6tSpAIC5c+ciPj4e//d//4eYmJhyrf1B2Xn5uJJ+DzezgUvp9+DgkKtaLdaSl5enSvuuZv4NgCM3RKQMQ1/yd14BLt7KKtfXVqsfLU95eXnIyFG3BlXDTU5ODvbv34/IyEh5m52dHYKDg5GUlFTkc5KSkhAREWG0LTQ0FBs2bChy/+zsbGRnZ8uPMzIyAAC5ubnIzVXuF+vwhXQ8+8leAA6IPrBTsePaHnXbp+R7VtLxrf06amH7tE/0NpZH+wry8wHc77efWrjdaq9TPNH/TgA+rvYYoPB7aM7vhKrh5vr168jPz4enp6fRdk9PT5w8ebLI56Smpha5f2pqapH7z58/H9HR0Sbbt23bBhcXlzJWbupcJuCos1fsePQvOuDxylnlNr8qPj6+XF5HLWyf9oneRmu2Lz0b8NDb47bKowsic7BT/j3Myir9KJvqp6WsLTIy0mikJyMjA97e3ggJCYGbm5uirzU2Nxfx8fHo1q0bHB0dFT22LcgVvH2A+G1k+7RP9DaWV/uG9rfaoUsk+vsHWK+NhjMvpaFquPHw8IC9vT3S0tKMtqelpcHLy6vI53h5eZm1v16vh16vN9nu6OhotV8sax7bFojePkD8NrJ92id6G9k+7VO6jeYcS9WrpZycnODv74+EhAR5W0FBARISEhAYGFjkcwIDA432B+4PfRW3PxEREVUsqp+WioiIwMiRI9G2bVu0a9cOS5Yswd27d+Wrp0aMGIE6depg/vz5AICXX34ZQUFBeOedd9CrVy+sWbMG+/btwyeffKJmM4iIiMhGqB5uBg0ahGvXrmH27NlITU2Fr68vtmzZIk8aTklJgZ1d4QBThw4dsHr1asycORMzZsxA48aNsWHDBrRo0UKtJhAREZENUT3cAEB4eDjCw8OL/F5iYqLJtoEDB2LgwIFWroqIiIi0SPUViomIiIiUxHBDREREQmG4ISIiIqEw3BAREZFQGG6IiIhIKAw3REREJBSGGyIiIhIKww0REREJheGGiIiIhGITKxSXJ0mSAJh36/TSys3NRVZWFjIyMoS826vo7QPEbyPbp32it5Ht0z5rtdHwd9vwd7wkFS7cZGZmAgC8vb1VroSIiIjMlZmZiapVq5a4j04qTQQSSEFBAS5fvowqVapAp9MpeuyMjAx4e3vjwoULcHNzU/TYtkD09gHit5Ht0z7R28j2aZ+12ihJEjIzM1G7dm2jG2oXpcKN3NjZ2aFu3bpWfQ03Nzdhf2kB8dsHiN9Gtk/7RG8j26d91mjjw0ZsDDihmIiIiITCcENERERCYbhRkF6vR1RUFPR6vdqlWIXo7QPEbyPbp32it5Ht0z5baGOFm1BMREREYuPIDREREQmF4YaIiIiEwnBDREREQmG4ISIiIqEw3Fjg3LlzGDNmDOrXr49KlSqhYcOGiIqKQk5OTonP+/vvvzFx4kQ88sgjcHV1xYABA5CWllZOVZtn3rx56NChA1xcXFCtWrVSPWfUqFHQ6XRGX927d7duoWVUlvZJkoTZs2ejVq1aqFSpEoKDg/Hnn39at1AL3Lx5E8OGDYObmxuqVauGMWPG4M6dOyU+p3Pnzibv4QsvvFBOFZds6dKl8PHxgbOzMwICArB3794S91+3bh2aNm0KZ2dntGzZEj/++GM5VVp25rQxNjbW5L1ydnYux2rN88svv6B3796oXbs2dDodNmzY8NDnJCYmok2bNtDr9WjUqBFiY2OtXmdZmdu+xMREk/dPp9MhNTW1fAo20/z58/HEE0+gSpUqqFmzJvr164dTp0499Hnl/e+Q4cYCJ0+eREFBAT7++GMcO3YM7777LmJiYjBjxowSnzdlyhR8//33WLduHXbs2IHLly/j6aefLqeqzZOTk4OBAwdiwoQJZj2ve/fuuHLlivz11VdfWalCy5SlfYsWLcL777+PmJgY7NmzB5UrV0ZoaCj+/vtvK1ZadsOGDcOxY8cQHx+PH374Ab/88gvGjRv30OeNHTvW6D1ctGhROVRbsri4OERERCAqKgoHDhxA69atERoaiqtXrxa5/2+//YYhQ4ZgzJgxOHjwIPr164d+/frh6NGj5Vx56ZnbRuD+SrAPvlfnz58vx4rNc/fuXbRu3RpLly4t1f7Jycno1asXunTpgkOHDmHy5Ml4/vnnsXXrVitXWjbmts/g1KlTRu9hzZo1rVShZXbs2IGJEydi9+7diI+PR25uLkJCQnD37t1in6PKv0OJFLVo0SKpfv36xX4/PT1dcnR0lNatWydvO3HihARASkpKKo8Sy2T58uVS1apVS7XvyJEjpb59+1q1HqWVtn0FBQWSl5eX9NZbb8nb0tPTJb1eL3311VdWrLBsjh8/LgGQfv/9d3nb5s2bJZ1OJ126dKnY5wUFBUkvv/xyOVRonnbt2kkTJ06UH+fn50u1a9eW5s+fX+T+zz77rNSrVy+jbQEBAdL48eOtWqclzG2jOf82bQ0Aaf369SXu89prr0nNmzc32jZo0CApNDTUipUpozTt2759uwRAunXrVrnUpLSrV69KAKQdO3YUu48a/w45cqOw27dvo3r16sV+f//+/cjNzUVwcLC8rWnTpnj00UeRlJRUHiWWi8TERNSsWRNNmjTBhAkTcOPGDbVLUkRycjJSU1ON3r+qVasiICDAJt+/pKQkVKtWDW3btpW3BQcHw87ODnv27CnxuatWrYKHhwdatGiByMhIZGVlWbvcEuXk5GD//v1GP3s7OzsEBwcX+7NPSkoy2h8AQkNDbfK9AsrWRgC4c+cO6tWrB29vb/Tt2xfHjh0rj3LLhdbew7Ly9fVFrVq10K1bN+zatUvtckrt9u3bAFDi3z013sMKd+NMazpz5gw++OADvP3228Xuk5qaCicnJ5P5HZ6enjZ7jtVc3bt3x9NPP4369evj7NmzmDFjBnr06IGkpCTY29urXZ5FDO+Rp6en0XZbff9SU1NNhrcdHBxQvXr1EusdOnQo6tWrh9q1a+OPP/7AtGnTcOrUKXz77bfWLrlY169fR35+fpE/+5MnTxb5nNTUVM28V0DZ2tikSRMsW7YMrVq1wu3bt/H222+jQ4cOOHbsmNVvElweinsPMzIycO/ePVSqVEmlypRRq1YtxMTEoG3btsjOzsZnn32Gzp07Y8+ePWjTpo3a5ZWooKAAkydPxpNPPokWLVoUu58a/w45clOE6dOnFznB68Gvf3c0ly5dQvfu3TFw4ECMHTtWpcpLpyztM8fgwYPRp08ftGzZEv369cMPP/yA33//HYmJico1ogTWbp8tsHYbx40bh9DQULRs2RLDhg3Dl19+ifXr1+Ps2bMKtoKUEBgYiBEjRsDX1xdBQUH49ttvUaNGDXz88cdql0al0KRJE4wfPx7+/v7o0KEDli1bhg4dOuDdd99Vu7SHmjhxIo4ePYo1a9aoXYoJjtwU4ZVXXsGoUaNK3KdBgwby/1++fBldunRBhw4d8Mknn5T4PC8vL+Tk5CA9Pd1o9CYtLQ1eXl6WlF1q5rbPUg0aNICHhwfOnDmDrl27Knbc4lizfYb3KC0tDbVq1ZK3p6WlwdfXt0zHLIvSttHLy8tkImpeXh5u3rxp1u9bQEAAgPujkw0bNjS7XiV4eHjA3t7e5MrCkv7teHl5mbW/2srSxn9zdHSEn58fzpw5Y40Sy11x76Gbm5vmR22K065dO/z6669ql1Gi8PBw+QKFh40QqvHvkOGmCDVq1ECNGjVKte+lS5fQpUsX+Pv7Y/ny5bCzK3kwzN/fH46OjkhISMCAAQMA3J8ln5KSgsDAQItrLw1z2qeEixcv4saNG0ZhwJqs2b769evDy8sLCQkJcpjJyMjAnj17zL6izBKlbWNgYCDS09Oxf/9++Pv7AwB+/vlnFBQUyIGlNA4dOgQA5fYeFsXJyQn+/v5ISEhAv379ANwfFk9ISEB4eHiRzwkMDERCQgImT54sb4uPjy+3f2vmKksb/y0/Px9HjhxBz549rVhp+QkMDDS5bNiW30MlHDp0SNV/ayWRJAkvvfQS1q9fj8TERNSvX/+hz1Hl36HVpipXABcvXpQaNWokde3aVbp48aJ05coV+evBfZo0aSLt2bNH3vbCCy9Ijz76qPTzzz9L+/btkwIDA6XAwEA1mvBQ58+flw4ePChFR0dLrq6u0sGDB6WDBw9KmZmZ8j5NmjSRvv32W0mSJCkzM1N69dVXpaSkJCk5OVn66aefpDZt2kiNGzeW/v77b7WaUSxz2ydJkrRgwQKpWrVq0saNG6U//vhD6tu3r1S/fn3p3r17ajThobp37y75+flJe/bskX799VepcePG0pAhQ+Tv//t39MyZM9KcOXOkffv2ScnJydLGjRulBg0aSJ06dVKrCbI1a9ZIer1eio2NlY4fPy6NGzdOqlatmpSamipJkiQNHz5cmj59urz/rl27JAcHB+ntt9+WTpw4IUVFRUmOjo7SkSNH1GrCQ5nbxujoaGnr1q3S2bNnpf3790uDBw+WnJ2dpWPHjqnVhBJlZmbK/84ASIsXL5YOHjwonT9/XpIkSZo+fbo0fPhwef+//vpLcnFxkaZOnSqdOHFCWrp0qWRvby9t2bJFrSaUyNz2vfvuu9KGDRukP//8Uzpy5Ij08ssvS3Z2dtJPP/2kVhNKNGHCBKlq1apSYmKi0d+8rKwseR9b+HfIcGOB5cuXSwCK/DJITk6WAEjbt2+Xt927d0968cUXJXd3d8nFxUXq37+/USCyJSNHjiyyfQ+2B4C0fPlySZIkKSsrSwoJCZFq1KghOTo6SvXq1ZPGjh0rd8y2xtz2SdL9y8FnzZoleXp6Snq9Xuratat06tSp8i++lG7cuCENGTJEcnV1ldzc3KSwsDCj8Pbv39GUlBSpU6dOUvXq1SW9Xi81atRImjp1qnT79m2VWmDsgw8+kB599FHJyclJateunbR79275e0FBQdLIkSON9l+7dq302GOPSU5OTlLz5s2lTZs2lXPF5jOnjZMnT5b39fT0lHr27CkdOHBAhapLx3Dp87+/DG0aOXKkFBQUZPIcX19fycnJSWrQoIHRv0dbY277Fi5cKDVs2FBydnaWqlevLnXu3Fn6+eef1Sm+FIr7m/fge2IL/w51/xRLREREJAReLUVERERCYbghIiIioTDcEBERkVAYboiIiEgoDDdEREQkFIYbIiIiEgrDDREREQmF4YaICMCNGzdQs2ZNnDt3TtHjHj9+HHXr1sXdu3cVPS4RFY/hhojMMmrUqCLvQt69e3e1S7PIvHnz0LdvX/j4+JRq/969exfb5p07d0Kn0+GPP/5As2bN0L59eyxevFjBaomoJFyhmIjMMmrUKKSlpWH58uVG2/V6Pdzd3a32ujk5OXBycrLKsbOyslCrVi1s3boV7du3L9VzNmzYgAEDBuD8+fMmd0UePXo0jhw5gt9//x0AsGnTJowdOxYpKSlwcOD9iomsjSM3RGQ2vV4PLy8vo68Hg41Op8Nnn32G/v37w8XFBY0bN8Z3331ndIyjR4+iR48ecHV1haenJ4YPH47r16/L3+/cuTPCw8MxefJkeHh4IDQ0FADw3XffoXHjxnB2dkaXLl3wxRdfQKfTIT09HXfv3oWbmxu+/vpro9fasGEDKleujMzMzCLb8+OPP0Kv15sEm5Jq/O9//4saNWogNjbW6Dl37tzBunXrMGbMGHlbt27dcPPmTezYsaOUP2EisgTDDRFZRXR0NJ599ln88ccf6NmzJ4YNG4abN28CANLT0/Gf//wHfn5+2LdvH7Zs2YK0tDQ8++yzRsf44osv4OTkhF27diEmJgbJycl45pln0K9fPxw+fBjjx4/H66+/Lu9fuXJlDB482GRUafny5XjmmWdQpUqVImvduXMn/P39jbY9rEYHBweMGDECsbGxeHAAfN26dcjPz8eQIUPkbU5OTvD19cXOnTvL8JMkIrNZ9bacRCSckSNHSvb29lLlypWNvubNmyfvA0CaOXOm/PjOnTsSAGnz5s2SJEnS3LlzpZCQEKPjXrhwQQIg32E9KChI8vPzM9pn2rRpUosWLYy2vf766xIA6datW5IkSdKePXske3t76fLly5IkSVJaWprk4OAgJSYmFtumvn37SqNHjzbaVpoaT5w4YXIX+Y4dO0rPPfecyWv0799fGjVqVLE1EJFyePKXiMzWpUsXfPTRR0bbqlevbvS4VatW8v9XrlwZbm5uuHr1KgDg8OHD2L59O1xdXU2OffbsWTz22GMAYDKacurUKTzxxBNG29q1a2fyuHnz5vjiiy8wffp0rFy5EvXq1UOnTp2Kbc+9e/fg7OxstK00NTZt2hQdOnTAsmXL0LlzZ5w5cwY7d+7EnDlzTJ5TqVIlZGVlFVsDESmH4YaIzFa5cmU0atSoxH0cHR2NHut0OhQUFAC4Py+ld+/eWLhwocnzatWqZfQ6ZfH8889j6dKlmD59OpYvX46wsDDodLpi9/fw8MCtW7eMtpW2xjFjxuCll17C0qVLsXz5cjRs2BBBQUEmz7l58yYaNmxYpvYQkXk454aIyl2bNm1w7Ngx+Pj4oFGjRkZfJQWaJk2aYN++fUbbDFckPei5557D+fPn8f777+P48eMYOXJkifX4+fnh+PHjZarx2WefhZ2dHVavXo0vv/wSo0ePLjJIHT16FH5+fiXWQUTKYLghIrNlZ2cjNTXV6OvBK50eZuLEibh58yaGDBmC33//HWfPnsXWrVsRFhaG/Pz8Yp83fvx4nDx5EtOmTcPp06exdu1a+WqlBwOFu7s7nn76aUydOhUhISEml2r/W2hoKI4dO2Y0elPaGl1dXTFo0CBERkbiypUrGDVqlMnxz507h0uXLiE4OLiUPyEisgTDDRGZbcuWLahVq5bR11NPPVXq59euXRu7du1Cfn4+QkJC0LJlS0yePBnVqlWDnV3x3VL9+vXx9ddf49tvv0WrVq3w0UcfyVdL6fV6o33HjBmDnJwcjB49+qH1tGzZEm3atMHatWvLVOOYMWNw69YthIaGonbt2ibH/+qrrxASEoJ69eo9tBYishwX8SMiTZs3bx5iYmJw4cIFo+0rVqzAlClTcPny5VIt/rdp0yZMnToVR48eLTFgmSsnJweNGzfG6tWr8eSTTyp2XCIqHicUE5GmfPjhh3jiiSfwyCOPYNeuXXjrrbcQHh4ufz8rKwtXrlzBggULMH78+FKvatyrVy/8+eefuHTpEry9vRWrNyUlBTNmzGCwISpHHLkhIk2ZMmUK4uLicPPmTTz66KMYPnw4IiMj5dsavPHGG5g3bx46deqEjRs3FnkpNxGJjeGGiIiIhMIJxURERCQUhhsiIiISCsMNERERCYXhhoiIiITCcENERERCYbghIiIioTDcEBERkVAYboiIiEgoDDdEREQklP8HBoE0jvCQ1NwAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/examples/atomic_chain_api/input_files/negf_chain_new.json b/examples/atomic_chain_api/input_files/negf_chain_new.json
index 2a2002e..0a05a2b 100644
--- a/examples/atomic_chain_api/input_files/negf_chain_new.json
+++ b/examples/atomic_chain_api/input_files/negf_chain_new.json
@@ -42,9 +42,9 @@
             "err": 1e-5
         },
         "sgf_solver": "Sancho-Rubio",
-        "espacing": 0.01,
-        "emin": -2,
-        "emax": 2,
+        "espacing": 0.02,
+        "emin": -1.5,
+        "emax": 1.5,
         "density_options": {
             "method": "Fiori",
             "integrate_way": "direct"
@@ -54,10 +54,10 @@
         "out_dos": true,
         "out_tc": true,
         "out_ldos": true,
-        "out_current_nscf": true
+        "out_current_nscf": false
 },    
 "AtomicData_options" :{
-    "r_max": 2.0
+    "r_max": 3.0
 },
 "structure":"./chain.vasp"
 }

From d73fb9c40deea02ab5d657c596f1eabe8d83c8b2 Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Mon, 15 Sep 2025 22:44:16 +0800
Subject: [PATCH 129/152] Update docs/CONTRIBUTING.md

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 docs/CONTRIBUTING.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
index 5c356fc..959d0e1 100644
--- a/docs/CONTRIBUTING.md
+++ b/docs/CONTRIBUTING.md
@@ -70,7 +70,7 @@ pytest ./dpnegf/tests/test_file.py
 
 ## Submitting a Pull Request
 
-1. [Fork](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) the [DPNEGF  repository](https://github.com/DeePTB-Lab/dpnegf). If you already had an existing fork, [sync](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork) the fork to keep your modification up-to-date.
+1. [Fork](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) the [DPNEGF repository](https://github.com/DeePTB-Lab/dpnegf). If you already had an existing fork, [sync](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork) the fork to keep your modification up-to-date.
 2. Create a new branch for your changes.
      ```shell
      git checkout -b my-fix-branch

From 64c202a2242ba8165f09dea6ffba74615ec21ab6 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 15 Sep 2025 22:56:50 +0800
Subject: [PATCH 130/152] update index.rst

---
 docs/hands_on/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/hands_on/index.rst b/docs/hands_on/index.rst
index 5dfe0e3..7591743 100644
--- a/docs/hands_on/index.rst
+++ b/docs/hands_on/index.rst
@@ -3,4 +3,4 @@ A quick Example
 =================================================
 
 .. toctree::
-   tutorial1_base_sk
+   tutorial1_c_chain

From 02fba706fc553cce9f5947df21fb793d7edf480d Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Tue, 16 Sep 2025 14:31:44 +0800
Subject: [PATCH 131/152] Update tutorial1  (#36)

* update totorial to delete unnecessary log info.

* add docs

* update index.rst

* add gen_input_docs

* add tpye check

* update gen_input_docs and conf.py

* fix typos
---
 docs/conf.py                          |   5 +
 docs/hands_on/tutorial1_c_chain.ipynb | 143 +++-----------------------
 docs/index.rst                        |   4 +-
 dpnegf/utils/gen_input_docs.py        |  69 +++++++++++++
 4 files changed, 92 insertions(+), 129 deletions(-)
 create mode 100644 dpnegf/utils/gen_input_docs.py

diff --git a/docs/conf.py b/docs/conf.py
index 925e70b..5b68934 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -94,3 +94,8 @@
 latex_elements = {
     'extraclassoptions':'openany,oneside'
 }
+
+# # -- Auto-generate input docs from argcheck ---------------------
+# def setup(app):
+#     from dpnegf.utils import gen_input_docs
+#     gen_input_docs.main()
diff --git a/docs/hands_on/tutorial1_c_chain.ipynb b/docs/hands_on/tutorial1_c_chain.ipynb
index 06b7638..21aab86 100644
--- a/docs/hands_on/tutorial1_c_chain.ipynb
+++ b/docs/hands_on/tutorial1_c_chain.ipynb
@@ -5,9 +5,7 @@
    "id": "f132d6ec",
    "metadata": {},
    "source": [
-    "# Tutorial 1:  Quantum Transport in a One-Dimensional Chain\n",
-    "\n",
-    "A quick start for DeePTB-NEGF."
+    "# Tutorial 1:  Quantum Transport in a One-Dimensional Chain"
    ]
   },
   {
@@ -28,11 +26,11 @@
     "\n",
     "In this tutorial, you will learn \n",
     "1. how to load DeePTB model and plot band structure\n",
-    "2. how to calculate the tranmission spectrum\n",
+    "2. how to calculate the transmission spectrum\n",
     "   \n",
     "For demonstration, we use a one-dimensional chain as an example.\n",
     "\n",
-    "### Requirement\n",
+    "### Requirements\n",
     "\n",
     "DeePTB and DPNEGF installed. Detailed installation instructions can be found in README.\n"
    ]
@@ -116,7 +114,9 @@
     "\n",
     " \n",
     "NEGF connects *microscopic Hamiltonians* (from first principles or tight-binding) with *observable transport quantities* (DOS,  current), incorporating the quantum effects naturally.\n",
-    "This makes it an essential tool for nanoelectronics and quantum device simulations.\n"
+    "This makes it an essential tool for nanoelectronics and quantum device simulations.\n",
+    "\n",
+    "In **DPNEGF**, the DeePTB model (either DeePTB-SK or DeePTB-E3) is employed to predict the electronic Hamiltonian with first-principles accuracy, after which the efficiently implemented NEGF method is used to calculate quantum transport properties.\n"
    ]
   },
   {
@@ -180,24 +180,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "463ca65d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DPNEGF INFO    ================================================================================\n",
-      "DPNEGF INFO                                      Version Info                                  \n",
-      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
-      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
-      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
-      "DPNEGF INFO    ================================================================================\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from dpnegf.utils.loggers import set_log_handles\n",
     "import logging\n",
@@ -218,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "04d77b4f",
    "metadata": {},
    "outputs": [
@@ -233,8 +219,9 @@
    "source": [
     "from dptb.nn.build import build_model\n",
     "import json\n",
-    "model =  \"nnsk_C_new.json\" # the model for demonsration\n",
-    "model_json = json.load(open(model))\n",
+    "model =  \"nnsk_C_new.json\" # the model for demonstration\n",
+    "with open(model) as f:\n",
+    "    model_json = json.load(f)\n",
     "model = build_model(model,\n",
     "                    model_options= model_json['model_options'],\n",
     "                    common_options=model_json['common_options'])"
@@ -245,7 +232,7 @@
    "id": "146abc51",
    "metadata": {},
    "source": [
-    "After the model is loaded, bands for specific structures can be ploted. \n",
+    "After the model is loaded, bands for specific structures can be plotted. \n",
     "\n",
     "Here we load the full system and split it into unit cell."
    ]
@@ -608,110 +595,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "524faa1e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_api/input_files\n",
-      "sisl is not installed.Thus the input for TBtrans can not be generated, please install it first!\n",
-      "tbtrans is not in the Environment PATH. Thus the input for TBtrans can be generated but not run.\n",
-      "================================================================================\n",
-      "            _______  .______   .__   __.  _______   _______  _______            \n",
-      "           |       \\ |   _  \\  |  \\ |  | |   ____| /  _____||   ____|           \n",
-      "           |  .--.  ||  |_)  | |   \\|  | |  |__   |  |  __  |  |__              \n",
-      "           |  |  |  ||   ___/  |  . `  | |   __|  |  | |_ | |   __|             \n",
-      "           |  '--'  ||  |      |  |\\   | |  |____ |  |__| | |  |                \n",
-      "             |_______/ | _|      |__| \\__| |_______| \\______| |__|              \n",
-      "--------------------------------------------------------------------------------\n",
-      "                       DPNEGF version 0.1.1.dev97+bccd946                       \n",
-      "================================================================================\n",
-      "DPNEGF INFO    ================================================================================\n",
-      "DPNEGF INFO                                      Version Info                                  \n",
-      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
-      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
-      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
-      "DPNEGF INFO    ================================================================================\n",
-      "\n",
-      "DPNEGF WARNING The model option atomic_radius in nnsk is not defined in input model_options, set to v1.\n",
-      "DPNEGF INFO    ------ k-point for NEGF -----\n",
-      "DPNEGF INFO    Gamma Center: True\n",
-      "DPNEGF INFO    Time Reversal: True\n",
-      "DPNEGF INFO    k-points Num: 1\n",
-      "DPNEGF INFO    k-points: [[0 0 0]]\n",
-      "DPNEGF INFO    k-points weights: [1.]\n",
-      "DPNEGF INFO    --------------------------------\n",
-      "DPNEGF WARNING AtomicData_options is extracted from input file. This may be not consistent with the model options. Please be careful and check the cutoffs.\n",
-      "DPNEGF INFO    The AtomicData_options is:\n",
-      "               {\n",
-      "                   \"r_max\": 3.0,\n",
-      "                   \"er_max\": null,\n",
-      "                   \"oer_max\": null\n",
-      "               }\n",
-      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732052e-10 (threshold: 1.000000e-05)\n",
-      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
-      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
-      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
-      "DPNEGF INFO    The coupling width of lead_L is 1.\n",
-      "DPNEGF INFO    The coupling width of lead_R is 1.\n",
-      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
-      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
-      "DPNEGF INFO    ================================================================================\n",
-      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
-      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0.0\n",
-      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0.0\n",
-      "DPNEGF INFO    Number of electrons in lead_L: {'C': 1.0}\n",
-      "DPNEGF INFO    Number of electrons in lead_R: {'C': 1.0}\n",
-      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
-      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
-      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 3.0\n",
-      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
-      "DPNEGF INFO    Getting eigenvalues from the model.\n",
-      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
-      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
-      "DPNEGF INFO    q_cal: 3.999994485346824, total_electrons: 4.0, diff q: 5.514653175886508e-06\n",
-      "DPNEGF INFO    Estimated E_fermi: -13.638588428497314 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
-      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
-      "DPNEGF INFO    KPOINTS  kmesh sampling: 11 kpoints\n",
-      "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 3.0\n",
-      "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
-      "DPNEGF INFO    Getting eigenvalues from the model.\n",
-      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
-      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 51 iterations.\n",
-      "DPNEGF INFO    q_cal: 4.000002782981872, total_electrons: 4.0, diff q: 2.7829818716185173e-06\n",
-      "DPNEGF INFO    Estimated E_fermi: -13.638587474822998 based on the valence electrons setting nel_atom : {'C': 1.0} .\n",
-      "DPNEGF INFO    -------------------------------------------------\n",
-      "DPNEGF INFO    Zero bias case detected.\n",
-      "DPNEGF INFO    Fermi level for lead_L: -13.638588428497314\n",
-      "DPNEGF INFO    Fermi level for lead_R: -13.638587474822998\n",
-      "DPNEGF INFO    Electrochemical potential for lead_L: -13.638588428497314\n",
-      "DPNEGF INFO    Electrochemical potential for lead_R: -13.638587474822998\n",
-      "DPNEGF INFO    Reference energy E_ref: -13.638588428497314\n",
-      "DPNEGF INFO    =================================================\n",
-      "\n",
-      "DPNEGF INFO    Merging 150 tmp self energy files into /personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_api/input_files/../negf_output_cli/results/self_energy/self_energy_leadL.h5\n",
-      "DPNEGF INFO    Merge complete.\n",
-      "DPNEGF INFO    Merging 150 tmp self energy files into /personal/DeepTB/dptb_Zjj/dpnegf/examples/atomic_chain_api/input_files/../negf_output_cli/results/self_energy/self_energy_leadR.h5\n",
-      "DPNEGF INFO    Merge complete.\n",
-      "DPNEGF INFO    Properties computation at k = [0.0000,0.0000,0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -1.500\n",
-      "DPNEGF INFO    computing green's function at e = -1.198\n",
-      "DPNEGF INFO    computing green's function at e = -0.896\n",
-      "DPNEGF INFO    computing green's function at e = -0.594\n",
-      "DPNEGF INFO    computing green's function at e = -0.292\n",
-      "DPNEGF INFO    computing green's function at e = 0.010\n",
-      "DPNEGF INFO    computing green's function at e = 0.312\n",
-      "DPNEGF INFO    computing green's function at e = 0.614\n",
-      "DPNEGF INFO    computing green's function at e = 0.916\n",
-      "DPNEGF INFO    computing green's function at e = 1.218\n",
-      "DPNEGF INFO    negf calculation successfully completed.\n",
-      "\u001b[0m\u001b[0m\u001b[0m\u001b[0m\u001b[0m"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Command line for DPNEGF\n",
     "! pwd\n",
diff --git a/docs/index.rst b/docs/index.rst
index 8a2f3dd..5fc05f5 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -10,9 +10,11 @@ DPNEGF Documentation
 establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy. 
 
 --------------
-Key Features:
+Overview
 --------------
 
+Key Features:
+~~~~~~~~~~~~~
 By using DeePTB-SK or DeePTB-E3—both available within the DeePTB package—DeePTB-NEGF can compute quantum transport 
 properties in open-boundary systems with either environment-corrected Slater-Koster(SK) TB Hamiltonian 
 or linear combination of atomic orbitals (LCAO) Kohn-Sham Hamiltonian.
diff --git a/dpnegf/utils/gen_input_docs.py b/dpnegf/utils/gen_input_docs.py
new file mode 100644
index 0000000..6714fb1
--- /dev/null
+++ b/dpnegf/utils/gen_input_docs.py
@@ -0,0 +1,69 @@
+def render_argument(arg, indent=0):
+    """Render an Argument object (and its children) into RST format."""
+    ind = "    " * indent
+    out = []
+
+    if isinstance(arg, str):
+        return f"{ind}{arg}"
+
+    out.append(f"{ind}{arg.name}:")
+    out.append(f"{ind}    | type: ``{arg.dtype}``")
+
+    if getattr(arg, "optional", False):
+        out.append(f"{ind}    | optional: True")
+
+    if getattr(arg, "default", None) is not None:
+        out.append(f"{ind}    | default: ``{arg.default}``")
+
+    if getattr(arg, "doc", ""):
+        doc_lines = arg.doc.strip().splitlines()
+        for line in doc_lines:
+            out.append(f"{ind}    {line.strip()}")
+
+    # 如果有子字段
+    for sub in getattr(arg, "sub_fields", []):
+        out.append("")  # 空行分隔
+        out.append(render_argument(sub, indent + 1))
+
+    # 如果有子变体
+    for var in getattr(arg, "sub_variants", []):
+        out.append("")
+        out.append(f"{ind}    Variant:")
+        out.append(render_argument(var, indent + 2))
+
+    return "\n".join(out)
+
+
+import os
+from dpnegf.utils import argcheck
+
+def generate_rst_from_argcheck(output_dir="docs/input_params"):
+    os.makedirs(output_dir, exist_ok=True)
+
+    # 这里你可以写死，也可以动态扫描
+    modules = {
+        "common_options": argcheck.common_options,
+        "run_options": argcheck.run_options,
+    }
+
+    for name, func in modules.items():
+        # arg = func() if callable(func) else func
+        # print(f"[DEBUG] {name} returned:", arg, type(arg))
+        arg = func()
+        rst = f"""
+========================================
+{name.replace("_", " ").title()}
+========================================
+
+.. _`{name}`:
+
+{render_argument(arg)}
+"""
+        with open(os.path.join(output_dir, f"{name}.rst"), "w", encoding="utf-8") as f:
+            f.write(rst)
+
+def main():
+    generate_rst_from_argcheck()
+
+if __name__ == "__main__":
+    main()

From 3ea46fa96b83e935621e7eaba30d16e4faf56e7c Mon Sep 17 00:00:00 2001
From: Jijie Zou <73353910+AsymmetryChou@users.noreply.github.com>
Date: Tue, 16 Sep 2025 19:53:39 +0800
Subject: [PATCH 132/152] Fix wrong cell outputs in tutorial notebook (#37)

* update totorial to delete unnecessary log info.

* add docs

* update index.rst

* add gen_input_docs

* add tpye check

* update gen_input_docs and conf.py

* fix typos

* update tutorial by delete wrong outputs

* update docs

* remove INPUT TAG for temp

* remove shell dependence

* remove shell dependency

* add docs

* add warning
---
 docs/hands_on/tutorial1_base_sk.ipynb | 1284 -------------------------
 docs/hands_on/tutorial1_c_chain.ipynb |   62 +-
 docs/index.rst                        |    8 +-
 3 files changed, 38 insertions(+), 1316 deletions(-)
 delete mode 100755 docs/hands_on/tutorial1_base_sk.ipynb

diff --git a/docs/hands_on/tutorial1_base_sk.ipynb b/docs/hands_on/tutorial1_base_sk.ipynb
deleted file mode 100755
index e9b6f9f..0000000
--- a/docs/hands_on/tutorial1_base_sk.ipynb
+++ /dev/null
@@ -1,1284 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "c7fed360",
-      "metadata": {},
-      "source": [
-        "# Tutorial 1: deeptb-sk baseline model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "8eaf407f",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "**DeePTB** is a method that uses deep learning to accelerate first-principles electronic structure simulations.\n",
-        "\n",
-        "### Version Features\n",
-        "- **v1**: Constructed tight-binding (TB) models with first-principles accuracy (DeePTB-SK)\n",
-        "- **v2**: Added E3 equivariant networks to represent single-electron operators (Hamiltonian, density matrix, and overlap matrix) (DeePTB-E3)\n",
-        "- **v2.2**: Incorporated built-in SK empirical parameters covering commonly used elements across the periodic table\n",
-        "\n",
-        "Through these capabilities, DeePTB provides multiple approaches to accelerate electronic structure simulations of materials.\n",
-        "\n",
-        "### Learning Objectives\n",
-        "\n",
-        "In this tutorial, you will:\n",
-        "1. Learn how to use built-in base model to plot band structure for given crystal structure\n",
-        "2. Learn how to generate a empirical sk model in deeptb-sk format for target system"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "0c5db933",
-      "metadata": {},
-      "source": [
-        "## 1. Calculating Band Structure for a Given Structure\n",
-        "\n",
-        "The deeptb-sk module now [since v2.2] has built-in empirical SK parameter models covering elements across the periodic table. \n",
-        "\n",
-        "These can be directly used to obtain empirical SKTB models for given structures. It also supports directly plotting band structures for a given structure."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "id": "a980d847",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[01;34m./\u001b[0m\n",
-            "\u251c\u2500\u2500 \u001b[00mgaas.vasp\u001b[0m\n",
-            "\u251c\u2500\u2500 \u001b[00mhBN.vasp\u001b[0m\n",
-            "\u2514\u2500\u2500 \u001b[00msilicon.vasp\u001b[0m\n",
-            "\n",
-            "0 directories, 3 files\n"
-          ]
-        }
-      ],
-      "source": [
-        "import os\n",
-        "workdir='/root/soft/DeePTB/examples/base_model/'\n",
-        "os.chdir(f\"{workdir}/structures\")\n",
-        "!tree -L 1 ./"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "c6d703e8",
-      "metadata": {},
-      "source": [
-        "Run the band structure plotting command.\n",
-        "**Note** that the selection of high-symmetry paths in the Brillouin zone is based on the seekpath.get_path_orig_cell function, which has the following characteristics to be aware of:\n",
-        "1. It does not support 2D materials and will treat 2D materials as 3D materials\n",
-        "   \n",
-        "2. If the input cell is a non-standard primitive unit cell, the returned k path is equivalent to the k path for the standard cell. For example, the band structure calculated along the k path for the standard and non-standard unit cells will be the same up to numerical errors.\n",
-        "   \n",
-        "3. If the input cell is a supercell of a smaller primitive cell, the returned k path is that of the associated primitive cell, in the basis of supercell reciprocal lattice. In this case, the k points are not the high-symmetry points of the first Brillouin zone of the given supercell, but the high-symmetry points of the Brillouin zone of the associated primitive cell.\n",
-        "\n",
-        "The command for plotting the band structure is as follows: "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "55ca0ab2",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
-            " \n",
-            " \n",
-            "#################################################################################\n",
-            "#                                                                               #\n",
-            "#                                                                               #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
-            "#                                                                               #\n",
-            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
-            "#                                                                               #\n",
-            "#################################################################################\n",
-            " \n",
-            " \n",
-            "/root/dptb_venv/lib/python3.10/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead\n",
-            "  conv_lattice = dataset[\"std_lattice\"]\n",
-            "DEEPTB INFO    The structure space group is: Fd-3m (No. 227)\n",
-            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
-            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
-            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
-            "/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
-            "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
-            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
-            "Figure(640x560)\n",
-            "DEEPTB INFO    band calculation successfully completed.\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# 1. Silicon\n",
-        "!dptb run band -i poly4 -stu silicon.vasp -o band_si\n",
-        "\n",
-        "# display the band plot:\n",
-        "from IPython.display import Image, display\n",
-        "import os\n",
-        "image_path = f'./band_si/results/band.png'\n",
-        "display(Image(filename=image_path))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "9e8fd859",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
-            " \n",
-            " \n",
-            "#################################################################################\n",
-            "#                                                                               #\n",
-            "#                                                                               #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
-            "#                                                                               #\n",
-            "#                         Version: 2.0.4.dev77+45eaa8c                          #\n",
-            "#                                                                               #\n",
-            "#################################################################################\n",
-            " \n",
-            " \n",
-            "/root/dptb_venv/lib/python3.10/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead\n",
-            "  conv_lattice = dataset[\"std_lattice\"]\n",
-            "DEEPTB INFO    The structure space group is: F-43m (No. 216)\n",
-            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
-            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
-            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
-            "/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
-            "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
-            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
-            "Figure(640x560)\n",
-            "DEEPTB INFO    band calculation successfully completed.\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# 2. GaAs\n",
-        "!dptb run band -i poly4 -stu gaas.vasp -o band_gaas\n",
-        "\n",
-        "# display the band plot:\n",
-        "from IPython.display import Image, display\n",
-        "import os\n",
-        "image_path = f'./band_gaas/results/band.png'\n",
-        "display(Image(filename=image_path))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "bdce4498",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
-            " \n",
-            " \n",
-            "#################################################################################\n",
-            "#                                                                               #\n",
-            "#                                                                               #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
-            "#                                                                               #\n",
-            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
-            "#                                                                               #\n",
-            "#################################################################################\n",
-            " \n",
-            " \n",
-            "/root/dptb_venv/lib/python3.10/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead\n",
-            "  conv_lattice = dataset[\"std_lattice\"]\n",
-            "DEEPTB INFO    The structure space group is: P-6m2 (No. 187)\n",
-            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
-            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
-            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
-            "/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
-            "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
-            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
-            "Figure(640x560)\n",
-            "DEEPTB INFO    band calculation successfully completed.\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# 3. hBN 2D \n",
-        "!dptb run band -i poly4 -stu hBN.vasp -o band_hBN\n",
-        "\n",
-        "# display the band plot:\n",
-        "from IPython.display import Image, display\n",
-        "import os\n",
-        "image_path = f'./band_hBN/results/band.png'\n",
-        "display(Image(filename=image_path))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "16a7eee3",
-      "metadata": {},
-      "source": [
-        "## 2. Extracting SK Parameter Files for a Given System\n",
-        "\n",
-        "Since there is a built-in baseline model covering the periodic table, for the target research system, you can extract the empirical parameter model for your target system from this built-in baseline model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "f4d144f5",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "\u001b[01;34m./\u001b[0m\n",
-            "\u251c\u2500\u2500 \u001b[00mgaas.json\u001b[0m\n",
-            "\u251c\u2500\u2500 \u001b[00mhbn_sp.json\u001b[0m\n",
-            "\u251c\u2500\u2500 \u001b[00mhbn_spd.json\u001b[0m\n",
-            "\u2514\u2500\u2500 \u001b[00msilicon.json\u001b[0m\n",
-            "\n",
-            "0 directories, 4 files\n"
-          ]
-        }
-      ],
-      "source": [
-        "os.chdir(f\"{workdir}/confs\")\n",
-        "!tree -L 1 ./"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "10376e0d",
-      "metadata": {},
-      "source": [
-        "For the target system, we first need to define the basis set configuration and save it in a JSON file. Below is the configuration we use for hBN."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "57b4a974",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "{\n",
-            "    \"common_options\": {\n",
-            "        \"basis\": {\n",
-            "            \"B\": [\"s\",\"p\",\"d\"],\n",
-            "            \"N\": [\"s\",\"p\",\"d\"]\n",
-            "        }\n",
-            "    }\n",
-            "}"
-          ]
-        }
-      ],
-      "source": [
-        "!cat hbn_spd.json"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "81f0228f",
-      "metadata": {},
-      "source": [
-        "Run the following command to extract the empirical model settings and parameters for the target system from the built-in empirical model covering the periodic table, and save them in the sktb.json file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "df8715cf",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
-            " \n",
-            " \n",
-            "#################################################################################\n",
-            "#                                                                               #\n",
-            "#                                                                               #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
-            "#                                                                               #\n",
-            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
-            "#                                                                               #\n",
-            "#################################################################################\n",
-            " \n",
-            " \n",
-            "DEEPTB INFO    Extracting empirical SK parameters for BN\n",
-            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
-            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
-            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
-            "DEEPTB INFO    Empirical SK parameters are saved in hbn_spd_model/sktb.json\n",
-            "DEEPTB INFO    If you want to further train the model, please use `dptb config` command to generate input template.\n"
-          ]
-        }
-      ],
-      "source": [
-        "!dptb esk hbn_spd.json -m poly4 -o hbn_spd_model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "7946902a",
-      "metadata": {},
-      "source": [
-        "The above command will create an hbn_spd_model folder and save the sktb.json model file in it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "bc7b427d",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "{\n",
-            "    \"version\": 2,\n",
-            "    \"unit\": \"eV\",\n",
-            "    \"model_options\": {\n",
-            "        \"nnsk\": {\n",
-            "            \"onsite\": {\n",
-            "                \"method\": \"uniform_noref\"\n",
-            "            },\n",
-            "            \"hopping\": {\n",
-            "                \"method\": \"poly4pow\",\n",
-            "                \"rs\": {\n",
-            "                    \"B-B\": 4.22,\n",
-            "                    \"B-N\": 4.04,\n",
-            "                    \"N-B\": 4.04,\n",
-            "                    \"N-N\": 3.85\n",
-            "                },\n",
-            "                \"w\": 0.2\n",
-            "            },\n",
-            "            \"soc\": {},\n",
-            "            \"freeze\": false,\n",
-            "            \"push\": false,\n",
-            "            \"std\": 0.01,\n",
-            "            \"atomic_radius\": \"cov\"\n",
-            "        }\n",
-            "    },\n",
-            "    \"common_options\": {\n",
-            "        \"basis\": {\n",
-            "            \"B\": [\n",
-            "                \"s\",\n",
-            "                \"p\",\n",
-            "                \"d\"\n",
-            "            ],\n",
-            "            \"N\": [\n",
-            "                \"s\",\n",
-            "                \"p\",\n",
-            "                \"d\"\n",
-            "            ]\n",
-            "        },\n",
-            "        \"dtype\": \"float32\",\n",
-            "        \"device\": \"cuda\",\n",
-            "        \"overlap\": true\n",
-            "    },\n",
-            "    \"model_params\": {\n",
-            "        \"onsite\": {\n",
-            "            \"B-s-0\": [\n",
-            "                -9.436724662780762\n",
-            "            ],\n",
-            "            \"B-p-0\": [\n",
-            "                -3.6036229133605957\n",
-            "            ],\n",
-            "            \"B-d-0\": [\n",
-            "                0.0\n",
-            "            ],\n",
-            "            \"N-s-0\": [\n",
-            "                -18.575620651245117\n",
-            "            ],\n",
-            "            \"N-p-0\": [\n",
-            "                -7.092767238616943\n",
-            "            ],\n",
-            "            \"N-d-0\": [\n",
-            "                0.0\n",
-            "            ]\n",
-            "        },\n",
-            "        \"hopping\": {\n",
-            "            \"B-B-s-s-0\": [\n",
-            "                -4.163856506347656,\n",
-            "                3.1430838108062744,\n",
-            "                3.5406551361083984,\n",
-            "                -13.840292930603027,\n",
-            "                4.28679084777832,\n",
-            "                0.0003413402009755373\n",
-            "            ],\n",
-            "            \"B-B-s-p-0\": [\n",
-            "                -4.2632155418396,\n",
-            "                1.855499505996704,\n",
-            "                5.719208240509033,\n",
-            "                -12.824378967285156,\n",
-            "                2.3826582431793213,\n",
-            "                0.00027443747967481613\n",
-            "            ],\n",
-            "            \"B-B-s-d-0\": [\n",
-            "                0.00014130362251307815,\n",
-            "                -0.00012631429126486182,\n",
-            "                0.0001882282958831638,\n",
-            "                -0.0005035149515606463,\n",
-            "                0.00022772896045353264,\n",
-            "                -0.40316706895828247\n",
-            "            ],\n",
-            "            \"B-B-p-p-0\": [\n",
-            "                4.029307842254639,\n",
-            "                -0.4571390151977539,\n",
-            "                -5.698226451873779,\n",
-            "                5.24634313583374,\n",
-            "                1.8383373022079468,\n",
-            "                0.00098962162155658\n",
-            "            ],\n",
-            "            \"B-B-p-p-1\": [\n",
-            "                -1.7277371883392334,\n",
-            "                1.7380834817886353,\n",
-            "                0.2498142123222351,\n",
-            "                -4.095367431640625,\n",
-            "                1.5244688987731934,\n",
-            "                0.6229994893074036\n",
-            "            ],\n",
-            "            \"B-B-p-d-0\": [\n",
-            "                -3.374056541360915e-05,\n",
-            "                0.00013169155863579363,\n",
-            "                -0.00022330175852403045,\n",
-            "                -8.510611951351166e-05,\n",
-            "                -6.722987745888531e-05,\n",
-            "                -0.3973250985145569\n",
-            "            ],\n",
-            "            \"B-B-p-d-1\": [\n",
-            "                -7.808039663359523e-05,\n",
-            "                3.817861215793528e-05,\n",
-            "                -5.0371396355330944e-05,\n",
-            "                -0.000102539241197519,\n",
-            "                -1.8067279597744346e-05,\n",
-            "                -0.3949469327926636\n",
-            "            ],\n",
-            "            \"B-B-d-d-0\": [\n",
-            "                -6.081238097976893e-05,\n",
-            "                9.681181109044701e-05,\n",
-            "                -9.043539466802031e-05,\n",
-            "                5.160560249350965e-05,\n",
-            "                -0.0002392987225903198,\n",
-            "                0.3956696689128876\n",
-            "            ],\n",
-            "            \"B-B-d-d-1\": [\n",
-            "                0.00015222658112179488,\n",
-            "                6.237948400666937e-05,\n",
-            "                0.0002462207048665732,\n",
-            "                -0.0003655400942079723,\n",
-            "                0.0004969655419699848,\n",
-            "                -0.39649420976638794\n",
-            "            ],\n",
-            "            \"B-B-d-d-2\": [\n",
-            "                -6.792173371650279e-05,\n",
-            "                0.00012900785077363253,\n",
-            "                1.0697433026507497e-05,\n",
-            "                -0.00010661676060408354,\n",
-            "                -9.13233234314248e-05,\n",
-            "                -0.3952234387397766\n",
-            "            ],\n",
-            "            \"N-B-s-s-0\": [\n",
-            "                -5.198118686676025,\n",
-            "                4.442707061767578,\n",
-            "                4.672472953796387,\n",
-            "                -22.44223403930664,\n",
-            "                8.299448013305664,\n",
-            "                -0.27995532751083374\n",
-            "            ],\n",
-            "            \"N-B-s-p-0\": [\n",
-            "                -6.1210198402404785,\n",
-            "                4.5004191398620605,\n",
-            "                6.178765296936035,\n",
-            "                -23.72483253479004,\n",
-            "                7.701132774353027,\n",
-            "                -0.17325514554977417\n",
-            "            ],\n",
-            "            \"N-B-p-s-0\": [\n",
-            "                -4.536301136016846,\n",
-            "                2.089078426361084,\n",
-            "                7.104267597198486,\n",
-            "                -18.932785034179688,\n",
-            "                5.050920486450195,\n",
-            "                -0.08721348643302917\n",
-            "            ],\n",
-            "            \"N-B-s-d-0\": [\n",
-            "                -7.312803063541651e-05,\n",
-            "                0.00010872803977690637,\n",
-            "                0.00023205213074106723,\n",
-            "                -0.0005027204751968384,\n",
-            "                0.0001486523833591491,\n",
-            "                -0.43798455595970154\n",
-            "            ],\n",
-            "            \"N-B-d-s-0\": [\n",
-            "                0.00018420522974338382,\n",
-            "                -0.00017437210772186518,\n",
-            "                -0.00016033451538532972,\n",
-            "                0.0007334152469411492,\n",
-            "                -8.776571485213935e-05,\n",
-            "                0.4466051459312439\n",
-            "            ],\n",
-            "            \"N-B-p-p-0\": [\n",
-            "                4.69775390625,\n",
-            "                -0.6529765129089355,\n",
-            "                -6.943027973175049,\n",
-            "                8.185653686523438,\n",
-            "                1.3665649890899658,\n",
-            "                -0.2770684063434601\n",
-            "            ],\n",
-            "            \"N-B-p-p-1\": [\n",
-            "                -1.963960886001587,\n",
-            "                1.9514763355255127,\n",
-            "                0.5242166519165039,\n",
-            "                -5.668179988861084,\n",
-            "                2.2336957454681396,\n",
-            "                -0.8325421810150146\n",
-            "            ],\n",
-            "            \"N-B-p-d-0\": [\n",
-            "                -6.0136051615700126e-05,\n",
-            "                8.034883649088442e-05,\n",
-            "                -0.0002142499142792076,\n",
-            "                0.00013417207810562104,\n",
-            "                3.860515425913036e-05,\n",
-            "                -0.44638267159461975\n",
-            "            ],\n",
-            "            \"N-B-d-p-0\": [\n",
-            "                -0.00015385006554424763,\n",
-            "                9.536759898765013e-05,\n",
-            "                -0.00010434392606839538,\n",
-            "                2.4020744604058564e-05,\n",
-            "                -0.00022013107081875205,\n",
-            "                0.44291141629219055\n",
-            "            ],\n",
-            "            \"N-B-p-d-1\": [\n",
-            "                -4.726650513475761e-05,\n",
-            "                8.19785927888006e-05,\n",
-            "                0.00011910804460057989,\n",
-            "                -0.0003437807899899781,\n",
-            "                0.00014456789358519018,\n",
-            "                0.4415556490421295\n",
-            "            ],\n",
-            "            \"N-B-d-p-1\": [\n",
-            "                9.281534585170448e-05,\n",
-            "                -0.000124435915495269,\n",
-            "                0.00014234631089493632,\n",
-            "                -0.0006778020178899169,\n",
-            "                0.00030078168492764235,\n",
-            "                -0.4489174485206604\n",
-            "            ],\n",
-            "            \"N-B-d-d-0\": [\n",
-            "                -0.0001388048694934696,\n",
-            "                0.00010272378858644515,\n",
-            "                -0.00019081367645412683,\n",
-            "                3.532186383381486e-07,\n",
-            "                -0.00017708863015286624,\n",
-            "                0.45609885454177856\n",
-            "            ],\n",
-            "            \"N-B-d-d-1\": [\n",
-            "                -0.0001369681558571756,\n",
-            "                0.0001792228576960042,\n",
-            "                -0.00011874250776600093,\n",
-            "                -0.00022725776943843812,\n",
-            "                -5.964709271211177e-05,\n",
-            "                -0.45647943019866943\n",
-            "            ],\n",
-            "            \"N-B-d-d-2\": [\n",
-            "                0.00014195215771906078,\n",
-            "                -7.780492160236463e-05,\n",
-            "                8.625858754385263e-05,\n",
-            "                0.0001418764004483819,\n",
-            "                3.1707189918961376e-05,\n",
-            "                -0.45413458347320557\n",
-            "            ],\n",
-            "            \"N-N-s-s-0\": [\n",
-            "                -6.06509256362915,\n",
-            "                4.933748245239258,\n",
-            "                7.251394271850586,\n",
-            "                -31.393178939819336,\n",
-            "                11.564867973327637,\n",
-            "                0.6752002239227295\n",
-            "            ],\n",
-            "            \"N-N-s-p-0\": [\n",
-            "                -6.407330513000488,\n",
-            "                4.119426727294922,\n",
-            "                8.804641723632812,\n",
-            "                -30.208786010742188,\n",
-            "                9.762639999389648,\n",
-            "                0.4003536105155945\n",
-            "            ],\n",
-            "            \"N-N-s-d-0\": [\n",
-            "                0.00011224352056160569,\n",
-            "                0.00017669328371994197,\n",
-            "                2.3582368157804012e-05,\n",
-            "                -0.0004224574367981404,\n",
-            "                0.000269725191174075,\n",
-            "                0.5320302248001099\n",
-            "            ],\n",
-            "            \"N-N-p-p-0\": [\n",
-            "                5.50653076171875,\n",
-            "                -1.4403566122055054,\n",
-            "                -8.006013870239258,\n",
-            "                13.777935981750488,\n",
-            "                -0.6363162994384766,\n",
-            "                0.36886876821517944\n",
-            "            ],\n",
-            "            \"N-N-p-p-1\": [\n",
-            "                -2.2638466358184814,\n",
-            "                2.1415014266967773,\n",
-            "                1.0750678777694702,\n",
-            "                -7.8354573249816895,\n",
-            "                3.0920794010162354,\n",
-            "                -1.063719391822815\n",
-            "            ],\n",
-            "            \"N-N-p-d-0\": [\n",
-            "                7.20867101335898e-05,\n",
-            "                0.00036340532824397087,\n",
-            "                0.00024355569621548057,\n",
-            "                -0.0004975462798029184,\n",
-            "                0.0004125885898247361,\n",
-            "                0.5261558294296265\n",
-            "            ],\n",
-            "            \"N-N-p-d-1\": [\n",
-            "                0.00015687875566072762,\n",
-            "                4.641729174181819e-05,\n",
-            "                1.3238663086667657e-05,\n",
-            "                0.00046566742821596563,\n",
-            "                -0.00020128212054260075,\n",
-            "                -0.533370852470398\n",
-            "            ],\n",
-            "            \"N-N-d-d-0\": [\n",
-            "                8.857469947542995e-05,\n",
-            "                0.00029952620388939977,\n",
-            "                0.00024031809880398214,\n",
-            "                -0.00025129984715022147,\n",
-            "                0.0003825896419584751,\n",
-            "                0.5308742523193359\n",
-            "            ],\n",
-            "            \"N-N-d-d-1\": [\n",
-            "                3.192495569237508e-05,\n",
-            "                -0.0001347306970274076,\n",
-            "                -0.00010394358105259016,\n",
-            "                -0.00015763661940582097,\n",
-            "                -0.0002910229086410254,\n",
-            "                0.527771532535553\n",
-            "            ],\n",
-            "            \"N-N-d-d-2\": [\n",
-            "                4.771947715198621e-05,\n",
-            "                7.819013262633234e-05,\n",
-            "                0.00018762303807307035,\n",
-            "                0.00027266336837783456,\n",
-            "                0.0002987241605296731,\n",
-            "                -0.5279330611228943\n",
-            "            ]\n",
-            "        },\n",
-            "        \"overlap\": {\n",
-            "            \"B-B-s-s-0\": [\n",
-            "                0.20907151699066162,\n",
-            "                -0.23900975286960602,\n",
-            "                -0.06029646843671799,\n",
-            "                0.805722713470459,\n",
-            "                -0.34798508882522583,\n",
-            "                -0.07375287264585495\n",
-            "            ],\n",
-            "            \"B-B-s-p-0\": [\n",
-            "                0.25513431429862976,\n",
-            "                -0.22145505249500275,\n",
-            "                -0.26228660345077515,\n",
-            "                1.159956932067871,\n",
-            "                -0.4085614085197449,\n",
-            "                -0.0003635674365796149\n",
-            "            ],\n",
-            "            \"B-B-s-d-0\": [\n",
-            "                -0.00010135513730347157,\n",
-            "                0.00012300396338105202,\n",
-            "                -0.00016775091353338212,\n",
-            "                1.0182542609982193e-05,\n",
-            "                -0.0003550578549038619,\n",
-            "                -0.3522164523601532\n",
-            "            ],\n",
-            "            \"B-B-p-p-0\": [\n",
-            "                -0.2968706786632538,\n",
-            "                0.1348806917667389,\n",
-            "                0.5835362076759338,\n",
-            "                -1.4401586055755615,\n",
-            "                0.34514784812927246,\n",
-            "                0.00011754724982893094\n",
-            "            ],\n",
-            "            \"B-B-p-p-1\": [\n",
-            "                0.0973825603723526,\n",
-            "                -0.14244243502616882,\n",
-            "                0.023147646337747574,\n",
-            "                0.47471076250076294,\n",
-            "                -0.2704032361507416,\n",
-            "                -0.667027473449707\n",
-            "            ],\n",
-            "            \"B-B-p-d-0\": [\n",
-            "                -0.00011588518100325018,\n",
-            "                0.00018029639613814652,\n",
-            "                -9.216874605044723e-05,\n",
-            "                -0.00048209051601588726,\n",
-            "                4.212089697830379e-05,\n",
-            "                0.3581341505050659\n",
-            "            ],\n",
-            "            \"B-B-p-d-1\": [\n",
-            "                -0.00011588512279558927,\n",
-            "                0.00018029661441687495,\n",
-            "                -9.216976468451321e-05,\n",
-            "                -0.000482094066683203,\n",
-            "                4.2125670006498694e-05,\n",
-            "                0.35813406109809875\n",
-            "            ],\n",
-            "            \"B-B-d-d-0\": [\n",
-            "                -0.0001013551518553868,\n",
-            "                0.0001230038469657302,\n",
-            "                -0.00016775041876826435,\n",
-            "                1.018380862660706e-05,\n",
-            "                -0.0003550600085873157,\n",
-            "                -0.3522164225578308\n",
-            "            ],\n",
-            "            \"B-B-d-d-1\": [\n",
-            "                0.00010135525371879339,\n",
-            "                -0.0001230034977197647,\n",
-            "                0.0001677492109593004,\n",
-            "                -1.018853799905628e-05,\n",
-            "                0.0003550658584572375,\n",
-            "                -0.35221636295318604\n",
-            "            ],\n",
-            "            \"B-B-d-d-2\": [\n",
-            "                9.815972589422017e-05,\n",
-            "                -0.00016340948059223592,\n",
-            "                0.0001705800968920812,\n",
-            "                0.0002916558878496289,\n",
-            "                -6.5286149038001895e-06,\n",
-            "                -0.3533816933631897\n",
-            "            ],\n",
-            "            \"N-B-s-s-0\": [\n",
-            "                0.1997109055519104,\n",
-            "                -0.2244972437620163,\n",
-            "                -0.11343343555927277,\n",
-            "                0.9763136506080627,\n",
-            "                -0.4251488149166107,\n",
-            "                -0.2723999619483948\n",
-            "            ],\n",
-            "            \"N-B-s-p-0\": [\n",
-            "                0.2620687186717987,\n",
-            "                -0.2704774737358093,\n",
-            "                -0.23450201749801636,\n",
-            "                1.4295225143432617,\n",
-            "                -0.593266487121582,\n",
-            "                -0.0280486810952425\n",
-            "            ],\n",
-            "            \"N-B-p-s-0\": [\n",
-            "                0.2336491048336029,\n",
-            "                -0.18477198481559753,\n",
-            "                -0.32471197843551636,\n",
-            "                1.258323073387146,\n",
-            "                -0.4374508261680603,\n",
-            "                -3.666881821118295e-05\n",
-            "            ],\n",
-            "            \"N-B-s-d-0\": [\n",
-            "                -2.9759947210550308e-05,\n",
-            "                9.752172627486289e-05,\n",
-            "                -0.0001534644834464416,\n",
-            "                0.00047143836854957044,\n",
-            "                1.3361132005229592e-05,\n",
-            "                0.40016892552375793\n",
-            "            ],\n",
-            "            \"N-B-d-s-0\": [\n",
-            "                -0.00019644841086119413,\n",
-            "                0.0001292879751417786,\n",
-            "                -0.00011083389108534902,\n",
-            "                -0.00012051903468091041,\n",
-            "                -0.00022029990213923156,\n",
-            "                -0.4031250476837158\n",
-            "            ],\n",
-            "            \"N-B-p-p-0\": [\n",
-            "                -0.280412495136261,\n",
-            "                0.11416203528642654,\n",
-            "                0.6316858530044556,\n",
-            "                -1.5773518085479736,\n",
-            "                0.4028981328010559,\n",
-            "                -0.00018129641830455512\n",
-            "            ],\n",
-            "            \"N-B-p-p-1\": [\n",
-            "                0.09812135994434357,\n",
-            "                -0.14181673526763916,\n",
-            "                0.005639200564473867,\n",
-            "                0.5506149530410767,\n",
-            "                -0.3099479675292969,\n",
-            "                0.6836565732955933\n",
-            "            ],\n",
-            "            \"N-B-p-d-0\": [\n",
-            "                8.620692824479192e-05,\n",
-            "                7.003633072599769e-05,\n",
-            "                0.00014002059469930828,\n",
-            "                1.5960773453116417e-05,\n",
-            "                0.0003343636344652623,\n",
-            "                0.39526382088661194\n",
-            "            ],\n",
-            "            \"N-B-d-p-0\": [\n",
-            "                -0.00017928905435837805,\n",
-            "                0.00014779999037273228,\n",
-            "                -6.557474262081087e-05,\n",
-            "                0.00033531803637742996,\n",
-            "                -1.5598576283082366e-05,\n",
-            "                -0.4068489074707031\n",
-            "            ],\n",
-            "            \"N-B-p-d-1\": [\n",
-            "                3.969529643654823e-05,\n",
-            "                -0.0001192440977320075,\n",
-            "                0.00016035677981562912,\n",
-            "                -0.0005162757006473839,\n",
-            "                -1.6432837583124638e-05,\n",
-            "                -0.3948792815208435\n",
-            "            ],\n",
-            "            \"N-B-d-p-1\": [\n",
-            "                0.0002542896254453808,\n",
-            "                -4.0774408262223005e-05,\n",
-            "                0.00016710262570995837,\n",
-            "                0.0006886226474307477,\n",
-            "                7.42142292438075e-05,\n",
-            "                0.4113677740097046\n",
-            "            ],\n",
-            "            \"N-B-d-d-0\": [\n",
-            "                -0.0001716944680083543,\n",
-            "                2.248838427476585e-05,\n",
-            "                -0.00012059728032909334,\n",
-            "                0.0001464982924517244,\n",
-            "                -6.761669646948576e-05,\n",
-            "                -0.4281955659389496\n",
-            "            ],\n",
-            "            \"N-B-d-d-1\": [\n",
-            "                -0.0001716944680083543,\n",
-            "                2.248838427476585e-05,\n",
-            "                -0.00012059728032909334,\n",
-            "                0.0001464982924517244,\n",
-            "                -6.761669646948576e-05,\n",
-            "                0.4281955659389496\n",
-            "            ],\n",
-            "            \"N-B-d-d-2\": [\n",
-            "                -9.511156531516463e-05,\n",
-            "                0.0001717804989311844,\n",
-            "                -1.6953305021161214e-05,\n",
-            "                0.0001179392565973103,\n",
-            "                9.918229625327513e-05,\n",
-            "                0.4204893112182617\n",
-            "            ],\n",
-            "            \"N-N-s-s-0\": [\n",
-            "                0.1918010711669922,\n",
-            "                -0.2127472460269928,\n",
-            "                -0.1606387048959732,\n",
-            "                1.155442476272583,\n",
-            "                -0.5151748657226562,\n",
-            "                0.48609858751296997\n",
-            "            ],\n",
-            "            \"N-N-s-p-0\": [\n",
-            "                0.24681918323040009,\n",
-            "                -0.23861254751682281,\n",
-            "                -0.31103551387786865,\n",
-            "                1.6039279699325562,\n",
-            "                -0.660601019859314,\n",
-            "                -0.07287845760583878\n",
-            "            ],\n",
-            "            \"N-N-s-d-0\": [\n",
-            "                -6.115203723311424e-05,\n",
-            "                -0.0002562953741289675,\n",
-            "                -0.00012758496450260282,\n",
-            "                0.0004416516749188304,\n",
-            "                -0.00025469131651334465,\n",
-            "                0.488688588142395\n",
-            "            ],\n",
-            "            \"N-N-p-p-0\": [\n",
-            "                -0.2732810080051422,\n",
-            "                0.10842154920101166,\n",
-            "                0.699445903301239,\n",
-            "                -1.881728172302246,\n",
-            "                0.5495500564575195,\n",
-            "                0.00020055289496667683\n",
-            "            ],\n",
-            "            \"N-N-p-p-1\": [\n",
-            "                0.10150457173585892,\n",
-            "                -0.1420295238494873,\n",
-            "                -0.021784797310829163,\n",
-            "                0.6614340543746948,\n",
-            "                -0.36609184741973877,\n",
-            "                -0.7560092806816101\n",
-            "            ],\n",
-            "            \"N-N-p-d-0\": [\n",
-            "                -1.3360753655433655e-05,\n",
-            "                -0.00014160110731609166,\n",
-            "                0.00010747101623564959,\n",
-            "                -0.00040430951048620045,\n",
-            "                0.0002254340797662735,\n",
-            "                -0.4823491871356964\n",
-            "            ],\n",
-            "            \"N-N-p-d-1\": [\n",
-            "                5.713030986953527e-05,\n",
-            "                0.00020219954603817314,\n",
-            "                -1.679777051322162e-05,\n",
-            "                -0.00015270523726940155,\n",
-            "                0.0001418725005351007,\n",
-            "                -0.4920305609703064\n",
-            "            ],\n",
-            "            \"N-N-d-d-0\": [\n",
-            "                2.117294025083538e-05,\n",
-            "                -0.00019494653679430485,\n",
-            "                9.384578152094036e-05,\n",
-            "                -0.00038998579839244485,\n",
-            "                -7.752966484986246e-05,\n",
-            "                0.487053245306015\n",
-            "            ],\n",
-            "            \"N-N-d-d-1\": [\n",
-            "                -3.1081654014997184e-06,\n",
-            "                -0.00021633837604895234,\n",
-            "                3.2148207537829876e-05,\n",
-            "                -3.418952110223472e-05,\n",
-            "                -0.00027515843976289034,\n",
-            "                0.4870029091835022\n",
-            "            ],\n",
-            "            \"N-N-d-d-2\": [\n",
-            "                -8.452979091089219e-05,\n",
-            "                -0.00021817802917212248,\n",
-            "                -5.2256466005928814e-05,\n",
-            "                -0.0005801816005259752,\n",
-            "                -6.465519254561514e-05,\n",
-            "                -0.4823436141014099\n",
-            "            ]\n",
-            "        }\n",
-            "    }\n",
-            "}"
-          ]
-        }
-      ],
-      "source": [
-        "!cat hbn_spd_model/sktb.json"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "db1704e2",
-      "metadata": {},
-      "source": [
-        "We can also load the generated sktb.json model file to plot the band structure:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "f3b9ee35",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
-            " \n",
-            " \n",
-            "#################################################################################\n",
-            "#                                                                               #\n",
-            "#                                                                               #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
-            "#                                                                               #\n",
-            "#                         Version: 2.1.2.dev50+d4f488d                          #\n",
-            "#                                                                               #\n",
-            "#################################################################################\n",
-            " \n",
-            " \n",
-            "DEEPTB INFO    The structure space group is: P-6m2 (No. 187)\n",
-            "DEEPTB WARNING CUDA is not available. The model will be loaded on CPU.\n",
-            "/Users/aisiqg/Software/venv/pydptb/lib/python3.9/site-packages/torch/nested/__init__.py:58: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/NestedTensorImpl.cpp:180.)\n",
-            "  return torch._nested_tensor_from_tensor_list(tensor_list, dtype, None, device, None)\n",
-            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
-            "DEEPTB INFO    band calculation successfully completed.\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "!dptb run band -i ./hbn_spd_model/sktb.json -stu ../structures/hBN.vasp -o band_hBN\n",
-        "\n",
-        "# display the band plot:\n",
-        "from IPython.display import Image, display\n",
-        "import os\n",
-        "image_path = f'./band_hBN/results/band.png'\n",
-        "display(Image(filename=image_path))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "20aca3a2",
-      "metadata": {},
-      "source": [
-        "We can see that the band structure is the same as before. \n",
-        "\n",
-        "Here we can choose different basis settings, e.g. `hbn_sp.json` as the input config:\n",
-        "```json\n",
-        "{\n",
-        "    \"common_options\": {\n",
-        "        \"basis\": {\n",
-        "            \"B\": [\"s\",\"p\"],\n",
-        "            \"N\": [\"s\",\"p\"]\n",
-        "        }\n",
-        "    }\n",
-        "}\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "5847351c",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
-            " \n",
-            " \n",
-            "#################################################################################\n",
-            "#                                                                               #\n",
-            "#                                                                               #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
-            "#                                                                               #\n",
-            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
-            "#                                                                               #\n",
-            "#################################################################################\n",
-            " \n",
-            " \n",
-            "DEEPTB INFO    Extracting empirical SK parameters for BN\n",
-            "DEEPTB INFO    dtype is not provided in the input json, set to the value torch.float32 in model ckpt.\n",
-            "DEEPTB INFO    device is not provided in the input json, set to the value cpu in model ckpt.\n",
-            "DEEPTB INFO    overlap is not provided in the input json, set to the value True in model ckpt.\n",
-            "DEEPTB INFO    Empirical SK parameters are saved in hbn_sp_model/sktb.json\n",
-            "DEEPTB INFO    If you want to further train the model, please use `dptb config` command to generate input template.\n"
-          ]
-        }
-      ],
-      "source": [
-        "!dptb esk hbn_sp.json -m poly4 -o hbn_sp_model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "cf264978",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
-            " \n",
-            " \n",
-            "#################################################################################\n",
-            "#                                                                               #\n",
-            "#                                                                               #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588                     \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2588\u2588\u2588                   \u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2588\u2591\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2588\u2591\u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588   \u2591\u2591\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588\u2588   \u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591   \u2591\u2588\u2588\u2588  \u2591  \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2588\u2588\u2588\u2591\u2591\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588     \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588\u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2591      \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588\u2591\u2591\u2591\u2591\u2591\u2588\u2588\u2588     #\n",
-            "#      \u2591\u2588\u2588\u2588    \u2588\u2588\u2588 \u2591\u2588\u2588\u2588\u2591\u2591\u2591  \u2591\u2588\u2588\u2588\u2591\u2591\u2591   \u2591\u2588\u2588\u2588            \u2591\u2588\u2588\u2588     \u2591\u2588\u2588\u2588    \u2591\u2588\u2588\u2588     #\n",
-            "#      \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588 \u2591\u2591\u2588\u2588\u2588\u2588\u2588\u2588  \u2588\u2588\u2588\u2588\u2588           \u2588\u2588\u2588\u2588\u2588    \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588      #\n",
-            "#     \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591   \u2591\u2591\u2591\u2591\u2591\u2591  \u2591\u2591\u2591\u2591\u2591           \u2591\u2591\u2591\u2591\u2591    \u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591\u2591       #\n",
-            "#                                                                               #\n",
-            "#                         Version: 2.0.4.dev87+5ed8d35                          #\n",
-            "#                                                                               #\n",
-            "#################################################################################\n",
-            " \n",
-            " \n",
-            "/root/dptb_venv/lib/python3.10/site-packages/seekpath/hpkot/__init__.py:172: DeprecationWarning: dict interface is deprecated. Use attribute interface instead\n",
-            "  conv_lattice = dataset[\"std_lattice\"]\n",
-            "DEEPTB INFO    The structure space group is: P-6m2 (No. 187)\n",
-            "/root/dptb_venv/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
-            "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
-            "DEEPTB INFO    No Fermi energy available, setting to 0.0 eV\n",
-            "Figure(640x560)\n",
-            "DEEPTB INFO    band calculation successfully completed.\n"
-          ]
-        },
-        {
-          "data": {
-            "image/png": 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167Hu2kXle+9T+cE2ovr0JqZ/fxSTqYEiF96uvrnlq+eraFi+nAc1P/9M6dq1VH/5VV3X/YCzzyai2/2EderUKF3zG2q8TKmpJEyaSPSAAZS9/DLlr71G7cGD5E2YQPkrl5AwbRqmFJnJ40/qk1smh4PzDh7EfJQGWP5CmmAJzfnDYnvRfLntdirffpuSdetw5uUDnmZWEXffRcT992NMSNA4wlNj27+fkmfXUv355wAEtGlD4ozpBLRurW1gQgjRAGr27aNkzTNYdu6s+17wtdcS9UBPAi+/3Gvu9p4MV3k5ZZu3UPriC6jWGpTAQOJGjiT87rt88vcRDc9tsfDbdR0BOGvHdnRHKZT94bq8efeqF0L4HJvNxueff46tAfZRbEyq00n5m29y6K67KZg9B2dePvroaGJHjKDVe+8SO2xYoxe/jTlW5nPOIXnhAhLnzkEfHk7twYNkPPgfSp9/HvXIbqSiWapvbvnK+Soaly/lge3AAbKGDiWzdx9P8avXE3b77Zzx2qukLFlM0BVXNHqx2FjjpY+IIObhgZyxaROBl16KWlNDwezZ5AwbjqOwsEGPJbxTfXLLbjCwr83ZPnG+NhYpgIUQXsVms/HFF1947RuzqqpUff456d17UDDzCZz5+RhiYogb/ThnvrWVqP882ChT5o6mKcYq7OabSduymeBrr0V1OChauoysgQ9jz85utGMK7dU3t7z9fBVNwxfywJ6dQ+7EiWQ88CDWr3eBwUB41zs54/XXSJw+DVNaWpPF0tjjZUxOpsXqp4kdORLFZMKycyfp3bpT+cG2Rjme8B71yS270cjPbdpgq61twsi8i6wBFkKIeqr5v58pWrSImr17AdCFhxHdrx8R996LrhmvpTHExJC8eBGVb71FwYIF1Pz4I+ndexA3cgThd98tU+uEEF7LVVlJyZo1lL/yKqrTCUDorZ2JGTS4Wa+PVXQ6oh58gOCrryJv8hRqDxwgb+JEqr/4gvixY9A3w/2ahagvKYCFEOIEnEVFFK1YSeU77wCgmM1E9uxB1EMPoT+8Tqa5UxSF8K5dCbr8cvKmTafm++8pmD2H6i++JHH27AbfB1MIIU6H6nJR/vrrlDz1NK6KCgCCrmpP7NChmNu21Ti6phNw5pm0XL+OkrXPUfLcc1R9+CHW778jae5c2TtY+C2ZAi2EEMeg2u2UbtjAn3ffXVf8ht12G2e8/jqxQ4b4TfF7pH9Mrduxg6yBA3EWF2sdmhBCAGD9/nsyHvwPhXOfxFVRgalVK1JWrqTFihV+Vfz+l2I0EjPoYVLXPYcpLQ1XcQnZQ4ZS/cUXWocmhCakC7TQnD90mxP153a7qaysJCwsDJ1Ou8/orIfvcNoPHQLAfN55xI0eTeAF52sW0//Seqxs+/eTPWw4rtJSjElJpKxaialFiyaPQzS8+uaW1jkovIO35IGzrIyiJUvrPrDUhYURM2gQEffcjWLwnkmPWo6X22Yjd9x4LF99BTod8RMmEHHXv5s0BtF4TpRbbouFgx2vx2o2c9GH2zAcpWeJP1yXSwEsNOcPJ5rwHc6yMoqWLaPyrbcB0EdGEjt8GGG33YYiF/j/YM/KInvIUBw5OegjI0lZvgzzOedoHZYQwo+objcVW9+iaNky3JWVoCiE330XMYMHY4iM1Do8r6M6nRTMnk3F1rcAiBk8iKh+/aSfgx+QbZA8pAAWmjvyROvQoQN6vf6UX6t379707t27gSITWqisrOS5556jb9++hIWFNdlxVVWlats2CufNr1svFn7P3cQOHYq+CeM4GVqN1f9ylpSQPWw4tQcOoAQFkjx/AcHt22kWjzh99c0tb8lBoS0t88CemUn+jJnU/PADAAFnn038hPEEXnBBk8ZxMrzhvFFVleJVqyh9bh0AEfffT9zjj6GcxjWY0N6JcsttsbC30y183OEaBjz2GBFxcf94jD8UwN4zH0QIYPv27af1/Ouvv75hAhGacbvdVFRU4Ha7m+yYzqIiCubMrVsPFdC6NfETJxB44YVNFsOp0GKsjsYQHU2L1avJHT0a65495AwfTsL06YR1vkXTuMSpq29ueUsOCm1pkQeqy0XZpk0Ur1yFWluLEhhIzOBBRHbr5lXTnY/GG84bRVGIHTIEQ3QMhQsWUL5lC67SEhJmzkRnMmkWlzg99cktt6JgDQrC7cf3QL37HUL4ndO9A5zWhPv4Cd+nqipV779PwfwFuCsrUQwGogf0J+qhh1CMRq3D8yn6kGCSly4hf8pUqj76iLyJE3GVlRLZo4fWoQkhmhl7ejr5M2ZQs/cnAILatSNh8iSMiYkaR+Z7Irt3Qx8VSf7kKVR9/Amu8gqSFi7wyyaPwn9IASy8ygcffNAsp1oI7+MoLKRgzhwsX34FQEDbtiROm0rAWWdpHJnv0plMJM6ehT46ivJNmylcsBBnSSkxQx6RtWVCiNOmulyUvbyR4qeeQq2tRRcUROzIkYTf9W95jzkNYZ06oY+IIPexx7F++y1ZAwaSsnIFhqgorUMTolFIRxchhFcxGAy0bNkSQyNNYVNVlYp33iX9/m5YvvwKxWAg5pFHaLl+nc8Vv409VqdC0emIe/xxYoYOBaB03TpK12/QOCpxsuqbW96Yg6LpNUUe1B46RGa//hQtWYJaW0tQ+/akvbKFiLvv8rni1xvPm+Arr6TFM2vQR0dT++uvZA8bhttq1ToscZLqk1t6t5vY4mIMfrzeW5pgCc35w2J74R2cxcXkPzHLs/0DYD7nHBKmTSWgdWuNI2ueyjZupHDBQgASpk8n/PbbNI5ICOFrVLebspdepnjVKlS7HV1wMLGjRhLetavPFb6+wJ6RQWbffrjKywm6qj0pixfLkqBmRLpAe8gdYCGEV3E6naSnp+N0Ohv0das+/5z0bt2xfPUVitFIzNChpK5f59PFb2ONVUOJ7NGDyF7/ASB/5kwsO3dqHJGor/rmlrfnoGgajZUHjvx8sgc/4rnra7cTfPVVpG3ZTMS/fXvKszefN6aWLUleuhTFbMb69S7yZz6B3CvzHfXJLZdOR0F0tFfmX1ORAlgI4VWqq6vZsGED1dXVDfJ67poa8p+YRe5jj+MqLyfg7LNp+dKLRPfp7fWdQk+koceqMcQ++iiht3YGp5OcMWOx7d+vdUiiHuqbW76Qg6LxNUYeVG77kPTuPbB++y2K2Uz8xIkkL1uGMSGhwY6hFW8/bwLPP4+kuXNBr6fy3XcpXrVK65BEPdUnt2oCAvjsmquptliaMDLvIgWwEKLZqvn5Z9J7PkDFG2+AohDZ6z+kblhPQKtWWofmNxSdjsQpUwhq1w61pobsYcOxZ2drHZYQwku5qi3kTZ5C3oQJuKuqMJ93HmkbX/bJtb6+LOTaDiRMnABA6XPrKNuyReOIhGg4UgALIZod1eWiZO1aMvv2w5GZiSE+nhZPrSJu+HDZ31ADislE0rx5BLRpg6u0lOyhj+IsK9M6LCGEl6n58UcyenSn8r33QKcjekB/Utc+iyk1VevQ/FJ4165ED3oYgMJ586n69DONIxKiYUgBLIRoVhw5OWQNGEjxqqfA6SS0UyfSNm0k6IortA7Nr+lDgklZthRjUiKOrCxyhg3HXVOjdVhCCC+gOhwUP/UUmQMG4sjNw5iUROqzzxAzaJA0YNJYdP/+hN9zN6gqeRMnYv3hB61DEuK0SQEshPAqQUFBdO3alaCgoJN6Xt32Rt17ULN3L7qgIBJmTPfsSxsW1kjRautUx0orhpgYUpYvRx8eju2XX8gdOw7V4dA6LHEU9c0tX8tB0ThOJw/s6elk9u1HybNrwe0m7PbbablxI4EXXdQIkXoHXzpvFEUhfswYQjpeh2q3kzNyFLV//ql1WOIY6pNbAXY7V/7wA0GBgU0YmXeRbZCE5vyh3bpoXM6yMgrmzKH6k08BCLzoIhJnzsCYnKxxZOJoavbtI2vQYFSbjfCuXYmfPEnW9gnhZ1RVpfyVVyhaugzVZkMXFkb8hPGE3Xyz1qGJo3DbbGQNHoztp30YEhJouX4dhthYrcMSJ0m2QfKQO8BCCK9itVp58803sVqt9Xq8ZedO0rt39xS/BgMxQ4bQ4pk1flH8nuxYeYvACy4gac5s0Omo2LqVijfe1Dok8T/qm1u+moOiYZ1sHjiLisgZNozCJ+eh2mwEtWtH2qZNflP8+uJ5ozObSVm8GFNaGs78fHInTET14210vFV9cqvWZGLXxRf7VP41NCmAhRBexW63s3fvXux2+3Ef57bZKJj7JNmPDsNVXILpjDNouWE90X37oOj1TRSttuo7Vt4o5LrriBnyCACF8+djO/irxhGJI9U3t3w5B0XDOZk8qPrkEw5164Zl59coAQHEjR5NyorlGOPjmiBS7+Cr540+IoLkRQtRggKp+f57ilev1jok8T/qk1sOvZ701BbY/XgJkhTAQgif49neqCflr7wCQGSP7rR88QXMbdtqHJk4GVG9ehHcoQOq3U7uuHG4qv13T0IhmjtXdTV5U6aSO2Ys7opKAtq2peWLLxDZvRuKTi5HfYWpZUsSJk0GPNsjVe/YoXFEQpw8eccRQvgMt91O8eo1ZPbpiyMjE0NcHCkrVxL3+ONHXccivJui05E4fRqGhAQcmZkUPPEE0pZCiObHsnsP6d27U/nuu6DTEdWvLy3XPUfAmWdqHZo4BWG3dCLivvsAyJ88BUd+vsYRCXFypAAWQng9VVWp+vQz0u+9j5I1a8DlqtveKLh9O63DE6dBHxFB0tw5YDBQ9dFHdXf1hRC+z56dQ87oMWQ/8gjOvHyMycmkPrOG2EceQZE92X1a7KiRBLRti6uigrwJE6Sjv/Ap0gVaaM4fus2JU6OqKrUHDlC0dBnWb74BwBAbS+yIEYR1vkXj6ERDKn3pJYoWLUYxGkld9xzmc87ROiQhxClyVVRQ+vwLlL30kqcw0uuJuOceYocOQSf/xjcb9uwcMh54AHd1NZG9/kPc8OFahyROQLpAexi0DkCII3Xu3Bn9aTQw6t27N7179264gESTUp1OHHl51Pz4I9Y932D99luchYUAKCYTkf95kOjevdH5wN6J4uRE9uxJzfc/UP355+SOHUfLl15EHxqqdVhCiBNQVRVXWRm1Bw9i/eYbLHv2UHvgIBy+vxLUrh1xo0YS0Lq1xpGKhmZKSSZh6lRyR4+m7PkXCLrkEkKuu07rsPyaIy8PV1XVMX+u2mxNGI33kgJYeJXt27ef1vOvv/76hglENCpHYSG1+/djz87GkZWNPTsLR1Y2jvx8qo1G3rn5Jm7/7DNCampQjEZC/vUvYocO8YutjU5GeXk5S5cuZfjw4URERGgdzmlRFIWEqVPI+PUgjpwc8qfPIGn+PNkfWCP1za3mlIPi+Nx2O7affsKelY0jKwtHTnbd15Wq6nnf/uhjQmpqAAho3ZqYRwYTfN11ch7/j+Z03oTe8C8ie3SnbOMm8qZOI+3llzAmJmodll+q+vhjfp02/R/n4v+qDgzknZtv4tGKCqL8tH+KFMDCq3To0OG07gCnpaU1XDCiQaluN9Y9eyjf8grVX30FbvdRH6ccnmoTcd99JLRvT+BFF0qDKz+hDwsj6cknyezbj+rPPqN80yYie/TQOiwh/Jo9O4eK11+jYutbuMrLj/6gw7NyQm++iYQrriDwssv9alsjfxc7bBg1P+3D9vPP5I4dR+qzz8gabw3U/nmo7mt9ZCT6Y8yW0wcEAKA7/Kc/kgJYeJUPPvigWa418Geu8nIq3n6H8ldfxZGdXff9gLPOwtSyJcYWLTCmJGNq0QJjSguqjQZYvpzoh3oR7OOfjIuTZz73XGJHjqRw3jyKlizFfP4FBF5wvtZhCeFXVJcLy86dlG95BcvXX9dNZ9bHRGM++2yMKS0wtkjxvG8np2AJDoKnnyZu+HDC5H3b7ygmE0lPziW95wPYfv6ZouXLiXvsMa3D8mtpLzx/zNkF5eXlsHRpk8bjbaQAFkI0Ctuvv1L20stUffgh6uEN2XXBwYTdfjsR995zzO0vlGPdYRB+I+L++6j5/nuqPv6YvAnjSdu0SRrnCNEE3BYLZZu3UP76azjz/traJqh9eyLuu5eQDh1QDP+8dKyR922/Z0xMJHH6NHJGjqLs5Y0EXdmOkGs7aB2WEEclBbAQokGpdjslzz1HyXPrwOUCIKBtWyLuuYewWzujCww87vNNJhMXXXQRJpk+dULNdawURSF+8mRsv/yMIzePouUriB83Vuuw/Ep9c6u55qA/suzaTf7MmTgP7+mqCw8j/I47ibjnbkypqcd9ruTByWmu4xVy3XVE9uxJ2csvUzBrFoGvbJFmhk3M6HJxtk533Nxqrvl3MmQbJKE5f2i37i9sBw6QP206tb/9BkDI9dcT1bs35vPPkyYo4qRZ9uwhe/AjALR4+imCrrhC44iEaH7cFgtFy5ZR/uprABiTEoke+DChN98k/RfESXPbbKT37IkjI5OwO+8gcepUrUPyG8VrnqFk9Woi7r2H+PHjT/l1/OG6XKd1AEII36fa7RSvXkPGQ72p/e039BERJM6ZTfLCBQRecP5JFb92u50ff/wR++Fp0+LYmvtYBV95JRH33gtA/oyZuK1WjSPyH/XNreaeg82dZc8eDnXrVlf8Rtx3H2mbNxN+x+0nVfxKHpyc5jxeOrPZU/QqCpVvvU31jh1ah+RXHHo9B1yu4+ZWc86/+pICWAhxWmwHfyXjod6UrFkDTichN95A2itbCOvU6ZRez2q1snXrVqxS7JyQP4xV7LBHMSQm4MjNpWj5Cq3D8Rv1zS1/yMHmyG21UjD3SbIHP4IzLx9jUiIpT60iftzYU9pnXfLg5DT38Qq86KK6Dv4FT8zCVV2tcUT+o9Zk4nOX67i51dzzrz5kDbAQ4pSoqkrZSy9TtHw5OJ3ow8OJGzuW0E43n/J0Z7fVSm16esMGKnyaLjiYhEmTyR4yhPItWwi96UaCLrtM67CE8Fm2gwfJfXw0jtxcACLuvYfYYcNOudGc6nTiOLxuWD3G9nbC/8QMeYTqr77CkZVF0aLFJEyZrHVI/kH1nIO2AwepOcY5bbNYmjIiryQFsBDipKluN0WLFlG2cRMAIf/6F/Hjx2GIjj7xc1WV2oMHsf38C46cHBy5uXV/usrLqQ4MhJtvImvoUOyXXELQlVcSdMUVshbNjwW3b0f43XdT8frr5E+fQdrmTSdspiaE+CfLnj3kPvY4bqsVQ0ICCVOmENzuyno911lainXPNzhysnHk/Pd9OwdHQSHVJhPcfBMZD/6HmgsvJOjKKwm++iqMCQmN/BsJb6Uzm0mYMoWsgQOp2LqV0JtvIviqq7QOq1mr/fVXVFstADmPP05FTc1RH/ff6yx//sBKCmAhxElx2+3kT5tO1bZtAMSOHEnkAz1PeNfXWVZG5fvvU7n1LWp///2Yj/vvXQh7egZl+w9Q9vJG9DHRxA0fTuitt0ozLT8VO3w4lp07ceTkULRiBfGjR2sdkhA+pfLDD8mfMhXV4SDwsktJXrjwhB16VacTy9e7qNi6leqvvgKn86iPU4xGAFyVlVR99BFVH30EOh0R99xDzOBB6MPDG/z3Ed4v6NJLiOzejbKNm8if+QRpW7agD2l+DZW8heuI7ciM8fEYj7LGV1XdUF7h+drhaKrQvI50gRaa84duc82Fq9pC7ujRWPfsQTEYSJg+jbDOnY/5eNXlwrp7t+fi6fMvUA9fPCkmE4GXXYqpRSrG5GSMSUkYk5MwJiejms1kHDhAVG4utd9+i2X7DpwFBQAEXnwxcWPGYG5zdpP8vt7O6XSSnZ1NSkoKhqPszdncWHbtInvIUFAUWqxZQ9Cll2gdUrNV39zytxz0VWWbNlO4YAGoKiE33kDizJnoAgKO+Xh7ZiYVb71N5Tvv4Cwqqvt+QJs2BJx1luc9O+Xwe3dSMkRGkJ2ZSUxFBbXffod19y5q9v4EgD4igpihQwnveieKTlrPgH+dN+6aGtK798CRnU343XeTMHGC1iE1W5n9B1C5cyeVl13KlevWHTW33BYLB67/F8WRkbR//TVMh6+/j+QP1+VSAAvN+cOJ1hw4S0rIHjac2gMHUAIDSV4wn+D27Y/6WNXhoGzjRso2ba4rXgECzmlL+J13Eta5M/qwsHod1223U/biS5SsXYtqs4FOR2SPHsQMeeS4F3Ciecqf+QQVb76JsUUL0jZtlKnxQhyHqqoUr1xF6bp1gKfLc9zox1H0+qM+3vr99xQ/9TQ1339f9z19RARhXboQfucdBJx1Vr2Pbf3mGwrmzcf+558AmM87j4Tp0wg444zT+I2EL7J+/z1ZAwYCkLJyJcHt22kcUfOU2X8Alu3bCTjnHM584/WjPsZtsfDbdR0BOGvH9qP+G+oP1+XyUZwQ4oTsWVlk9ulL7YED6CMjSV2z+pjFb81PP5H+4IMULV2Gs6AAXXgYEd270fLll0l78UUi77//uMVvdXU169evp/pw10idyUR03z6c8dqrhN58M7jdlL30EpkP9ab2jz8a5ff1Ff87Vv4gduQIDPHxOLKyKF65Sutwmq365pY/5qCvUJ1O8mfMqCt+YwYPIm7smKMWv66KCvJnzCRrwEBP8avTEXzNNSTNe5JW779H3GOjjlv8Hi0Pgq64grSXXyJ21Eh0QUHYfv6ZjAf/Q/lrr+Pv91787bwJuvRSIrp1AyB/5kzc0oSp0dSYzXzYutVxc6smIIBPrr7Kb/LvaKQAFkIcl+3AATL79MWRk4MxKYnU59ZiPvfcfzzOVVVFwZw5ZPbth/33P9BHRJAwdQqtPviA+NGj6z1t2el0kpGRgfN/1poZExJImjuH5CVL0EdFUfvbb2T8pxdlm7f47cXUscaqOdOHhJAwaSIAZRs3UvPjj9oG1EzVN7f8MQd9gbu2lpzHR1P51tug0xE/aRLR/fv/o4eCqqpUvv8+h+65l4qtWwEIv+duznznbVKWLSX0xhtRTKYTHu9YeaAYjUQ98ABpr71KULt2qDYbBbNnk/v4aJxlZQ33C/sYfzxvYh8dijE5GWd+PkXLlmkdTrPl1uspCAk5bm65dDqKYmJwulxNGJl3kQJYCHFM9sxMsocMxVVWRkCbNqSuew5TaurfHqOqKpUffsihe++l/NXXQFUJ73onZ7z2KuF33omuHhdPJyPk2g6kbdpI8NVXo9bWUjhvHjkjR/2t+YNo3oKvvprwrneCqpI/YybuozT6EMJfqS4XeZMmY/nqK5SAAJLmzyPirn//43H27Gyyhwwlb9JkXGVlmFq1IvW5tSRMmIAxPr5BYzLGxZGyYjmxI0agGAxUf/45Gd17YP322wY9jvBeusDAuq2Qyl99DesR0+yFaGpSAAshjspZVOQpfsvLCWjblhZrVmOIifnbYxz5+eQMH0He+Am4ikswtWxJizWrSZgyBX1ERKPFZoiOJnnZUuLGjEExmbB89RXpDzxIzU8/NdoxhXeJHTkSfUw09owMyl58SetwhPAKqqpS8OSTVH/6KYrRSPKSxYRef/3fH+N0UvLcOtLv74Z1924Uk4mYRx4h7cUXCLzookaLTdHpiPrPg6Q+vwHTGWfgLC4ma/AjlDz7rF9vx+JPgi6/nPB77gagYO6Tft2FWGhLCmAhxD+4qqvJHjYcR24uxpQUUpYtRf8/nQJr9u4l44EHsezYgWI0Ej1wIC03bSTosstO69g6nY7w8HB0J+gWqigKkd3uJ3X9eoypqTjz88kcMJDS51/wm4up+o5Vc6QPDSVuxAgAStauxZGfr21AzUx9c8ufc9Ablax5horXXgdFIfGJmQRf+fc9fl3VFrJHjKB45UrU2lqCrryStC2bie7Xt15TnY/lZPLA3KYNLV94nrA7bge3m+KnniZ76KM4S0tP+fi+xp/Pm9ghQ9BHRGD/4w/KNm/ROpxmR3G7Cbbbj5tbOlUlyGpF58fbSkoXaKG5I7vNdejQAf0xulPWR+/evendu3cDReaf3LW1ZA8bRs2336GPivJMe05J+dtjKj/YRv706ah2OwFt25L4xExNO3u6LRbyZ82u25s4+NprSZw2tVHvQgvtqapK1sCHqfn+e0L+9S+SF8zXOiQhNFP+2msUzJ4DQNzYMUTef//ffu7IyyN7xAjsv/+BYjYTP348Ybd10XRv9Yq33/bcCbTZMMTEkDh71ml/iCq8X8XWreTPmIkSFMgZr76GMT5O65CaBekCXX/Ne/Mx4XO2b99+Ws+//n+meomTo7pc5E+ZQs2336ELCiJl+bK/Fb+qqlLyzLOUrF4NQEjHjiTOegJdYGCDxeB2u6msrCQsLKzen47rgoNJnPUEQZddRuGCBXVTopPmzCbwwgsbLDZvcypj1ZwoikL82DGk93yA6s8+o3rHDkKuuUbrsJqF+uaWv+egt6j69FMK5j4JQPSA/v8ofmv2/R85o0bhKi3FEBND8pLFmM85p8GOf6p5EH7HHZjPPZfcceOx//knWYMGEzPoYaL69GnWewb7+3kTdscdlL/5Jraf9lG0dClJs2dpHVKz4VYUqk1G3G73MXPLrShYzWbPY5o4Pm/hr7+38FIdOnSgY8eOp/xfWlqa1r+Cz1JVlcL5C6j6+BMUo5GkhQswt21b93O33U7+lCl1xW/kfx4kaf68Bi1+ASorK1m6dCmVlZUn9TxFUYi4525S16/7+5ToF15stl2iT3WsmpOA1q2J7NEDgMJ583HX1mocUfNQ39ySHNSe9bvvyJs4Cdxuwu+6i+iHH/7bz6s+/pishx/GVVpKwNlnk/r8hgYtfuH08iCgVStaPr+BsNsPT4le9RTZjw5r1lOi/f28UXQ64seOBZ2Oqm3bsOzeo3VIzUZNUBBvnHvucXPLajbzzs03UVlV1YSReRe5Ayy8ygcffNAsp1r4gtK1ayl/5RVQFBJmTP/b2jFnWRm5j4/2bDmj1xM/diwRhxtZeBvP+rIXKJg1i6oPP6RoyRKs330nU6KbsZiBA6jatg1Hdjalzz9PzIABWockRJOw/forOaMeQ7XbCenYkfhxY+umNKuqSum69RSvXAlAcIcOJM2ehc4L/43VBQaSOH0aQZdfRsHcJ7Hu2kVGj54yJboZM7dtS8S991K+ZQuF8+aRtvHl01qHLsTJkDvAQggq3n6H4qeeBiDu8ccJ69Sp7mf2zEwye/eh5scf0QUHk7JsmdcWv/+lDwkmcfYs4ieMly7RfkAXHEzsyJEAlK5bjyMnR+OIhGh8jsJCch4dhru6msCLLyZx9iwUg+e+hup0kj9jRl3xG9mjO8mLFnpl8Xuk8DvuoOWRXaIHDaZk7Vq/aWzob2IeGYw+Kgp7ejplGzdqHY7wI1IAC+HnbPv3UzB7NgBRffoQ2b1b3c/s2TlkDXwYR3Y2xqREUtevI7h9O61CPSmeKdH3eKZEt2jhF1Oi/Vlop5sJuuIK1NpaChYs0DocIRqVareTO3YszuJiTK1akbx4UV0zG9XlIm/qVCrfeht0OuLGjCHu8cdRTqPBZFMKaNXK0yX6ttv8Zkq0v9KHhhI7fDgAxWuekW7+oslIASyEH3OWlZEzejSq3U7wddcS88jgup85CgrJHjwYZ1ERpjPPJHX9egLOPLPRYzKbzXTs2BHzUToTntLrtWlDyxdfJLRTJ3A6KVqyxNMMpqKiQV5fSw09Vr5MURTPvtAGA5Yvv6L6yy+1Dsmn1Te3JAe1Ubh4Cbaf9qELCSF54QL0YWHA4X2A58yl6oNtYDCQvGA+kd3uP8Grnb6GzgNdYCCJM6aTMG0qitnsmRLd8wGs33/fIK+vNTlv/hJ2WxcCL74Y1WajaPFircPxeUa7nQvz84+bWyaHg/MOHsQcENCEkXkX2QZJaM4f2q17I9XlIvvRYVh378bYogUtX3gefWgoAM7SUrIGDMSeno4xJYXUZ5/BEBurccSnR1VVKl5/ncIFC1HtdgwJCSTNnUPgBRdoHZpoQEXLllO6YQPGpCTSXtly1C0ehPBlFe+8S/7UqQAkL1lMyLXXAp73uKJFiyl7+WXQ6Uic9cTflrP4qto//iB37Djshw6BTkfMoEFE9endrLtE+xvbr7+S8eB/wOUiZeUKgtu31zoknyTbINWfvHv4MJfLxU8//cTatWsZPHgwl19+OSaTCUVRUBTltLYE+uSTT+jVqxdnn302wcHBREVFceGFFzJ69GgOHDjQcL+E0EzxU09j3b0bxWwmef68uuLXVVlJ9pCh2NPTMSQk0OLpp5q0+LXZbHz++efYbLYGfd2jTonuP8Cnp0Q31lj5suj+/TDEx+PIzaV0/Qatw/FZ9c0tycGmZTtwoG7JSvTAgXXFL0DJ6jWe4hdImDypSYvfxsyDf06JXuWZEl1W1uDHaipy3vyd+eyz62YqFDw5D7fdrnFEvstuNLI3If64uWU3GNjX5my/zj8pgH3Um2++SVhYGBdddBH9+/fn6aef5rvvvsPhcJzW61ZWVtK9e3duuukmXnjhBX777TesVitlZWXs27ePBQsWcOGFFzJnzpwG+k2EFqo+/YzSdesASJg8mYCzzgI8nwxmDxtO7a+/oo+OpsWqlRgTE5s0NpvNxhdffNFob8zNaUp0Y4+VL9IFBRE36nBDrA0bsGdlaRyRb6pvbkkONh1XRQW5o0ej1tYSfM01RA/oX/ez0udfoOSZZwCIGz2a8DvvbNLYGjsPjjolukdPn50SLefNP0U//DD6mGgcmZmUvfCC1uH4LIfJxE8JCccvgI1Gfm7TBpsfbxsoBbCPKi8vx2q1NuhrOhwO7rrrLjZv3lz3vfPPP59evXpx//33k3i4EHI4HEyYMIEZM2Y06PFF06g9dKhu+lxkzx6Edb4FALfNRs6ox7Dt24cuLIwWK1dgatlSy1AbTV2X6PHjPF2iv/yK9J4PULNvn9ahiQYQcuONBLVvj2q3UzhfGmIJ36e6XOROnIgjNw9jcjKJT8ysmwJc9sorFC1dCkDMkCF/a2TY3ITfcQctN6zHlJaGs6iIrIcHUbL2OekS3QzoQ0KIO9zNv+S5ddIQSzQqKYB9XHx8PLfffjvTp0/nvffeY/jhbnqnYubMmXz66aeAp0HDxo0b2bdvHxs2bGDz5s2kp6czevTousdPmzaNL7744rR/B9F03BYLuaPH4LZaCbz0UmKHDQMOdxQdMxbrt9+iCwoiZcXyurvCzZWiKETcey+p655rNlOihYeiKMSPGQ0GA5YdO7B8/bXWIQlxWkrWrMH69S4Us5mk+fPrml5VvPsuhXOfBCCqbx+i+/bRMswmEdC6tWdKdJcuzWZKtPAIveUWAi+91NMQa/lyrcMRzZgUwD6qc+fOZGRkkJ+fz9tvv82UKVO49dZbiYiIOKXXKywsZNGiRXV/X7JkCd27d//bY0wmE/PmzaNbN8+ny6qqMn78+FP+HUTTUlWV/BkzsB86hCE2lqS5c1CMRs/3Z8/GsmOHZz3w0qUEnnee1uE2GXPbtp4p0Tff/NeU6GHDcRYVaR2aOA2mli2JvP8+wNMxV3U6NY5IiFNT/cUXlDy7FoCESRMxtzkbAMuu3eRP98zEiujejZhHHtEsxqamCwoiYcZ0EqZO+WtKdPceWHbu1Do0cRoURSHusVGgKFR9sI2aff+ndUiimZIC2EclJCSQmpraYK+3YcMGLBYLAGeffTYDBw485mPnzZuH7vDUq6+//poffvihweIQjad80yaqPv4ExWAgad6TGKKjASjbuJHKt98BnY6keU8SdOklmsYZFhbG8OHDCTt8h6Mp6EOCSZwz+68p0Tt3kt6tO1WffNJkMZwKLcbKl0QPGIAuPAz7H39Q8eabWofjU+qbW5KDjcuRm0veFM+SlYju3Qi79VYA7FlZ5I4fDy4XYV1uJe6xx1AURbM4tcgDRVEIv/NOz5ToM87AWVxM9qPDKJj7JO6amiaL41TIeXNs5rZtCbv9NgAKFy2UGVknKdBq5a5ffjlubgXZbNz+0ceEHW5+6o+kABaAp6nWf/Xu3fu4/5CmpqZyww031P39jTfeaMzQRAOw/forRUuXARA7ciSBF14IgGXXLooWLwEgbtRIQq65RqsQ6+h0OiIiIuo+ZGkq/50S3fLFFwho08bTcGbMWPImT8FVVdWksdSXVmPlK/RhYcQMfBiA4qdX46qu1jgi31Hf3JIcbDyq00nepMm4q6sxX3ABcSNGAOCqtpAzchTuykrMF1xA/KRJmm8JpGUeBLRuTcsXXyDi8Oy08ldeOdzTwXvvHsp5c3wxjzyCEhiI7ad9VH34kdbh+BSdqhJidxw3t3SqSkhNjV/nn//+5qKOzWZj165ddX+vz/ZJ//rXv+q+/u+6YeGd3DYbeRMnoTocBF97LRGHtxqwZ2aSO248uN2E3XkHEf8z5V0rlZWVLFmyhMrKSk2OH9CqFS3XryOqbx/Q6ah87z3S77uf6i+/1CSe49F6rHxBxD13Y2rZEldZGaXPPad1OD6jvrklOdh4Sp5bR83eveiCgkia9YRnyYrLRd6kSZ6lLHFxJM+fhy4gQOtQNc8DndlM/JjRpKxciSEuDkdmJpl9+1K4ZAluL+y0rPV4eTtjXBxRD/UCoGj5Mtx+3K34ZFkDA3n93HOOm1tWs5m3brrRr/NPCmDBwYMHcR/uoKgoCpdccuIpsJdeemnd1/v372+02MTpK1q6DPuff6KPifasl1IUXNXVnjsIVVWYL7yA+PHjNZ0+dyS3201FRUVdTmpBMZmIHTKE1GefwZiairOoiJyRo8idMNGrGq14w1h5O8VoJHbkCADKXt6IPTtH24B8RH1zS3KwcdT89BMlzz4LQPyE8RiTkwHP/u2Wr75CCQggacH8Jt2j/Xi8JQ+C27cjbfMmQm/tDG43ZS+8SHr3Hl63XZK3jJc3i/rPfzDEx+PMy6fspZe1DsdnqDodFpPpuLnlVhSsQUG4/Xh6uRTAgoMHD9Z9HRcXh9lsPuFzjlx/XFpaSpE0DPJK1V99RfmWLQAkTp+OITLScwdhwkTs6ekY4uNJnj8fncmkcaTeKfCii0jb+DKRvf4DOh1V27aRfu99VL73nqxL8iHBHToQ1K4dqsNBsXQWFV7OVV1N3sSJ4HIRemvnunW/lR9s+2v/9imT/apZ4cnQh4WR9MQTJC9Z7LkbnJVF1oCBFMyZg8uP73j5Gp3ZTOzQIQCUrHsOZ0mJxhGJ5kQKYEHJEW8q8fHx9XpOQkLC3/5eWlraoDGJ0+csKiJ/2nQAInv2JLh9ewCKV67ydHwOCCB54QIMMTFahun1dGYzccOHk7p+HabWrXCVl5M3eQpZAx+m9vfftQ5P1IOiKMSNHOH5EOPjj7FK4z7hxQrmzvXs95uUSPy4cQDY9u8nf4an43PUQw8R1rmzliH6hJBrryXtlS2E33UXAOWvvsahu++h4u23Zd9gHxHauTPmc89FtdZQ/NTTWocjmhGD1gEI7VUf0RgmMDCwXs/538dVN1BzmYyMDIKCghrktY4mIiLilLeK8iWq203etOm4yssJOPtsYh4dCkDl++9TumEDAAlTpmA+5xwtwzwqg8FAy5YtMRi86+0p8LzzSHvhBUpffImStWup+f570ns+QGSPHkQPGIA+JLjJY/LWsfJGAWedRXjXrlS88QZFCxeR+vwGzRsHebP65pbkYMOqfP99qt7/APR6Ep94An1ICM7iYnJGPYZaW0twhw7EDPG+7Y68NQ/0ISEkTJpIWOdbKJj7JPZDh8ifNp2KN94kbuwYzG3aaBKXt46Xt1F0OmJHjSSr/wAqtm4l4v77MJ99ttZheTWdy0V8dfVxc0vvdhNbXIxBr2/CyLyLnHkC2xENIkz1nAob8D9NN2oaaMuB8xp5StfUqVOZNm1aox7DG5Rt3Ih11y6UgAASZz2BzmTCduAA+TNmAhDVpw9hnW/ROMqjCwkJoXfv3lqHcVSKyUR03z6E3dqZwoWLqP7sM8pefJHKd94hesAAIu6+C6UJp5N781h5o5jBg6jatg3b/v1UffABYV26aB2S16pvbkkONhxHTg4Fs+cAEN2vH4EXXYTqcJAzejTOwkJMaWkkznoCxQsvWr09D4Iuv5y0l1+ibNMmitc8Q83evWQ88CBht91GzOBBGP9nVltj8/bx8iZBl1xC6E03UfXxxxQtXkLKqpVe07PEGwXabHT6/Q9CQkKO/ZjaWm7c+fVxH9PcSQEs/rbm12631+s5tf/Tka++d45P5Oeff270O8DNne3gQYqXrwAgduQIAs48E1e1hdyx41DtdoKvvZaYRwZrHOWxOZ1OsrOzSUlJ8dpPx42JiSQvmE/1jh0ULlyIIyOTwvnzKdu4kZhHHiH05pua5O6iL4yVNzFERxPVty/FK1ZQtGIlITfcgK4ePQ/8UX1zS3KwYahOJ7mTJuG2Wgm86CKi+/UFoGjVKmw/7UMXGkry4kXovfSC1RfyQDGZiOrVi9BbbqFoyVKqPvyQynfeoerDD4no3o3oPn3QN9G+vL4wXt4kdtijVH/xBdY9e7Bs307ItddqHZLXcul05IcEk+p0HjO3XDodxZGRnOF04q8dYGT+l/jbJ0D1vZP7v49rqE+RWrZsSVpaWqP919wL4CO3PArpeB0R996LqqoUzJ6NIzsbQ0ICiTOme/XUz+rqajZs2NBg0+obU8g113DG5s3Ejx+HPjoaR3Y2eRMmkNGjJ5UffIDqdDbq8X1prLxFZM8eGBITcBYUUPr8C1qH47Xqm1uSgw2jZO1znkI3OJjEJ2aiGAxYdu6k7HCOJkyZgumI5pPexpfywBgfT9Kc2aRu2EDgZZei2u2UPf8Cf95+B0XLVzRJsyVfGi9vYExOJrJnTwCKFi9BdTg0jsh72QID+ah16+PmVk1AAJ9dczXVFksTRuZdvPcqWDSZ6Ojouq8LCgrq9Zz8/Py//T0qKqpBYxKnpnjlKs/+kDExxE+ejKIoVL71FlXbtoFeT9LsWU32Cbe/UIxGIu69lzPffIPogQNRggKp/f138iZO4tDd91D+2mteuQ+lv9IFBBD76DAASjdswFFYqHFEwt/V/PwzJWvXAhA/YQLGpCQchYXkTZkKQMT99xN6w7+0DLFZCjz/PFqsXk3ykiWYWrfCbbFQun49f95xJwVPzsORI1umeZOovn3RR0Ziz8ig/LXXtQ5H+DgpgAVtjmgCUVhY+Lc1wceSmZlZ93VUVBSxXrIXoT+zfv8DZRs3AhA/ZTKGyEhq//yTgnnzAYgZPJjAiy7SMsRmTRcURMzDA2n17rvEDB6EPjy8bk3fH51vpeDJedh+/VXrMAUQ2ulmAi+6ENVmo3jVKq3DEX7MXVvr6dbvchHaqRNhnW9BdbnInzwFV1kZAW3aEDtiuNZhNluKohBybQfSNm4kaeECzOedh1pbS/mWLfzZ9d9kDRlK1SefyB1HL6APCSZm8CAAitesli2txGmRAljQpk0bdIenxKqqyo8//njC53x/xKby53hhJ2F/466pIX/aNFBVwrt2JeSaa3DbbOSOH49qsxHUrh1RD/XSOky/oA8LI7p/f8589x1iHxuFITEBd1UV5Vu2kNGjJxm9HqL0xZewZ2drHarfUhSF2JGjAKh85135YEJopmT1Gux//ok+Opr4sWMAKF23Duu336IEBpI0exa6/2k6KRqeotMRev31pG5YT8qqVQS1aweqinXXLnLHjOWPLrdRuGAB1m+/bfSlLeLYwrt29dytr6ik5PCe2EKcCimABWazmfaH94gF+Pzzz0/4nC+++KLu6xtuuKExwhInoWjZchw5ORgSEogdNRKAwkWLsP/+B/roaBJnzvDqdb9HCgoKomvXro3aDK0p6AIDierZkzPfeouUlSsIufEGMBiw/fwzRYsXc6jrvznUrRtFq1Zh2bUL1ymsBWsuY6WFwAvOJ/Tmm0FV65rGib/UN7ckB09dzb59lL7gWeMbP2E8+ogIrN//QPHqNZ7vjRuHKS1Nwwjrr7nkgaIoBLe7kharVnLG1jeJ6tMHfUw0rtJSyjZuIuvhQfzeqRN5U6ZS+f772LNzUFX1pI/TXMarqSkGQ90SlvJNm3H8z3I8AabaWq7OzDxubgXY7Vz5ww8ENVADW1+kqKdy5gqvNW3aNKZPnw5Ax44d61XMAsyfP58xYzyfPrdp04YDBw4c87FZWVmcccYZuFwuAL777jsuvfTSU47ZYrHUNdGqrq4mOLjp91P1ZdZvviFrkKerc8rKlQS3b0flRx+RN248KAopK1YQ3L6dxlEKAGdJCVUffUT1519g/f57OHwOAaAomM44A/P552Fq2RJTixYYU1IwJiejCw6WbR8agT0ri0P33gdOJylPrSL4yiu1Dkn4CbfNRnrPnjgyMgnr0oXEmTNwlZeT3vMBnAUFhN12G4kzpmsdpgBUhwPLzp1UffoZlq++wlVR8bef66OiCLzgfEytW3vet5NTMKYkY4iO9sotq3ydqqpkDRpEzbffyXnyPzL7D8CyfTsB55zDmW8cfZ2022Lht+s6AnDWju1H3QnBH67Lpfe6AOChhx5i+vTpWCwWDh48yLPPPkv//v2P+tixY8fWFb9XXXXVaRW/4vS4LRbyZ8wAIOLeewhu386z7nTmEwBE9entc8Wv1Wrlww8/pFOnTs3u03FDdDSR3bsT2b07rooKLDt2UL1zJ7af9uHIycH+55/Y//zzKE80oA8JQRcWhj44GIwGFL2BWqORPeFhXFlZhdntRlXdoKrgVj1/qm7Q6dEFBqILDkYXFIQuOMjzZ1AQSlAQhqgoAlq3xtiihc/MEmgophYtiLjnHso3b6Zo2TKCnn/e78bgWOp7Hjbn87UxFa96CkdGJoaYGOJGP46qquRNn46zoABjy1Tix43VOsST0pzzQDEaCenYkZCOHVGdTmr27qX6y6+o+fFHag8cwFVaSvUXX8IXX/7jubrgYHRhoejDwlFMJk9BbNBTazCyJzTE897tcqFyxPu22w2oKObAuvfquvfv/76HB4dgSmtJQOvW6PzsLp6iKMQOG0Zmr4eofO89Ih94AHObs7UOy2vUmkx8m9qCBKv1mOdircnED+eei27UY5iP8uG61Q/WvEsBLACIi4tj1KhRzJw5E4Bhw4YRFhbG/fffX/cYh8PB5MmT2Xi40RLAnDlzmjxW8ZfCpUtx5OZhTEokdtgwVIeD3PETcFssBF50ITEPP6x1iCfNbrezd+9err/++mZ3IXUkfXg4YV26ENalC+C5O1yzbx+1Bw5iz8rEkZ2DIzsbV3k5OJ24ystxlZdz5D9L1YGB/H7zTbTdvgN3PbcwOxYlMJCA1q0xtzmbgLMP/+cHF1fRA/pT+fbb1O4/QNWHHxHW+RatQ/IK9T0P/eV8bUjW73+g7OWXAYifPBl9WBhlGzdi+fIrFJOJpDlz0PnYWPpLHigGA0GXXUbQZZcBniZmtQcOUPN//4c9I8Pzvp2VhaOgAFwu3BYLbosFZ97fp+o22Hu3omBKTf3rPbvN2QScdRaG2NhmPWso8LzzCO3UiaoPP6Ro+TJarJBlLP/lNBr5MyoKu91+1HNRMZlwhYWRntqC8z/6+Kj5Zz1ydlozJQWwD+vSpQu5ubl/+96R2xN9++23XHzxxf943nvvvUdSUtI/vj958mR27NjBp59+Sk1NDd26deOJJ57g0ksvxWaz8eWXX5KXl1f3+OnTp9OxY8eG+4XESbHs2kXF4a0AEqZORRccTPHTT2P7+Wd0YWEkzp6NcoxN0IX3MURHE3r99YRef/3fvu+22XBVVOKuqsRVWYnbYkF1OFFdTiqsVvjxR+Ief4xw8+FCVaeAonjuZCoKqsuFWlOD22o9fDFm/evrmhqcBQXU/vEHak0Ntn37sO3b99fBFYWA1q093Wm73IoxIaHpBqSJGCIjier9EMWrnqJ45UpCbvgXOpNJ67BEM+WuqSF/+vTDDQvvJKTDNdT+/jtFS5cBEDtiBOYjdmYQ3k0XEEDgRRf9Y4cF1enEVVWFu7ISV2UVrsoKTydplwvV4aSixgo//UTc6McJDzCDooBO8RSt/21KarPhslhQrUe8Z1utuC1WXJWV1P7xB66SEuwZGdgzMqj66KO64+ujogi57jrCutxK4CWXNMuZLTFDHqH600+xfr0Ly67dPjfbTSuK0UjS/PnwxuvEjhpJ+FGa7FlsNrj3Xg2iazpydezDfvnlFzIyMo75c4vFwt69e//xfbvdftTHG41GXn/9dQYOHMiWLVsA2LdvH/uOvCA+/Lhp06YxYcKE04henA5XdTX5h6c5R3TrRtDll1Oz7/8oec7TFTF+wvhmWaz4I53Z7FmjEx/3j5+5y8vhxx8Jvf56wiIiTvkYqtOJPTOL2l9/9fz326/Yfv0VV3EJtb/9Ru1vv1G8ciWBl11KWJcuhN54I/rQ0FP/pbxMZM+elG95BUduLuWvvkpUz55ahySaqaLlK3BkZ2OIjyd21ChUu528qdNQHQ6Cr72WiPvv0zpE0QAUgwFDZCRERh715+7ycvjpJ0I7djyt925ncbHnPfrXX7Ed/JXa337Dnp6Oq7SUijffpOLNNzEkJBB2662EdelCwJlnnPKxvI0pJYXwe++hfNNmipYvJ+jKK5plod8YAlJbABB2ww2EHyX/DBZLE0fU9KQAFn8THh7O5s2bGTBgABs2bODrr78mLy8Po9FIixYtuOWWW+jXr59sfaSxokWLcObnY2zRgthHh+K22cibOsWzl2TnWwi7+WatQxQ+RDEYCDjzDM/F0RFTgJ0lJVi2b6fivfeo+fY7ar77nprvvqfwyXl1dxeCr7oKxcfvmOoCA4keNIiCJ56g5NlnCb/jjmZV4AvvYP3mG8o3bwYgYcpk9CEhFD/1FLUHDqAPDydh0sRmPW1VNDxDTAyGmBiCr7qq7ntum42an36i6oMPqPr4E5z5+ZSuW0fpunUEtG1L2G1dCOvUCUNMjIaRN4zo/v2pfPsdag8coGrbNsJuvVXrkISPkC7QQnP+0G2uIVm+/prsoY+CotDimTUEXXIJBfPmU755M4bYWNK2bEYfFqZ1mKKZceTnU/n+B1S+997fGnUZYmKIHjiA8K5dfXrKvep0kt69B/ZDh4jq3ZvYR4dqHZJoRtw1NaTf3w1Hbi7h99xNwoQJ1OzbR2bffuB2k/TkXEJvuknrMEUz47bZqP7ySyrffx/Lzq/hv3sY6/WE33kn0QMHYIz75+wiX1Ky9jmKV63CkJjAGa+/7tdLWOrTBbo+/OG6XOYKCOFD3BYL+U/MAiCyR3eCLrkEy+49f91VmDZVil/RKIwJCUT36U3als20fOlFIh94AH1MNM7iYgpmzyG9W3eqPvvslPbE9AaKwUDssEcBKNu4UfaXFA2qeNUqHLm5GBISiBs+/PCsnangdhN6a2cpfkWj0JnNhHXqRMrixbT64H3ixozBfMEF4HJR8cYbHLrrbopWrcJV7btTXiMf6IkhNhZnXj7lW17ROhzhI6QAFsKHFK1Y6Zn6nJREzCOP4Kqq8jRUASLuu4/g9u01jvD0lZeXM336dMrLy7UOxetpMVaKomBu25a4USNp9fbbxI1+HH1EBPb0dHIfH01Wv37U/Phjk8XTkIKvvZbASy9Fra2lePVqrcPRVH1zS87XE6v56SfKNm4CIGHiBHTBwRQtW+7ZBikujvgxYzSO8PRJHpwcLcbLEBlJZLf7abl+Halrn8V84QWoNhula5/jUNeulG3ahHqMHjHeTGc2EzN4EAAla9fiqqzUOCJtWYKDeeHii46bW3K+SgEshM+wfv8D5Yebk8VPmoQuMJDC+Qs8+0a2aEHs8GEaRyj8jWIyEdm9O2dsfZOofn1RzGZq9v5EZr/+5Dz2OLWHDmkd4kn57/6SgGdd2W+/aRyR8HVuu538GTNBVQm77TaCr74ay67df83amTpFZu2IJhd48cWkPvccSfPnY2rZEld5OYXzF3Do3vuo/GAbqtutdYgnJez22zG1aoW7spLS9eu1Dkf4ACmAhfABbpuN/MN7NIf/+98Et7uSqk8/pfLdd0GnI3H6tGa/X6vwXvqQEGIfeYQz3nid8LvuAp2O6s8/J71bdwrmPonbatU6xHoLvOB8z3RUVaVouewtKU5P6dq12A8dQh8dTdxjozyzdmbMAJrPrB3hmxRFIfSGf5G2ZTPxE8ajj4nGkZND3sSJZPZ6CNvBg1qHWG+KXk/so/9dwrJJlrCIE/LdjiWiWercuTN6vf6Un9+7d2969+7dcAF5iZI1z+DIzMQQG0vsiOE4S0oomDUbgKiHHvrHHoRCaMEYF0fCpIlE9uxB8YoVVH/xJeWvvIJl1y4Sn3iCwPPP0zrEeokZOoSqzz/HsmMHlj17CL7ySq1DEj7IdvBXStZvACB+7Bj04eHkTZ4is3aEV1EMBiLuuYewLl0offFFSp9/Htv+/WT2eoiYIUOIfPABn9heKLjDNQRedik1331P8VNPkzh9mtYhCS8mBbDwKtu3bz+t519//fUNE4gXse3fT+mLLwIQP34cupAQch97DFd5OQFnn03MwAEaR9iwTCYTF110ESY/7uRYX946VgFnnknyokVY9uwhf/oMHFlZZPbtS8zDA4nq3RvlND7kagqmFi2IuPtuyrdsoWjZMoKef94nLgAbUn1zy1tzUGuq0+m50+t0EnLDDYTeeKNn1s5773lm7cyY3qxm7UgenBxvHC9dYCAxAwYQcffdFMyeQ/Xnn1O0dCmWnTtJmD4NY3y81iEel2cJy3AyH3qIynffJbJnT8xtztY6rCZncDg4s7T0uLnljfnX1GQbJKG5I9utd+jQQe4AH0F1OMjo9RC1v/5K6C23kDR7FpXvvUfe5CkoBgOpLzyP+Wz/e4MXvsNVWUnBnLlUffgh4Fl7ljhzBsakJI0jOz5naSl/du2Kaq0hcdYswo7YH1mIEylZv57i5SvQhYZyxquvgF5P+n334yorI6pPH2KHDtE6RCGOSVVVKt7cSuGCBag2G7qwMBImTvCJbuW548ZT9dFHBF99FSnLl2sdTpOSbZDqTwpgoTl/ONFOVcmzz1L81NPow8NJe+1VcLk4dN/9uCsriXlkMNH9+mkdYoOz2+388ssvnHvuuX796WR9+MpYqapK5XvvUXh4PbAuOJj48eMIu/VWrUM7rv+ef8akJNJee9Wv9pesb275Sg42JXtGBunde6Da7SRMm0r4HXeQO3EiVR9sw9SqFS1ffKHZ5ZLkwcnxlfGyZ2SQN3EStv37AQi743biR49G58XXafbsbNLvuRfV6SRl1SqC2/nPEpbM/gMo37WLgnZX0vGpp46ZWyfKP3+4LvevOV1C+JDaP/+k5Nm1AMSNGY0hMpKCefNwV1YS0LYtUb16aRxh47BarWzduhWrDzVO0oqvjJWiKITfdhtpmzYSeNFFuC0W8iZNJnfCRFxVVVqHd0yRDzyAISYGR24uFa+9pnU4Taq+ueUrOdhUVLeb/BkzUe12gtq3J+z226n+4guqPtgGOh0JU6c0u+IXJA9Olq+Ml6llS1LXPUdUv76g01H59juk9+hJzU8/aR3aMZlSUgi/5x4AipYt87mO1qfLHhDAztTU4+aWr+RfY5ICWAgvpLpcnosoh4PgDh0IveUWqj7+mOpPPgWDgYQpk1GMRq3DFOKkGJOTabFmNdGDHga9nqpt28jo2ZPa33/XOrSj0gUGEv3wwwCUPPMsrupqjSMS3q781Vep+fFHlMBAEiZOwF1dTcHsOQBE/edBAs/zjUZwQvyXYjQS+8gjtFi9GkNiAo6cHDL7D6Bs02a8dRJp9ID+6IKCqD1woG75jRBHkgJYCC9Utnkztn37PFNFJ4zHVV5OwZPzAIju0xtzmzYaRyjEqVEMBmIGDCB17VqMyck4cvPI7NOX6u07tA7tqMLvvANTWhquigpKD3f0FeJoHHl5FB1ecxj76KMYk5IoWrwYZ3ExxpapRA8cqHGEQpy6oEsvIW3TJkJvuQVcLgrnz6dw7pOoDofWof2DITKSqN4PAVC8ciVuu13jiIS3kQJYCC9jz86heOUqAGKHD8cYH0/hwoW4SksxtWpFVN++GkcoxOkLvOB8Wj6/gcDLL8NttZIzciRlGzd63R0FxWAgdtjh/SVffhlHQYHGEQlvpKoq+bNmoVprCLzoIiLuuxfLrl1UbH0LFIXEKVPQmc1ahynEadGHhJA46wnPFl6KQvmrr5I9fIRXLmWJfOABDLGxOHLzKH/lFa3DEV5GCmAhvIiqqhTMmoVqsxF4+WWE3/Vvqr/8kqr3P/CsH5vSPNePHSkkJISHHnqorgGDODZfHyt9RAQtli8n/N//BrebwgULKZgzx+vuKARfdx2Bl1yCWltL8dNPax1Ok6hvbvl6DjaUynffw/r1LhSTiYQpk1Frasif+QQAEd3uJ/Dii7UNsJFJHpwcXx4vRVGI6tWL5IULUAIDse7eTeZDvbFnZWkd2t/ozGbPchugZO1aryzSG4O5poabf//9uLnly/nXUKQAFsKLVGzdinXPHhSzmYRJk3BbLHXrxyIffIDA85v/+jGDwUBaWhoGg2xTfiLNYawUk4n4SROJHTkSFIWK114ne9hwXJWVWodWR1EUzx0PoPKdd6n97TeNI2p89c2t5pCDp8tZXEzhwoUARA8ciCktjaLlK3Dm52NMSiJ26FCNI2x8kgcnpzmMV0jHjqSuXYshIQF7RgYZDz2E9dtvtQ7rb8Jvvx3TmWfirqikdN16rcNpEnq3m4Rqy3Fzqznk3+mSAlgIL+EoLKRo8RIAYgYNwtSiBUWLl+AsKsLYMpWYw814mrvq6mrWr19PtTQcOqHmMlaKohD14AMkL1qIEhSIdc8eMnr3xp6ZqXVodQIvuIDQm24Et5ui5Su0DqfR1Te3mksOno7C+fP/6s7/4ANYv/uubspl/ORJ6AIDNY6w8UkenJzmMl7mNmfTcsN6zOedh7uikqwhQyl/802tw6rztyUsGzfiyM/XOKLGV2M282HrVsfNreaSf6dDCmAhvICqqhTOnYu7uhrzeecR2bPH4fVjW/1u/ZjT6SQjIwOn06l1KF6vuY1VyHXX1d1RcGRkkvFQb6zffKN1WHVihgwBgwHLjh1eFVdjqG9uNbccPFlVn35K1cefgF7vmfp8uIM/QPg9dxN8pX/sQerveXCymtN4GWJiaLFmtac5ltNJwcwnKFyyxGu2Hwru0IHASy9Ftdv9YgmLW6+nICTkuLnVnPLvVEkBLIQXqPrwI6q/+BLl8BZHqs3mV+vHhPgv89ln0/L5DZgvuAB3ZSXZjw6j6tNPtQ4LAFNqKhF33w1A0VL/219S/J2rooKCuU8CEPVQL8xt2lC8chWO7GwM8fHEDhuucYRCNA2d2UzirCfqOp2XvfAieZMne0U/B88SFs+5WPnOu9h+/VXjiIQ3kAJYCI05y8oonD8fgKi+fQlo3drv1o8JcSRDdDQtVj9N6E03oToc5I4d55kN4QWiB/RHCQrEtn8/VR99rHU4QkOFixfjKinBlJZGdP/+1Pz0E2UbNwIQP3EC+pBgjSMUoukoikLMwwNJnDULxWCg6oNt5Dw+GrfNpnVoBJ5/HqE33QSqSrEfLGERJyYFsBAaK1ywAFdZGabWrYju0/vv68cm+cf6sSPpdDrCw8PR6eTt6USa81jpAgJInD2rrkN0/oyZlL74ktZhYYiKIqpXLwCKV6xotvtL1je3mnMOHo/l66+pfPsdUBQSpkwGIH/6DFBVwm6/nZBrrtE4wqblr3lwqprzeIV1voXkxYtQzGYs27eTPfRRXF6w1jRm6OElLDt3Ytm9R+twGo3idhNstx83t5pz/tWXonrbpovC71gslrpW7NXV1QQH+8+n5tVffUXOiJGg05G6fh0BrVqR3q07juxswu++m4SJE7QOUQhNqapK0bJllD3/AgBR/foSM3gwiqJoFpO7poY///1vXMUlxD42iqiePTWLRTQ9t8XCofu74czPJ6J7N+JHj6ZoxUpK161DHxPNGVu2oA8P1zpMITRl/eEHckaMxF1dTUDbtqQsX4YhKkrTmAqenEf5li0EtG1LyxeeR2lmBWBm/wFYtm8n4JxzOPON10/5dfzhurx5/Z8Xwoe4qqv/2uLogZ4Enncexaue+mv92OFtV/yN2+2mvLwct6yvPCF/GCtFUYgbPpyYw0sBStc+R+HcJzVdf6sLDCRm4OH9JZ991qu2bGoo9c0tf8jB/1W0YuXhJSqJxA4Zgm3/fkqffx6A+HHj/LL49cc8OB3+MF5Bl1xCizWr0UdFUXvgAJn9+2vehTl64AB0QUHUHjhA5fvvaxpLY3ErCtUm43Fzyx/y70SkABZCI0WLFuMsLMTYogUxgwZRs28fZS+/DED8hAno/XSD8srKSpYuXUplMywqGpo/jVV0n97ETxgPikL5q6+SN0nbBivhXe/E1KoV7opKSp5dq1kcjaW+ueVPOQh4lqhs2QJA/MRJKAaDZ+qzy0Vop06E/utfGkeoDX/Lg9PlL+NlbtOG1Gefqevsn9m3H7WHDmkWjyEykqi+fQEoXrnKK9YnN7SaoCDeOPfc4+aWv+Tf8UgBLIQGjtziKGHKFFCUv9aP3XYbIR38a/2YEPURcc89JD7xBBgMVG3bRs5jj2t2AaMYDMSNHAFA+ebN2LOyNIlDNB13Tc1fWxzddRfB7dtRsn4Dtb/9hj4igrgxozWOUAjvY2rZktTn1mJKS8NZUEBW/wHY9u/XLJ7Inj0wJCbgLCig7MUXNYtDaMugdQBCHKlz587o9fpTfn7v3r3p3bt3wwXUCFzVlr+2OLr/foIuvYSilSuxHzqEPjqauMdGaRyhEN4rrPMt6EJCyB0zBsuOHWQ/OoyUJYvRabBGKfiqqwi++iosO7+maNlykufPa/IYRNMpXrnyryUqI4ZT+9tvlK713P2PGz0aQ2SkxhEK4Z2M8fG0ePYZsh99lNr9B8ga+DApy5dpssWjLiCA2KGPkjdxIiXrNxDetSuG2Ngmj0NoSwpg4VW2b99+Ws+//vrrGyaQRlS0bKln/VhyMrGPDsV24AClG/x7/ZgQJyOkwzWkrFhBzogR1Hz/PVlDh5KyfLkmywZiR4zAsms31Z9+ivX7Hwi69JImj0E0vpoff6Rs02YAEiZNRGc2kz99BqrTSUjHjoTe0knjCIXwbobISFo8vZqcUSOp+e57soY+SsqSxQRdfnmTxxJ6SyfKNm3Ctm8fxU+vJmHypCaPQWhLCmDhVTp06HBad4DT0tIaLphGYNm9h4rXPJ35EqZMRtHryZ823bN+7OabCb3BP9ePHclsNtOxY0fMZrPWoXg9fx6roEsvIeWpVWQPGYrtp31kD36ElJUr0IeFNWkcAa1aEX7Xv6l47XWKFi0i9fkNzaKzaH1zyx9y0G2zkfffJSp33kHw1VdTumEDtv370YWGEjdurKZdyb2BP+RBQ/LX8dKHBJOybBk5ox7Duns32cNHkLxwIcHt2zVpHIqiEDdyBJl9+1GxdSsR99+Puc3ZTRpDYzHa7VyYn3/c3PLX/DuSbIMkNOcP7dYB3FYr6d264cjNI+Lee4kfP47iZ56h5OnV6MPDSXv1Fc23CBDC19gOHCD7kSG4KioIaNOGlJUrmnwqqrOkhEP/vgu31UrizBmEdenSpMcXjatw8RLKXnwRQ2wsaa9swVVSQnqPnqh2OwnTphJ+xx1ahyiET3HX1pI72rOMRTGZSFowX5O9s3PHjafqo48IuvJKUlat9PkPsmQbpPrz/Y+phfARRctX4MjNw5CYQOywR6n9/XdKD3ePjRszRorfw2w2G59//jm2ZtidsaHJWIG5bVtarH7as9XGwYNkDxqMs7S0SWMwREfXdRYtWrGyWXQWrW9uNfcc/Ft3/okT0QUFkT9jBqrdTtBV7Qm7/XaNI/QOzT0PGpq/j5cuIMBT9HbsiGq3k/vY41R/8UWTxxH76FAUoxHrnj1Ytu9o8uM3BrvRyN6E+OPmlr/nH0gBLESTOHLrjIRJk1ECAo5YP3adrB87gs1m44svvvDrN+b6krHyCDjrLFqsXo0hJoba338na+DDOIuKmjSGv3UWfemlJj12Y6hvbjXnHHTX1nqWqLjdhHXpQsi1HSh/5RVq9v6ELiiIhEmTfP6OUUNpznnQGGS8QGcykfTkXEJvuhHV4SBnzFiqPv64SWMwJicT2bMHAEVLlmi6tV5DcZhM/JSQcMIC2O/zT+sAhGju3DU1ni2O+GvrjLKXX8b2yy+H14+Nk4soIU5TwJln0OKZNRji47EfOkTmwIdxFBQ02fH/21kUoGTdepzFxU12bNE4SlavwZ6ejj4mmrjHH8OenUPR8hUAxI4YjjEhQeMIhfBtitFI4qxZhHa+BZxOcidMpPKDD5o0hqi+fdFHRGBPT6f8jTea9NhCO1IAC9HIilaswJGTU7d1hj09neKnngYg7rFRGOPiNI5QiObBlJpK6jNrMCQm4MjMJKv/ABy5uU12/NBbOmE+/3zUmpq6c1z4ppr/+5nSF14APN35dWFhFMyciWqzEXT55YTfdZfGEQrRPCgGA4kzZniWE7hc5E2eQsXb7zTZ8fUhIUQ/PBDwfOjlqqpqsmML7UgBLEQjsuzeQ/mRW2cEBZE/Y6asHxOikRiTk0l95hmMyck4cnPJHDAQe3Z2kxxbURTiRo0EoGLrVmy//tokxxUNy22zkTdlMrjdhN5yC6H/+hcVr7+B9dtvUcxm4idPahadvoXwFopeT8LUKZ4Pltxu8qdPp/yNN5vs+BF3343pjDNwlZdT8txzTXZcoR3pAi0011y7zbmqqkjv1h1nQQHh99xNwoQJlG3aROH8BShBgZzxyisyhe4o3G43lZWVhIWFoZOLzOOSsTo2R0EhWYMH4cjIxBAXR4unVmFqom3S6jqLtmtHysoVPrnEob651RxzsODJeZRv2eLp+rx5E+6aGtLvux+31Urc448R2aOH1iF6neaYB41JxuvoVLebwvkL6nqmxI0ZQ2S3+5vk2NXbd5AzfDiK0cgZr72KMTm5SY7bkDL7D6Bqxw5cF17A+Rs3HjO3TpR/zfW6/Ehy1gnRSArnzcdZUIAxJYW4ESP+tn4sbrisHzsWnU5HRESEXBTUg4zVsRnj40hdswbTmWfiLCwk8+GHqf3zzyY5dl1n0d27fbazaH1zq7nloGXX7r8aFk6d4pn6PGsWbquVwIsuJOL+prkY9zXNLQ8am4zX0Sk6HXFjRhPZsycAhfPmUdpETQWDr7maoHbtUB0OipYtb5JjNgadqhJidxw3tyT/pAAWolFUffIJle+9BzodiTNnoAQG1q0fC7z8MsLvvlvrEL1WZWUlS5YsobKyUutQvJ6M1fEZYmJosfppAlq3xlVcQtbAh5tkWvKRnUULFy7EXVvb6MdsaPXNreaUg67KSvKnTwcg4r77CL7qKirfeRfLzq9RTCbiJ09G0es1jtI7Nac8aAoyXsemKAqxo0YS1bs3AEWLFlOyfn2THDdu5AjQ6aj6+GMse/Y0+jEbgzUwkNfPPee4uSX5JwWwEA3OWVREwazZAET1fojACy/82/qxhMmTZf3YcbjdbioqKnC73VqH4vVkrE7MEBXlKYLbtsVVVkbWoEHYDhxo9ONG9+uHITYWR1YWpc8/3+jHa2j1za3mlIMFTz6Js7AQY2oqscOH4SwqonDhQgCiHx5IwBlnaByh92pOedAUZLyOT1EUYoYOIXqgpzlV8fIVFD/zTKMfN+Css4i4914ACp+ch2q3N/oxG5qq02ExmY6bW5J/UgAL0aBUVSV/5hO4KioIaNuWmAEDcOTnU7RkCQAxQx7BlJKibZBC+Bl9RAQtnn4K83nn4a6oJGvQYGr+7+dGPaYuOJjYESMAKF23HkdOTqMeT5yeyg8/pOqDbaDXkzhjBorZTMGcubirqgg4py1RDz6odYhC+BVFUYh5eCAxjzwCQMnTqyl+6ikau3VRzCOD0UdFYU9Pp/Tllxv1WEI7UgAL0YAqXn8Dy44dKCYTiTOmg9FYt37MfOEFRHbrpnWIQvglfWgoKatWEXjRhbirqsgePJiavXsb9Ziht3Qi6IorUGtrKZi/oFGPJU6do7CQgjlzAYju24fAC86natuHVH/xhWeLlqlTUQwGjaMUwj9F9+tL7PDhAJQ8u5aiZcsatQjWh4b+dbxnnsWRn99oxxLakQJYiAZiz86mcPFiAGKGDCGgVSvKX33Vs34sIICEKVNk/Vg9GAwGWrZsiUEuOE9Ixurk6EOCSVmxgsDLLsVttZI1ZCjW775rtOMpikLc2DEoBgOWr76i+osvGu1YDa2+ueXrOaiqKvkzZuCurCTgnLZE9+uHo6CAgrmegjiqXz8CzjpL4yi9n6/nQVOT8To5Ub3+Q9zoxwEoe/4FihYubNQiOOy2LgRecgmqzUbhwkWNdpzGoHO5iK+uPm5uSf7JNkjCCzSHduuqy0XWgAHU7P2JwMsvo8VTT+HIzia9R09Um022zhDCi7htNnJGPYZ1924Us5nkRYsIbndlox2vaPkKStevx5CYwBmvvorObG60Y4mTU/bKKxTOfRIlIICWL76IKa0l2UMfxbp7N+ZzzyX1ubUoRqPWYQohgPLXXqNg9hwAIu691/MBYyP1VKn97TfSH3gQXC5Sli8j+OqrG+U4DSmz/wAs27cTcM45nPnG66f8Os3huvxE5A6wEA2gdP16avb+hC44mMRp08DtJm/yFFSbjaArryRCpj7Xm9PpJD09HafTqXUoXk/G6tTozGaSFy8i+OqrUW02ckaOxLJzZ6MdL7p/PwwJCTjz8ildt67RjtOQ6ptbvpyD9vR0ipYsBTxbVwWceQblr7xa98FI4swZUvzWky/ngRZkvE5NxD33kDB1CigK5a++SsETs1BdrkY5VsBZZxHZvTvg2RvcV7r5u3Q68kOCj5tbkn9SAAtx2qw//EDx6jUAxI0ejTExkZJ167D93/+hCwkhYeoU6fp8Eqqrq9mwYQPV1dVah+L1ZKxOnS4ggKSFCwjpeB1qbS05ox6j+ssvG+dYgYHEPfYYAKUbnseemdkox2lI9c0tX81Bt81G7rjxf/uQsvbQIYqWHi6Ihw3DlJambZA+xFfzQCsyXqcu/M47PT1WdDoqtm4lf9p01EYq5GIeHujp5p+d7TPd/G2BgXzUuvVxc0vyD/x38rfwSp07d0Z/Gutke/fuTe/De8c1BVd5OXkTJ4HLRViXWwm7/TZs+/dT8uxaAOLHjcWYkNBk8Qgh6k9nMpE0dy65kyZR/cmn5IweQ9LsWYTeeGODHyvkX9cTfPXVWHbupODJeaSsWI6iKA1+HFE/RYsXU/vbb+gjIz0X0y4X+ZOnoNbWEtS+PRH336d1iEKIYwjr0gX0BvImT6byvfdQXS4Sp09r8BkbuuBgYkeOJG/CBErXrSe8SxeMyckNegyhDSmAhVfZvn37aT3/+uuvb5hA6kFVVfKmTsNZUICxZSrx48ah1taSN2kyOJ2E3nQToZ07N1k8QoiTp5hMJM2eTd6UqVRt20bu+AkkzphBWOdbGvY4hxtipd93P9Zdu6j+5BNCb7qpQY8h6qfyww8pf/U1ABKfmIkhNpbi1Wuw7d+PLizM07BQPpwQwquF3dIJxWgkb/x4qrZtQ3U4SJr1BIrJ1KDHCe10MxVbt2LdvZuC+QtIWbK4QV9faEMKYOFVOnTocFp3gNOacMpa2UsvY9m+3XMBPWcuuuBgCubPx56ejiEmhvjx4+QiSggfoBgMnvWeBgOV775L3uTJqE4n4bff1qDHMaWkENW7NyVr1lC40LMGWRcU1KDHEMdnz86m4IlZAET16UNw+/bU/N/PlKw9PGtn7FiM8XFahiiEqKfQG/6FsmA+uaPHUP3pp+SMHUvSk0+ia8AiWFEU4kY/Tkb3HnXd/EM6dmyw1xfakAJYeJUPPvjAJ7rN1fzfzxQtXw5A3GOjMLc5G8uu3ZRv2gxAwtQp6CMiNIzQdwUFBdG1a1eCpDA4IRmrhqPo9SRMm4piNFLx5pvkT5uG6nQQ8e9/N+hxoh7qReU77+DIzaX4mWeIO7zfpLepb275Ug667XZyx43DbbEQeNFFxAx6GLfNRt6UyeByEXrLLQ1+599f+FIeeAMZr4YTcu21JC9eRM5jj2P58ityH3uMpPnzG7TbfsAZZxD54IOUrl9Pwfz5BLVr57Xd/E21tVydmXnc3JL8kyZYQpw0V1UVeePH1U1zDr/nHlyVleTPmAF4WvP7Qrt8b2Uymbj44osxNfA0puZIxqphKTod8RMnEHHffaCqFMx8grLNWxr0GDqzmbgxYwAoe3kjtX/80aCv31Dqm1u+lIPFy5ZRu/8AuvAwEmfPQjEYKFq6DEdGJoa4OOLHjdU6RJ/lS3ngDWS8GlbwVVeRvGQxitmMZefX5IwYidtiadBjHNnNv+S55xr0tRuS0emkVWnZcXNL8k8KYCFOiqqq5M+YiSM3D2NSEvGTJwFQMHuOZy1waiqxI7zzjo6vsFqtvPnmm1itVq1D8XoyVg1P0emIGzuGyJ49ASicN4+ilStRVbXBjhFybQfPFDqn09PB1OFosNduKPXNLV/JwarPPqNs4yYAEqdNw5iQQPVX2ynf4vmAI2HqVPRhYVqG6NN8JQ+8hYxXwwu+8kpSli9HCQrE+s03ZA58GGdJSYO9/v9287ft399gr92Qak0mdqS2OG5uSf5JASzESSnf8grVn36KYjCQ9ORc9CEhlL/yClUffQQGA4kzZqALDNQ6TJ9mt9vZu3cvdrtd61C8kup04iwrw56RQeVP+9i7dy/V6ek+s0ehL1AUhdhRI4kZPAiA0ufWkT+9YQvVuLFj0YWFYfvlF0qe8769get7HvrC+erIyyN/umeGTuQDDxBy3XU48vLImzIFgIju3Qhu307LEH2eL+SBllRVxVVdjSM3F9uBA1R8+y179+6lpqSkQT9c83dBl15C6urV6CMjqT1wgMw+fRt027mQf13vaV7odJI3eQpum63BXruhOI1G/oyKOu65aC0sZO/evZTv3oPlaP99820TRqwNWQMsRD3ZDhygaLGn+1/siOGYzz0X2y+/ULTo8PeGPUrgBedrGaJoZlSnE9v+A9h+/hnbz/+H7edfsGdk1P28OjAQbr6JrIEPU1ZTgz4qCkN8HMaERM+f8QkYW6QQdMUV6ENCNPxNfI+iKET3748+OoaC2bOpfPsdXCWlJM17skE+5DLGxxE/dix5EydSsnYtwR2uIfC88xogcnEk1W4nd8IE3FVVmM87j9ihQzzfGzced2Ul5nPPJdZL12EL3+XIzaXmp32H37d/xnbwV9QjiqX/vnen9+hJCWCMj8eQkOD5Mz4eY1IigRdeiKllS+1+CR9lPvdcUtc9R/aQoThycsjs05fkZUsb5P1VURTix4+j5scfsR86RPHKlXV3hX2F6nSSPfRRuOJy8iZPpqqm5h+PsbpcGkTWtKQAFqIenCUl5Dz2OKrDQUjH64jo3h1XZSW5Y8d6vnf99XVTJoU4HaqqUnvwIJXvvkvltg9xHWMKly4oCENMDABKQADU1OAqLcVVWkrt/gN/f7DBQOBFFxLSoQPB11yD6cwzpUN5PUXc9W8M0VHkjp+AZedOsgY+TPKypRgiI0/7tcM630L1l19StW0b+ZOn0PLll7y2sYovUlWVgnnzsf20D11ICElzZqOYTBQuXIjt//4PXWgoSU/ObdCOscJ/OcvKqPrwIyrffRfbzz8f9TGKyYQuLBTjEU0y1Zoa7Onp2NPT//F4Y4sWBF9zNSEdOhB46aXoAgIaKfrmxdSihacIHjac2gMHPO/b8+c1SH8WfUQE8VMmkzNsOGUvbyS4w7UEt7uyAaJuGmptLa7ycgBMLVsS4HT+4zEupxP2/ti0gTUxKYCFOAG33U7O44/jzM/H2DKVhOnTAcifNt2zFjg52dM9VgoKcRpUh4OKt96mbNMm7H/+Wfd9XVgYgRdeiPncczGffx7ms89GHxmJYjBQXl4OS5dyxquvEKYoOPLzcRYU4MgvOPxnPrUHDmDPyKDmu++p+e57ipYuw5CYQMg1HQjucA1Bl18u0/ZPIOS662jx9FNkDx+O7ZdfyOzbj5TlyzGlJJ/2a8ePHUPN999jz8igaOky4seOaYCIBUD55i1UvPEGKAqJs2ZhTE6m6tNPKXt5IwCJM6ZjTErSOErh62r+72dKn3uO6h074L/FhF6PuW1bzOefj/m8czGfey7GxMS6D7jq3rtfe5UQm83znl1YgCPP8x5uz8rEtvcnHFlZlG/aTPmmzShmM0FXXkHINdcQ3KEDxoQE7X5pH2CIjiZ1zWpyxozFumsX2SNHkTBpEuF33H7arx1yzTVE3HsP5a++Rv706aRt3oQ+NLQBom5ayYsWEhUf/4/vWywWeP01DSJqOooqiw+ExiwWCyGHp2dWV1d71TZIqqqSP306lW+/gy4khJYb1mNKS6P0xZcoWrwYxWgkdf06zG3bah2q8FGq00nl++9TsuYZHLm5gOcuQUjHjoR1uZXgq65CMRpP6xj27BwsO3Zg2b4d63ffoR6xXlgxmwm7pRPhd92N+fzz5IOc46g9dIjsRx/FmZePPjqalGVLG+Tct+zaRfaQoQCkrFwp61EbgGXXbrKHDQOXi9jhw4jq1Qt7djYZPR/AbbEQ9VAvYocN0zpM4cNsBw9S/PTTWL78qu575nPOIey2LoR26oQhOvq0Xt9tsWDZswfL9h1YduzAWVT0t58HXnwx4XffReiNN8rMkeNQ7XbyZ86k8r33AYgZOpSo3g+d9r917poa0nv0xJGVRViXW0mcObMhwj0tmf0HYNm+nYBzzuHMN14/6mPcFgu/XefZx/isHduPmjvefF3eUKQAFprz5hPtv4UuOh0py5cR3L49NXv3kjnwYXA6iR8/joh779U6TOGjqrfvoHDRQhwZniYd+uhoono/RPiddzbaml23zYb1m2+x7NhB9favcObl1/0soHVrwu++m7Aut/rkp9lNwVFYSM7wEdT++itKUCDJ8+YRfNVVp/26BU/Oo3zLFgxxcZ67CdKR+JTZMzPJ6PUQ7qoqwm67jYTp01DtdjL79qP2wAECL76YFk8/ddofLAn/5CgooGjRYqo+/tjzDZ2OsNu6ENWrFwFnntkox1RVldpff8OyfTuWHTuo2bcP3G7P4UNDCevShYi77yKgdetGOb6vU91uilesoHTD8wBE3H8/cY+NQjGc3kTYmn37yOzbD9xukp6c62mQpSEpgOtPukALcQzVO3ZQtHQpAHGjRhLcvj3OsjJyx0/w7AF8yy2E33OPxlE2P+Xl5UyfPt0zRayZcpaVkTtpEjnDh+PIyEQfHk7s8GGc+dZWonr2rHfxeypjpTObCbm2A/HjxnLm22/T4tlnCLvtNpSAAGp//53CefP4o/Ot5E2dRs3evdKh9H8Y4+Jo8cwagi6/HNVaQ/aw4ZS+8OJpj1Ps8GEYW6biLCykYO6TDRTtqatvbnnb+eqqriZn5ChP06sLLiB+4gQURaFo0SJqDxxAHxFB4uzZUvw2MG/Lg8agqirlr71O+n33e4pfRSG08y2c8eorJE6bdlLF78mOl6IomNucTXS/vqQ+t5Yz33mHmMGDMCQm4K6qonzzZtK7dSejTx8q3n7bK7sTa0nR6YgdNozYx0YBUL5lC9mPDqtbC3uqAi+4gKg+vQHInz37H3fptWAJDuaFiy86bm5VBway6c47KK+oaLrAvIwUwEIcRe2hQ+SNnwBuN+FduxLRvTuq203+lCk4CwowtWxJwuELKyHqS1VVKj/80HMB9f4HoNMR+eCDnPn2W0T16tXk09gURSHokktInDGdVh+8T9yYMQS0bo1qs1H5zjtk9u1HevfuVLz1Fm7Z3qSOPiSE5OXLCLvzDnC7KVqyhPwpp7clhs5sJnHGTNDrqdq2jcptHzZgxP5BdbnIGz8ee3o6hvh4khfMRxcQQOUHH1D+6muH1wI/gTE+TutQhY+xZ2eTPWgwBbNn47ZYMJ9/PmkbXyZp1ixNOjUb4+OI7t+fM996i5QVywm58QYwGLD9tI/8adP545bOFC5egiM//8Qv5keievYkaf48lMBArHv2kPGfXtT+9ttpvWZM//4EtG2Lu6KS/Bkz5UNjHyEFsBD/w1VR4bmDYLEQePHFxI8bi6IolKxejWXn1yhms6dzaDOcEiIaj7umhvwpU8gbPwFXWRmmVq1IXbeOuJEjvCKX9GFhRHa7n5abNpK6fh3hXe9EMZux//4H+dNncOiOOylZvx5XVZXWoXoFnclEwpQpxI0eDXo9le+9T1b/ATgKCk75NQPPP4/ovn0BKJg7F0dBYUOF6xeKli6re49OXrQQQ0wMtoMHyX9iFgDR/fsT3L69xlEKX1P5wQekd+uO9dtvUcxmYkeNJPW5tQScdZbWoaHodARfdRXJ8+bR6t13iBk6FGNyMu7qaspefJE/u/6bvMmTsR38VetQvUboDTfQct1zGJOScOTmktGnL1WffnrKr6eYTCTOnIFiMmHZuZOK148+9Vh4FymAhTiC6nSSO34CjqwsDAkJnk8KTSbK33iTkmfXAhA/bpxX/MMnfIc9I4OM3r09TTj0eqIH9Kfliy8QeL737fuqKAqBF1xAwpQptNr2AbHDh2GIi8NZXEzx8hX80aULhQsXyZ0FPGMV2b0bKSuWow8Px7Z/Pxn/6UXNjz+e8mtG9+uL+ZxzcFdWkj9zhtxNqKeKt9+m7KWXAEiYOgVz27Y48vPJGTYctaaGoHbtiB7QX+MohS9R7XYKnpxH3sRJqDYbgZddStrmTUQ98ACKXq91eP9giIkhuk9vznjzDZKXLSXo8svB6aTyvffJ6NmTrCFDsezaLe8pQMBZZ9HyhecJuvJK1JoackePofjp1aiH11Wf9OudeSYxQ4cAULhoMfasrIYMVzQCKYCFOEx1u8mfPgPr7t0ogYEkL1qEISqK6h07KJgzB4DoAf0bpIW+ODaTycRFF12EqZnszVn16Wdk/KcX9t//QB8dTYunVhEzaFCD7D3a2GOlDwkhqlcvztz6JgnTp3umR1trKHv5Zf68syu5kyZhO3iwUY7tS4KvvJKWLzxPwFln4SopIevhQZS/8eYpvZZiNJIwcwZKQADWr3dRumFDwwZbT/XNLW84X6u376Dgv3d5B/QnrFMnXJWVZD86DGdxMQGtW5M070mvLFqaC2/Ig4bkKCggc+DDlG/ZAkBUv760eOopTCkpDfL6jTleik5HyDXX0GL107R84XlCb7kFdDrPVkBDhpDxwINUvv8+6lH2f/Un+ogIUpYvI7JnDwBKnnmG3MdH47ZYTun1Inv08PSGsNnIHT9es3XYBoeDM0tLj5tbRpeLtMwsTH7cC0G6QAvNeUO3OVVVKZw717NOTK8nef48Qjp2xLZ/P5kDBqLW1BB2++2y36+oN1VVKVm9hpJnngE8W1YkzZ2DITZW48hOnaqqWL/+mtIXXsS6Z0/d94M7dCC6X18CL7xQw+i057ZayZ8+naqPPwEOdxodNfKUGi6Vv/oqBXPmAnhFd1FvZdmzh5zhI1DtdkJvuYXEJ2aiOp1kDx1KzXffY4iLI3X9OoxH2etSiKOp2bePnJGjcJWVoQsNJXHmDEKuvVbrsE6LIzeX0pdeouLNraiHCzNjUhJRvXsTdsftDfKBrC+rePsdCmbPRrXbMbVqRfLCBZhatDjp13Hk55PR8wFcFRWE/OtfJD05t0k/eJMu0PUnd4CF31NVlaLFS/5qkjJjBiEdO+LIzSV7+Ii66XMJkyZK8dsE7HY7P/74I3Yfbrqk2u3kT51WV/xG9uxJi6efavDit6nHSlEUgq++mhZPraLlSy/W3VmwbN9OZp++ZA0ajPWbb/x2ip0uKIjEuXOJeWQw4Ok0mtmv/ylNh4u4914iunUDIG/KVM+2J02ovrml5flas3cvOaMeQ7XbCel4HYnTpwGQP206Nd99jy44mJRlS6X4bQLN4X0bPDN2sh4ehKusjIA2bWj54ouNUvw29XgZk5KIHz2aVu+9S8wjg9FHRuLIzaVg9mwOdf03ZRs3+nXn6PA7bqfFmjUYYmKw//FH3V3yk2VMSCB50UIUk4nqzz6jaOmyRoj2+BwGA39ERR43txx6PX+2SPH58/V0nN4GWEI0sM6dO6M/jU/LevfuTe/evU/qOSWr19StHYufNJGwzrfgqqgge+ijuEpKCDj7bJLmzZNtM5qI1Wpl69atpKWl+eR0OldVFbmjx2D95hvQ64kfN46Iu+9qlGNpOVbmtm1Jmj0L++BBlK5bT8W772L95hus33xD4EUXEtW3H8HXXO13HxopikJ0v34EnHUWeVOmYvv5Z9J79CTu8ccI79r1pMYj7rFROPJysXz5FTkjR9Fyw3qMycmNGP1f6ptbWuWg7cABsh8dVvcBZeKcOShGI4VLl1K1bRuKwUDS/PnSr6GJ+Pr7NkDpyy9TtGgxqCrB115L0uxZ6IKCGuVYWo2XPjyc6H79iHzgASrefJPSDc/jLCykcMFCStY+R+SDDxBx733oQ5rfHb8TCbzgfFq++AK5Y8dRs3cveZMmU/3VduLHj0MfGlr/17n4YhKmTiVv4kTKXnoJY0oykfff34iR/509IICdqalcYbUeM7dqTSb2XHIJ7WpqMPvpnvNSAAuvsn379tN6/vXXX39Sjy/dsKHuLl3c6MeJ+Pe/cdfWkjPqMewZGRgSEkhessQv/zEQJ89RUED2o8Ow//EHSlAgyU8+SfDVV2sdVqMytWhBwpTJRA8cQOmG56l4801q9v5EzvDhBLRtS3TfPoT8618oOv+acBRy3XWkbdpI3tSp1Hz7HQUzn8CyfQcJkyaij4io12soej1Js2aROWAgtQcOkD1sOKnrnkPvpxcs/1X7++9kPzLE06n/kktIXrQQXUAAZZu3UPb8CwDET5lMcLsrNY5U+ALV7aZo0SLKNm4CPLMv4kY/jmJovpfIOrOZyO7dCb/7birffofS9etx5OZSvHwFpes3ENm9O5E9uqMPD9c61CZliI2lxZrVlKxbR8kzz1K1bRs1e/eSOGM6QZddVu/XCet8C46cHIpXraJw/gKMScmEdLimESMXJ6v5nt3CJ3Xo0OG07gCnpaXV+7Flm7dQtGw5ADFDhhBZt9fvVGp+/BFdSMjh6XOyZ6Q4MUduLlkPD8KRm4shJobkZUsxt2mjdVhNxpiQQPzYMUT37UPpSy9R/upr1B44QO6YsZjOPJPovn0IvfnmZn1R+b+MCQm0eOopyl58keKVq6j+7DPS9+0jYfp0gtu3q9dr6IKCSF68iMzefbCnp5M7egwpy5eh+OhdttNlz8wka8gQXBUVmM89l+QlS9CZzVR99hmF8+cDEPPII4TfdpvGkQpfoLpcFMyaRcXWtwCIHfYokb16+c3MFZ3JRMQ9dxPe9U4qt22j9Ll12NPTKXnmGUpfepHI++4j8oEHMERHax1qk1EMBmIGDCC4/VXkTZqEIzubrIcHEfXQQ8Q8PLDe771RffvgyMmmYutb5I4fR+qzz/rVNYG3858rEeETPvjggyZZbF/x1lsUzpsHeLo7Rvftg2q3kzd9OlUff4xiNJK8cAEBrVo1eizC99kP/wPpzM/HmJJCi6efwpiYqHVYmjDExhI3YgTRvXtTtmkTZRs3Yf/zT/ImTab4qaeJ6tOH8Nu6+E0Bp+h0RPXqRdCVV5I3cRL29HSyhwwhsmcPYoYORRcQcMLXMMbFkbxkMZn9+mH99lvyZ832y4Z8jrw8sgY/gqu4hICzzvJsPxUSTNXHH5M3aTKoKuH33E1U3z5ahyp8gOpykT9tOpXvvQc6HYnTpxHWpYvWYWlCMRgIv+02wjp3pvqzzyhZ+xy1v/5K6YbnKdu0mfB//5uoXv/BmJCgdahNJvCC80l7+SUKFy6kYutblK5fj2XXLpJmPYGpHjdbFEUhfvx4HHn5WPfsIWfESGnI50WkC7TQXFN3myvbtJnChQvB7SayZw9iR43CbbGSO3q0p7OtwUDizBmEderUqHGIo3M6nWRnZ5OSkoLBB+4W2jMzPcVvYSHGlqm0eOrpJps14Atj5aqupnzLK5S99BKu8nIADPHxRPXqRfi/ux61A2Vz5bbZKFqylPJXXgEgoHVrz561555br+dbdu4ke8RIcLmIGTyI6P6Nt69tfXOrqXKw9o8/yB4+HGdePqaWLWnxzBoM0dGe9/MFC0BVCb3pRhJnzfKrWQbewhfei46kOp3kTZlK1bZtoNeT+MTMJv0339vHS1VVLNt3ULJ2LbbDDfgUg4Gw228nqk/vBtsOyldUffIJ+U/Mwl1ZiWI2E/voo0Tcd2+9Ojy7qqrI7NsP+59/EtCmDf/P3n2HN1W2Dxz/niRN0pXuFmhpCyhLkOVAQAQFQQF5FfTnYroHoq97vILj1VdcgIoKioB7AoIKTqaCgKLsDaWF0t20STPP+f0RqKCMFJombe7PdfWi4yS5+3Dn6bnPeUbmtKnoAnSem3PjTVh//hlrl86c8847R80t1WZjc6/eFCUk0PWLzzEePP8+XDisAi0FsAi6unqjaR4PBS++VL2vX/zQIaQ+9BDeoiJyx96Nc8sW37zN558numvXgMQgGhbnzl3k3nYbnqIijM2a0fT1KfV6m6NAUquqKJs9m9JZ7+IpLARAn5RE4nXXEj90aMBOCEJR5dKl5D/xJN7SUlAU4gYPJvnOOzAkJJzwsWWff86BZ3z7kjf+79NY+vcPdLhBZ/vlF/Y9+BCqzUZE06Y0ffMNDKmpvvmKB/dJjr/ySt+8TdnrV5yA5naz/7HHfNuVGQw0efYZYi+8MNhhhSRN07CvWkXJ29Oxr17t+6ZOR2y/i0kaPRpT8+bBDbAOuQ8UkD9+fPUWgKaWLUl94H6iOnU68WP37WPPiJF4S0qI7t7dt1J0AC58yDZI/guvVUlE2PJW2si759/VxW/ymDtJfegh3Hv2kDN6NM4tW9AnJpI5bZoUv0FWWVnJjBkzqKysDHYox+Xcvp29t9yCp6gI02mn0XTqm3Ve/NaXtgLQRUaSeO21NJs7h7SHHyKiSWO8xcUUTn6FHQMHUfTmVLxWa7DDrBMx559P9scfYbn0EtA0yufMYdflV1D68SdoHs9xHxs/ZAgJw64HfNv9VCxaFJAY/c2tQOdg2WefkTv27uoFrzLfmY4hMZH8x8dVF7/Jt99O6oMPSPEbRPWlL9JcLvY99DAV3//gm+r0/ISgFL/1pb0URSH6nHNo+uYbZE5/m+ju3UFVqfhmAbuvvIq8+x/AsXlzsMOsExFpqWS89iqpDz6ALjYW59at7L3xJvY99hjugoLjP7ZJE9JffhnFbMa2fDn5Tz6FFqAtiKrMZr49rcVxc6vKZOKHbueFfP4FkhTAosFz5+ez94YbsP38M4rZTJMJz5E0ciSO9evJGX0D7n37iWjalMx33sHcunWwww17Ho+HPXv24DlBIRBMji1bfXtFlpRgatWKjDdex5CYWOdx1Ie2+judyUT80KE0++ILGo0fjzErC9VqpXjqVHYOGEjhK6/iKSkJdpgBZ0hKovFTT9H0rWmYWrVCraigYMIE9lw/DPtvvx33sSl33UVsn4vQ3G723XsfxW+/Xet7L/ubW4HKQc3rpeDFlzjw7P/A68UyYAAZU15DZzSSe/c9vnmbej2Nxo8j6YbRYTcfOtTUh75IdTrJu/8BKhctQjEaafLiC8T07BmUWOpDe/1dZIcOZEyeRNb77xFz8KJB5Y8/sue668m9ayxVf/wR5AgDT9HpSLjqKprN/oK4yy8HRaHimwXsGjKEklmzjlvURrY7g8ZPPQU6HdavvmLvbbcH5G+dqtdzICbmuLnl1ekoTE7G4/XW+uvXF1IAi2ozZsxAUZQafdwYwDlotaFqwwb2DB+Bc/t29MlJNJ36JrEXXUTl0qXsvfW26pVEM9+ZjjGjbvbXFPWbY/Nm9t52K96yMkxtWvuGPfsxdFUcSYmIIG7QQLI//YTGzz6D6fTTUe12SmbMYOegyyh44QXcBw4EO8yAi+rUiax3Z5H60IPoLBac27ax96ab2ffoo7gPHP2ugqLT0fjpp4k/uLdk0ZTX2f/Io6gOR12GHjCq3U7effdR+sEHACTffhuNnhiPWlFBzi23YF+xAsVsJv3ll4gbNCjI0Yr6QHU4yLv3XmzLlqGYTKS//DIx3WVbmpNhbt2a9OcnkP3Jx8Re0h90OmzLl5Mz+gb23nIrtpW/1voFuVBjSEig0WOPkjVrJub27dHsVRROmszuq6/BtmLFMR8Xe2Fv0ie+jC46mqq1a9kzbDiOLVvqMHJxiBTAosGq+OEH9t58C97iYkynnUbWzJmYmjWj4KWXyfv3vWgOB9Hdu/uGrkoBI/xQtX4De2+9DbXcirldO5q+/nrY7ZNY2xS9HsvFF5P14Qekv/wS5jPOQHM4KP3wI3ZdNpj8/z6DKzcv2GEGlKLXk3DllTT74nPihlzhu6uwYCG7hgyhYNKkow6vUyIiSHvwAdIeeQQMBiq+/ZacG2+s9xcN3AcO+OaxLVmKYjTS+NlnSBw9msqffmLPddfj3LQZfUICTae+KQWM8IvqcJB3z7+x/+K7cJIxaaLf25CJYzO1aEGTp5/29Vv/+heKwYB99Wpyb7+dnFGjqVy6tMEXwua2bcmc/jaNxo9Dn5iIa88ecu+4k9wxY7CvWXPU3z+me3cyZ84gIjMTT34+OaNvoOKHH4IQfXgLvaXnREho3bo1F1100QmP69atWx1EUzOe0lIKX3oJ69ffABDdvTtNnn0G+5o1HHhuAp78fADiLr+ctAcfQImICGa44m90Oh1xcXHodKF1fa7qzz/JvXOMby5ihw6kT56MPia4C0OEaludDEVRiOnZk+jzz8e+8leKp79N1ZrfKP/iC8rnzsXSrx+Jo0Zhat4s2KEGjCEhgUaPPEL8FVdw4LnncPy5jtJZ71L24UdYBgwgcfgwjFlZRzwmfsgVGJtls+/+B3Bu2syeYcNJf+F5Is8885Ri8Te3aisHNVWlfPZsCie/glpZiT4xkfSXXsSQkkLev/+NbclSACKyMsmYOBFjZuYpvZ6oXaHaF6l2O7l3303Vmt9QoiLJmDSZqM4nXrQo0EK1vU6GsWlTGv3nMZJuupGSWe9SPmcOjnXryLv7HkytWpE0ehQxF16I0gB+16NRdDriBg0ipndvit+cSunHH2P7+RdsP/+CuX17EkcMJ+aCC474/U3NmpE1cwb7HnoY+8qV7HvgQZJuvpmkm2485XZSVJVol+u4uaXTNKLsdnRhPHVEVoEW1WbMmMGoUb79E0eMGMGMGTPq5HVra7U5TdOwzptHwcSJqOVWUBQSrr+O+KuuonDiRCp/+BHwLUaQ9vBDRIdg8S5Ck/2338kdexeavYrIzp3JmDQRXVRUsMNq8Oy//07J9OnYfv7F9w1FIfaiC0kcPRpzq1bBDS7ANFXFtnw5JTNmUrV2re+bikJM794kjhhBZLszjjjevW8feff8G+f27b67w48+StyggXUf+Elw7tjBgf8+Uz2H0NymDY2ffYbKpUspmvI6WlUVisFA4sgRJI4e7dfeyUJ4K23k3XUXVX/8gS46moxXJhPZoUOww2rwPMXFlL7/PqWffopmrwLAmJ1N4uhRWPr1a/DblLlycyl9913Kv5xXPSfYmJ1N4vBhWC65BMVorD5W83gonDSJ0g8+BCDmogtp/MQT6CIjT+q1ZRVo/0kBLKrV5wLYtXs3+c8+S9XqNYBvefq0hx/CsWkzRa++imq3g15P4vXXk3TzTWG192h9o6oqVqsVi8USElfH7atWkXv3PWgOB1HnnEP6Sy+e9B+n2hZqbRUojo0bKX57OpWHrXgc3aMHSTeMPuU7nfVB1dq1FM+cWX0XFCDqrLNIHDGcqHPPrV79WLXb2f/4OCp/+gmAhOuuI/n2206qv/M3t04lB1WHg+Lp0ymdOQvN40GJiiTl9tsxt2/PgWf/h/Pg6rKRHTuS9ugjYbXlSn0Tan2Rt6KC3DF34Vi3Dl1sLBmvvvqPi0bBFGrtFQjesjJKP/qY0o8+Qq2oACAiPZ3EESOwDBqI7rBCsCHyFBdT+uFHlH36KerB1ZYNKSkkXHcdcf8ajD42tvrY8rlzOfDMs2geD6aWLWnyv2f/MdrHHzk33kTF8uV4z2xPuw8/PGpuqTYbWy7ohd1spsO3CzEc5WJ+OBTADfNdJ8KG6nJRNHUau6++hqrVa1DMZpJuvYX4K4dy4LkJFEyYgGq3Y27Xjqz33iXlrjFS/IY4q9XKpEmTsIbAlji2FSvJHXu3r/g9ryvpL78UMsUvhFZbBZK5bVvSX3yB7I8/IrZfP9+iK8uWkTNqNDm33ELlsuUNeq5ZZMeOZLz8MtmffIxl4EA4NNduzF3suPRSDjw3Aftvv/+1yv1NvsUJS99/n50DB1H89nS8B09A/eVvbp1sDtpW/sruq6+h5O3paB4P0T170ujxx3Fs3ETOqNE4N29GFxtL2mOP0XTaVCl+Q1wo9UXe8nJyb7vdV/zGWWj6xushVfxCaLVXoOjj40m+9Raaz59P8p13ok9IwJ2Xx4FnnmHX4H9RMmsW3ga8DY8hKYmUO++g+VdfkTJ2LIaUFDyFhRROnMiOPn3Jvfseyr/6Cm9lJXGDB9P0zTfQJybi3LqVXUOvZN8jj+LYurXGr1sVFcXstm2Pm1t2s5n5fftgreHfhYakYY9DEA2W+8ABrPPmUT53Lu59+wEwtWlDRJPGlMychVblG3aji44m+c47iB8yRPaIFDVSuXw5++67H83lIrpHD5pMeE6GXgaZ6bTTaPLMf3HdegslM2ZQ/tXXVK1eQ97qNRibNydx2PXE9u/fYO8smFq0oPET40m+7VZK3/+A8nnz8BYVU/bJJ5R98gmGlBRiLroQS9++GE87jaJJk3Dv20/RlCmUzJxJ/JVXknDtNRiSkoISv+pyUfnTIsrnzsW+ciUA+sREos46i6qNG9n/0MPVx8b270fqv/8dtFhF/eQpLSX3jjtxbtmCPj6ejClTMLdqGeywwpo+JpqkUSNJuOZqymfPpmTmLDwFBRROmkzxtLeIu+IKEq65mohGjYIdakDoY6JJHD6M+Kv/j4pvvqHkgw9wbd+BbelSbEuXokREEN3tPGL79iXjjTcomjQJ2/LlVCxcSMXChb7RTqNHyfD9WiYFsKg3NJeLyqVLKZ8z17fMvKqiaRqK0YguOhrHxo04N20CfAulxA8ejGXQoKDszyrqt8olS9j3wINobjcxvXrR5Nlnjpi3I4LLmJlJo8cfJ+nmmyn98CPKv/gC186d5D/xJIWvvUbC/11N/NAh6C2WYIcaEBGNGpF6779JGXMntl9XUfHdd1QuWoSnsJCyjz6m7KOPMaSmEt2zJ2ga9pUrcOfmUTJjBqUffkjc4MEkDrueiCZN6iRex9atlM+di/Wbb3zrM+Db59eQloanuJiKb78FQImKxHJxP+L+9S8i27erk9hEw+EpKSH3ttt92x4mJtL09SmYTjst2GGJg3RmMwnXXEPckCFULFhAybvv4dq5k9L33qP0o4+I7duHxGHDGuz6DjqjkbjBg4kbPBjnjh1UfP8DFd99h2vXLioXL6Fy8RIUo5Hobt1IGD4cx8aNVK1Zg23ZMmzLlhHZuTNJo0cR1bWr7HteC6QAFkdVVlbGp59+yoYNGygvL8disdCkSRPOO+882rdvXydvPs3jwVNQgCs3F9uyZZR/OQ9vaamv8FXV6oUUNJcL1e1GFxWF5eK+xF12GeYOHaSDECel4scf2ffwI+DxENunD42ffkpWCg9REY0akXrP3STddCPlX3xB6Ycf4SkooOi11yiePp34fw0m4ZpriEhvmHt8K0YjMT26E9OjO6rLhX3lr38VwwUFlH/2GeBbIFCJiEAB1IoKSj/4gLJPPyXmwgsxt2pFRGZTjJmZGJs2PeXF3TRNw1tWhnvfPhwbNlA+ew6OTZv+6rd1OtA0MBjwFhWhAJGdOhE3eDCxfS4KqSkGov7wFBWx97bbce3ciSE5mYw3XsfUrOGuGF+f6YxG4i67DMvAgdh+/oXSd9/Fvno1Fd8soOKbBUSdey6J119H1HnnNdjzOFOLFphatCDp5ptwHSqGv/0W1549R6x1oSkKushIVKsV+6+/Yl+9GnPr1kT36I4xKxtjVhbG7Kwj5hML/8giWKLa4YtgHc/pp5/Ogw8+yOjRo2ulczp8sv36K4ZgqrDiLbei2my+E6XDPxQFDAYUgwFFp0MxGDC3a0fcZYOI7dMHXQOcqB9uHA4HK1asoGvXrpjreL62dcFC9j/+OHi9xPbrR+MnnwjpFSuD2VahSHO5sH77HaXvvotz+3bfN3U6Ynr2JOHaa4js3LnBnlAdTnW5sK9Yge2XFTg2bcK5dSua0+mbJ62qaG43eL2+/hSO+FdnNqOzxOJNSmJLVjbtNBWzcuzlQhyqyjrVS6tdu9GXlKBWVKB5PEf22wB6ve9Ckk6HoihENGlC7MUXE3fZoJNa7EWElmD2Re78fPbefjvuPTkYUlNp+uYbIb9NlvTdR3Js2kTJu+9R8f33vr4J38rJ8Vf/H3EDB4bFhTFN03Bt307FokU4NmzEsXEj3uJi389U1devut2+gxXlrw98d9f18fF4rVYcDgc7e3TnkpdfPmpuqTYbGy68iC0tmnPplClExcf/45hwWARLCmBRzd8C+JCBAwfy0UcfnfIb4/A32urTWxL191XrdDrQ6dCZTJhat8Z8RlvMrVpjbtsGY4sWDXa+n6hbZV/M5sAzz4CmYbn0UhqNHyfzxuspTdOwr1xJyax3q+eagm91+IRrria2X7+wms+teTw4d+7EuWkTjo2bcGzahGPjxiOK4iOK1dqgKL5iV68nIiODyPbtMLVug7lNa0ytWmFISKi91xJhy7V3L3tvuw3P/nwMjRr5it+MjGCHJU6Se98+Sj/4gPK5X/p27wB0sbHEXf4vEq66iojGjYMcYd3RNA1PQSGOTRtxbt6MY8NGqtavx1tcjHaozz7079+YO5xJs48/PurzyjZIPlIAi2ozZsxg3LhxXHnllVx00UW0b9+elJQUvF4vubm5/PDDD0yePJnNB7emALjsssuYPXv2KS3jf/gb7bfLBhOVnIySnISSmoqSkoISE4uSEI/StOkp342Lj48n/ihXu0ToCMaV8ZL33qfw5ZcBiB86lNQHHzjlzejrgtxFODHnzp2UfvQR1q++RnM4ANAnJBA/dAjxQ4ZgSEkJcoTBoblcuAsKUSsrUCsrUW023IWFuPfm4t6/H1thIRuMEbTauxejx3vM53EZDGzJzqKd10tMSgqGRo2JyEjHkJCAzmLB1KJFg52LLf4SjL7IuX07e++4A29RMRGZmTR9fUq9WUhJ+u7j81basM77ktKPPsadm+v7pk5HTO9eJFxzDZEdO4bFaJ6/0zQNT2Eh3rIyVKsVr7UCT1Ehrj05uPNy8RwowOF2k3P5v+g5ZIjcAT4OKYBFtbKyshPuSedyubj11lt55513qr/37rvvcv3115/06x7+Rgu0cePGMX78+Dp5LXFyysrKmDRpEmPHjg34xQpN0yieOo3iqVMBSBwxnOQxY+rNH9a6bKv6zlteTtns2ZR9+hme/HwAFIOBmIsuIv7KoWF7QnUs/uaW5KCAus8Dx8aN7L3zTtRyK6bTTiNjymv1asVwed/4R/N6sS1fTumHH2H/9dfq75tatyb+yqFY+veXrS3/5kS5pdps/N6vP/P79mHMrbeSmJb2j2PCoQAO3cltos750wkbjUbeeusttm/fztKlSwF47rnnTqkAPtyGDRuIOsVFWI5H/tCIQzSPh4LnX6Ds4EJBybffTuLoUVIENVD6uDiSRo4k8frrqfjpJ8o+/IiqP/6o3mrCdNppvhOqSy6RtQSECGG2X34h74EH0OxVmNu1I2PyJPRxccEOSwSAotcT07MnMT174ty2jdKPPsb6zTc4N2/mwFNPUzhxEnGDBhI/dKisJSBqRApgUWM6nY5x48bRp08fANavX09ubi4ZtTDvJisrq0FeaRKhRa2qYt8jj2BbshQUhdT77iPh6v8LdliiDigGA5a+fbH07Ytj82bKPvkU68KFOLdv58Cz/6Nw0mQsAwcSP3QIphYtgh2uEOIw5XPnkv/fZ8DrJerss0l/8QW5YBUmTKefTqP/PEbKmDsp/3IeZZ99hjsvj9IPPqT0gw+JOvdc4ocOJabn+SG9eKUIDaE/yU2EpJ49exJx2NYwmw7uvytEqPOUlLD35luwLVmKYjLR5Ln/SfEbpsytW9Po8f/Q4puvSbn330RkZqLa7ZR98gm7r/o/cm6+GeuChaguV7BDFSKsaZpG0ZtTyX/yKfB6sVx6CRmTJ0nxG4b08fEkDh9GszmzyXhlMtE9zwdFwb5yJfvuv5+dgy6jaOo03AcOBDtUEcJkDrA4aU2aNGH//v0AfPDBB1xzzTUn9TzhMNdA+E9VVaxW6wnno58M586d5N19D+68PHRxFjJefpnIDh1q9TXqUiDbKhxpqor911WUffYZlYsX+1bYxDd82jJgAHGXX46peXjsLepvbkkOCghsHqhOJweeeRbr/PkAJI4eRfLtt9fr6Sryvqld7n37KPv8C8rnzMFbVub7pk5HdPfuxF/+L6K7dw+bu8Inyi3VZmPLBb2wm810+HYhhqNMOwyH83IpgMVJi4uLw2q1AjB37lwuu+yyk3qecHijieCr+P579o9/Aq2qioj0dDJemSxzhsQxufPzKZ8zh/K5X+IpKKj+fmTHjsRdfjmxfS6SxVeECDB3fj777rsfx6ZNoNeT9uCDxA+5IthhiRClulxU/vADZV/Mpuq336q/b0hJIW7wZcQNHkxEkyZBjDD4ZBskH7nsJE7Kzp07q4tf8N0NFqI2WK1WJk6ceER+nQrN46Fw8mT2PfgQWlUVUWefTebMGQ2i+K3tthJ/iWjUiORbb6X5vC9Jn/gyMRf0BL2eqrVryR83jh39+nPguQk4DtsWriHxN7ckBwUEJg/sq1ax57rrcWzahD4ujozJkxtM8Svvm8DQGY1YLrmEzGlTyf7sUxKuvx59fDyewkKK33qbnZcNZu8dd2L97rsGO7XFn9yym8182eeisM6/8BgPIGrd9OnTqz+Pi4ujY8eOwQtGNCiqqlJeXo56cPjpqfAUFrJ/3HjsK1cCkDB8GCl33NFghkLVZluJo1MMBmLOP5+Y88/HXVCAdd48yufMwb1vP2WffELZJ59gatkSy6CBWPr3x5CYGOyQa4W/uSU5KKB280Dzeil97z0KX30NVBVT69akPz+hQd25k/dN4JmaNSP1nrtJvuN2KhctonzOXOwrV2JfsQL7ihXoLBYs/fsTN2ggpjZt6vWQ+sP5k1uqomCPikIN40HADeMsUJyyyspKv/fi/fnnn3nxxRerv7766qsxNJCCQjQMmqZRsfBbDjz3HKrVihIZSaNxj2Pp2zfYoYl6LCI1laQbbiBx1Cjsv66ifPZsKhcvxrl1K4UvvkThpMnEdO+OZdBAYrp3RzEagx2yEPWKa+9e8seNp+qPPwCwDBxI2sMPyXQDcdJ0RiOWiy/GcvHFuHLzKJ87B+v8r/AUFFRfxDS2aEHcwIFYLr0EQ3JysEMWdUCqFgHAZ599xpQpU7jzzjsZPHgwcUfZU8/hcDB16lQeeughHA4H4NtXd9y4cXUdrhDH5CktpeB/z1Hx/fcAmFq3pvFTT2Jq3jzIkYmGQtHpiO56LtFdz8VbXo7122+xzpuPY8MGKhcvpnLxYvTx8Vgu6Y9l0CDMrVoFO2QhQpqmqpR99hmFkyajORwoUZGk3nsvcYMHN5g7cyL4jBnppNxxB8m33op91SrKv5xH5aJFuHbsoHDSJApffZXo884jbtBAonv2RCcXMRssKYBFtVWrVjFixAgMBgOtW7emdevWJCQk4PV6ycvL45dffjlivkBkZCRz586lcePGQYxaNDQGg4GsrKwajyrQPB7K586l6PU38JaWgsFA0ujRJI0ehXLYll0Nycm2lag9+rg4Eq68koQrr8S5Ywfl8+dj/fprvEXFlH74EaUffvTXEOlLLsGQkBDskP3ib25JDgo4tTxwbNxIwcsTqxctijrrLBqNe7xBDXn+O3nfBJei1xPdtSvRXbviraig4tvvKJ8/D8ef67AtW4Zt2TJ0cRYs/foTd9kgTK1b15sLMf7kll5VSSkqwqDX12FkoUVWgRYAzJgxg1GjRvl9/DnnnMOMGTNo06bNKb92OKw2JwJH0zRsS5ZQOPkVXLt3A2Bs0YLGT4zHXAv5KURNaR4PthUrsc6bR+XixWhut+8HBoMMkRbiIHdeHoWvTaFi4UIAFLOZlDFjiL/qShTZGkgEgWv3bsrnf4X1q6+OWP3feFoL4gYOwnJJ/3o/RFpWgfaRAlgA4HQ6WbNmDT///DMrVqxgx44dFBUVUVxcjKqqxMXF0axZM7p27crQoUPp0aNHrb12OLzRhP88Hg+5ublkZGQc9wqm5nZTuXgxpR9+RNXatYDvblzSzTcRf8UVYVFc+NtWIni85eVYFy70DZHeuLH6+9VDpAcOxNSqVcjdXfA3tyQHBdQsDxxbt1L++eeUz/3Sd3FIUbBccgnJt99GRJiMKJP3TWjTvN6/hkj/9BPaoRWj9Xqiu3UjbuCAkB0ifaLcUm02NvfqTVFCAl2/+BzjUdb/CYfzcimARdCFwxtN+K+srIxJkyYxduxY4uPj//FzV24e5V/OpXzuXLxFxQAoJhMJ11xD4qiR6P1czK0hOFFbidDy9yHSh5hOOw3LwIFY+vfDkJISxAj/4m9uSQ4KOHEeqFVVVPzwI2Wff4bjz3XV348691xS7hqDuXXrOow2+OR9U3/4hkh/S/m8+TjW/ZW7OovFt7jWwAGY27ULmYuYJ3wv2mz83q8/8/v2Ycytt5KYlvaPY8LhvFwuOwlxkjRNQ7XZUSuseK1WVJsNzeVG83jQ3C7flW2vF83tRvN4Qaf4hnUpuiM/V5Rj/kzR60Cn9/2rN6AY9L6vDXrQ6VD0ehS9HgwG32MOfX3w3+rPdbq/jqknNFXFW1aGp7gYx8aN2NesoWrNb7j37as+Rp+URNzgy4gfOpSIo3TiQoQSU4sWpI4dS8odd2D7ZQXW+fOoXLwE5/btFE6cSOHkyUR3PRfLgIHE9LpAVr4NENXpRLVa8VZUoFZUoDqd4PH4+uqD/fWhz1EUFJ0Ch/rkQ5+jgF539D5dp/j6Xp0OpbpvNhzRb/+9j64+9rCfVf+r04XMybU/1KoqvCUluPbswb7mN+xr1uDYtAk8Ht8BBgOxvS4gfuhQIs86q179biL86GNjiR8yhPghQ3Du2oV1/ldYv/7at4r0Z59R9tlnRGRlEjdgAJYBA4ho1CjYIQs/SAEsxFFobjfuAwW4c3Nx5+Xizsvzfezbj9dqxVthRa2ohPq2h5+iHHZypUPR/a2APvxE5NDnRzs5OeIw5Wjf/Odjj/d8h6mMiIAz2rLz8iuIqaz85wF6PVFnnUX8FZcTc8EFDXaBK9Fw+fYW7kHM+T3wWq1UfPcd5fPn+xZg+fkXbD//gi46mti+fbEMHEBkx45SJPhB0zS8paV/9de5ubgOfu4tKfX129aKv4Yz1ieHF8N6PRj0f/Xfh1/YDFa/bYyAtm3ZOWQo0WVlRz0mIj2duMGDiRt8Wb2fRynCk6lZM1LG3Eny7bdhX7Ua61fzqfjxJ9x7ciia8jpFU14n6qyzsAwcSOxFF6KLiqrT+Jzbt5P79H+hWTY5t9xCicv9z4Pq23lrgEgBLMKe6nLh3LIVx/p1VK1bj2PDBtz794PX69fjFaMRnSUWXXQ0OqMJxWCACANKRASKIcL3r16HpmmgaqCqaJp61M/RVLRD31e9vu97vWher+9rj+9zVNX3r8eDpqpoXo/vWI/H9/1jzWzQDh5z8Ep8KM5/cEdGwhltq9tfFxODsXkzojp3IapLZyI7dEDXAIfjiPCkt1iq7y64cnKwfvUV5fO/wpOfT/mcOZTPmUNEejqWg3cXjBnpwQ45JGiahjtvn6/f/uNPHOvW4dy9G62qyr8nUBR0sbHoY2JQoiJ9fbXhYL8dcehz3ymSdqgfPtRXa8fox73qwc8PHq+qf/XJqhfNe7Cv9h7Zf1cfe7y/OQf/DkAI99tt26I5nYDv76IhLY3Ijh2I6nIWUV06N+hVnUV48a0i7dsKL+0hGxU//oh1/lfYV6+u/jjwv/8Re+GFWAYMIOrss3wXqwKs4qefcG7fDs2yceftw32s/jAy0vd7hPH8c5kDLIKurucaeCsrsf3yC44//6Rq3Xqcmzf/tUrrYRSTiYj09IMfTTBmZGBo3BhDfDw6iwVdTAx6iyUkhylWn1QdVixrHo/vc4/XV2h7vOD1oHnVg/8e5eTr8O7h4OdHdBnaUY479M2jPPbw7x2r63F5PGwrKqLtmWcSlZISFotZnSyXy8XGjRtp27YtRmmnBkNTVap++43yr76i4vvv0ex/ncREduqEZcAAYvv2Ceh8d39zq65yUPN6caxbh33Nb1StX4dj3Xrfdmd/pygY0tKISG9CRHo6xoN9uCE5GZ3Fgj421td/R0WF3JSQIwrnQ/330f5VVV9/fuh7f+9Lg9Rvbz1wgLZntCMqNQUlKkpGLRyH9N0Nk3v/fqxff035/K9w5+RUf9+QloblkkuwDByAqVmzgL1+0dRp5L/1FoW9e9Np6BCMxyhwXR4POz0e2nXpctT8C4c5wFIAi6A7/I3Wo0cP9KdwlWzkyJGMHDnyH9935eZhW7qEysVLsP/++19zkQ7Sx8djPrM9ke3aY27XDlPzZuiTk+UPuBAiqNSqKioXLaJ83nzsv/5aXYwoJhMxvXph6d+P6K5dG+SFItVmw7ZiBZVLlmJbtgzv34bWKhERmFq3JrJ9O8zt2mNu1RJDkyYhuTKrECJ8aJqGY/16rPPnY134LWpFRfXPzG3bYhlwKbF9+tT6VICiqdMofvNN4ocOIe3hh0/6eaQAFqIOHP5GO1Xjxo1j/PjxvrsF69dTuWQplUuW4Nq584jjjNnZRJ1zDpFntsfcvj0R6elS7IYIu93Ot99+y8UXX0xUHc+fqW+krcKL+8ABrN98g3X+V7h27ar+vs5iIfbCC4ntdzFRXbrUylA7f3OrtnPQnZ9P5ZIlVC5ZQtXqNUeMztHFxhJ97rmYO5xJZPv2mFq1kmI3REhfVDPSXuFDdTqxLV1K+VdfYVv+819THXQ6os46i9h+FxN74YXoLZZTfq2iqdPY9847bLj0Egbfe+8xc+tE+RcOBXD4Dv4WIelU7gAbVY2Objf7x4/Htmz5kcPj9HoiO3UkpmdPYnr2xNi0aS1FLGqby+Xijz/+oFevXnJicALSVuElIi2NpJEjSRwxAuemTVi//hrrd9/hLSquni+sT04itk8fLP37n9LWHP7m1qnmoKaqODZtwrZ0qW9F7K1bj/h5REaGr9++oCeRHTrIonchSvqimpH2Ch86k4nYPn2I7dMHT0kJFQsXYl2wEMf69dh//RX7r79S8Oz/iO7ejdiL+xFzQU90B+fongy3Xs9WVcXlch0ztyT/pAAWIWbBggV+X2nSNA3P/v1ULl+ObckS7KtWoy1YiPXgz3UxMUR3705Mz55EdzuvVq6uCSFEsCmKgrltW8xt25Jyzz3Y16yhYuG3VPz4I96iYso++piyjz7G0LgR0V3PI+rss4k652wMCQnBDh3wDW22//YblYuXYFu6FE9R0V8/1OmIPPNMYnqeT/T5PTE2y5bROUKIBsGQmEjCNdeQcM01uHLzqFi4kIpvv8W5fTuVi33T9BSzubrPjj7nHIwtWkgfGABSAIt6Q/N6cW7fTtXaP6hau5aqtWvxFBQccUz13YKe5/u2DpG7BUKIBkzR64k+5xyizzmHtAcfwLZyJdYFC6hcvATP/nzKZ8+mfPZsAEwtWx48sTqHqM6d6myLDveBAqr+WOvru//4w3eX97CtOJSoSGK6dSP6/J5Ed+8WMoW6EEIEijEjnaQbRpN0w2icO3ZgXbiQigULceflYVu6FNvSpRQC+qQkos46q7ogltXUa4cUwCKkuPfvp8rhxFtagrekBE9JKd6SEpw7d+L4809Uu/3IBxgMRJ5xBtE9zyem5wVyt0AIEbYUo5GY888n5vzzUauqfNtx/Por9l9X4dy+HefWrTi3bqX0/ffBYMB0+mlEpKaiT0hEn5SIITERfUIihsQEnCYTAJrnGCvEH3RoT13nzp3YXG68ZaV4SkrwlpTi3r8Px5/rcO/b94/HRTRpQnSPHr6hzZ07y1xeIUTYMrVoQcrtt5N82204t2zBvnIltl9XUbV2Ld7iYt+d4oULAV/fGZGViSE5GUNKCobkFAzJSRhSUv6xUKA4NlkESwTd4ZPtf+vQkajjzAHWRUcT2eFMIjt2JLJjR8xnnBGS2xAJIUQo8ZSUYP91FfZVvg93Xl7dvbhOh6llSyI7diCyQwciO3QkIi217l5fCCHqIdXl8m3/tmoV9l9/pWrDxn/sYnI0sgr0iUkBLILu8Dfa72efQ2xKCoaEBPSJiegTEzAkJmFo3IjIMztgOq1FnWwmLoQQDZk7Lw/H1q14S0rxlBTjPTjaxlNagrfY969abj3xEwHodOjj4339dUKir+9OiMeQlIy5bVsi27dD1wBPoIQQoi6pNhtV69bjKTiAp7AIT9Ghj0I8hYV4i4rRNI3GT4zH0r//Sb+OFMBC1IHD32gVFRW1tiWSqJ/KysqYNGkSY8eOJT4+PtjhhDRpKxEoh3LrzlGjiI+LO/ZxViuvTp8uORjmpC+qGWkvEQiaplFaWMgrr79+3Nw6Uf6FQwEsc4BFSJH5u0IIETr0Fgv64xTAermGLoQQIUFRFFlPwU+6YAcghBBCCCGEEELUBSmAhRBCCCGEEEKEBSmAhRAhxWg00qFDB4wyjOeEpK1EoPibW5KDAiQPakraSwSKP7kl+SeLYIkQEA6T7YUQQgghhAh14XBeLneAhRAhxeVysXbtWlwuV7BDCXnSViJQ/M0tyUEBkgc1Je0lAsWf3JL8kwJYCBFi7HY7c+fOxW63BzuUkCdtJQLF39ySHBQgeVBT0l4iUPzJLck/KYCFEEIIIYQQQoQJKYCFEEIIIYQQQoQFKYCFEEIIIYQQQoQFWQVaBN3hq8316NEDvV5/0s81cuRIRo4cWUuRiWDweDzk5uaSkZGBwWAIdjghTdpKBIq/uSU5KEDyoKakvUSg+JNbJzomHFaBlgJYBN3hb7RTNW7cOMaPH18rzyWEEEIIIUQ4CYcCWC47iZByqneAs7Ozay8YERSVlZV89tlnDB06tNYujDRU0lYiUPzNLclBAZIHNSXtJQLFn9yS/JMCWISYBQsWNMgrTcJ/Ho+HPXv24PF4gh1KyJO2EoHib25JDgqQPKgpaS8RKP7kluSfLIIlhBBCCCGEECJMSAEshBBCCCGEECIsSAEshAgpOp2OuLg4dDrpnk5E2koEir+5JTkoQPKgpqS9RKD4k1uSf7IKtAgB4bDanBBCCCGEEKEuHM7Lw7f0F0KEJFVVKSsrQ1XVYIcS8qStRKD4m1uSgwIkD2pK2ksEij+5JfknBbAQIsRYrVYmTZqE1WoNdighT9pKBIq/uSU5KEDyoKakvUSg+JNbkn9SAAshhBBCCCGECBNSAAshhBBCCCGECAtSAAshhBBCCCGECAtSAAshQorZbOaCCy7AbDYHO5SQJ20lAsXf3JIcFCB5UFPSXiJQ/MktyT/ZBkmEgHBYbl0IIYQQQohQFw7n5XIHWAgRUhwOB4sWLcLhcAQ7lJAnbSUCxd/ckhwUIHlQU9JeIlD8yS3JPymAhRAhxuFwsHjx4rDumP0lbSUCxd/ckhwUIHlQU9JeIlD8yS3JPymAhRBCCCGEEEKECSmAhRBCCCGEEEKEBUOwAxDicP3790ev15/040eOHMnIkSNrLyAhhBBCCCFEgyGrQIugO3y1uVM1btw4xo8fXyvPJYJDVVWsVisWiwWdTgapHI+0lQgUf3NLclCA5EFNSXuJQPEnt050TDisAi13gEVI6dGjxyndAc7Ozq69YERQ6HQ64uPjgx1GvSBtJQLF39ySHBQgeVBT0l4iUPzJLck/KYBFiPnw5puxpKSii4lBHxuDLjYWXXQM+oR4dEZjsMMTdcBqtTJ9+nRGjx6NxWIJdjghTdpKnCpNVVFtNtTKSrwVFaiVNtTKCspLSvh4w0aGJCcToz/2HapKr5fPi4q4qnUb4hIT0MfGoouN9f1riUMf0/DuHIh/kr6oZqS9xKnQNM3Xb5eX4y0vx2u14i0rx2stp8Lp4ovCAkbfeOMxc0vyTwpgEWIKX3wJ29HuAOv1GLOzMZ1+GuaWLTG1bInp9NMxJCfXfZAioFRVpby8HFVVgx1KyJO2Ev7SVBXXnj04NmzEsWEDjvXrceXkoNpscJSZUJWRkVT07UPRu+/iqKo65vMeOu7Ayy9jO8pxhpQUTKefhun0g/12y9MxZmaiGOT0oyGRvqhmpL2EvzylpUf0246tW/CWloHXe9TjKyMjKe/b57i5JfknBbAIMTEXXIDZ6Tx4J6IStaICb2UleL24duzAtWMHFQsWVh+vT0zE3O4MYs7vScz5PTCkpAQxeiGECA3eykrsq1cfPGnagGPDBl+xewyK0YguJgZdbAz66Bi8cXEAWPr2IdYQcczH6bxe0FSiunTGXG49ot/WHA48hYV4Cgux/fzLX69lMmE67TSiu51H9PnnY27TBkXmQQohwpymqjg2bKDqzz99Re+6dbj37Tvm8YrZjD4uDr3Fgi7OgqewEAoK6zDi+ksKYBFSmjzz339Mttc0DU9BAc6t23Bu3Ypzm+9fV04O3pISbEuWYluylAOAuU0bonv2JKbn+ZhatUJRlOD8IkIIUcdUh4PKJUuoWPgttuXL0dzuI36umM2YW7fGfMYZmM84A9Ppp6OPs/gKX5PpiGPLyspg0iRSxo497lwx88HjGj/xxD+OU202nDt2VPfbjq1bcW7bjlZV5SvMN2ygeNpbGJKTiT7/fGJ6nk/UOeegM5trp0GEECLEaZqGY+NGKhYspOK773xF7N8Ys7Or+21z27YY0lJ9Re/f+sqiqdMonTWrrkKv16QAFiFPURQi0tKISEsj5vwe1d9XHQ6c27Zj/3UllYuX+E6oNm3CsWkTxW++iaFRI2Ivuoj4oUMwZmYG8TcQNWEwGMjKysIgQyRPSNpKaC4XtpUrsS5YQOXiJWiHDUM2ZmUR2bGj76SpXTtMLZr7PfTY39w63nG66GgizzyTyDPP/CteVcWdl0fV2j+oXLoE2y+/4Ckqonz2bMpnz0YxmYju2pW4yy8nutt5KKewKKKoO9IX1Yy0l3Du2IF14UIqFn6LOze3+vu6mBiiunT5q+A9oy362Fi/n1evqjRWlOPmluSfbIMkQkBtLbfuKSqictky3x3hlSvRHI7qn0V17Ur8lUOJOf98OaESQtR7jk2bKPvscyp+/BHVaq3+fkSTJsT264el38UYTzst5EfBqC4XVWvWULlkKZVLluDJz6/+WUSTJsQPHYLlssswJCQEMUohhDh13vJyyr6YjXXBN7i276j+vmI2E3PBBVj6XUzUeeed9KKvRVOnUfzmm8QPHULaww+fdJzhsA2SFMAi6ALxRlMdDmwrVlA+ew625curF3kxNGpE/BVXEPevwRiSkk75dUTt83g85ObmkpGREdZXJ/0hbRVeNE2javVqit+ZgX3lyurv65OTsPTtS2y//pjbnVErRa+/uVWbOahpGs6t27B+9RXl8+ZVF/aK0Uhsnz7EX3VVrf1+onZJX1Qz0l7hxX2ggNL336fsiy+qR+koBgPR3bsRe3E/Yi7oiS4y8pRfp2jqNAqmTcPxr8Gc+eCDx8ytE+VfOBTAsuqEaJB0ZjOxvXqRMWkizefOIXHEcPRxcXjy8ymaMoWdlw4g/8mncO/fH+xQxd9UVlYyc+ZMKisrgx1KyJO2Cg+aqlKxaBE5I0ex99bbfMWvXk/sJf3JeH0KLb7+mtT77iOyfbtaKw79za3azEFFUTC3aknqv++hxTdf02jc45jatEZzubB+/TU5I0eSM3wElcuXI9fuQ4v0RTUj7RUeXDk55D/1NLsGD6b0/ffRqqowtWxJ2n8eo8V335L+0ktY+verleL3kCqTiXkez3FzS/JP5gCLMBCRnk7KXXeRdMstVHz/PWWffIpj/XrK587F+vXXxF1xBUmjR8mWSkKIkKJ5PFR8+y3FM2bi2uEbLqcYjcQNHkzisOuJSE8PcoSBozObibvsMiyDBuHYsJGyTz+l4ttvcWzcSN5dY4ns0IHkO+8gqnPnYIcqhBBHcGzZQsmMmVR8/z0c3GooslMnEkeNJLpbNxnFEgKkABZhQ2cyETdgAHEDBlD1558Uvf4G9l9/pezjjymfO5eEq68mcfgw9Ae3/xBCiGDQVBXrN99Q/MYbuPf5RqnooqOJv/JKEq69JqymbyiKQmS7M4hsdwYpd4+lZOZMyj75lKo//mDvTTcT1bUrKXfcjrlt22CHKoQIc86dOymcNBnbsmXV34vu0YOkUSOJ7NgxeIGJf5ACWISlyDPPpOnrU7D9+itFU17HsW4dJTNmUPbppyQMu57E665DFxUV7DCFEGHGsXkzB557Dsef6wDQJySQcM01xF91ZY1WAm2IDAkJpN59NwnXXkvJ29MpmzMH+4oV7FmxgpjevUm+/TZMzZsHO0whRJjxVlZS/OablH78CXi9oNMR27cviSNHYG7ZMtjhiaOQRbBE0AV7sr2madiWLqVoyus4t20DwJCWRur99xHTq5cMValjLpeLjRs30rZtW4wnuRJiuJC2ajg8paUUTXmd8tmzQdNQIiNJGj2ahGuvCcq+uP7mVjBz0JWbR/HUqVi/+cY3zFCvJ+Haa0i++Wa5gFnHpC+qGWmvhkFTVazz51P4yqt4S0oAiLngAlLuHhuU7TeLpk4j/623KL5sEOc98MAxc+tE+Rfs8/K6IAWwCLpQeaNpqkrFt99R9Nqr1cMOo3ueT9oDDxDRuHFQYhJCNGyax0PZ519Q9MYb1asex/brR8rYsUSkpQY5uvrBuXMnRa++RuXixYBvtf/U++8jtlev4AYmhGiwqjZsoGDC8zjWrwcgIiuTtPvvJ/q884IWk2yD5D9ZBVqIgxSdDkv/fmR/+imJo0ehGAzYlixl19ArKZk5E83tDnaIYcFutzNnzhzsdnuwQwl50lb1m/2339hz/TAKJkxAtVoxnX46TadNpckz/w168etvboVCDpqaNyf9pRdJnziRiCaN8eTns+/e+8j79724D9tXWAROKORBfSLtVX95SkrIf/IpckaMxLF+PUpUJClj76LZRx8Ftfg9xGk08qPHc9zckvyTAliIf9CZzaTccQdZH35AZOfOaA4HhZNfYff111O1dm2ww2vwXC4Xf/zxBy6XK9ihhDxpq/pJtdnIf+pp9t50M85t29BZLKQ++ABZ770bMqsa+5tboZSDMef38F3AHDUKDAYqFy9m15ChlMx6Vy5gBlgo5UF9IO1V/2iahnXBAnZdMYTyuXNB07BceinNPv+cxOHDUUJkKLtbr2erqh43tyT/pAAW4phMzZvTdOqbNBo/Hn18PK7tO8i54UYOTHge1eEIdnhCiHrI/vvv7L76GsrnzAFFIW7IFTT74nMSrroKxSDrUp4qndlMyp13kP3B+0R26uS7gDlpEntGjMS5c2ewwxNC1EPe8nL2P/wI+x99DLWiAlOrVmS+/RaNn3qSiFSZqlIfyV9bEVL69++PXq8/6cePHDmSkSNH1lo8iqIQN2ggMef3oPCVVymfM4eyjz/G/uuvNH76KcytW9faawkhGi7V5aL4jTcomfUuaBqGxo1o/MQTRHXpEuzQGiRTixY0nTbVt0DNyxNxbtnCnuuHkXLXXcRfdSWKTq7/CyFOzPbzz+Q/8SSeoiLQ60m64QaSRo9CiYgIdmjiFEgBLELKssP2TjsZvQK06Ik+Pp5G/3mM2D4XkT/+CVy7dpEzYiRJt91K4rBhKKdQtAshGjbH1q3kPz6uepV5y6CBpN53P/qYhrewSCjxXcAcRPR555H/xJPYfv6Zguefp3LZUho9/rjcuRFCHJNaVUXhxEmUffYZAMbsbBo/9aTsOd5AyCrQIugOX22uR48eIXUH+Gi8ZWXk//cZKn/8EYDIzp1p/OQTslK0EOIImtdL6XvvUfT6G2huN/qEBNIefYTY3r2DHVrY0TSNsk8/pXDiJDSnE12chUaPPUbshRcGOzQhRIipWreO/f95HPfevQDEX/1/pIwZE5Qt6WpCVoH2nxTAIujq4xtN0zSs8+Zx4Pnn0exV6KKjSXv4IWL795d9g4UQuPfvZ/9j/6leOC/mgp6kPfYYhsTE4AYW5pw7d7H/P//BuXkzAJbLBpF2333o6sHfHSFEYGkeD8XTplE8/R1QVQxpaTR6/HGiu54b7ND8IgWw/2QSjBAnQVEU4i67jOwPP8R8ZntUm439j/2H/CeflAWyTlFZWRlPPPEEZWVlwQ4l5ElbhSbbL7+w+7rrqFq7FiUqkkaP/4cmL75Yr4pff3OrvuWgqXkzst6Z7lspWlGwfjmP3dcPw7l9e7BDq9fqWx4Em7RX6PEUF5N7x50Uv/U2qCqWSy8h++OP6k3xe0hlZCRvuFzHzS3JPymAhTglxowMMqdNI+mWW0Cnw/rlPHJG34ArNy/YoQkh6pimqhRNnUbumLtQy62Y2rSm2UcfETd4sIwMCSGK0UjKnXfQdOpUDI0a4c7JYc+IkVi/+SbYoQkhgqDqjz/Yc9312FevRomMpPF//0vjp55CHxsb7NBEgEgBLMQpUgwGkm++iYxXX0WfkHBwtdHrqVyyJNihCSHqiLe8nLyxd1P85pugacRdcQWZb79NRHp6sEMTxxDVuZNv7+Vzz0VzONj/2H848NwE1DDeG1OIcKJpGqUffUzOzbfgKSzEmJ1N1qxZWPr3C3ZoIsCkABailkSfew5Z77+HuX171IoK8u75N4WvvYbm9QY7NCFEADk2b2bP9ddj+/lnFJOJRuPH0ejRR9CZTMEOTZyAISGBjFcmk3TTjQCUffIJe2+6GXd+fpAjE0IEkmq3s//Rxyh4/nnweIjt25esWTMxNW8W7NBEHZACWByVy+Xi3Xff5dJLLyUrKwuz2Uzjxo3p1q0bL7zwAkVFRcEOMSRFpKWROfVN4v/v/wAomf4OuWPuwlNaGuTI6g+j0UiHDh0wGo3BDiXkSVsFX9mcOeSMGo17334i0tPJfGc6cYMGBTusU+ZvbjWEHFT0epJvvZX0iRPRWSw41q9nz3XXY1uxMtih1RsNIQ/qkrRXcDl37WLPiJFULFwIBgMp9/6bxs8+0yAWw4vwemmp0x03tyT/ZBVocRSbN2/mmmuuYe3B1UuPJjU1lXfeeYdLL730lF+voa42Z12wkPynnkJzODCkpZH+4guY27QJdlhCiFqgOp0UPDeB8rlzAYjueT6Nn3xS5ozVc+68PPIeeNC3SrSikHznHSSOGCFzuIVoICp++IH948ej2aswJCfT+H/PEtWpU7DDqhWyCrT/5A6wOEJubi4XXXRRdfGrKAoXXHABo0ePZtCgQURGRgJQUFDAv/71L348uBeu+CdL/35kzZqJMSsLz4ED5Nx4E9bvvgt2WCHP5XKxdu1aXDIP74SkrYLDU1zM3ltv9RW/Oh3Jt99O+osvNqji19/camg5GJGeTub0t4kbPBg0jaJXXiX/8XGoTmewQwtpDS0PAk3aq+5pmkbR1Gnse+BBNHsVkV06k/X+ew2m+D3Erdez2es9bm5J/kkBLP7m2muvZd++fQBkZWXx+++/s2jRIt5++22+/PJLcnJyuOiiiwBwu91ceeWVYb2M+omYWrQgc9ZMort18y2y8tDDFE2dhgy8ODa73c7cuXOx2+3BDiXkSVvVPef27ewZMQLHn+vQxcaS8corJN0wGkXXsP6c+ptbDTEHdSYTjR7/D6kPPgB6Pdavv2bvrbfiKS4OdmghqyHmQSBJe9Ut1elk/2OP+RYpBBKuvZamU6ZgSE4OcmS1z2k0ssjrPW5uSf5JASwO8/XXX7N06VLANz9g3rx5dOjQ4YhjkpOTmTt3Ls2bNwegpKSECRMm1Hms9Yk+Job0iS+TcO21ABS/+Sb7H3lU9gsWop6pXLacnFGj8ezPJyIzk6yZM+rdHpHCfwlXXUXG5MnoYmNx/LmOPcNH4NiyNdhhCSFq4NCInYoFvvm+aY8+Suq9/0YxGIIdmggiKYBFtddee6368xEjRtC+ffujHhcdHc2TTz5Z/fWbb76Jx+MJeHz1maLXk3rvv0l77DEwGKj49lv23ngT7oKCYIcmhDgB31YZH5F3zz2odjuRZ3Uha8Y7GLOygh2aCLDorueSNXMGEVmZePLzybnhBioWLQp2WEIIP/xzxM5k4q+4PNhhiRAgBbAAfJPcf/jhh+qvR40addzjhwwZUj1BvqSkhCWy561f4i//F02nvIY+Lg7Hpk3kDB+BY9OmYIclhDgGze2m4H//o+D5F0BViRs8mKavvII+Li7YoYk6YszKImvGDN9+wVVV7LvvfopnzJCpLEKEsKOO2DnnnGCHJUKEFMACgJ9//hnnwUU+oqOjOfvss497vNls5rzzzqv+WhbD8l9Uly5kzpqFsXlzPIWF5Nx4k9xROExMTAwjRoyovsAijk3aKrC8FRXkjr2bss8+B0Uh5e67SfvPYyhhsHWEv7kVLjmot1jImDSR+Kuu+mtxrCefRHO7gx1aSAiXPKgt0l6Bo2kapR9+WD1iJ+qss8JqxE6k08kgg+G4uSX5JwWwOGjTYXch27dvj8GPuRGdO3c+6uPFiRkzfPuFRnc7D83hYN9991P68SfBDiskGAwGsrOz/crBcCdtFTjuvDxyRo7CvnIlSmQk6S++QOKw68NmOxx/cyucclCJiCDtwQf+Whzry3nkjr0bb6Ut2KEFXTjlQW2Q9goMzeOh4H/PUfDCi9UjdjJemRxWI3b0qkq6Tnfc3JL8kwJYHLRly5bqz7P8vEqWmZlZ/fnmzZtrPaaGTh8TQ/rLLxN3xRWgaRRMmEDByxPRVDXYoQVVZWUlM2bMoLKyMtihhDxpq8BwbN7MnlGjce3ejSEtjcy33yLmgguCHVad8je3wjEHE666ivSXXkKJjMS+ciV7b7wR94EDwQ4rqMIxD06FtFftUx0O8u6/n7LPPgu7ETuHqzKZmOt2Hze3JP+kABYHFR+2vUNaWppfj2nUqFH15yUlJbUeUzhQDAbSHnmY5DvuAKD0vffY//DDYb1CtMfjYc+ePbKwmh+krWqf7ZdfyLnpJrzFxZhatiRzxjuYW7UKdlh1zt/cCtccjOnRncy3pqFPTsK5bRs5I0fh2Bq+K0SHax6cLGmv2uUpLWXvrbdhW7IUxWSiyYQJYTVi53BenY79mnbc3JL8g/C99y2OcPhVoMjISL8ec/hxtXUVac+ePURFRdXKcx1NfHw88fHxAXv+k6EoCkmjRxHRpDH548ZT8f0PeAoLafLiixgSEoIdnhBho3z+V+Q/9RR4PESdcw5Nnn8efUx0sMMSIcrcujVZM2aQO+YuXLt2sfeGG2ny/POyNZYQdcidl8feO8fgzslBZ7GQ8fJLRHbsGOywRIiTAlgA4DjsjqPRz+EiJpOp+vOqqqpaieOMM86olec5lnHjxjF+/PiAvsbJsvTvjyE5mbz77qfqjz/JGX0DGa9MxpiREezQhGjQNE2jZPo7FE2ZAkDsJf1p/PjjYTd0TtRcROPGZE5/m7z776dq9Rpyx46l0aOPEHfZZcEOTYgGz7F5M7l3jcVbXIyhUSMyXnkFU/NmwQ5L1ANSAAvAt6rzIS6Xy6/HHFo1Gvy/a3wiGzZsCPgd4FAWddZZZE5/m9wxd+HOySFn5CjSJ00kMsAXBkKJTqcjLi4OnU5maJyItNWp07xeCp5/gbJPPwUgccRwku+8EyXM29Tf3JIcPLhC9CuvkP/EE1QsWEj+E0/izj9A0k03hs0QTMmDmpH2OnW2FSvIu/9+NHsVppYtSZ80kYjU1GCHFXQ6TSMGjptbkn+gaLKRnQD+7//+j08+8a1CfNdddzFp0qQTPuaLL75gyJAhgG8+8P79+0/qtW02W/VS7JWVlURHy5BDT2EhuXffg3PzZhSzmSYTniOme/dghyVEg6I6HOx/9DEqFy0CRSH1vntJuPrqYIcl6ilNVSma8jol77wDQNwVV5D24AMoYbzSqhCBcMR0lbPPpskLz6MP4y19DimaOo3iN98kfugQ0h5++KSfJxzOy8O39BdHSEpKqv78gJ+rWebn51d/npiYWOsxhTNDSgqZU98kqmtXNIeDvHv+Tfm8ecEOq06oqkpZWRlqmK+G7Q9pq5PnLS8n9/bbqVy0CMVopMn/npXi9zD+5pbk4F8UnY6UO+/wbZOkKJR/8QX7HnggLBY1lDyoGWmvk6NpGsXvzCB/3DjweIjt34+MyZOk+D2MqihYNe24uSX5JwWwOKjVYauc7tmzx6/H5OTkVH/eunXrWo8p3Omio8l4+SUsl14KXi/545+g+O23aeiDNqxWK5MmTcJqtQY7lJAnbXVy3AcKyLnpZqr++BNdbCwZr71KbJ8+wQ4rpPibW5KD/5Rw1VU0mTABxWikcvES9t52G96ysmCHFVCSBzUj7VVzmqpS+NJLFL36KgAJw4fR+KmnZK2Gv7GbzXzgdh83tyT/pAAWB7Vp06b683Xr1vm1NPpvv/121MeL2qMYjTR68gkSR44EoGjK6xT87zk0rze4gQlRT7n27CHnhtG4duzwjbR4axpRnTsHOyzRwMRe2JuMKa+hs1hw/LmOnNE34N63L9hhCVEvaW43+ePGUfrBhwCk/PseUseODfu1GsTJk8wRAHTr1q16VWebzcbq1auPe7zT6WTFihXVX1944YUBjS+cKYpCypg7SX3AN6yu7LPP2PfAg2ExrE6I2uTYtImcG27Esz+fiMxMMqe/jem004Idlmigojp1IvPttzA0aoRrzx72jBqFY8uWYIclRL2iOhzk3Xc/1q+/Ab2eRk88QeJ11wU7LFHPSQEsAIiJieGiiy6q/nrGjBnHPf6LL76goqIC8M3/7dmzZyDDE0DC/11Fk/896xtWt2gRubffjre8PNhhCVEv2FetYu/Nt+AtLcXUujWZb79FRJMmwQ5LNHCm5s3JnD4d02mn4S0qZu+NN2Fb+WuwwxKiXvBareTefge2ZctQTCbSX3yBuIEDgh2WaACkABbVbr/99urPZ8yYwYYNG456nN1u5/HHH6/++uabb8Ygq1zWidg+fch47VV0sbHVewW7T3L17VBlNpu54IILjtiaSxydtJV/Kn78kdwxd6Ha7USddRZN33wTgyzcd1z+5pbk4IlFpKXS9O23iDyrC6rdTt5dd2FdsCDYYdUqyYOakfY6MU9hIXtvupmqP/6oXqsh5vzzgx1WyDO63XTR6Y6bW5J/sg2S+JuePXuydOlSALKzs5k7dy5nnnlm9c+Li4u55ppr+O677wDf3d8dO3ac0v664bDcem1z7thB7pi78Bw4gCElhfTJkzC3bBnssIQIOWWz53DgmWdAVYm58EIaP/0UuoPTPYSoS6rLRf7j46g4+PczZexYEoZdHzZ7BQvhL9feveTefgfuffswJCeT/spkOcfxg2yD5D+5AyyO8MEHH9C4cWMAdu/eTceOHenduzc33ngjgwcPJjMzs7r4NRgMfPLJJ6dU/IqTY2rRgszpb2Ns0cJ3lfTGm7CvWhXssGqFw+Fg0aJFOGSO8wlJWx3boe0yDjz9NKgqcf/6F03+96wUv37yN7ckB/2nMxpp/Mx/Sbj2GgAKJ02i8KWX0BrAViSSBzUj7XVsji1byLnhRtz79hGRkUHm9Lel+K0Bl8HAKo/nuLkl+ScFsPibjIwMfvzxRzp27Aj4TiIXLVrE22+/zZdffondbgcgJSWFOXPmHDFvWNStiEaNyHz7LSI7d0a12cgdcxfWhd8GO6xT5nA4WLx4cVh3zP6Stjo6TVUpfHli9XYZiaNGkfbYoyh6fZAjqz/8zS3JwZpRdDpS772XlLvvBqD0gw/Z/8ijqC5XcAM7RZIHNSPtdXT2335j70034y0uxtSyJZnT3yYiPT3YYdUrrogI1qjqCQvgcM8/KYDFP7Ru3ZqVK1cyc+ZM+vfvT9OmTTEajaSmptK1a1cmTJjAxo0bGTBAFiIINn1sLBmvvkJsn4vQ3G72P/IIJe+/H+ywhAgaze0m/4knKT34Pki55x5S7rxDhpmKkJI47Hoa//dpFIOBiu++I3fMGLyVlcEOS4igqVy8mNw7x6DabER27kzTaVMxJCUFOyzRQMnKReKojEYjw4cPZ/jw4cEORZyAzmSi8TPPoE96ibKPP6bwpZfxFBSSMvYu2SNPhBXV4WDfQw9jW7rUt13G44/LiqEiZFn690efkMi+++6javUa9t5wI+mTJxORlhrs0ISoU+Xz5pH/1NPg9RJzQU8aP/MMujBeoEkEnpwdC9EAKHo9qfffR8pdYwAofe899j/2n3o/rE4If3krKsi9405sS5f6tst44XkpfkXIiz73HJq+NQ19chLO7dvJGT0a586dwQ5LiDpTMmsW+eOfAK8Xy2WDaDJhghS/IuBkFWgRdOGw2lxdKv/qK/KffAo8HqLOPpsmL7yAPqb+tKmqqlitViwWCzq5g31c0lY+nqIicu8cg3PbNnQxMaRPfJmoTp2CHVa95m9uSQ7WDve+feTeOQbXnj3oLBbSX3qxXuWw5EHNSHv51pgpeuUVSmbOAiBh+DBS7rpLpqucgqKp0yicOhXDFZfT4qGHjplbJ8q/cDgvD893nRANWNyAAWRMmogSFYl91Sr23nQT7oKCYIflN51OR3x8fNieFNSEtBW4cnPJGX0Dzm3b0Ccn0XTa1HpVOIQqf3NLcrB2RDRp4lvttn17VKuV3DvupOLHn4Idlt8kD2om3NtL83g48NRT1cVvyti7SB07VorfWqDTNCyKctzcCvf8AymAhWiQort2JXPaNPRJSTi3bvUNq9u1K9hh+cVqtTJx4kSsVmuwQwl54d5Wji1byRl9A+68PCLS08l8e7psl1FL/M2tcM/B2qSPj6fpG68Tc0FPNKeTfQ88QOknnwQ7LL9IHtRMOLeX6nSy78GHKJ/7Jeh0NBr3OImy3kytsZvNvOdyHTe3wjn/DpFFsERI6d+/P/pT2Kpk5MiRjBw5svYCqsfMrVuTOX06uWPG4M7JIeeGG8h4+WUiO3QIdmjHpaoq5eXlqA1gb8xAC+e2sq9ZQ96/70WtrMTUsiUZr0zGkJwc7LAaDH9zK5xzMBB0ZjNNJkzgwHMTKP/iCwqem4CnsJDk228P6btjkgc1E67t5a2oIO/ee6la8xuK0UjjZ58htlevYIfVoKiKQiUcN7fCNf8OJwWwCCnLli07pcf3ko70CMaMdDKnv03e3ffgWL+evbfdTuOnnyb2wt7BDk2Ik1bxww/sf+w/aC4XkZ06kf7yS+hjY4MdlhC1QjEYSHvkYQypKRS/8SYl09/BU1BIo0cfQTEagx2eECfFXVBA3pi7cG7fji46mvSXXyKqS5dghyXCVMgWwKqqkpeXR35+PjabDYDo6GgaNWpEenp6WI9bb8h69OhxSneAs7Ozay+YBsKQkEDTN15n38OPYFu6lH3330/ynXeSOHJESN9REOJoSj/9lILnJoCmEdO7N42ffkpWDBUNjqIoJN90E4aUFA488yzW+fNx5+XR5PkJGBISgh2eEDXi3LWL3DvH4MnPx5CcTPrkyZhbyXQVETwhUwBbrVa+++47vvvuO3799Vc2bNiAx+M56rEGg4EzzjiDc845h759+9K3b18sFksdRywCYcGCBQ1ytblg00VGkv7C8xS8+BJln3xC0auv4tq5k7THHkVnMgU7vCMYDAaysrIwGEKmewpZ4dRWmqZR/MYbFL/1NgDxQ4eQ+sADKKdwwUwcm7+5FU45GAzx//oXEWlp7HvwIap+/52c4SNIn/gyphYtgh3aESQPaiac2qtq3Tpyx45FLbcSkZVJ01dfJaJJk2CH1WDpVZXGinLc3Aqn/DuWoG+D9M033zB9+nTmz5+P67A9S08U1uF3roxGIwMGDGD06NFceumlAYtVBEY4LLceSko//ZSC518Arxfzme1Jf+EFDElJwQ5LiGPSPB4OPPMs5XPnApB06y0k3XijjGAQYcO5cxd5d9+NOy8PXVQUjZ99lpge3YMdlhDHVbl0KfseehjN4cB8xhmkT5ooIxgCqGjqNIrffJP4oUNIe/jhk36ecDgvD8o4YlVVmT59Om3atGHgwIF88cUXOJ1ONE2r/gDfMt0JCQmkp6fTpEkTEhISqk94Dj/W6XQye/ZsBg0aROvWrZk+fXpYT+wW4ngSrrySjMmT0cXG4vhzHXuGj8CxZWuww6rm8XjYvXv3MUeAiL+EQ1upDgd599/vK351OtIefZTkm26S4jfA/M2tcMjBUGBq3oysWTOJ7NIZ1W4n7557KHn//RPeLKgrkgc1Ew7tVT53Lnn33ofmcBDdrRtN33xDit864NXpyFPV4+ZWOOTfidR5ATxnzhzatGnDTTfdxNatW6uL2Li4OAYOHMhzzz3HwoULycnJwel0UlxczN69e8nNzaW4uBin08mePXtYsGABzz33HAMHDiQuLq76ebZt28ZNN91EmzZtmDNnTl3/ekLUC9FdzyVr5gwisjLx5OeTc8MNVC5eHOywAN/VxpkzZ1JZWRnsUEJeQ28rb1kZe2+7DduSpSgmE02en0D8FZcHO6yw4G9uNfQcDCX6+HiavvoqcZdfDqpK4Usvc+Dp/6IdNnouWCQPaqYht5emaRS//Tb5Tz4FXi+WgQNJf+lFdJGRwQ4tLFSZTMzzeI6bWw05//xVp4O/+/Xrx/fffw/43iAxMTH83//9H1dddRUXXnihX4sfGQwGmjZtStOmTbn44ou5//778Xq9/Pjjj3z66ad89NFHVFZWsm3bNoYMGULfvn1ZsGBBoH81IeodY1YWWTNmsO/Bh7D/+it5995H0s03k3TjDSiyyJwIMndeHrl3jcW1ezc6i4WMl18ismPHYIclRFApRiNpjz6CsXkzCl+eSPmcObh276bxs88QkZoa7PBEmNM8Hgqef4Gyzz4DIHHUKJLvCO0tvER4qtOz3O+++w5N02jWrBlTpkxh//79TJs2jb59+57Syr96vZ6+ffsydepU9u/fz2uvvUbz5s3RNI3vvvuuFn8DIRoWvcVCxuRJxF95JWgaxW++Se6Yu/CUlgY7NBHGqv78kz0jRuLavRtDWhqZb78lxa8QBymKQuK115I+8WV00dFUrV3Lnmuvw7ZiZbBDE2HMW2kj755/+4pfRSH1/vtIufMOKX5FSKrTAjgrK4uZM2eydetWbr311oBMqo6Ojua2225j69atzJgxg8zMzFp/DSEaEiUigrSHHqTR+PEoZjP2FSvYc+112H//PdihiTBk/fZb9t5yK97SUkytW5P5zjuYmjcPdlhChJyY7t3Jeu9dTC1b4i0tJffOOyl6cyqa1xvs0ESYcefns/eGG7D9/DOK2UyTCRNIuPrqYIclxDHVaQG8ZcsWhg0bVid7+Op0OoYPH86WLVsC/lpCNARxgwaSNXMGxuxsPAUF7L3lVkpmzUKr4wXloqKiGDx4MFFRUXX6uvVRQ2or37yx6ex/+BE0l4vonueTOW0qEWkyrDMY/M2thpSD9ZExM5PMd6b75gVrGsVTp/pG8ZSU1Gkckgc105Day7FpEzkjRuLcvh19chJNp75J7IW9gx1W2DK5XPTS64+bWw0p/05W0LdBEiIclluvT1S7nfz//peKBQsBiO55Po3Hj0cfFxfkyERDpblc5D/7LNYv5wGQcO01pNx9t+zxK0QNlH/1FQeeeRbN4cCQnEzjZ58lqnOnYIclGrCKRYvY/+hjaA4HxtNakDFpEhGNGgU7rLAl2yD5T1a6EUIcQRcVReOnnybtkUdQjEZsS5ay+7rrsK9ZUyevb7fbmTNnDna7vU5erz5rCG3ltVrJHXOXr/jV6Uh98AFS771Xit8g8ze3GkIONhRxAwaQ9e4sjM2a4SkqYu+tt1I0bRqa2x3w15Y8qJn63l6aplHy3vvsu+9+NIeDqPO6kvn2dCl+Q4DTaORHj+e4uVXf86821HkBPHr0aJYuXVrXLyuEqAFFUYgfcgWZ70wnIiMDz/589t58C/nPPIM3wMvmu1wu/vjjD1whsLVHqKvvbeXKzSNn1Gjsq1ejREWSPvFlEq66KthhCfzPrfqegw2NqXlzst6dheXSS8DrpfiNN9kzbDiOjRsD+rqSBzVTn9tL83go+N//KHz5ZdA04ocOIWPiRPQxDe8uYX3k1uvZqqrHza36nH+1pc4L4BkzZtCrVy+aN2/OE088wc6dO+s6BCGEn8ytW5P1/vvEDbkCgPLPv2D3lVdRuWRJkCMT9Z1t5a/kDB/+10rPb71NTPfuwQ5LiHpPFxlJoyefpPHTT6GPi8O5bRt7RoykYNIkVIcj2OGJesxTWkrumLso++xzUBRS7rmH1IceQjHU6a6qQpyyoA2B3r17N08++SSnn346PXv2ZPr06VRUVAQrHCHEMehjomn0yCM0nfomEU2b4ikoIO+ef7PvkUfrfKEVUf9pmkbxOzPIvfNOvOXlmNq0JnPGO5hbtQx2aEI0GIqiYLnkErI//4zYfv1AVSmd9S67r74G++rVwQ5P1ENVGzaw5/ph2H/91bfS8/MTSLz+OtnmSNRLdV4Ad+nShUPrbmmahqZpLF++nJtuuolGjRpx/fXX8+233yJrcwkRWqK6dCH7ow9JHDEcdDoqFi5k99ArKf/qqzpfKVrUT95KG/vuf4CiV18FVcVy2SAy33qLiFRZ6VmIQDAkJNDkmf+SPvFlDKmpuPfuZe8tt5L/32fwWq3BDk/UE2Wz57D3hhvx5OcTkZlJ1qyZxPaWlZ5F/RWUVaA3bdrEzJkz+eCDD8jNzT0yoINXkho3bsywYcMYPnw4bdq0qesQRR0Kh9XmGhrHpk3kP/kUzq1bATC1bEnSTTcR0+sClDrY5kzUP86du9h333249uxBMRhIfeB+4q64Qu4eCFFHvJWVFL3yim/4KqCLiSHh2mtJuPYa9LGxQY5OhCLV6aRgwvOUz5kDQMwFF9DoySfQHzxnE6FFVoH2X1C3QdI0jR9//JGZM2cye/ZsbDbbET8/dGLUpUsXRo4cydVXX01iYmIwQhUBFA5vtIZIc7spefc9St55B/XgSoJSCIujsX73HflPPolmr8KQlkaT554jsn27YIclRFiyr1nDgQkTcG3fAYAuOvqvQthiCXJ0IlS48/PZd9/9ODZtAkUh+bbbSBw1Uv62hzApgP0XMvsA2+12PvvsM959911++ukn1MOGVB4qhCMiIhgwYAAjRoxgwIAB6GWbjAbh8Ddajx49Tun/deTIkYwcObKWIhP+8JaXU/rBB5R+8GGtFMJlZWVMmjSJsWPHEh8fH4CIG45QbyvN46Hw1Vcpffc9AKLOOovGzz6DQS5khjx/cyvUc1AcnaaqVP74I0XTptVKISx5UDOh3l62FSvZ/8gjeMvL0cVZaPLMM0R37RrssMQJFE2dxp5Zs5jft89xc+tE+RcOBXDILNsWFRXF8OHDGT58OHl5ebz77ru89957bNy4sXo+sMvlYs6cOcyZM4fk5GSuvfZahg8fTqdOstF7Q7Fs2bJTenyvXr1qJxDhN31cHMm33UbCtddWF8LOrVvZd//9GJs1I7ZvX2J69cLU8nQZ7hpG3Pv3s3/cOKrW/AZAwvBhpNxxh6wWKkQIUHQ6Yvv0IebCC6n86SeKp07DuX07xdOmUfrBB8RcdCGxvXsTde656EymYIcr6ojmdlM8/R2K33oLVBVT69akPz+BiCZNgh2aELUqJM9E0tPTeeihh3jooYdYs2YNM2fO5KOPPqKoqKi6GC4sLGTy5MlMnjyZM844g5EjR3LdddeRlpYW5OjFqTjVO8DZ2dm1F4yokaMVwq5duyieOpXiqVOJSE8nplcvYnr3IvLMM1FkBEeDpGka1vnzKZjwPKrdjhIVSeNx44jt0yfYoQkh/kbR6Yi96CJievc+ohC2fjkP65fzUCIjienejZjevYnu0UPmfjZgzl27yP/P474hz4DlskGkPfggOrM5yJEJUftCsgA+XJcuXejSpQsvvfQSX3/9NbNmzWL+/Pm4XK7qYnj9+vXcf//9PPTQQ1x88cXMnz8/yFGLk7VgwYIGOdQinFQXwtdfT+WixVT+9CO2FStx5+VR+v77lL7/PvqkJKI6d8bQKI2ItDQMqakYUn3/agYpjOsrT0kJB57+L5WLFwNgPrM9jZ98EmPTpkGOTAhxPIcXwlVr1lDx409ULl6M58ABKr7/gYrvf0AxGIjs0gVj06a+PjstDUNqChFpaahSJNVbmqpS9vHHFL7yKprTiS42lrSHHiS2Xz8ZtSUarJAvgA8xGAxcdtllXHbZZZSVlfHhhx/y7rvvsmLFChRFQdM0PB4P33zzTbBDFUIA+thY4gYNJG7QQNSqKmw//0zFTz9hW7oMb3ExFd99d9THOc1mmrVvR+5V/0cRoBgMKCYTuqgo30dkJPo4C/r4BPQJvg9DcjKGtFQiGjVCFxsbNn+0jUYjHTp0wGg0BjsUKn78kQP/fQZvWRmKwUDSrbeSOHyY3Omvp/zNrVDKQXHqFJ2OqLPPJurss0l94H4cGzdS+dMiKn/6Cdfu3dhXrsS+cuU/Huc0Gml2Znv2XnUVRRooej1KRAS6qCiUqEh0UdHoY6KP6Lf1iQm+C6BpaRiSk8OqrwiV9417/37yxz9RvTd0VNeuNHr8cSLSZGu6+irC6yU7N5e9Vwyh0O0+6jGOiAhO794t6PkXTCGzCNbJyM/PZ/z48UydOrW6CFYUBa/XG+zQRA2Ew2R78RfN5cL+2284t+/Ac+AAnoIC3AUH8BQU4ikqAo/nlJ5fiYrEmNEUY3b2kR9ZmTKUKwC8FRUUTHge69dfA2A6/XQaPfEE5lYtgxyZEKI2OXftomrNGtwFBXgKCnx99oEDuA8cQKuqOrUn1+uJSEs9rM9uhrGZ73N9QkLYXNSsK5qmYZ03j4LnX/BNVTGbSbl7LPFDh0pb12OVixeTd+994Gdpd/ryZUc9LwqH8/J6VwA7HA5mz57NrFmz+OGHH44odqUArp/C4Y0m/KN5vVQdOMDGTZtolZFBhKKA14vqcKDa7ag2m+9fqxVPaSne0lK8pWV4Cgvx5OfjLS8/9pMrChGNG2HMzsbUshXmM9pibtsWQ1pavf2D73K52LhxI23btg3KlVzbL7+Q/9TTeA4cAJ2OxOHDSbrlZnRhfFW5ofA3t4KdgyL4NE3DUVrKhrVraZWRgVGnQ/N40NxuVJvd12fb7aiVFXjLyvCWlvr676Ji3AcO4CkshOOct+ksFkzNsjE2b4G5TRvMZ7TF1KIFSkREHf6WtSuY7xt3QQEF//sflYuXADJVpaGx5+ezceNGWjdrhvEo7xHVZmf7sGHsbdKY3rNmYT7Kiu/hcF5eb4ZAL1q0iFmzZvH5559TWVkJwN9r9x49ejBixIhghCeEqAWKXo8rKoqvli3jtLFjia3h9hCqw4EnPx/X3r24du3Gtdv34dy9C7Xcinvfftz79mP7+Zfqx+iTkjC39RXD1UVxQkIt/2aBYbfbmTt3LtnZ2XV6EuXcuZPCiZOwLV8OQERGBo2ffILIDh3qLAYRWP7mVrByUIQORVFw6nR8tXQpp40di6mG/bbm9eIpKsadl4tr9x5fv71rF67du3Hv349qtVL1x59U/fEn5bNn+17TZMLUsuUR/bYxK6ve7FEbjPeNWlVFyax3KZk1C83hkKkqDZTLbPa9Fzt3JuYo70XVZsNpNPJrp06cW1V11AI4HIR0AbxlyxZmzZrF+++/z969e4F/Fr3Z2dnV2yc1b948GGEKIUKEzmyuHkLH+ecf8TNPaanvxGrnThybNuPYuBHn9u14i4uxLV2KbenS6mMjsjKJ6tSZqC6diezcmYhGjer4NwlNnpISit+cStns2b47NgYDCVcOJfmOO9BFRgY7PCFEPaQcHP4ckZZKVOfOR/xMdThw5eTg2rUb57ZtODZuxLFxI2pFBY5163CsW1d9rC42lshOHYnq3IXIzp0wt2ol267hu8BgnT+foimv+6YZ4bvrm/bQwzJVRYStkOsZiouL+eijj5g1axarD07K/3vRGxsby9ChQxkxYgQ9e/YMRphCiHrGkJCAISGBqMP2DVcdDpxbt/pOqjZswLFhI649e3DvyaF8Tw7lc+b4Htu4UfVJVVSXLkRkZNTbYdMnQ3U6Kf3gQ0qmT0e12wGI6dWLlLvGYMzKCnJ0QoiGSmc2Y27ZEnPLltDvYsB3Tujeu/eIftuxZQtqRQW2JUuxLfFdzFSiIons0KH6Yqa5bVuUMBulYFuxksJJk3Bu3QpARJMmJN81htg+fcLqb5gQfxcSBbDb7WbevHnMmjWLBQsW4D64atnhha9Op+PCCy9kxIgRXHHFFUTK3QYhxCnSmc1EnnkmkWeeWf09b0UFVWvXYl+zhqrffsexeTOe/flYv/oK61dfAWBITSXqrLOIOvssos4+m4jGjYP1KwSUpqpUfPstha+8iic/HwBTm9ak3nMPUV26BDk6IUQ4UhQFY2YmxsxMLP37A6B5PDi2bKHqt9+x/7aGqt/XolZUYP9lBfZfVvgeZzb7CuKzfP22uU3rBnuH2LlzF4WTJmFbtgwAXUwMSTfeSPz/XSVrNAhBkBfB+uWXX5g1axaffPIJZWVlwD/v9rZq1YoRI0YwbNgw0tPTgxClCLRwmGwv/OfxeMjNzSUjIwNDCJycqDYbVevW/VUQr1+P9reVqiPS030nVeecTVSXLhhSUuoktkC1lbfShvXrryj7/HNc23cAYEhLI+XOO4jt37/ezLMTJ8/f3Aq196sIjlDLA83rxbl9B1W//4b9t9+o+u13vKWlRxyji4oisnNn37ZPZ3XB1LJlnfVtgWgvTVWx/7qK8tlfUPHTouppKvFDh5B8003oazg3W9RPJ8ot1WZjc6/eFCUk0PWLzzEePP8+XDicl9d5Abxr1y7effdd3n33XXbu3An8s+hNSEjg6quvZsSIEZxzzjl1GZ4IgnB4o4mGQ3U4qFr7B/Y1q7GvWo1j48Z/rGBqzM6uPqmK7NKl3iyq5di8mbLPPse6YEH1tiZKVCRJI0eRcN21so2UEKJe0jQN144d2Fevwb5qFfbffkO1Wo84RhdnIapzl4P7IJ+FsVmzejFM2FNainXefMq++AL3wfVyQKapiKNTbTa29bwAkG2Q6rQA1ul01Xv2Hs5gMNC/f39GjBjBoEGDZDXJMBIObzThv8rKSj777DOGDh1anRehTLXZsP++FvvqVdhXrca5Zcs/9uAznX569XDpyM6d0dfS71UbbaVWVVHx7beUffa5r5g/yNisGfFDh2C59FL0YbpKZDjzN7fq2/tVBEZ9ywPN68W5bRv2Vat9BfHvv6HZj9zLWJ+U5BvZ06ULUeecXatrP5xqe2mahuOPPyj7/HMqvv8BzeUCfHe1LQMGEHfF5b550yLsnCi3VJuNP/tezM9dOnP9uHFYkpP/cUw4nJcHfZxKhw4dGDFiBNdddx0pdTRsUAgRujweD3v27MHzt2HGoUoXHU1Mj+7E9OgOgNdqxb5mDfbVq7GvXo1r+w6c27bh3LaN0g8+BJ0OU4sWmM9sT2T7M4ls346IzMyTGnp3Mm2laRqu3bup+n0tVb//TuWSJagHt5ZTDAZiLrqI+KFDiOzUqV7c/RCB4W9u1bf3qwiM+pYHil6PuXVrzK1bkzjsejS3G8fmzb6CePVqqv74A29xMRULF1KxcCEAhuRkzGf6+mxz+/aY27Q56VExJ9Ne3vJy3/oUv/2O7ZdfcO3YUf0zU+vWxA8ZgqV/P3RRUScVk2gY/Mktr05HYXIynuPsv93QBaUATklJ4brrrmPEiBGcedjiM0IIUd/pLRZie/cmtndvwLd10KFi2L56Ne49OdUFcfnnXwC+q/amli0xtWqFuVVLjNnZRGRmoo+PP+UiVPN4fHc6fv+9uuj9+1y4iPR04odcgWXQIAyJiaf0ekIIUd8oERFEtm9PZPv2JI0ehepy4Vi/vrogdqxbh6eoiMoff6Tyxx99D9LpMGZnY2rVEnOr1phaNCciM4uIxo1qZV9d94EDB/vs37D/vvaIghd8i3pZ+l1M3JAhvhWu5YKlEH6r8wJ43rx59O/fH71sui2ECAOGxEQsF1+M5WLfFh6ewkKq1q2nat2fOP5ch2PTJlS7naq1a6lau/aIx+osFowZGRhSUzEkJ6FPSsaQlIhiNqMYjdgODrWu+Okn1Koq1PJyvGVleMsO/ltejjsvr3rrokMUkwlzuzOI6tSJqLPOIrJLF1nYSgghDtIZjUR17uzbl/iWm1EdDhybNuFYt46qP9dRte5PvEXFuHbuxLVzJxXfLKh+rBIRQURGBoZGaRiSkjEkJ2NISUYXFYViNKGYTdgP3nmzfv01Xrsd79/6bk9xMZ4DB/4RlzE7m8hOnYjs1JGYnj3Rx8bWWZuIhqfqjz+xHWWKk62q6ihHNyx1XgAPGDCgrl9SCFGP6HQ64uLi0DXQgsyQkkLshb2JvdB3h1jzeHDt2YNzyxYcm7fg3L4d1549ePLzUa1W37zcw+bmHs5uNhPVoztFL7yI3eE45mvqoqOJ7NiRyE6diOrUEVPbtrIVhjguf9+HDf39KvzT0PNAZzb7Lhgeto+8p7AQx5atOLdsxrl1K87du3Hn7EVzuXDt2oVr165jPt+hvrv4tSlUHavv1ukwtWpJVKdOvqK3Y0cZoSNO6ITvRZ0OnaYRZbdT8MQTVB4l/+xhMDQ6qNsgnUhpaSnLly9n7969lJaW4vF4ePzxx4Mdlqhl4TDZXoiaUh0OXDk5uHPz8BQX4S0uxlNUjLekBNXpRHO50BwONFVFb7Ggj49HHxfn+zc+rvprQ3IyxubNa2VInhBCiGPTVBVPfj6unBw8hYV4CosO9t8lqHY7mtNZ3X/rIiP/2W9Xfx6PscVp6GPkfEjUvoIXX8K+atUxf273eDjj88+AhnteHpIF8MqVK3nyySdZuHDhP1aL9v7tqsSBAwcYPHgwqqpy3nnnMWnSpLoMVdSCwwvgHj16nNLw+JEjRzJy5MhaikwEg6qqWK1WLBZLg72bUFukrUSg+JtbkoMCJA9qStpLBIo/uXWiY8LhxlTIvev+97//cf7557NgwQJUVUXTtOqPo0lLS6NRo0asXr2aN998k+Li4jqOWNSmZcuWsXjx4pP+2L17d7B/BXGKrFYrkyZNwvq3PRrFP0lbiUDxN7ckBwVIHtSUtJcIFH9yS/IvBLZBOtxrr73GI488Uv1169at6d27N6tWrWL16tXHfNyoUaP48ssvcbvdfP311wwbNqwuwhUBcKp3gLOzs2svGCGEEEIIIUSDEjIF8P79+3nggQcAMJvNvP7664wYMQKAMWPGHLcA7t+/PyaTCZfLxU8//SQFcD22YMGCBjnUQgghhBBCCBF8ITME+o033qCqqgpFUXj55Zeri19/mEwm2rVrh6ZprFu3LoBRCiGEEEIIIYSor0KmAF64cCEATZo04eabb67x41u0aAHAnj17ajUuIUTdMpvNXHDBBZjN5mCHEvKkrUSg+JtbkoMCJA9qStpLBIo/uSX5F0JDoHfu3ImiKHTv3h1FUWr8+Pj4eICwntAtRENgNpvp1atXsMOoF6StRKD4m1uSgwIkD2pK2ksEij+5JfkXQneAy8vLAUg8yU2+nU4nABEREbUWkxCi7jkcDhYtWoTjKJuziyNJW4lA8Te3JAcFSB7UlLSXCBR/ckvyL4QK4ISEBABKS0tP6vE5OTkAJCcn11pMQoi653A4WLx4cVh3zP6SthKB4m9uSQ4KkDyoKWkvESj+5JbkXwgVwFlZWWiaxpo1a2r8WJvNxsqVK1EUhbZt2wYgOiGEEEIIIYQQ9V3IFMB9+vQBYMeOHSxdurRGj33llVew2+1HPI8QQgghhBBCCHG4kCmAhw0bhl6vB+CWW26hpKTEr8f98MMPjB8/HoCoqCiGDx8eqBCFEEIIIYQQQtRjiqZpWrCDOOTWW29l6tSpKIpCixYteOmllxgwYAB33XUXr732Goqi4PV6Ad+q0VOmTOGVV17B7XajKAr/+c9/qothUX/YbDZiYmIAqKysJDo6OsgRiWBSVRWr1YrFYkGnC5lrdCFJ2koEir+5JTkoQPKgpqS9RKD4k1snOiYczstDqgB2uVxceOGF/Pzzz9VbIVksFiIiIigqKkJRFM4880zy8/MpKCgA4FD4/fr14+uvvz6pLZREcIXDG00IIYQQQohQFw7n5SF12cloNPLtt98ybNgwNE1D0zTKy8spLi6uLmz//PNPDhw4UP1zgBEjRjB37lwpfoVoAKxWKxMnTpQ9vf0gbSUCxd/ckhwUIHlQU9JeIlD8yS3JvxArgME3j3fmzJksXryYwYMHEx0dXV3sHl70mkwmLrnkEhYtWsQ777yD0WgMcuRCiNqgqirl5eWoqhrsUEKetJUIFH9zS3JQgORBTUl7iUDxJ7ck/8AQ7ACO5fzzz+f888/H6/Xy559/sm/fPsrLy4mOjiYtLY2OHTtiNpuDHaYQQgghhBBCiHoiZAvgQ/R6PZ06daJTp07BDkUIIYQQQgghRD0WckOghRDhzWAwkJWVhcEQ8tfngk7aSgSKv7klOShA8qCmpL1EoPiTW5J/IbYKtAhPh68216NHj+r9oE/GyJEjGTlyZC1FJoQQQgghRPgIh1Wgw7f0FyFp2bJlp/T4Xr161U4gImg8Hg+5ublkZGSE9dVJf0hbiUDxN7ckBwVIHtSUtJcIFH9yS/KvjodAd+7cme+//77OXu/bb7+lc+fOdfZ64tT16NGDCy644KQ/srOzg/0riFNUWVnJzJkzqaysDHYoIU/aSgSKv7klOShA8qCmpL1EoPiTW5J/dXwHeO3atfTr14/zzjuPhx56iIEDBwbkdb788kuee+45VqxYEZDnF4GzYMGCBjnUQgghhBBCCBF8dXoHuFOnTmiaxi+//MLgwYNp1qwZTz31FFu2bDnl596yZQtPPvkk2dnZXH755axYsQJN0+QOsBBCCCGEEEIIoI4L4NWrVzNlyhSSk5PRNI2cnBzGjx9P27ZtadOmDXfccQcfffQRGzduxOPxHPN5PB4PGzdu5MMPP+SOO+6gTZs2tG3blieeeIKcnBw0TSM5OZnXX3+dX3/9tQ5/QyGEEEIIIYQQoSooq0DbbDYmTpzI5MmTKSws9AWiKEcGpiikpqaSlJREVFQUmqZRVVVFUVERhYWF/D3sQ1+npKRw9913c9ddd8lQ2noiHFabE/5zuVxs3LiRtm3bYjQagx1OSJO2EoHib25JDgqQPKgpaS8RKP7k1omOCYfz8qBug+R0OnnvvfeYPn06v/zyy1GP+XthfKxwzzvvPG644Qauu+46TCZTrccqAicc3mhCCCGEEEKEunA4L6/TIdB/ZzKZuOGGG1i+fDk7duxg0qRJDBw4kNTU1OpjNE074uOQ1NRUBg4cyKRJk9ixYwfLly9n9OjRUvyepBkzZqAoSo0+brzxxmCHLRogu93OnDlzsNvtwQ4l5ElbiUDxN7ckBwVIHtSUtJcIFH9yS/IvhPYBbtasGWPGjGHMmDEAHDhwgF27dnHgwAFsNhsA0dHRNGrUiGbNmh1RJAshGg6Xy8Uff/xBr169iIqKCnY4IU3aSgSKv7klOShA8qCmpL1EoPiTW5J/IVQA/11aWhppaWnBDiMstW7dmosuuuiEx3Xr1q0OohFCCCGEEEKI2hGyBbAInnPPPZdXX3012GEIIYQQQgghRK0K6hxgIYQQQgghhBCirgR1FWgROmbMmMGoUaMAGDFiBDNmzKiz1w6H1eaEEEIIIYQIdeFwXi53gIUQQgghhBBChAUpgIUQIaWsrIwnnniCsrKyYIcS8qStRKD4m1uSgwIkD2pK2ksEij+5Jfkni2CJoygrK+PTTz9lw4YNlJeXY7FYaNKkCeeddx7t27dHUZRghyiEEEIIIYQQNSYFsPiHuXPnMnfu3KP+7PTTT+fBBx9k9OjRUggLIYQQQggh6hUZAi1qZNu2bdx4441cdtll2Gy2YIcjhBBCCCGEEH6TO8CiWmZmJldeeSUXXXQR7du3JyUlBa/XS25uLj/88AOTJ09m8+bNAMyfP59rr72W2bNno9PV3nWUPXv2EBUVVWvP93fx8fHEx8cH7PnFqTMajXTo0AGj0RjsUEKetJUIFH9zS3JQgORBTUl7iUDxJ7ck/2QbJHFQWVkZFovluMWsy+Xi1ltv5Z133qn+3rvvvsv1119/Sq99+HLrgTZu3DjGjx9fJ68lhBBCCCFEfRIO2yBJARzCXn31VV599dVafc6nn36aoUOHnvTjVVWlV69eLF26FIB27dqxbt26U4rp8Dfahg0b5A5wmHO5XGzcuJG2bduG9dVJf0hbiUDxN7ckBwVIHtSUtJcIFH9y60THhEMBLEOgQ1hRURFbtmyp1ec81SXPdTod48aNo0+fPgCsX7+e3NxcMjIyaiE6yMrKapBvNOE/u93O3Llzyc7OlhODE5C2EoHib25JDgqQPKgpaS8RKP7kluSfLIIlTkLPnj2JiIio/nrTpk1BjEYIIYQQQggh/CMFcAgbP348mqbV6seNN954ynFFRESQnJxc/XVRUdEpP6cQQgghhBBCBFqdF8CjR49m9OjRfPDBB8c9rqysjJycHHJycvx63gcffJDExESSkpJqI0xxAodvgSRDloUQQgghhBD1QZ0vgqXT6VAUhTvuuIPJkycf87gxY8YwZcoUFEXB4/Gc8HnHjBnDa6+9hqIoeL3e2gxZ/M3OnTtp0aJF9derVq3irLPOOunnC4fJ9sJ/Ho+nel65wSDLFByPtJUIFH9zS3JQgORBTUl7iUDxJ7dOdEw4nJeH9LtOFqgOTdOnT6/+PC4ujo4dOwYvGNHgGAwGsrOzgx1GvSBtJQLF39ySHBQgeVBT0l4iUPzJLck/mQMs8F3d8dfPP//Miy++WP311VdfLVcvRa2qrKxkxowZNcrLcCVtJQLF39ySHBQgeVBT0l4iUPzJLck/KYAF8Nlnn3HOOecwa9YsysvLj3qMw+Fg8uTJ9OnTB4fDAfj21B03blxdhirCgMfjYc+ePX5NfQh30lYiUPzNLclBAZIHNSXtJQLFn9yS/AvxIdCi7qxatYoRI0ZgMBho3bo1rVu3JiEhAa/XS15eHr/88gtWq7X6+MjISObOnUvjxo2DGLUQQgghhBBC+E8KYHEEj8fD+vXrWb9+/TGPOeecc5gxYwZt2rSpw8iEEEIIIYQQ4tRIASy45ppraNmyJT///DMrVqxgx44dFBUVUVxcjKqqxMXF0axZM7p27crQoUPp0aNHsEMWDZhOpyMuLg6dTmZonIi0lQgUf3NLclCA5EFNSXuJQPEntyT/QnwbpJpsayTbINVf4bDcuhBCCCGEEKEuHM7Lw7f0F0KEJFVVKSsrQ1XVYIcS8qStRKD4m1uSgwIkD2pK2ksEij+5JfknBbAQIsRYrVYmTZp0xKJr4uikrUSg+JtbkoMCJA9qStpLBIo/uSX5JwWwEEIIIYQQQogwEbRFsH799VeefPLJ4/78kOMdd7TjRf3Vv39/9Hr9ST9+5MiRjBw5svYCEkIIIYQQQjQYQSuAV61axapVq457jKIoADzxxBN1EZIIAcuWLTulx/fq1at2AhFCCCGEEEI0OEErgOt48WlRT/To0eOU7gBnZ2fXXjAiKMxmMxdccAFmsznYoYQ8aSsRKP7mluSgAMmDmpL2EoHiT25J/gVhG6RevXpV39kNlJ9++imgzy9qVzgsty6EEEIIIUSoC4fz8jq/A7xo0aK6fkkhRD3icDhYsWIFXbt2Deurk/6QthKB4m9uSQ4KkDyoKWkvESj+5Jbkn6wCLYQIMQ6Hg8WLF+NwOIIdSsiTthKB4m9uSQ4KkDyoKWkvESj+5JbknxTAQgghhBBCCCHChBTAQgghhBBCCCHCghTAQgghhBBCCCHCQp2vAi3E34XDanPCf6qqYrVasVgs6HRyje54pK1EoPibW5KDAiQPakraSwSKP7l1omPC4bxcCmARdOHwRhNCCCGEECLUhcN5uVx2EkKEFKvVysSJE7FarcEOJeRJW4lA8Te3JAcFSB7UlLSXCBR/ckvyTwpgIUSIUVWV8vJyVFUNdighT9pKBIq/uSU5KEDyoKakvUSg+JNbkn9SAAshhBBCCCGECBOGYAcgxOHc+/ahNmmCEhmJIgtDCCFEndO8XtxlZQBUrvwVvdl0zGMrqxwAeK1WtOholAZnCOoAAN5vSURBVIiIughRCCHEQZqmoVVV4SkqwllYGOxw6gUpgEVI2TFgIFF6Peh0KBER6CIj0UVFoU9OxtymDcbsLIxZWRizsjFmpKMYjcEOWdQyg8FAVlYWBoN0TycibSVqQrXZcO7YgXPbdpzbt+PKycFbUoK3vBy1sgLVXoXmdKJ5PFSZTKR060bBgw9S4XAc8zmrzGZSunVjz+B/UeB0ohgMKCaTr++Oi8Pc8nSMzZv7+uysLIzZWehjY+vwtxZ1QfqimpH2Ev7SPB5ce/f6+uzt23Hu2ImnoABPSQmqtRzVZkd1OsHrBU2jymym8WWDjptbkn+yCrQIAYevNrf69JZEHevOr06HYjCAwYCiKKDXE9GkCabTTyeyU0eiOnfGdPrpKHp9HUYvRO3SvF48RUW48/bh3rcPz4F8PCUleMvK8JaW4bWWo7ncaC4XmssFqoouJgZdbAz6WIvvX0tc9df6xATMLVtibNbM9/4RYcFdUEDV2rV/nTRt24573z7Ad0KleTy+E6bjUZS/Po5F0+B488gO9tVKRET1qB59QgLGZs2I7NCByE4diezQAf3BvwFC1FfeigrceXm49+3HvX8f3uJiPKWlvn67rAzN4UB1OX19t9uNzmRGZ4n19dOWWHQxsb6vY2LRx1kwZmdjat0GfUzDW4FXHJ3qcFD1x584t27Fuf3ghcqdO305o6pobjd4PCd8HlObNjSf/cUxf77vscew/7rqmD+3ez10+PFHoOGuAi0FsAi6wwvg0t27iSgswrVrF669e3Hn5eHZvx/n1q2+OxMHT7YUgwEU5R8n9Lro6IMnVZ2I6twJc9u2cpe4nvF4POTm5pKRkdGgr05qmoY7NxfHxk2+fN+1C+euXbhzcnx/5Pzg1ekoSkggubQU/QkWs1DMZkwtT8fcpi3mNm0wt2ntK4rlglGDoHk8VK1bh+3nn7Et/xnnli1H/lzTwONBU1Vf0arToSgKupgYDKmpGBqlYcxoSkRmJqbsLHRZWeR7vWRmZh73fejxeMjZvp1UVUXNycG1J8fXd+fn49qxA29xcfVrKjqdryA+dBHzEJ0OU8uWRHXqSGTnzkR26oQhISFQTSUCIFz6bfAN93ds3IRzx3ZcO3fh2rUT567dqDVYUbcmfXdEVubBPrst5jatMbVqLUVxA+LOy6Ny+XJsy3/Gvno12t9G3GiH+m2v19dv63QoRiOGtDQimjQmIjMLU3Y2xtNPo/i1KVSsWYO1S2fOeeedo74XVZuNzb16Hzf/7F4vnf9YC0gBLETA+LPfmGqzUfHDD5TPm0/Vb7/5TuY0DZ3JhKllS3TR0Tg2bEC12Y54nC46mugePYjp3YuY7t3RRUXVxa8kTkFZWRmTJk1i7NixxMfHBzucWuMpLcWxYSOODRtwrF9H1YYNqOXHOGHS64lISyMiPR1DozQMiUnoExPRJ8Sjt8ShmIzoTCasbjdvfPUVN/fuTYzHi1pZgdda4fu33IpaWYF7fz6OLZvR7FX/eBnFbMZ8Rltie/cm5sKLiEhLDXAriNrkKSnB9vMv2JYvx/bLL6gVFX/9UFEwtWqJISkZ94EDOLdv9xWhioIhJYX4oUOIGzwYQ0rKUZ/b3/fh8Y7TVJWqtWuxzp+P9bvv0OxV1RcxjdnZGDOb4tqTgzs398gnVRQizzyTmN69iendG2NG+km2kKgrDbXfVl0unFu3HdFvu/fkHPN4fUICEU2aENGkCYaUZPTxCegTEtDHx6OLikIxGlGMRqxOB2/Mn8+N53Uj1uvx9dsV1ur+21NSinPbNjz5+f98EUXBmJVFdLduxPbtg7ldO1kzpR7RXC7sv6/19dvLl+PavfuInxtSUjC1PB3VZsexZYvvvFZR0EVEENO3DwlXXXXM//OcG2+i4Pff+eqyQcd8L6o2G7/368/8vn245ZJLSDjKxUab3U5Sx45Awy2AG/ZlOlHv9O/fH/0J7kglRkZytsPBOY4qEhwOKletwq4ofB0dTYcrh3Jxdjb2336j6rff8ZaWUrFwIRULF6IYjUSdey6xvXsR3bOn3GEQAaM6nTi3bKFq/Xoc6zfgWL8ed17eP45TIiIwtWyJ6bQWGJs1x9i8GabsbAxpaX4NV3YeXKgo8swziT3OSaemqrj27MG5eTOOjZtwbN6MY/MmNHsVVWt+o2rNbxS88CKRHTsS27cvMRf2JiJViuFQpDqdVP60iPI5c7CvXu27m3uQzmIh+rzziOnRHdXrpeyDD7EtXw6AAkR26UL8VVcS26tXnSxWpeh0RHXuTFTnzqTefz+VP/1E+bz52Fetwr13L+69e7EMGkiT5yfg2rWbqt9/x/77b7i276Dqjz+o+uMPCidOxHT66b5iuFcvTC1PP/LusRC1RNM03Hv34tiwgap163GsX+8bfXaUETkRGRm+vrtFc4zZzTA2y8aYmYkuMtKv1zrUd0efczaW4/TdntJSnJs24di0CcemzTg2bcKTn49r925cu3dT+sEHGBo1Ivaii4jt2xdzuzPk/RGCNE3DuWkTZXPmULFg4ZE3a/R6Ijt0ILpbN0wtT6d8/ldU/vhj9VDniLQ04oZcQfzll2NITq7VuIxZWZjS0v7xfc/fbiY1RFIAi5CybNkyv46bje+E7uzIKO5OSeE0o5HLy8qwf/U15ilTSLj6ajRVxbF+PRU//UTljz/hzs3FtnQptqVLQacj6pxziBs0iJheF6AzmwP6e4mG61Bx6Vi/wXfitH4dzm3bjzpPJyIrk8gz2mFu1w5zuzMwnX46ujoYoq/odJiaNcPUrBmWSy45Im77ypVUfPudr+BYu5aqtWspeOGF6mI49qILa/2Prqg5x5atlM+di/Wbb44Yamlq3ZqYHt2J7tYNc7t2eK1WCp6bQMV33wG+u/yWSy4h/sorMbdqGazw0UVGYrn0UiyXXop7/36Kp73l+33mzafyp0Uk33Ybqffdi2Iw4M7Pp3LxEip/+gn7b7/h3LYN57ZtFE+dSkTTplgGXErcwIFENG4ctN9H1H+e0tIj+m3Hho1HHcasi7Mc0W+b27atswvohoQEDN26Ed2t2xFxV/2+looff6By8WI8+fmUvv8+pe+/7yuG+/TxFcNntJViOMi85eVYv/mG8jlzcW7bVv19fVIS0d3OI7p7d6K7dkUXHU3ZZ5+x76GHqkdrRXbsSPz/XUVs796yun4ASAEsQkqPHj1OeAf476ZpGt2rqri00kZSeTk5I0cSN3gwyWPuJPLMM4k880xS7roL1/btVPy0iMpFi3Bu2YJ9xQrsK1agi44mtl8/4gYNxNy+vfzBEMflKSo6eMK0vnpIs1pZ+Y/j9AkJmNu1I7LdGb4Tp7Zt0VssQYj46A4vihOuvhr3gQNU/PADld9/T9Uff1L1++9U/f47BS+9RGzfPiRefz3m1q2DHXZY8VZWUrFgAeVz5uLYtKn6+4ZGjYi77DLiLhtUXQRqmkbFt99RMGEC3rIy0OtJHDaMxOHD0MfFBek3OLqIxo1p9Ph/iLv8Xxz433M4N2+m4PnnKZ8zh9QHHiCqcycS/u8qEv7vKv6fvfsOb6p6Azj+zU460r0LZW+VoYA4AAcgglvEhZUhiOJCENmIgAwFZMhSAQfiVhBZiigKCjJlz0L3btqk2ff3R7DCjwIpJE3ans/z9AEybk4O7709555z3uMoLKTkt98o2bQJ47Y/sZ05Q96CheQtWEjADTeg79mD4Ntuc3vkTaiZnGbzf7Ny9h/AvG9fWVK4c8nUajSNG6Nt3gzdNdegbd4cVWKiX7ULlGFhBN/WmeDbOuM0mzFu3Urxxo2U/PqrqzP88ccUfPwxmiZNCH/icYLvuEN0oCqR5HRi2r6Dou++o2TTJleySlyxFXT7bYTeey+6Nm3KpjBbU1NJGzqU0r93AqBr1YroV4eK37deJtYACz7nzhpgd9jz8sh5dw6G1asBkAcHE/Xii4Tcd+8Fv7ysqakYVv9A0epV2DP+W2Ojql2bkB490PfsIaaA+ojVauXAgQM0a9YMtY8TmDmNRsyHD58dJfiH0n/2l7smS6bVom3SBG3z5mhbNEfXogXKuDivN5q8VVe2zEyKf/qJ4vUbMP/zT9njATfcQNgTTxDY4Uax5syLbOnp5C//iKJVq8oSosiUSoI6dSLkvnsJaNv2vORl9txcst6aSsmmTQBoGjQgdvw4tE2bXnEZ3I2tq41ByeGg6NtvyZk3r2xNvL77XUQPH37BdklOk4niTZswnJ1G/S9ZgA79nXei79kTXcuWftVZqSn86bot2e2uWTkHDmD+x3Wz0nKs/Fk56qQk1w3K5s3RXdMCTYMGlZI40xv15TSbMf5xtjO8eXPZtUMZE0NY70cIeeABkW3di5wWC4ZVq8j/6OPz8hpoGjUi5L770N/V7byb4JLDQcHKleTOm49kNiPTaokaMoTQXg9f8e/X0/0HULhtG1nt2tLxvffKjS2n0ciBzrdxJj6OzsuXoy3nxryn2uX+THSABZ/z9IlWumcPWVOnlWVB1ffoQczrI8qd5iw5nZT+/TdFq1ZT/NNP/2XfUyoJ7tSJ0Icfct2pEw2qas9eUIDl8BEshw9hPnQY86FD2E6Xk+xEJkNdr55rhOBsw0lTv361vcNuPniQ/I8+pnjjxrJtc9T16hH2+GPo77oLuUbj4xJWH5aTJ8n/cCmGtWvPq+uQ++5F3737BdMuJUnCsGYN2TPedk3dVCqJ6NuXiKeTq1z2e0dhITnz5lP0zTcgSahq1SJhxnQ0DRqU+3pbejpFP/yAYdXq89bXaxo0IPThh1yxWQ0bbcL5nFYr1hMnMB885MpxcPgwlqNHL8ikC6AIDy+7Qalt3gJt82bVdk9qR2EhhV99RcHKz3Hk5QEgDwgg5L77CHvsUbF8wIOcJhOFX31NwccfY8/NBVwJWPV33UXIffeiadLkwkGYU6fIfOMNSvfsBVw3l2NGj77qhH+n+w/AuGXLJbdBchqNHL21IwANf99SbttYdIAFoRJ440STHA7yP/qI3HnzwelE06QJCdOnoYqPv+h7nEYjxT//TNE331K6Z0/Z4+q6dQl9+GH0d3cXd08rgclkYv369XTp0oUAL2TtdhQXYz2VUpZExHryBObDR8rPtgkoo6NdI7vNm6Nr0RxN02Z+swWFt+vqXLbMTApWfEbR11/jNJkAV4MyrHdvwh7tLTKsXwXzoUPkffABJT9vKktqFdCuHRFPJ6O7/vpyb8DZc3LInDTZldMA11rg2HFj0TbyzDpfd2PL0zFYuu8f0l8fgT0jE5lWS+yYMei7db3o6yVJonTXbopWfU/x+g1lHR95QAD6Hj0IfehBNPXrX3W5hEvz9rVIstmwpqaevW6nuLaNO3YM6/Hjrj2t/49Mp0PbuHHZul1dixYoY2P95mZ2ZV27nVYrxT/+SP7Hn2A9ccL1oEJB8B13ENG/H5p69bz22dWdo6iIgpWfU/DZirLZK8qYGML7PEnIvfeWuyxDcjop+PgTchcsQLJYkAcEEPXSi4Q88IBHYvN0/wHk//UX+zp34sEpU8qNLafRyD933MmuZs14cPo0gsLDL3iN6AALQiXw5olm/OsvMl4fiaOwEHmInvjJkwls3/6y7zMfOULRV19R9MMapFJXQgKZToe++12E9ep10VEJ4epd7XYakt2OPS8Pe3Y29qwsbFlZ2E6fxnK2w+vIzbvoe1WJiWibNEHTpMnZ/RYb+3W2cF9sPeIoKaHom28pWLECe1YW4EroETGgP6H33VdtR8K9wbRrF/kffIDxj61ljwV16kT400+ja9H8ou8zHzxI2suvYM/JQaZSETFgAOF9nvRo3XtiG6QrZS8oIGPUaEx//glA2GOPEfXCkMt+P4fBQNHq1RR+8eV5szd0bVoT1qsXQZ06uZVdXai4q75uSxJOgwFbVhb2nBzsWdnYyjq8p7CmpZU7hRlc2c+1TZqgadwIbZOmaJs0RlWrll/vcV7Z125JkjBt3Ur+Rx9j+usv14NyOSE9exIx8BlU5WQCFspnz82l4NNPKfjii7KEVaratYlIfgr9XXdddPaNs7SUjDFjy5aqBLRvT+yY0ahiYz1WtopugzRk0CDCy/m/rwkdYPGbQKjWAtu2JemTj0kfNhzzgQOkPj+EyMGDCX86+ZJ327SNGqF9/XUih7yA4YcfKPzyS6wnTlD01dcUffU1AW3bEvbYowTedJNYC+lBkiThOJtQypaTg81oRHI4cJaacRpLcBqNOI1GHMXFZX93lhix5+Viz8p2dXpzc6Gcjd3PpYyKcu1DWqcO6jpJaBo2RNO4sRjhd4MiKIjwJ58grPcjFG/YQO6ChdjS0sh+ayoFn64gcvCzrqQrfjLS4o/MR46Q8+67mLZucz0glxPctQsRycmXvblW/NNPZIwdh2Q2o65Xj/i3plS7EU5lWBiJc94l970F5H/4IQWffor58CHip0xBGRFx0fcp9HrCH3uMsN69Mf21ncIvv6Rk8+ayrb6UsbGEPdKLkPvu86uEdNWB02IBzl63TSYkpxPJaj17jS7BUVJSdr0ue6ywEHt2NrZs17W7vGnL55LpdKjrJKGuUwdNnTqupShNmlRKvoWqTiaTEXg2m7T58GHyFi9xbUv23XcY1q4lrHdvwpOfEufFJTiNRvKWLaPgk0/LYlXTsCHhTz9N8B23X/KGiy0ri7RXhmI5dAiZSkX08OGE3H+fV+PWXlCA/exSmvO+x9lBnZpOdICFak8VG0utJYvJnubKMpo7bx7m/fuJnTDhslNZFUGBhD3Si9BeD1O6cycFK1dSsukXTH/9hemvv1DVqkVY796E9Owh1pu5QXI6sRw9Runu3djOnMZeUICjoBBHQQGO/HwchYUUq1Rw5x2cfrov+Vd6oVYqUUZFooqOQRkdjapW4n8d3qQ6fjOFuSqTqVTou3cn+I47KPzmW/IWL8Z2+jQZI16noNlHRL34AgHXX+/rYvoVW3Y2eQsWUPT9KpAkZEol+nvuIfypPqgTEy/5XkmSyP/gQ3LnzwcgsEMH4qZMqbaxLFMoiHr+ObTNmpE5bhylf+8k5YkniZ82Fd0111z6vXI5ge3bEdi+HbbMTIq++YbCL7/CnplJzux3yV24iJAedxP6SG809epW0jeq2uw5OZh27cJy+AiOgnzs+QVl1217YQHFEld/3QYUoaEoY85et+Pizl6zk1DXrYMyOlp0dD1A27gxCTOmU7p3Lzmz36V0927yly2j8JtviHj6aUIf6SVyO5xDstsp/OYb8hYtxpGfD4C2RQsi+vcj8OabLxuT5gMHXDN2cnNRhIWR8PYMdNdd5/VypzzZh7yLnYsia77oAAs1g1yjIXbMaLQtWpA9dSolv/zCmX79SJw31609TmUyGQFt2hDQpg22jAwKVq6k6JtvsZ05Q/b06eTOn0/IvfcS+sgjV53EoDqRbDbMhw5Tumsnpp27KN29G2dx8aXfdHaao0ytQiZJyORyZAE65IGBKAICkQcHIw8MdP0Euf5URkSijI5CdbbhpAgPFyPzlUSmVrtG1XrcTf7Hn5D/0UeYDxzgzMBBBHboQOSQ5z22LrWqchqN5H/0EfkffVw2chB8xx1EDnn+sh1fcK3jy5r4JoY1awAI7f0I0S+/XCOm8wbf1hl13TqkvzoM66lTnBnwDHFTJhPcubNb71fFxhL57LOE9+1L8bp1FHy6AsvRoxR++RWFX35FwI3tCX/sMQLatxfXjLMkScKekeG6Zu/ciWnXrvITAp7rbINaplQi02pddalSoggMQh4UdPZ6HYQ8MADFv//W68uu2cqYGJRRUaLjVYl0115LrSWLMW7ZQs6cuViPHydn9mwKVq4kctAg9N3v8utp5N4mSRLGX38l5905WE+dAlxTnaNeGOJaTuHGzZjijRvJGDfeNWOnfn0SZ828ZC6aq6VKTITdu1z/kMlcP+U5+3hVS5boSWINsOBzlb3WoPSf/aQPHYo9NxdVrVrUem/+FWVEdJpMrvVmn63EmpLielAmI6hjR8IeexRd69Y18m61ZLVS8vvvGFatxvjnnxdMa5PpdOiuuw5t40YowsJRhIehDAtDER6OIjQURViYaARVYfa8PPKWvE/h11+71uzJ5YQ+9BCRg5+tthlXL0ay2yn67ntyFy4sy8Squ+5aol56Cd2117p1DHtBAelDX3Ul5lMoiB72KmEPP+zNYvslp9HoWj+3eTMoFMSOG0vI3XdX+DiSJFG6YwcFKz6j5Ndfy5KOqZOSCH20NyE9etTYPYUtx465dkTYuPHCpIAyGZqGDdFde43rJmNYOIqwUJTh4SjOXr/lgYE18ndedSA5HBh++IHcBQvLcjtomzUjesRr6JpfPB9BdWU+cIDsWbPK9uZVhIQQMXAgoQ/c71auBdeMnQ/Inf8eUHkzdnIXLSZv4UJCH3qQmNdfv+Lj1IQ1wKIDLPicL040a2oqqc8OxpaejjImhlrz56GuU+eKjiU5nRi3bqVgxYr/1vTh2vst7NHeBHftWiM6dObDRzCs+h7Dj2txFBaWPS4P0RPQshW61q0JaN0KTaNGNWLkqqaznjlD7ty5FG/8CQBFWBhRL76A/u67a8RIm/HPv8ieMaMs86qqVi2ihjxP0G23ud1JsBw/TtpLL2FLz0AeFET81KkEtm/nzWL7NcluJ/PNNzGscu31Hv3acMJ69bri41lTUylcuZKib78ry2wuDw4m5P77CHvkEY8mp/FXjsJCDOvWU7TqeywHD/33hFKJtmlTAlq3QteqNbqW19W4G1g1kdNspnDlSvLe/wCn0QgyGSH330/Uc4NRVFKiRV+y5+SQ8+67GNb8CJyd4fTYY4Q/nex2jhCnxULmxIkU/7gWgLBHexP10kuV0u4RHWD3iQ6w4HO+OtFsWdmkPvcc1pMnUYSFkTh3DtomTa7qmJYTJylc+RlFq38oG/lUhIUR+tCDhD74IMqoKE8U3W84ioowrFlD0arVZfsuAygiI9B3746+Wzc0DRtWqMPji8zGVVVVqCvjn3+RPX061pMnAdBeew0xr7121eeav7Ln5pL9zkyK160Dzo4cPDOA0AceqNB0M+O2P0kfNgynyYQqMZGEWTPR1K289aq+zAJ9KZLTSc4771Cw4jMAIp977rJJDS/HUWLEsHoVBSs+w5aa6npQoSC4cydCe/dG17JltRrZlJxOjL//QdGq7zFu/rVsGyGZUkngrbcS0uNuAtq2rdBIeFW4FvkTf68ve24uObPfLVt2IQ/RE/Xc84Tcd2+1nBYtORwUfvkVufPmuTr+gL57dyKfG1yhG2GOoiLSXn7Ztb+vQkHMa8MJffBBbxX7ArmLFpOyfDmr77zjkrF1ufirCR1gMQwj1FiqmGhqLV5E6pAhWA4e4szAQSTOnoWuZcsrPqamXl1iXn+dyMGDKfzmGwo//wJ7VhZ5i5eQ/+FSgrvcSdhjj6Ft2tRzX8QH7Dk55H/8CYVfffXfNlEqFUEdO6Lv2ZPA9u3EKK8AQGC7ttT59BMKPvuM3EWLMe/dR8qTfVzTop8dVG2yjl7QgJLLCX34Ydd3rODIWclvv5E+bDiSzYaudWsSpk+rEaMv7pDJ5UQNHYo8KJi8xYtd9V1STOSQIVfcSVUEBRLWuzehDz+M8fffKfh0Babt2yne+BPFG39C07QJYY8+iv7OO6v0mjnJZsOwbj35H35YtqYRXHtIh/TsQXDXrn697ZtQeZSRkcRNfIOQ++8ne+pULMeOkTV5MkXffEP06yOq1bRo84EDZE6eXDYDQtu0KTEjX0fbrFmFjmMvKCB18HNYjhxBHhxM/Ftv1egZO/5OtFCFGk0ZFkatBQtIe+llSnft4sxzz5Pw9gy39gq+FEVICBHJyYQ//jjFmzZR+NlnlO7Zi2HNjxjW/IjuuusIe+zRKrcvpS0tjfzlyyn6fhWS1Qq4tgEIeeB+9F27oggJ8XEJBX8kU6sJ79OH4K5dyZk1m+L16yn8/HOKN2xwTYvu0aNKj7CV24AaNfKKbnQV//QT6SNHgd1OUOfOxE+eVKU7Xd4gk8mIHDQQeVAQOTNnkr9sOY6SEmJee+2qRqdkCgVBt95K0K23Yjl6lIIVn2H48UcsBw+ROXYcOe++S+hDD7lm84SHe/AbeZfTYqHo++8pWL4cW3oGcHaqd88e6Hv0RNu4ZiepEy4uoHUrkj75mMIvviD3vQWYDx7k9FPJhNx3H1FDnq/Sv/MdxcXkzn+Pwi++AElCHhRE5HODCX3wwQpfR+w5OZx5drBrRmFEBLXem1/ttqerbqpOy1sQvEQRFETi3DmkDxuG8Y+tpL30MnFvTSG4U6erPrZMpULfpQv6Ll0o3b+fghUrKN6wkdI9eyjds6fK7EtpOXGS/KVLMaxdC2f3ldNddx3h/foS2KFDle68CJVHFRND/JTJGO+/j+xprmnRmeMnYFjzIzGjRrqVEdmfOEpKyJ033yMNKADD2rVkjB0HDgfBXbsSN2G8WwlXaqrwJx5HHhhI1qRJFH31NU6jkbg33vDIFE1Nw4bEjh1D5JDnKfr6a9dsntxc8hYsJP+DDwnu1pWw3o/6defRaTJR+OVX5H/yMY5cVxI2RXg4YY8/RuhDD1fbLbQEz5IplYQ9+ijBd95ZNi266JtvKNm8mehhrxJ8551Vqg0gSRLF69aT/c47ZckJg+/qRvRLL7m1K8j/s2VmcmbQs9jOnEEZHU2tBe+hTkrydLEFDxNrgAWf85e1BpLVSsaYsRRv3IhMpSJh1syrHgkujy07m6KvXFtw/JssSqbVEtKjB6G9H6nUdX6XY0tPJ2f+fIrXrivLlhrQvj0R/fqia9XKK7/0TCYT69evp0uXLgQEBHj8+NVJVa4ryWqlYMUKchctRjKbkWm1RA58hrDHHqsSsyKKf/qJrGnTyjoWV9OAAij6/nsy35gIkoS+Zw9ix4zx6Vo7d2PLH2LQsG49GWPHgt1OyL33EDN6tMcTrUlWK8U//0zBJ59iPnCg7HHd9W0I692boFtv9Zu1kU6rlcIvvyR/yfs4iooAUMbGEt6nDyH33oNcq/X4Z/pDHFQlVbm+TDt3kTV5clleh8BbbyFmxAhUMTE+Ltnl2dLTyXxzEqY//wRAlVSbmNdGENiu7ZUdLy2NM4MGYUvPQBUfR+J7C3y6FWbuosWkf/gh+7vfxb1Dh140ti4Xf/7SLvcm0QEWfM6fTjTJbidj1CiKN/6ETKslce4cAlq18spnOc3m//alPHas7PHADjcS9u++lD66q+owGMh7/wMKV65EstkACOrcmfCnk6vV2h/B96ypqWRNmozpr78A13rE2DGj/TZJlr2ggOyp0yjesAG4+gYUQMEXX5D91lQAQh58gJgRI2pEpmxPKv7pJ9JHvA5Opyvr6tChXrl+SpKEed8+ClZ8RvFPP5XNiFHFxxPa+xFC7r3X7Wyx3ihb8br15M6bhy093VWu2rWJeDoZfbduYiq94DFOq5X8D5eS/8EHSHY78oAAIp9/ntCHH/LLa5fkdFL45ZfkvDsHqbQUmVpNRL++hPXpg/wKzwtrSgpnnh2MPSvLtaXmgvd8njleZIF2n+gACz7nbyeaZLWSNnQoxj+2Ig8MpNaihV5tjP+3L+UKSn797b99KevUIbRXL/R3d6+0BpXTaqVw5efkffABToMBgIC2bYl68YVK65BYrVYOHDhAs2bNUIsG2yVVl7qSJAnD6tVkvzPTFXcKBWGPP0bkwIFeGa26EpIkUbxhA9lTp7lmbigUhD/Vh4gBA664AQWQ/8kn5LwzE8CrHbeKcje2/CkGi1atJnP8eAAiBgwgctBAr36eLTOTwi++pPDrr8uul7IAHSE97yH0oQfR1Kvn1c8/l2nnTnJmziobnVZGRhLx7CBCevSolBkV/hQHVUF1qS/L8eNkvvkm5r37ANc+5zGjR1dq7F+ONTWVzIkTKd3xNwC6Vq2IHTsGde3aV3xMy4kTnHn2WRy5eajr1CHxvfmooqM9VeQrlrtoMZlLlpB3T09uHD78orF1ufjzt3a5N/j/PDOhRunWrRuKq5hGlpycTHJy8lWVQaZWEz99OqlDXqB0505SBz9HrSWLvXZBl8lkBNxwAwE33ODal/Kzzyj67nusp06RPW0aOe++S/CddxB6//1or73WO6MaTifF6zeQO29uWZIUdYP6RL/4IgE33lipDXKTycR3331HnTp1qnTDoDJUl7qSyWSE9OxJYIcOZM94m+L16ylY/hElP/1MzKhRVzW66gn2vDyy3ppKyc8/A6Bp0IDY8eOuOpt73gcfkjtvHgDhTz1F5JDn/aLzC+7Hlj/FYEjPHjhNJrKnTSNv8WLkAQGE93nSa5+nio0lasjzRAzoj+GHHyhY8RnWkycpXLmSwpUr0V13LSH3P0DwnXd47UaO5cRJcua8i/HX3wBXBzy8Tx/Cn3iiQtsYXS1/ioOqoLrUl6Z+fWq//z6FX3xJztw5lO7ZS8qjjxHety/hTydf1c3BqyU5nRR+/jk5c+aWLbOJGjKE0F4PX9UotfnwEVIHD8ZRWIimQQMS35vvV0nxLGo1vzgcXGcyXTS2qkv8XQ3RARb8ypYtW67q/Z08kLgKQK7VkjBzJqmDBmE+ePBsJ3iJ19d2qBMTiX71VSIGPYth9SoKv/oa64kTGFatxrBqNer69Qm9/z703bt7LPui+dAhsqZOLbuDq4yKIvLZQa7MvH6ypk2oGZQREcRPmUzJXd3IemsqtrQ0UgcPRt+zB9Evv1zpGUf/nVKaNW0qziIDKJVEPP00EX2fvurppHlLlpD73gIAIp55hohnBvhN57cqC3ukF06jkdx588iZPRt5YCChDz7g1c+Ua7WEPvggIQ88gOnPPyn84ktKfvuN0j17Kd2zl+wZM9DfdRch99+HtnFjj3ymo6SEvIULKVj5uWsatkJB6P33E/HMAJQRER75DEFwh0wuJ+yRXgR1vJWst6Zi/O038hYtonjjRmJHj0J33XWVXibr6dNkvjGR0l27ANda/dgxY6+6DWc+fIQzgwbhNBjQNG1CrblzxRZ1VZToAAt+5eabb76qEeA6dep4rCyKoEAS587h9DMDsR4/Tuqzz1JryRJUMd6f5lK2L+Ujj2Deu5fCb76heMNGrMePkz3jbXLenUNQ587ou3cnsF3bK8oUay8oIHf+exR98w1IErIAHRHJyYQ9/rjfTDsVaqagW29F17oNufPmUfjFFxhWrcb4+x9Ev/oqwV0qJ+OoPS+PrEmTKdm8GQBNo0auUV8PdGDyP/6krPMb+fzzRDydfNXHFP4T0fdpnEYj+UuXkjVlCvLAAPTdunn9c2UyGYHt2xPYvj227GwMq1ZT9O232NLTKfziCwq/+AJt06boe/QguGuXK9pzV3I6MaxeTc6cuTjy8wEI6tiRqBeGoPbg7z9BqChVbCwJM9+heP0GsmfMwHriBKf79Xfth/7cc5WSdVxyOl17zs+b7xr11emIemEIoQ9d/dpky4mTpA4ejNNgQNuiBYlz51R4j3fBf4gOsOBX1q5d61drDRShodSaN5fT/QdgS00l9bnnqLV40RU1XK6ETCZDd9116K67juhXX8Xw448UffMtlsOHKV63juJ161CEhRHctQv67t3RNmt22c6B5HBQ+NVX5L63oGzdWvBd3Yh64QW/WMMiCOC6CRTz2nD03bqSOfFNrCdPkjFyJIYffyRmxGteTTZSvGkTWW9OwlFYiEypJLxfPyKeTvbIlkSFX35JzkzXmt+IQQNF59dLIp9/DqfRSOEXX5AxdhwyrdYjW9u5SxUdTUQ/1zRQ01/bKfr2W0o2bcJ88CDmgwfJfucdAm+8EX337gR1vNWtm47mAwfImjoN8z//AK48EdHDhhHYvp23v44guEUmk6Hv2oXA9u3InjULw/erKPz8c0o2bybm9REE3XKL1z7blplJ5rjxmHbsACDghhuIHTsGVXz8VR/bmppK6rPPuqY9N2lC4ry5Pkt2J3iGSIIl+FxVWGxvS0/ndP8B2LOy0LZoQa2FC3w2SipJEpZDhzD88AOGdevLRgEA1ElJ6LvfhaZhQ2QqletHrXb9KJXY8/LJefddLIcPA65Rrehhwwho7Z1M11fCbreTmppKYmIiyiqwHY4v1ZS6+v+Mo7IAHVHPDyH0oSvbb/ein2M0kv322xR99z1wdi/YNyagbeSZvV6LVv9A5rhxgP+t+f1/7saWP8eg5HSSOW48hjVrkGk01Hpvvk+mY/7LXlBA8bp1GFb/gPngwbLH5QEBBN1xO4EdOiDXBZx/7VapQAaFn39B0bffls3WiRzwDGG9H/GbzM7+HAf+qKbUl3Hbn2RNnowtLQ2A4DvvJHrYqx6dpi9JEsVr15L11lScJSWutb4vvega9fXA9dWWmcnp/v2xZ2Sirl+fWgsXVNogSEXlLlpM9uLFmO+7l2tfe+2isXW5+KsK7fKrJTrAgs9VlRPNcvIkp/v1w1lkIKhTJ+KnTfX5GlnJZsP4518Y1qyhZPNmJLPZrffJ9Xoinx1E6AMPVIk9VwUBXJk3MydOLFuvrr32GmJHj0ZTv/5VH7t0924yxox1bR8jkxH25BNEPvusx5K4GDZsIGPkKHA6CX3kEaKHveq3nd/qRLLbSRs2DOOvv6EICaH20g+vKvurp1hOnsTw448Y1qzBnpHp9vv03bsT9cIQlFFRXiydIHiO02wmd+FCCj7+BJxO5Ho90S+/hL5nz6u+BjqKish6ayrF69cDoG3enLiJb6BOSvJE0bHn5nJ6wDPYTp9GVbs2tRcvuuK93iuD2AbJfaIDLPhcVTrRTLt2kTr4OSSr1e8asY4SIyWbfqb4p59xFBQg2WyuH6u17O84HATdfhuRgwf77R3MkpISvvzySx566KGyuBDKVxPrqmw/xzlzkEylrinKTz9NeN+nr6izKlmt5C5aTP6yZeB0ooyNJe6NCQS0aeOxMpf89htprw4Du52Qe+8lZvQov9wr81zuxlZViEFnaSlnBjyD+eBBVImJ1F76od9c/ySnk9LdezD8+CPW48dw/nu9ttqQbGf/brGiTkoi6qUXvbYv/dWqCnHgT2pifZkPHSJz4ptYDh0CXFOUY0aPQp2YeEXHM277k8zx47Hn5IBCQUT//q4EhR66qW8vKODMwEFYjx9HGRdL7SVLfL7P7+XkLlpM6tKl/NWtK4+98spFY+ty8VeV2uVXSgz9CEIFBLRqReyE8WS8PpLClStRxccT/sTjvi4W4FozGdKzJyE9e5Y9JkkS1uPHKfnlF4p/+QXLwUMYf/0NyWoj8MYbCWzfzu8yGNrtdlJSUrDb7b4uit+riXUlk8sJ69WLoFvPyTi6ePF/GUdbtnT7WJYTJ8gYM7asQaa/+26ihw/z6Nou47Y/SR/+GtjtBHfrSsyokX7f+QX3Y6sqxKBcpyNh9ixOJydjS00l7aWXfbqM5VwyuZyA1q0uWIbiKCykZMvvlGzahHHbNixHjpC3eAnmG28ksMONqOvV85ubr1A14sCf1MT60jZpQtKypRR88gm5Cxdh2r6dU70eIXLgM4Q9/rjbHVen2UzOnDkUfrYSAFXt2sRNnIiuRXOPldVRXEzq80Ncnd+oKGotWOj3nd9/OeRyMiTpkrFVE+Pv/4kOsCBUkL5LF+yZWeTMnk3OzJkoY6LR33mnr4tVRnI4KN27l5JfNlPyyy/YUlPPe96em4th9WoMq1eDTIa2WTMCO9xIYPv2riRafrKmTBAupSzj6IYNZE+fgfXkSVfG0YceInLIkItmHHWWllKy+VeK16/D+PsfSHY78hA9sSNHEnzHHR4to2nXLtKGDkWyWgnq1Im48eN9vmyiplJGRJDw7ruc7tsP8z//kDFqtF8sYzmXLSODks2bKdn0C6Zdu1zbG53D9OefmP78k5xZs1DGxBDYvj0BN7Yn8IYb/O5GpiCUR6ZUEv7UUwTddhtZkyZj2r6dnHfnYFi/gdgxo9E2aVLu+yRJwvzPforXrcWwYQOO3DwAQh9+mKgXX/DontdOk4nUF17AcugQirAwEt+b7/UtMIXKJzrAgnAFwp58AltGBoWff07m2HGooqIqNPLkDZIkYfhhDblz57qmBJ0lU6sJaNeOoE4dCWzfHmvKaYxb/8C0dRuWY8cw79+Pef9+8hYvQabVom3eDF3Llq7s09deK9L8C35LJpOh79KFwHbtyJk9m6Lvvqfwyy8p+fVXYka8RlDHjoBrmrNx2zYMa9ddsFY+sEMHYseO8fiaSvOBA6S98CKS2UxghxuJmzLZI1mkhSunqVuXhHfeJnXwc5T88gvZb7/jF8tYLCdOkD3jbUx//nne45qGDQnq1ImgTh2RqTUYt23F+McflO7chT0ri6LvvqPou+8AUNet67pmt2yJruV1qBITff69BOFi1LVqkfjefAyrV5P9zkwshw6R8mQfwh5/jMhBg8pmZ1iOHcOwbh3F69aXJdICUEZGEjN2DEE33eTRcjnNZtJefgXz3n3I9XoS581DU7euRz9D8A+iAywIV0AmkxH96lDsWZmUbP6V1FdeIenDDz2WeKGizEeOkD11GqW7dwMgDwoi8JabCe7cmcAbb0QeEFD2WlVsLIHt2sJLYMvKxvTnNox/bMX01184iooo/XsnpX/vdL1YJkNTvz7a665F27gxmkaN0DRo4NG7rf9PLpcTEhKCvApME/U1UVcuipAQYseOJbhbN7ImTXZNc31lKMF33I48KJjin38u2/ILQBUfT3C3rui7dkXToIHHy2M5cZLU54fgNJnQXd+G+OnTPZZMq7K4G1tVLQYvWMaSEE/4475ZxuI0GsldvJiCFZ+B3Q5yObqWLQnq1JGgjp0uGHXS1KtL+GOP4TSbKd21C+MfWzFu24b1xAmsJ09iPXnSlSkaUEREoLvuOrTNmqFp1BBNw4Yoo6K81imuanHga6K+XO2okJ49CezQgewZb1O8fj0FH31Myc+bCO7WlZLNm7EeO/7f67Vagjp2RN+tK4Ht23t8tppkt5MxciSmHTuQBwSQOGcO2sae2QGgMskliSC4ZGyJ+BNJsAQ/UJUX2zvNZs48MxDz/v2oEhJcyVXCwyvt8x0lJeQtWEDB51+Aw4FMqyViQH/CHnuswg1uyenEeiqF0j17KN2zm9Lde7CdOXPhC2UyVLVqoT3bqFLXr486MRFVQsJ5HW1B8AWn2UzeosXkf/zxeVNIFZER6O+8k+Cu3dC2aO61joAtM5PTffu5tkxr1oxaC95DXoWuaTVF/vKPyJk9G2Qy4t+a4vHp75ciSRLF6zeQM3Nm2WydoI4diR76CqqEik+1tBcUYN6713Xt3r0b84GDrqSH/0cRGoqmoeu6rWnUCHVSbVQJCSjCw8VoseBzJb/9RtaUt7BnZZU9JlMqCbypA8Fdurr2y/bSzXfJ6SRz/AQMP/yATK0mce4cjyZCrCwiC7T7RAdY8LmqfqLZ8/M5nfw0trQ0dNddS+J77yHXaLz6mZIkYVizhpzZ7+LIc62FCb7jdqJeftmjiRrseXmU7tmL+Z99mI8cwXL0aNnam/IowsNRJSSgio9HlZiAKiYWRVgYitBQFGGhrj/1+kuuu3M6nRgMBvR6fY2+O+kOUVcXZz58mPxly5HrdAR37UJAmzZeX+9pz8/ndP/+2FJOo65bl1qLF/lNtuGKcje2qmoMSpJE9rTpFH7+uWuP4MWL0DX3XBKdi7GcOEn2tGmYtm8HQJWQQPSwYQTdcrPHPsNpsWA5eJDSPXswH3Zdt60pKResKf6XTKt1XbcT4lEnJKCMj0cZHnH+dTs09JK/16pqHPiKqK/yOY1G8j74EGtKCoE330Twbbeh0Ou9+pmSJJHz9tuumRgKBQkzphN0661e/UxvyV20mJxFi1A+cD/1R4y4aGxdLv6qervcHaIDLPhcdTjRrKdOkZL8NM7iYvTduxP7xgSv3VG3HD1K1tRplO7aBYAqqTYxw4cT2L69Vz7v/9nz87EcOYrlyBEsR49gOXESW3r6eVNML0kuR6HXIw8MRKZWu35UKtePWk2JRsPnoSE8plIRqg9BERyEKiEBTZOmqBLixUjFOQoLC5k9ezYvvvgioSIJznkkSXJld5bJ0DRs6PXOr6OkhDMDB2E5dAhlbCy1P3gfVUyMVz/Tm9yNraocg5LDQdorQzFu2YIyMpLay5d57f/MaTK5pjt/ugLsdmQaDeHJyYQ/1cfrN0zBNTPCevIkliNHMB85gvXYcaypqa7RNjebgbIAHQp9CDKNGrlKXXbNLrtuh4fxGBASrEehD0YREemaft2gQaV8x6qkKp833mbLzsZ2+jSaJk0vmszQk3IXLyZvwUIAYt+YQMjdd3v9M70ld9FiUpYvZ/Wdd/CIwUDwRc7tYpmMlXr9ReOvOrTLL0esARYED1DXqUP8W2+R+sILGNasQV23LhF9n/boZzhKSshbuJCClZ//N925f3/CHq/4dOeroQwPR9m+HYHt251fvuJibGlp5/9kZeMoLCz7cRYXg9NZ9u/ymHQ6uPMODGt+xFlaet5z8qAgNE0aE9C6DUGdOqFp1FB0iIULOEpKyJo0meL16wFX3OhatiSgTWt0rdugbdLYY3tFwn+JU/7NGlpr/rwq3fmtKWQKBXGTJnG6X1+sx46T9spQai9Z7NFplpIkUbxhAzkzZ2HPzgYgqOOtRA8dekXTna+UXKtF27Qp2qZNCTnncafVij0zs+yabU1Lw56egb2wwHWdLjh7rXY4kEyl2E2l5R6/7Lq9YeMF120UCjT16qFt3pygjrcS0K6d6BALF5AkCcP335M1bborUaFcjrZxY3Rt2riu3a1aeTwpZ8Hnn5d1fqOHvVqlO78AyvD/ZhwZ//wL2f+fi/8+d/Z8dVqtlVU0vyM6wILgIYHt2xE97FWy35pK7rx5qOskEXzbbVd9XEmSKF67luxZs8qmHwfdfhvRr7ziV/vSKYKDUTRpctFtDAAkmw1HURGOggKcJhOSzeb6sVpdF2KbjcKSEjh4kLBHexNoMuEoKsJ66hTWY8dxlpRQuuNvSnf8Td6iRagSEgjq3Bn9Xd0u+blCzVH6z34yXn8dW3o6KBTINRqcJSUYt2zBuGUL4BrJ0l13HQFtrkfftQuq+Pgr/jzJZiP99dcp3bkTeWAgiXPn+CwZnlBxiqBAEmfOJKXPU1gOHSJj7Djip77lkb2aLSdPkj1tOqa//gJcydeihw8j6JZbrvrYniJXq1HXro26du2LvkaSJJwlJTgKCnAUGZBsViSrzfWnzYZktVFYUgz79xP+5JMElppwGoqxZWZiOXQIR1ERlqNHsRw9StG33yLT6Qi6qQNBt99OcKdOYus9AUeJkazJkyletw4AeYgeZ5EB88GDmA8epODjj12zeRo1IqB1KwJvvoWAdm2v6ga4Ye1asqdNByBiwADCevf2yHfxJf099xCt0cDOnUQ9/zz6cs4tyWqlZO7cs/+ouZOARQdYEDwo7OGHsZ48ReHKlWSMGYsqPv6qOmaWY8dc0513urIyq2rXJmb4MAJvvNFTRa5UMpUKZWQkysjIi77GWVgIBw8S/vjj503NkaxWLCdPYT5wAONvv2Hctg1bWhoFH39Mwccfo2vVirBHexPUsaNHR/eEqkFyOin4+GNy5s0Hux1VfBxxkyejbdoUy9GjmP7eSenOvzHt2o3TYMC0dRumrdvIW7SI8D5PEp6cXOGRP8npJPONNzD++hsyjYaEWTPFjZgqSBUfT8KM6ZwZ9CwlP/9M7nvvEfXcc1d8PKfJRN6S9yn45BMkux2ZWv3fdOez27tUJTKZzHWD8xKjb87CQti/n7BHep1/3ZYk7FlZmA8dwvTXdkp++QV7VhbFG3+ieONP5ERGEvLQg4Q++GClJpAU/Efp/v1kvD7Stc2RQkHkoEGEJz+FPSeH0p07Mf29E9POv7GlnMZy+DCWw4cpWPEZAe3aEf3qUDT16lX4M0u2/E7GuPEgSYT26kXEwGc8/8V8QK5WE3zLLbBzJ/puXcud3uw0GuHfDnANJtYACz5X3dYaSHY7qS+9hGnrNpTR0SQtX1bhPUYdxcXkLVpMwcqV/0137tuXsCefqHLbqVSU2Wxm27ZttG/fHu0lGovO0lKMf/xB8YaNFG/a5NpGBFDGxhL+5JOEPHC/qKsawp6XR8a4cZi2bgMg+I47iBk9qtwGu+R0Yjl6jNJdOyn+6eeym0vKmBiiXniB4K5d3BpVkJxOst9+m8LPVroSp7w9w69G9q6Wu7FVnWKw6IcfyBw7DoC4iW+g7969Qu+XJInidevJeffdsky2gTffTPSwYRdsaVTduBsHkiRhPnCAkp83UbR6VdmsJplajf6uu4gY0B9VXFxlFdtnqtN5c6VcNy0/IWfePLDbUcbFEj95Mrprry339facHEw7d2Havh3D6tWuTOcKBWGP9CLimWfcnh5t2rmL1OefR7JYCO7WlbiJEz0y48NfXC62nEYj+2+7ncP169F9/nwCaugaYNEBFnyuOp5ojpISTic/jfXkSddWKIsXuXXnX7LZKPz6G/IWLsRRVARAUOfOru0xakCj4ErZsrIp+vorCr/8qmxtsSo+jshnnyW4W7dq9ctNOJ9x259kjB2LIy8PmUZD9NChhDxwv3udWEmiZNMmcmbOxJaeAYCuVSuih72KtnHjC17vtFgw7dhByebNGH/9rWwLm7g3J6K/6y7PfjHBJ3LmziP/ww+RqVTUWrgA3XXXufW+0n3/kP3O25j37gNc15/oYcOqbDbZyiBZrRT//DMFn67AvH8/4JolFPrww4T3fbrKZlAXLs+en0/muHEY/9gKuHaxiBk92u1OrDU1lZx3ZlKyeTMAirAwIp97jpB7el6Q8FCSJKzHj1OyeTMlm38ti7XAm28mYcZ0ZCqVB7+Z/3MajRy9tSMADX/fUm7btDq2y/+f6AALPnfuiXbzzTejuIpsrcnJySQnJ3uoZFfHmprK6T5P4SgqIuj224gaMgRVYmK5DXNJkjD+/gc5M2diPXUKAHW9ekS/8nKVne58pa7mzrjTYsGwejV5ixZjz80FQNOoEVEvvURgu7beKK5P1eRRBMlmI3fBQvKXLQNJQl2/PvFTJqOpX7/Cx3KazeR/9BH5S5eVJV8JfeB+IgYNQiaTUfL7765O79atSOckAZIF6Ih+6WVCH3zAk1/NL9TEEWBwjUqlD3+Nkk2bUISFEf/WFLTXXnvR2SS2zExy5syheK1r7aJMqyX8qacI7/NklZzufKWuNg5K9+4ld/57ZdtDyQMDCX/qqWo766m6nTcVYdz2JxnjxuLIrfhNywuPtY3sGW9jPXkSAE2TJsQMexVtixaU7t5d1um1paWd976gjh2JmzypWp6jYgTYPWKhnOBXtpxNUnOlOnXq5JmCeIA6MZH4GdNJfXYwJT/9TMlPP6OIiEDX8jpXRtqWLdE0aoT11CmyZ87CtM01fVMRGkrEoIGE3n9/jVzLajab2bx5My1btqxww0Cu0RD64IPo776bgk9XkL9sGZYjR0gdPBh997uIeuWVajWqcDV1VZXZ0tNJHzkK8z7XaFvIgw8Q/corV9yYkWu1RA4YQEjPnuS8O4fideso/PIrin5Yg2S1nrd/qjIqiqCOtxLUsSO666+vlo1zcD+2qlsMyuRy4ia+wel+/bEcPsyZgYOQqdVomzdH17Kl6+faa5ApleQvW0b+Rx8jWSwgk6HvcTeRgwejio729deodFcbB7prryXxvfmY/vyTnDlzsRw6RO78+RjWrCFm1EgCWrf2Qql9p7qdN+6QbDZyFy4if+lS103LevVcNy0bNLjiYwa2b0+dFZ9S8MUX5C1YiOXQIU736488MNC11vUsmVpNQNu2BHXsSNAtN1d4WVpV4k5sWVUq9jduzG0WCwGVXD5/UfNa14Jfu9oR4Dp16niuMB4Q0Lo18TOmk//hUswHDuDIyyvrDAPIdDpX48npRKZUEvpobyL69fN4qv+aRq7VEtH3aUIfuJ/chYso/OILDGt+pOT334l+6SX0PXuK7ZOqKMOGDWS9OQlnSQnyoCBix4wm+I47PHJsVWws8ZMnYXroQbKnz8By5AgAmoYNyzq9mqZNRexUc3KdjoRZM8mZNRvT9u048vMp3bWrbO91ZDJkOm3ZbABd69ZED31FJEC7SjKZjMD27Qlo25bidevInjkL66lTnBnwDCH33kvUiy+gCAm5/IEEv2NLTyd91KiyJQIhDzxA9NArv2l5LplKRfhjj6Hv1o3cefMp+u47nEYjipAQAm+5haCOHQls3w55QE3t6gnlER1gwa+sXbu22k21CLrlFoJuuQWnxYL5wAFKd++hdPduSvfsce2Li2tbo6gXXkCdmOjj0lYvitBQYl4bjv7uu8maNAnLkSNkTngDw9p1xI4bhyqm5o3UVFVOs5nst9+h6OuvAdBeew3xkyZd1RZGFxPQujVJH3+Eed8+lFFRlbpfq+AfVNHRxE+ehCRJ2M6ccV2zd+/GtGs3ttOnkUylqBITiXrxRYI6dxI3RTxIJpejv+suAm++mZw5cyj66muKvvuOkt9+I3bsmGqVbK4mKN64kcyJb5bdtIwZPQr9nXd6/HOU4eHEjhlNePJTOAqL0DZresF6YEH4l+gAC0IlkWs0BLRqRUCrVoBrrZn1xAlQKNDUrevj0lVvuhbNSfpoOQWfriB34UJMf/7Jqd69iR010mOjh4L3WI4fJ/31kViPHweZjPDkZCIHPuPV5CUyhQJdy5ZeO75QNchksrJ9ckPuuQdwZR23paejady42k6B9weK4GBiR44k5O67yZw0Gevx46S99DKhDz1I1EsvVXjbMqFyOc1mst95h6Kvzt60bNGC+MmTvH5DUV2rFtSq5dXPEKo+kQRL8LnqvthestuRLBbk1ex7eYvT6cRgMKDX65F7IXuz9dQp0kePxnLwEAD6u+8mevhwFEFV7//H23Xla5IkUfT1N2S//TaSxYIiMoK4CW8Q2L6dT8vlNJsx79+P5cQJAtq0uaJ9KP2du7FVnWPQUVKCXKcTo0hu8HYcOC0WcufNp+CTTwBQJdUmbuJEdM2be/yzKkN1Pm/g7E3LkSOxHjt70/Kpp4gcNNCnGZclScJ68hTmfXtRhIYSePPN1fLcvlxsOY1GDnfshEmr5br161CWMzW8urfLQXSABT9QXU80p9lM0TffkL/8I+zZ2QTecgvhfZ5E16qVmC7nY5LNRu7ixeR/uBScTlS1ahE/dSraxo18XTThLIfBQNakyRRv3AhAwI3tiXvjDZTh4ZVeFnt+/nlLF8yHDpXtO41MRtBtnYno21esAa0GJEmidMcO8t7/ANP27agSEwl7/DFC7rmnWmaMrWqMf/5F5vjx2LOzkSmVRL38MqGP9BK/U/2EJEkUffOt66al2YwiIoK4N3xz09JptWI5cADT2et26Z49OIsMZc+rk5II79sXfdcuNWorJLENkovoAAs+V91ONEeJkcIvv6Dg409wFBRc8Ly2eXPC+zxJUOfO1fLu49UyGAx88MEH9O3bF71e79XPMu3aRcboMdgzM5FpNMS8PoKQnj29+pmeVJl1VZlMO3eRMcb1/4JSSdRzzxH2xOOVup+zvaCAvAULMP61Hdvp0xc8/+/a4NLdu8seC7z5ZiL690N3zTWVVk5vcTe2qksM/rsVXf4H71O6Z+8FzytCQwnt1YvQXg9Xq0zynlKZceAoKiJz0qSyZJLBd95J7JjRVWqWVXU5b87lKCoi881JlPzs+n8JaN+euDcmoIyIqLQySJJE0VdfYVjzI+YDB5BstvOel2m1aJs2xXLsWFkOFlV8POHJyeh79qgWSxouF1tOo5E9Xbqy8eabGDB0KKHlZK2vbu3y8og1wFWUw+Fg//79bN++nR07drB9+3b27t2L7ezJ3rFjR3755ZcrPv5PP/3EsmXL2LZtG2lpaWg0GhITE+natSv9+vWjiRjpuICjqIiCFZ9R8Nln51xY4whPTkZ33XUUfP45hlWrMe/fT/prI1AlJBD2+OOE3CtGFs7ldDopKirC6XR6/bMCWrWizicfkzFmLMY//iBz/ARKd+0meviwKvF/Upl1VRkku528Je+T9/77rpH5xETiJk1C16Jypzk6iotJfX4IlkOuafLIZKjr1yOgZUt017m2MVPGxSGTybAcO0beBx9SvGEDxi1bMG7ZQkDbtkT064uuTZsqOzLlbmxV9RiUnE5KNm0i74MPy/6/ZWo1IffeS2ivXpj++ouCTz7Glp5B3qJF5C9bRkjPnoQ98bhrraEAVG4cKEJCiJ86lcKVK8meOYviDRuwHDlC/LSpV7WlTmWq6ufN/zPt2EHGmLFlI/ORzw0m7IknKvWmJUDekiXkLVhY9m9FeLhr67Kz121to4bI1GrXQMUXX1DwySfY0tPJmjyZvCVLCH+qDyH33Vclfv9fjDux5ZTJMAUE4KzBY6CiA1wFffvttzz++OOYTCaPH9tgMPDMM8+wcuXK8x43mUwUFBSwb98+Zs+ezYQJE3j99dc9/vlVkTU1lcKVKyn89tuybTHKm1oTO3IkkYMGUfj55xR8/jm2tDSyp00jf+lSai1ejDpRZJr1BUVoKAmzZ5H/4YfkLlhI0XffYT5ymIS33xFZoivR/2+Toe/Rg5jhwyp9VMdpMpH6wgtYDh1CERZGzOhRBLRujeIiozSaBg2InzwJ66CB5C9dStEPazD99Remv/5Cd911hD35BEG33ipme/gZp9mM4Yc1FKxYgfXkScC1LV3ogw8S/sTjZfuEaurVJfShByn+6WfyP1qO5eAhCr/8ksKvvybmteGEPvSQL79GjSWTyQjr3Rtts2akj3gda0oKKU8lEzfxDYJvu83XxasxJJuN3EWLXMuJJAlV7drET56EtmnTSi9L/seflHV+Iwb0R3/33agSE8u9CakICiTi6WTCHu3tWqq2bDn27Gyyp88g7/0PCHv0UULuv0/M9qjGRAe4CiosLPRK59dms3H//ffz89npKwAtWrSgdevWmM1mfvvtNzIyMrDZbIwcORKbzcbYsWM9Xo6qQJIkTH/+RcFnn2HcsgXO3kXTNGxIeL++BN92W7kNXmV4OJGDBhH+1FMUfb+K/OXLsWdmkvbKK9T+4IMqmYipOpDJ5UT064e2xTVkjByJ5eAhTvfpQ/zbb1f66GNNZFi7jqzJk3EajcgDA4kZORJ9t66VXg6n2Uzay69g3rsPuV5P4vx5aBu5ty5cXbs2sWPHEjFgAPnLllP07bdl686UsbGEPvwQIffeKxpUPmbLyKDg888p+vY7nAbXekB5UBBhvR8htHfvcv9/ZEol+q5dCO5yp2t98LJlmLZuI2vadFSJtXyelK0m0117LUmffEzGqNGY/vyT9GHDiXx2EOH9+lXZ2RdVhTU1jYxRozD/8w8AIffeS/SrQ32y327hl1+SM3MmAJGDnyWiXz+33ifXal2d3QcfxLBqFflLl2JLzyB33jzyFi8muGsXwh55xCcdesG7ql/quRokJiaGHj16MGHCBNasWcOLL754VcebOHFiWedXq9WyYsUK9u3bx7Jly1i5ciWnTp1i2LBhZa8fP348mzdvvqrPrGqcpaUUfvklpx7uRepzz2H87TeQJAI7dCDh3dkkrfgU/Z13Xna0R67TEfZIL2p/+AHKqCisx4+TMXo0ksNRSd/EfymVSpKSklAqK//+XGC7tiQtX4a6QX3submcGTAAw9q1lV4Od/myrjzBaTSSMW48GaNG4TQa0V13LXU+W+GTzq9ktZL+2ghMO3YgDwggcc4ctzu/51LFxREz4jXqfv8d4cnJKEJCsGdmkjtnLie6303G+PGYDx70wjfwLHdjqyrEoCRJmP7+m7RXh3HinnspWP4RToMBVUICUa+8TL0fVhP57LOXvTkhk8kIuOEGEufMQd+jBzgcpI8YgTUlpZK+if/yZRwow8JIfHc2YY/2BiD3vQVkjBqN02yu9LK4qyqcN5diWLOGlEcfxfzPP8iDg4l7awqxY8f4pPNb9MMPZL01FYDw5GTC+/at8DHkajWhDz5I3a+/JnbCBDRNmyBZrRhWrSbliSdJSX4aw48/Ilmtni6+x7kTWwqnk6jcXJQ1eGaSSIJVBWVmZmK1Wqldu/Z5j48fP54JEyYAFV8DnJ2dTb169TAajQAsWLCAgQMHlvva3r17l02RvvHGG/njjz+u4Fv8x98X20uShHn/AQyrV2NYu7Zsfa8sQEdIz3sI6/Uw6jp1rvj4pfv3c2bAM0gWC+FP9SHqhRc8VHLhSjmNRjLGjKFk86+A65dq5HODK309U3VW+s9+MkaNwpaaCnI5Ef37E9GvLzIfNAglu52MUaMo3vgTMq2WxDnvEtC6tUeO7bRYKF6/gYKVn5VtvQWgvfYawh55hKDOnZFrNB75LOF89rw8DD+upWjV967tWM4KaNeOsN6PEHjTTVc8Nd1ptXJm4EDMe/ehSqpN0tKlF50mL1Sewq+/IWvqVLDb0TRtIpayeJijxEjWW1Mo/tF1Y1jXqhVxb05EFRvrk/IU//QT6SNeB6eT0EceIXrYqx4Z+ZckCfM/+yn8fCXF6zcgnc36r4iIIPSB+wm57z6ffeerJbJAu4gOcDVyNR3g6dOnM3z4cAAaNWrEoUOHLnoROX36NHXr1i1bYL9z505atWp1xeX21xPNlpWN4cc1GFb/ULZGDHBti9H7EfQ97/HYlGXD2nVkjBoFQOyECYT0uNsjx62K7HY7qampJCYm+vTuuOR0kjtvPvlLlwIQ1PFW4iZO9KtMo/5SVxUhOZ3kL1tO7oIFYLejjI0lbuJEAlpf+TXkasuTOX4Chh9+QKZSkTDzHQJvvNHznyNJmP/5h4KVKynZsLGsQSUPDCTo9tvQd+1GwA3X+81aYXdjy99i0Gm1Yvz1V4pW/4Dxjz/g7KwamVZLSI+7Ce3VC039+h75LHteHil9nsKemUlA+/Ykzp7lkxs4/sCf4sD099+kDxuOo6gIZWSkXy5l8af6clfpvn1kjByFLT0dFAoiBvQnom9fn12zSrb8TvrQoUh2OyH33kPM6NFeuUltz8uj6JtvKPzyK+w5Oa4HZTJ0rVuh73YXwbffhiIkxOOfe6UuF1tOo5FDnTqTGxZG+6+/Qn22/X0uf22Xe5IYzhAAV2KtfyUnJ1/yDlrt2rW57ZwkE9988403i1apnEYjhrXrOPP885zo0YPcOXOxnjyJTKMhuFtXEufNpe43XxP26KMeXa+r79aViP6uNStZb75J6d4Lt+GoKUpKSli2bBklJSU+LYdMLidqyPPETXwDmVpNyeZfSenbF1tamk/LdS5/qSt32bKySR38HLlz54LdTvAdd1Bnxae+6/xKEtlTp2H44QdQKIibMtkrnV9wTZ/VXXMN8W++Sb01PxAxaCDK2FjXNef7VaQ+9xwnut9N9tvvuLbv8PG9aXdjyx9iUHI6Kd27l6y3pnK8azfSXxvhWp7icKBt0YLoEa9R/8c1xLz+usc6vwDKiAgS3p6BTKvFtG0bObNmeezYVY0/xMG/Atq0Iemj5X69lMWf6utyJIeDvPff53S//tjS01HFx1F78SIiBwzwWefXtH076cOHI9ntBHfpQsyoUV6boaWMiCCif3/qrfqe+Klvobu+DUgSpX/vJGvSJI536UraK0MxbNjgF9Pu3YmtUo2GTTd1oOTsrM+aqGrcdhK8ymw2s23btrJ/d+rU6bLv6dy5Mxs3bgTg559/5o033vBW8bzOnpNDya+/UfLLL5i2bz9v3zhdy5boe/Qg+M47UJRzl8yTIgYOxHL8BCWbNpE29FWSPlpeZafYVCf67t1R1apN2qtDsR47Tkqfp4ifPt1nnbaqqnjTJjInTsRZZECm0xEzfBj6nj19lqhGkiRyZs+m8MsvQSYj7o03CO7cuVI+WxkRQeSAAUT060fp7j0Y1v5I8caN2HNzKfj0Uwo+/RR1UhLBXbsS1PFWNI0aien3/8dptWLavp2SzZsp2bwZR25e2XPK6Gj03buj73E3mrp1vVoObZMmxL0xgfThr1Gw4jPU9eoT+sD9Xv1M4fJUCQkkffAB6aNHY/z1NzJGjcZy/DiRzz4rzqUKsGVmkjFmLKU7dwIQ3LUrMSNf93p76FJK9+4l9eVXkCwW18ysNyZUSkdcplIRfMcdBN9xB7bMTIrXrcPw41osR4+WXYfkAQEE3XYbwXfcTsD11yPX6bxeLuHKiA6wwOHDh8umM8tkMremM7c+Z33cwSqQ0OVckiRhPXmSkl9/peSXzZj37TvveVWtWui7dUV/992Vus+jTC4n7o0JnO6XhuXIEdJeGUrt95eIC6gf0F3TgqTly0l7ZSiWQ4dIHTyY2LFj0Hfv7uui+T2n2UzOzJkUfvkVAJqmTYifNAl1UpLPyiRJErnz5lPw0ccAxIwe5ZPEWzK5nIDWrQho3YqYV1/FuHUrhh/XUvLrr1hTUshbtIi8RYtQREQQ2L49gR06ENCubY3NJO0oKsK4dSslv2ym5PctZdvOAcgDAgi89RZCevYk4IYbKnVkKvj224kYNJC8BQvJmjoVdVJtAtq0qbTPF8onDwwkYcYMcue/R/7SpeR/8CG202eInTC+Su/zWlmKf/qJzDcn4TQYkAXoiHltBPq7u/s0u3bpvn9IfX4IUmkpAe3aETdlStlWk5VJFRtL+FNPEf7UU1iOHcPw41oMa9diz8x05YtZvRqZSoWuVSsCb7yRwA43oq5fX2Qm9yOiAyxw+PDhsr9HR0ejdeMXw7kJuPLz88nJySHq7L6J/kZyOrGeOIHp752U7tyJadcuHHl5571G27w5QZ06EdSpE+q6dXx2kZIHBJDwztuk9HkKy+HDZIwbR/xbb4k71n5AFRND7feXkDF2LCU//UzGmLFYz6QS8cwA8UvtIsxHjpAxajTWEycACOvzJFHPPotMrfZZmSRJInfOXPKXLQMgetgwQu+7z2fl+ZdMrSaoY0eCOnbEaTRSvOkXin/aiGn7Dhx5eRh++ME1VVsmQ9usGYEdOhDYvh2apk2rbRIte36+65q9cxelO3diOXasbMs5AGVUFEEdbyWoUyd0bdog92FcRfTvj/XESYrXryd92HDXDJ4Esbe7r8kUCqKGPI+6bl2yJk6keONGbJmZJLzzNsqICF8Xzy85S0vJfvsdis4ub9M2a0bc5EmVOiBQntK9e0l97nmcJhO61q1JeHuGX1z7NA0aEDXkeSKfG0zp7j0Ur1+P8fct2NIzyvaEz5k9G2V0dFlnWNe6NcrwcF8XvUYTSbCqkStNgvXee+8xePBgAK699lr27Nlz2ffk5+cTcc4vj0OHDtG4ceOKFxrPLraXJAlHXh6WY8exHD3q2odz504cRUXnvU6mVqNr05rgTp0IvPVWVNH+lSWydPduzgwchGS3EzFgAJGDys/IXR1ZrVYOHDhAs2bNUPuwQXsxktNJ7ty55C9bDkDwXd2IHTvWJ41vf60ryeGg4OOPyX1vAZLNhiIygrgJb/h8v1RJksh5910Kln8EQPTw4YQ90sunZbocp9WKec8ejFu3YvxjK5ajR89/gVKJpkEDtE2bun6aN0NTr95V32RwN7Y8FYNOsxnriRNYjh7FfOAApr93npd88F/q+vVdnd6OndA2a+pXNwedZjOn+/fHcvAQ6gb1SfrgA79KmudN/notOte/W2E5DQZU8XEkzJrl0TXhFeGv9VW6bx8ZY8dhO30aZDLCn3qKyEEDfTLKel659uwh9fkhrs7v9W1InDXLr2fHSZKELSXl7HX7D0x/70SyWM57jTI2Fm2zZq7rdrNmaJs28UgyrcvFltNo5EDn2zgTH0fn5cvRlpO9viYkwRId4GrkSjvA52aAbteu3XnrgS+mtLSUgHP2e9uxYwdtrnDK17kn2v79+8877sVINhtSQQHk5eFMS0c6nYIzJQUp5TSSoejCN6g1yJs0IbBNG8Jvuglti+Y+HS1wR9GqVWSOd/1/xk2ZjL5LFx+XSDjXudtt6Fq2JH7G9Bo7NfVc1tQ0MseNo3T3bgACb72F2LFjfV43kiSRM3MWBZ98AlSNzm95bFnZmP7chvGPrZh27MBRUHDBa2RqNZqGDdE0aYw6IQFlXByquHhU8XEowsN9uu7aUViIPSsLW3o6lqPHsBw/huXYMWynz5w3uvsvTYMG6Fq3JqBNa3StWvn9qJ0tK5vTffpgz80l8NZbSJgxw28yewtgTUkh9cWXsJ05gzwwkPhp03x+Y84fSFYruUuWkP/hUnA6UUZHEzthPIFt2/q6aJh27SL1hReQTKUEXH89CbNm+nXntzxOs5nSXbsw/vEHxm1/um7ulXO9UyUmom3WDHVSbVRxcShj41AlxKOKjvbYzCmxDZKLmAItYD4na527dyI1/zftpLS09CKvrJghbdsRIJejkcvQyGRo5XI0MhnBcgUxSiXRSiVRSiXhl2hQSMAZm41jFgsHLGZ2lpZyyGzG/uc2xtVJYnwVSV4U0rMnlmPHKfj4YzInvIG6Vi20TZv6ulheZzKZWL9+PV26dHHrZoivhD5wP6r4eNJfe43S3bs5nfw0ibNnXdWe0BXlT3UlSRJF33xL9jvvIJWWIg8IIPrVoejvucfnU8QlSSLnnXco+HQFADGvjyD0oYd8WqYrpYqJJuSeewi55x4kScKelYX5wAHMBw66/jx4EKfBgHn/fsz791/wfplGgyo2FmV8HMqoKOQaLTK1GplGjUytRq7RINNoMcvlbMnKpGP9+gQEBoK8/GuuyWJh85HD3Bofj8buQLJacJotSBYLTpMJe24u9qws7NnZ2HNyzksy+P8UoaGujnujRuhatSSgVSsUoaGeqrpKoYqJJv7ttzkzYADGX38jd/57RA153tfF8jp/uhZdijopiaSlH5L26jBKz3asYkaMqPTEZf5UX5Zjx8gYOw7L2eVw+u53ET1smF/sa23aebbzW1pKQNu2JMx8p0qu35Zrta7pz2d3GXCUGLEcPuS6bh90XbttZ85gS03Flpp64QFkMpRRUWc7xbEogoORabXIdTrkOi0y7b9/arHoAvg9N4cuXbteNLYsajW7mjUjwWQiqArWpyeIDrCHzJ07l7lz53r0mG+++SYPVUIj7dw1v1ar1a33WP5vKofOQ3fjptWvR4C7d8uVKmRhYchiY5AnJSGrnYS8ThKyWrWI1mgobzw6tIo1pqJeGIL1xAmMf/xB2itDSVq+DKWfrrX2FKvVyp49e+jUqZPPGwaXE9i+HbU//IC0F1/ElppKytN9SZg+jYDrr6+Uz/eXurLn5pI58U2MW7YAoGvTmrjx41HFx/usTP+SJInsGTMo/GwlADEjRxL64AM+LpVnyGQyVLGxqGJjCT67NZ0kSdjS0jDvP4Dl+DHsGRnY0jOwZWRgz85GsliwpqRgTUm55LFLdDoO3HkH9T5bSdAlbnCWve6TTy/5unMpwsNRxkSjqd8ATYMGrk5vg/ooIiJ8frPEE3QtmhM7biwZo0aTv3Qpmvr1qn3CPH+5FrlDERpK4vx5ZE2ciGHNj2RNmoT1zGmihgyptCn1/lBfksNBwacryJ03z7VUJSSEmJGvE3zHHT4pz/8z/f03qS++VJbwKuGdt6tk57c8iqBAAtq0OS9ZnsNgwHzwEJbDh7ClpWFLT3dduzMzkcxm103E7Gy4zDLFEp2OPXfeQafOnS8aWzaFglO1a2G9xA3J6k50gD0kNzf3vGRSnlBYWOjR411M0Dnp7N0dyf3/1wV5KCV+YJs2BAUGIdNqkWnUyLU6ZBo1iqAglNHRrp+oKJQxMShCQ6tFY+lSZGf3Jj2d/DTWkydJG/oqtRYtrDa/BKoDTb161F66lLShr2Let4/U554nZswYQnrc7euiVYrijRvJmjwFR1ERMrWayOeeI+yxR/1ibaYkSWRPm07h558DEDN6NKH33+fbQnmZTCZDnZiIOjEROH/ZhGS1YsvOwZaehj0zE3tuHpLV6hq1tViQrDYki8X1mM11M1TbogVao7Hc6XoANqWrGaFr2ZIgtfq/a7dGi0ynRRkZ6Rq5iI5GGRODMiLCp0nQKou+Wzcsx4+T/8GHZE58E1WtWuiuucbXxRLOkqvVxL7xBqpatclbuJCC5R9hO5NK3JsTa8TvV1t6OhnjxpdtbxR4yy3Ejh6FMjLSxyVzMe3Y4er8ms0EtG/vSnhVzf9fFHo9ge3aEtju/GnnkiThKCg4eyMzHXtmFk6TCWepCclswVlaimQuxVlqdk2tLmdZjHAh0QEWzktmlZWV5dZ7MjMzz/t3uIey2dWaM6darjW4GoqgIBJmvkPKU09h3r+fzDffJG7ixGrf+a9KlBER1FrwHpnjJ1C8YQOZ48ZhO3OaiIED/aIj6A2OwkKypk+neO06ADRn90P1VVKZ/yc5na7O7xdfgExG7JjRhNx7r6+L5VMytRp1YgLqxMtnJw4oLITZs0l4a8olZ84Unn1d/JsTq9wMG2+LfPZZrMdPULJ5s2tv9+XLxN7ufkQmkxH5zADUtWuROX4CJZs2cab/ABJmvlNtZ1pJkkTR19+QPWsmkqkUWYCO6KFDCbn3Xr9pUxj/+ou0l19BMpsJ7HAj8TP8I9uzr8hkMpTh4SjDw9G1aH7J1+YuWkzB8uWVVLKqrXq2zHxg/PjxSJLk0Z/+/ftXStnPzd6cnZ193prgizl9+nTZ38PDw/12C6TqQl2rFglTp4JSSfGPa8lfuszXRRL+j1yrJW7yJML7Pg1A3pL3yRg1Guf/LReoDoo3buTkw71cnV+FgogB/Un68AP/6fzabGSOG/df53fc2Brf+RUqn0wuJ27iG2gaNMCRl0fa0FdxeihfhuA5+m7dSHxvPoqQEMwHD5KS/PSF2darAWtqGqnPDiZr8mQkUym6Vq2os+IzQu+7z286v8U/byLt7Mhv4E031fjOr+A9Igt0NXKlWaDNZjOBgYE4nU4Atm7dSvv27S/5nsmTJzNq1CgAbrrpJracXft3JWpCtjlPKfzyS7KmvAUyGfEzphPcqZOviySUo+j778mcNBnsdrTXXkPC229Xiz3/7Lm5ZE2dRsnPPwOuLWlix41F1/zSd6Urk7O0lPThwzH+sRUUCuLGj6v26y8F/2ZLTyflyT44CgsJvuN24qZMqbYzQ6oy65kzpL74IraU08gDAoif+haBHTr4ulhXTXI4KPz8c3LmzkMym5FptUQ+N5iwRx7xqwzlhV9/Q9aUKeB0EtSpE3FTJvv9bh3+JnfRYvIWLiT0oQeJef31cl8jskC7iCuwgFarPa/D607HefPmzWV/v+1s8hXB+0IfeojQXr1AksgYPQbzwYO+LpJQjpB77qHW3DnIg4Mx791HSp8+mA8f8XWxrpgkSRSt/oGTD/dydX6VSiIGDCDp44/8qvPrKCzkzKBnMf6xFZlWS8LMd0TnV/A5VXw88dOnI1MqKd74E7nvvefrIgnlUNeqRdKHH6K7vg1Ok4nUF18i/+NPqMrjRJaTJzkzYADZM952rae9/nrqrPyM8Mce85vOryRJ5C1ZQtakSeB0EnLffcRPfUt0fgWvEh1gAYD77ruv7O9Lly695GvPnDnDTz/9VO57Be+LfuVlAtq1QyotJfWll7BlZPi6SB5VWFjIhAkTKi0JnLcE3HADtT/8AFXt2tgzMjndty/FGzd69DMqo65smZmkvfAimePG4TQY0DRpQtLy5UQOGuhXDRRbZian+/XH/M8/yEP01FrwHkE33eTrYlVZ7sZWdTlfvS2gdStiRrtmTeV/8CGFX3/j4xJ5VnWJA0VICLXmzCHk3nvA6SRn5kwyx0/w+FIWb9eXZLeT9+FSUh57nNI9e5EHBBAzciSJ780/myDPP/ybqyH3vQUARPTvR8zoUciUIkXRlSrR6VhgtV4ytkp0Oj67pyeFRUWVVzA/IzrAAgBPPfVU2RSHw4cPs2TJkou+9rXXXsPhcABw44030rp160opo+AiU6mInzbNta4sN4/UF1/CUVzs62IJ5dDUrUvSsqUEtG+PZDaT/toIchcsRDq73MCfSQ4HBV98wamHe2H84w9Xhufnnydp2VK0jRv5unjnsRw/zum+/bCeOoUyJobaS94XGXcFvxPSsycRA1y5PbLeegvj1q0+LpFQHplaTcyYMUQPexUUCgyrV3PmmYHYc3J8XTS3mA8dIiU5mdy5c5GsVgI7dKDOF58T+uADfjX13mm1kjFylCtLv0xG9LBhRD77rN+sRxaqN/85EwSfio6O5pVXXin79wsvvMDnZ7cO+ZfNZmPEiBGsWLGi7LEpU6ZUWhmF/yiCAkmYPQtlVBTW48dJHzYcyc09nIXKpdDrSZw9i7DHHwcgb/Fi0l5+BYfB4OOSXZz5wAFOP5VM9ltTcZpMaK+9hqRPPyHi6WS/uzNfumcPp/sPwJ6VhbpuXWp/8D6aenV9XSxBKFfEwIGuafkOB2nDh1fppRHVmUwmI6x3bxLffRe5Xo/5n3849cQTmHbu8nXRLspRXEzW1GmkPNkHy8FDyPV6YidMIOHd2X6XfdxpNJL24ksUb9iATKkk7s03Cev9iK+LJdQg/tWSEdzWvXt30tPTz3vs3K2JduzYQcuWLS9435o1a4iPjy/3mGPGjOH333/n559/prS0lEceeYQ333yT1q1bYzab+fXXX8k4Z7rthAkT6Nixo2e+kFBhqthYEmbP4nT//pi2bydz0mRix48Td0/9kEypJPqVl9E0bEDW5CkYt2wh5fEniJ8+DW2TJr4uXhmHwUDuvPkUfvUVSBLywEAiBw8m9OGH/Ga92LlKfttC+ogRSGYz2muuIXHWTBRiKx7Bj8nObsllz87GtGMHaS+9RO2lH6KKifF10YRyBLZvR9LyZaS9MhTriROcGTSIqBdeIOzxx/zmd60kSRT/+CPZs2bjyMsDILhrV6Jffskvt3Oy5+eT+sILWA4eQqbTkTBjBoHt2/m6WEINIzrAVdSBAwdISUm56PNGo5E9e/Zc8Lj1EqOEKpWKr7/+mmeeeaZs9Hffvn3s27fvgteNHz+ekSNHXmHpBU/RNm5M/FtvkfbyKxhWr0YVH0/kwGd8Xayrolarue6661D70fpSTwnp2RNNw4akDxuOLT2d00/3Jfq14YRe4Tp6T9WVJEkYVv9AzuzZOAoKAAi+qxvRL72EMjLyqo7tDZIkUbBiBTmzZoPD4douY+pbyHU6Xxet2nA3tqrz+eotMrWa+OnTXNP2T54k7cWXqLVkCYqgqptptTrHgbpWLZKWLSVz0iSK164jZ+ZMSvfuIXbsuCv+P/NUfVmOHydr6lRK/97pOm5SEtGvvUZgu7ZXdVxvMR8+Qvqrr2JLT0cRGkrinHfRNmvm62JVKyqHg0Zy+SVjS+VwUOf0GdQqVSWWzL+IbZCqqDp16lyyA3wxJ0+epE6dOpd93caNG1m2bBlbt24lIyMDlUpFrVq16Nq1K/369aNp06ZXUOry1YR0695W+NXXZE2eDEDs+PGE9Ozh4xIJl+IwGMgYNw7jr78Brrv1Ma+PQBEcXOllsRw9StZbUyndvRsAdb16xLw2nIDrr6/0srjDaTaTNXkKhh9+AEDfswexo0Yhq8G/yIWqyZaeTkry0zjy8gho357EWTNFHPsxSZIo/OILct5+B8luR5WYSNybE32Sb8BpMpG7aBEFKz4Dux2ZVktEv36EP/E4Mj+9CWFYv57MCW8gmc2oEhJInPMu6qQkXxerWhHbILlPdIAFn6sJJ1plyJkzl/ylS0GpJPHdd/32DvDlWK1WDhw4QLNmzarlaMK/JKeT/KXLyF2wABwOlLGxxE0YX6GO59XUlT03l7z3P3BNd3Y4XHtDPjOAsEcf9dsGlC0zk7RXX8Vy8BAoFES//BKhvXv7zVTE6sTd2Kop56u3mA8c4PSAZ5DMZkLuvZeYMaOrZDzXpDgo3fcP6SNGYM/MBIWCiH79iOjXt0L5Ea60viS7HcOPP5L73gLsWVkABHXqRPSrQ1HFxVX4u1QGyeEgd9588pctAyCgfXviJ09CERLi45JVP7mLFpO5ZAl59/TkxuHDy40tp9HIgc63cSY+js7Ll6PV6y94TU1ol4skWIJQTUQ+N5jgrl3Bbif91Ver7B7BJpOJ7777DpPJ5OuieJVMLiei79PUfn8JqsRE7JmZnBn0LDnvvuv2lhtXUleOoiJy3p3DiXvvc2XfdDgIuv026n71JeFPPeW3nV/Tzp2kPPEkloOHUISEkDh3jquzXgU7C1WBu7FVU85Xb9E2a0b8lMkgl1P03XfkLVjg6yJdkZoUB7prWlDnsxUE39UNHA7yFi3idL/+WE+fdvsYFa0vyenEsG49p3o9Qub4CdizslDFx5MwaxYJb8/w286vw2Ag9cUXyzq/4U/1IfHd2aLz60UWtZpfHI5LxpZFreavVq0wlZZWYsn8i+gAC0I1IZPLiR03Ft31bXCaTKQ+PwTrqVO+LpZwGbprrqHOik8Jue8+kCTyly3nVK9HKPn9d49+jqPESO7CRZzoeQ/5y5a5Eke1aEHi/PkkTJvmd1lC/yVJEgUrP+fMs4NxFBSgadyYpI8/IrBt1ZzhIAj/L+jWW4kZ8RoAeUveJ//TT31cIuFyFMHBxL/5JnGTJiEPCnJlie71CLkLFuA0mz32OZIkUbJ5MymPPU7GyJFYU1JQhIQQ9eKL1Pnic4Juudljn+VpluPHSXmyD6at25BptcRNmkTUCy/4ZUJFoeYRHWBBqEbkGg0Jb7+DpmkTHIWFnHnueWznZAcX/JM8IIDYMaOJnzEdZWQkttRU0l54kbShrmQhV8NpNpO/fDkn77mHvEWLcBqNaBo1ImHWTGov/dCvp8o7rVayJk4ke9o0sNsJ7tqV2h+8j+oimewFoaoKffBBIgc/C0DO2+9QtPoHH5dIcIe+W1fqrPzMtde7zUbe4iWcevhhin/5hatZYShJEsZtf3I6+WnSXhmK5ehR5IGBRAwaSN3vvye8z5Plrt30F8U//0zKU8nYUlNRxsVS+4P30Xfr6utiCUIZkQVaEKoZRVAgiXPmcKZff6wpKaQ+9zy1lixGGRbm66IJlxHcuTOBbduSu3gxBSs+o+SXXzBu3Yq+x92E9e6Npl49t47jtFgo3bMH0/btFH3/PY5c19YY6qQkIgYNJPiOO5DJ/fv+pzUlhYwxYzHv3w9yOVFDhhD25BNiyrNQbYX37YujsIiCTz8l8403UAQHESS2GvR7qthYEufOoWTTJrLffgdbegbpQ19F16oVYY89SlDHjm6NekqShPXkSUzbd1C8cSOlO12ZnWVaLWG9exPe50m/nzrstFrJW7CA/GXLAQi44QbipkwW7Q/B74gkWILPnbvY/uabb0ZxFdNjkpOTSU5O9lDJqjZbZian+/bDnpWFtmlTai1cgLwKJDKw2+2kpqaSmJiIsgJJRaoby4kTZE+dhmnHjrLHAtq1I7TXw+iuuQZFeDgOh4PU1FQSYmOxHz2K6a/tmLZvp3TPHqRztjxTxccR8cxA9Hd1q1CiFl+QnE4KV64kZ+48JLMZuV5P/JQpYp/ISubueSjOV8+SnE4yJ7yBYfVqZGo1iXPe9duM7OcSceDiLC0l74MPKFj+EZLdDoAyLpawXo8Q1PFWVAkJyJTKsvqKUSiw7tyJcft2SrfvwJ6bW3YsmUpF6EMPEp6c7Jfb0f0/84EDZIwbj/XECQDCHnuMqBdf8PvfOdVJ7qLFZC9ejPm+e7n2tdfKPRedRiOHOnUmNyyM9l9/hfps+/tcNSEJlugACz537ol2tcaNG8f48eM9cqzqwHLiJGf698dRVETADTeQMHsWco3G18US3CRJEqU7d7pGgzdvBqez7Dl5iB5N3XrIAwIo3bMHp9F43nuVUVEE3HADgR1uJPj22/02udW5rKlpZE6YUDbyEdCuHbFjx/jt+mRB8AbJbid9+HBKNv+KPCCAWosXoW3SxNfFEirAlpVN4ZdfUPTV1ziKisoel6lUqJJqo05IxHL8OLbU1PPeJ9No0LVsScANN6C/q1uVuPZJVit5779P3odLweFAER5OzMjXCe7c2ddFq3Equg2SvkePcmcnmKxW6k96ExAdYEHwGjEC7F3mAwc4PXAgkqmUoM6diX9ril/fkS0pKeHLL7/koYce8tiNkerAlpZGwedfUPLLL9jS0kCSKNVo+KNNazr8vZNAtZqA668n4IYbCGh7A+o6darMdGHJ6aTwyy/JeXcOUmkpMp2O6JdfIuSBB6rMd6hu3D0PxfnqHU6LhdQhQyj9eyeKsDBqv7/Er/dMFXFQPqfZjGHtWoq++w7LkaNIZxNklV27d+8mrH4DAtreQOANN6C99toqdZPafPgwmePGYzl6FHDtaR897FUx5dlHchctJnXpUv7q1pXHXnml3HPRabWyr2s3fm/RnA5/70RXzq4TJoeD1nt2A9W3A+y/rWChRlq7dm21PNF8SdusGQlvv03aCy9SsmkTmRPeIHb8OL/NxGi320lJScF+dvqY4KJKSCD65ZeIfvklnGYz1pQUcg4dImf3buJmzyK6dWu//T+9FFtGBplvTMT0118A6Nq0Jm7cOFQJCT4uWc3m7nkozlfvkGs0JLwzkzMDB2I5dIgzg5+j1sKFqBP987wQcVA+uVZL6H33EXrffUhOJ/bMTCwnTpJ34jg5J09S67OVRCRUvaR+ks1G3tKl5C15H+x2FKGhRI94Df2dd/q6aDWeQy4nQ5Iuei7K1Wqi35hAzs8/E5KcjL6cARGjxQJnk/JVV6IDLAg1QGDbtsRNmUz6ayMwrFmD5HQQN2GCX48ECxcn12rRNm5McEwM7N6NpmHDKtf5lex2ir79lpzZ7+I0mZBptUQNeZ7QXr38PkGXIFQGRVAgiXP/S2h45plnqLVwAepatXxdNOEKyORyVPHxqOLjsbdoDrNnowgM8HWxKsx86BCZE9/EcugQAEGdOxPz+giUERE+LpngLt0118DPPxPW62FCQ0MveF5rNFb7DrBoZQhCDRHcuTPxUyaDUknx2nVkjB1XlqRDECqL5HRiWLuOk716kTXlLZwmE7rrrqXOik8J691bdH4F4RzKsDBXp7dOHexZWZwZ8AzWlBRfF0uogSwnTpA2fDgpjz+B5dAh5Ho9cZPeJH76NNH5FaocMfwjCDVI8O23E/+WnIwRr1O8bh04HMS9ORGZSuXropWRy+WEhIQgFx2hy6pKdSVJEsZffyX3vQVl68UUoaFE9O/nGvWtYiPY1Z27sVWVYrCqUkZFUWvRQs4MehbriROceWYgiQveQ1O3rq+LVkbEQcVUpfqypqaRt2gRhh9/dCVilMkI7taVqBdeQBUd7eviCf9HLkkEwSVjqyrFn7eIJFiCz9WEdOv+puTXX0kf/hqSzUbQbbcRP+nNKpElWKiajH/+Re78+Zj/+QcAeWAgYU8+Qfhjj1WJrbkEwR/Y8/NJfXYwlmPHUEREUGvBe27vDS4IFWXLyibv/SUUffc9nJ0tFtS5M5GDBqJp0MDHpRPK404WaHfUhHZ5ze36C0INFnTrrcTPmIFMpaLk559JH/H6efvG+pLT6aSwsBDnOVv+COXz57qSnE6Mf/3FmYGDSB08GPM//yDTaglPTqbequ+JHDBAdH79mLux5c8xWN0ow8NdI7+NGuHIy+PMwEFYjh3zdbEAEQcV5c/1ZcvMJPudmZy87z6Kvvoa7HYCbmxP0kfLSZgxXXR+/ZxTJsMgSZeMLX+Ov8oiOsCCUEMF3XwTCe+8jUytpmTzZtKGv4bTDzrBBoOB2bNnYzAYfF0UvyU5ndhzcsjevoPZs2eTd/Qokh/8IpMkidL9+8l++x1O3N2D1GcHY9qxA5lKRWjvR6j33bdEDXkeRUiIr4sqXIa756E4XyuXMiyMWu/NR9OkCY78fM4MHIT5yBFfF0vEgZscJUYsx4+TuXkzs2fPpjAnx9dFAsCem0vBZys53bcvJ+7uQcEnnyBZrehataLW4kXUmjsXbbNmvi6m4AaTVsunNtslz8W84yeYPXs2GT/9TMmW3y/8+WNrJZbYN8QaYEGowQI7dCBh5kzSXnkF42+/kTpkCAnTp6PQ631dNOEc9oICTNu2UfL775j37MWenY1kt1Oi08Gdd3C6X38KnE7UtWujTkpCXScJdVISqqQkNHXqeH2k1XLsGIZ16yhet961R/FZ8qAg9N26Ev7006hiY71aBkGoKRShodR6bz6pg5/DfPAgZ54ZSMKM6QRcf72viyacQ7LZKN2zB+Mff2D88y9sZ87gNBoByq7dJx96mIIQPeqkOudcu+ugrlMHZXSUV/dBdxQVUfzzJorXr8e0Y4drfS+ATIauTWsikpMJaN9e7MVezUg2G2kvvADt25E5YQIlpaUXvMbkcPigZJVLdIAFoYYLbN+OhFkzSR/6KqU7/ub0031JfHe22IfVx+z5+RjW/Ejxhg2Y9++H/0/XIJejjHRl3pSpVEgGA5ajR8sSTJ1LFR+PplEjNA0bomnUEE3DhqgSEq4o47KjsBDrqVNYTp3CevIUxq1bsR4/Xva8TKslqGNHgrvcSWCHDsjF2nJB8DiFXk/ie/NJe/FFSvfsJfW554kZO4aQu+/2ddFqNMlmw/jHHxjWrMH4x1acJtMFr5EHB6OOizv7Bgl7Rib2jExM27Zd8DpNw4b//TRqiKZ+feRabYXL5bRYsJ0547p2nzyJ+Z/9mLZtO28nCG2LFui7dSXo9ttFcqtqTLJacRQXA6CpXx9tOZ1dp90Oe3ZXcskql+gAC4JAYNu21Hp/CWkvvYz11ClSkp8mYeZMdC2a+7poNcq/jaei71dRsmVLWeIRAE3DhgTedBMBbduiTqqNMiKCIqMRZs+m7hefE2g2Yz15CmtKytmfU1hPpeDIy8OWno4tPZ2SX34pO55Mp0PToAHq2rWR67TI1Gpkag0ytRq5VoNMo0GmUuM0mbCeOlX24ygsvKDcMpWKwA4dCO7ShaCOtyLX6SqhtgShZlMEB5M4fz6Z48ZTvHEjmWPHYUtLJ2JAfzFqV8ksJ05Q9P33GNb8iCMvr+xxRVgYgTfeSOBNN6Fp1AhVbAzygAAKCwth9mySPvmYgPx81zX71H/Xb1tqGs7iYkp37qR0587/PkguR12rFpqGDZAH613XbY0auUbjun5r1GUJLW2paWXXbVt6+n8jvOfQNGxIcNeu6LvcKW5610Dx06YSHhNzweNGoxFWfuaDElUe0QEWBAEAbaNG1P7wQ9JefhnL4cOcGTiQuIkTCb6tc+WWQ6ulY8eOaK/gLndVZTl+nKLvV2H48fzGk7ZZM/Q9exDUsROqmAvvyP9bV7qgINSRkagTE+GWm897jaOw0DUyfOwY5iNHsBw5ivXECaTSUsz79mHet6/C5VXGxqKuUwd1nSS0TZoS1KkjiuDgin9xwW+5ex7WxPPVn8i1WuKmTEaVkED+smXkLVyILS2N2FEjKzWzf02MA0dJCcXr1lH03feuWTpnKcLD0XfvTvCdd6Jt1rTcmTb/1ldQTAzapCQCWrU673mn1Yr15CnXtfvIESzHjmE5cgRHQUFZJ7mi5IGBqOvWRV2nDpp6dQm85RaRRbwaUttstJHLL3kuqm02mh8+jFajqcSS+RexDZLgczUh3XpV4jQaSX99JMbffweZjKiXXyLsscfEiIKHOYqLXY2n71eV23gKuacnmvr1vfLZkt2O9fRpLEeOYsvIQLJaz/5YkKxWnGbXn5LFgkytPtvZrVO2tliM8AqC/yn86iuypk4Dh4OA668nfsZ0cWPKwySnE9P2HRR9/x0lm35BslhcTyiVBN10E/p7ehJ0003IVCrPf7Yk4cjNdXWGz97EdFqtSJaz12+LGafFgmS1gcOOKj7+nGt3HRQREeL3eDXnzjZITqORo7d2BKDh71vKnVJfE9rlYgRYEITzyAMDSXjnbbKnT6fwy6/IeWcmttQ0ooe+gkzp/UuG2Wxm27ZttG/fvtqNJni68XSldSVTKtHUqyfu/gsX5W5sVefztaoJffBBlLGxpI8YgWnHDlc+h9mzKmVqa3WPA1taGkWrVlO0ehX2jMyyx9X16xPSsyf67nehjIhw+3hXUl8ymQxlVBTKqCgCb7yxwt9BqBmsSiXb7XbuMJsvGltWpZLD9etRy2wmoBqer+4QHWBBEC4gUyqJHjECVUIiObNnU/j551gOHyLuzTdRxcd79bPNZjObN2+mZcuW1aYhdcnG0z33uBpP4eEVPm51rCvBP7gbWyIG/UvQTTdRe8n7pL30EtaTJzn1+BPEjBqJ/s47vfq51TEOnGYzxT/9hOH7Va4syWe5stt3Q39PT7TNml3RqGp1rC/BP1hVKv52Orn5Uh1glYr9jRtzm8VCQCWXz1+IDrAgCOWSyWSE93kSVWICmeMnULpnL6d6P0rMyNfRd+vm6+L5PYfBQMmmTRjW/OjxxpMgCMLFaBs3ovbSD0kfNhzz/v1kjHgd4x9/EDNsGPKAmtrcdY/kcFC6cyeGtesoXr/+vyzOMhkBbdsScs89BHXqeEWZmAVB8B+iAywIwiUF33Yb2saNyRgzhtI9e8kYNRrj778T/doIFEHVb13I1XCWllKy+VeK16/D+Psf/20xIRpPgiBUIlVMDLXfX0Lu4sXkf/Ahhu9XUbprN3GT3kTXXGT3P5ckSZj/+Yfideso3rARe25u2XOqhAT0PXsQ0rOn2MtcEKoR0QEW/Eq3bt1QKBRX/P7k5GSSk5M9VyABcDUCai1aRN77H5C3ZAmGNT9SunuPqzF17bW+Lp5POa1WTFu3Yli3npLNm5HM5rLnNA0aENylC/q7u4vGkyAIlUqmUhE1eDCB7dqRMWYstjNnON23H5GDBhHe50lkV/G7tqqTJAnL0aMUr99A8bp1rm2CzpLr9QTffjv6bl3RtW59RfulC4Lg30QWaMHnzs02d7XGjRvH+PHjPXIsoXylu3eTPnq0ay2rQkFE//6EJz+F3ENbbjidTgwGA3q9HrmfNjwcRUUYt26l5NffMP7+O86SkrLnVImJ6Lt1JbhLF69lcf5XVagroWpyN7ZEDFYNDoOBrEmTKd64EQDd9W2IGzfOYzkdqkIcSHY7pXv3UvLbbxh//Q3rqVNlz8l0OoI6dUTftSuB7dp5fQupqlBfQtWTu2gxOYsWoXzgfuqPGFFubDmNRg537IRJq+W69etQlrMsQmSBFoRKdvPNN1/VCHCdOnU8VxihXLqWLanz2WdkTZ5C8bp15C1ciGHV90QOHkxw165XfbdcLpcTGhrqmcJ6iCRJWE+exPjbb5T8toXSPXvA6Sx7XhkdTfCddxLctUulruv1x7oSqgd3Y0vEYNWg0OuJe2sKgas6kDVtOqU7/ubkAw8S+vDDhPd9GmVY2FUd31/j4LyblX/8gbO4uOw5mVpN4E03EdzlToJuvbVSl6b4a30JVZ9cktDLZJe8sSKXJIJKS2v0zRcxAiz4XE2401QdSZJE8dq15MyaXbZmStOoEVFDnifgxhuvuBNoMBj44IMP6Nu3L3q93pNFrhB7QQGlu3Zh2r4D45Yt502RA9f05sBbbiHolpvRXnONT6bJ+UtdCdWPu7ElYrDqsZ4+TdbkKZi2bwdcW9+FP9WHsMceu+I9vv0lDpxWK5YDBzD9vRPj1q0X3KxUhIQQeNNNBN5yM4EdOqDw0OyzivKX+hKql9xFizmzbBk/d7mTAS+/XG5sOY1G9nTpysabb2LA0KGERkdf8Jqa0C4XI8CCIFwRmUyG/q67COrUiYJPV5C/bBmWI0dIHfICAddfT+QLQ64o2YrT6aSoqAjnOY2WymDLyqZ01y5Kd+3E9PdOrCdPnve8TK0m4PrrCbzlZoJuuQVVXFyllq88vqorofpzN7ZEDFY96tq1SXxvPqZt28iZMxfL4cPkzn+Pgs8/J/KZZwi55x639yL/l6/iwGk2U7p3L6U7d2HatRPzvn/+21/9rPNuVrZo4Rdrn8V5I3iLUyajBC4ZW06ZDFNAAM4aPAYqOsCCIFwVuU5HRL++hD74AHkffEjh559j2rGD032eIqhjR/Q97iawQwe/ynzsNJuxHDmC+eAhzAcOULprF7a0tAtep25Qn4CWrQjscCMBbdte8eiIIAiCP5HJZATeeCMB7dpRvH4DufPmYUtPJ2vyFPI//oTQBx8k+I7b/Sp5n+R0YjtzxnXdPniQ0r17MB84CP9m2z9LERaGrnUrAtpcT9AtN3t973pBEKoe0QEWBMEjFKGhRL/yMmGP9iZ3wUIMP/xAyebNlGze7EowcuutBN9xB4Edbqy0zrBktWLLyMBy4gTW48exnDiJ5dgxV/ITh+P8F8vlaBs3Rte6tavx1LIlCrFGSxCEakwml7uS9t3WmcKvvyFvyRJsp0+TM3MmOTNnor32GoLvuIPg2yuvMyxJEo7cXKwpKViOn8B64jiWY8exHD2K02i84PXKmBjXNbuV69qtrlNH7K8uCMIliQ6wIAgepYqLI27CeML7PEnRqlUUb9yIPSPTtcfiunXIAnQE3XIrgTe2RxUbizI2FmV0NHKNBgClUklSUhJK5aUvT5LNhsNgwFFQgD03F3t2NrasLOzZOdjS0rCdOY0tM+u89V/nUkREoG3SBG2zpuiuvRbttddVuX2N3a0rQagod2NLxGD1IFOrCev9CCE9e1C0+geKN26kdNcuzHv3Yd67j5x3ZqK77lqCOnVGXbcOyugYVLExyPV6ZDKZ+9dtScJZUoKjsBBHXh62rGzs2a4fW0YGttRUrKmpSKWl5ZdTo0HTqJHr2t28OQGtW6GMj69yHV5x3gjeonA6iTt7Tl7qNVG5uSj9YDmAr4gkWILP1YTF9jWZJEmY9x+geMMGV2c4M7Pc1ynCwlDGxKCKiUGm04LDieR0gN2O02xBMpfiNJXiNBpxFBefl83zUmRaLeo6ddDUr4+mfj3U9eqjadwIVTmJHwRBEAQXe04OxT9vonjDBkp374ZymosyrRZVTAzK2FgUYaEgAQ47kt2BZLPhLC3FaTLhLDXhLDHiKCq6YMpyueRyVHFxaBrUd12zG9RHU78+6rp1kYlOoyCUK3fRYvIWLkRVuza6a64p9zWS3U7xunUANPx9S7kz8mpCu1x0gAWfqwknmuAiOZ2Y9++neMNGLMeOYc/MxJaVhWQ2l73GIZeTGxZGZEEBisslCJHJUOj1KKIiUUVHo4yKRhkdjSohHlViIupatVBERFS50QF32e12UlNTSUxMFCMJgke5G1siBmsGW1Y2JZt+xvTXdtdMm8xMHIWFZc9X6LoNyAJ0KENdNz2V0dEoY6JRxcSiqpWIOjERVVyc1/fi9SVx3gjeULDyczJmzLjsueiQy8mLi6X9ypWoysltUhPa5eKsEwSh0sjkcnTXXHPenUlJknAWFbkaVVlZ5J0+zRfHj5Ncrx56rRaZXIFcp0Wm0yHXBSAP0Lk6vaGhyIOD/SKjp6+UlJSwbNkyXnzxRbGnpOBR7saWiMGaQRUTTVjv3oT17l32mNNsdk1dzswk7/QZvtj/D8l166LXaJEpFchUKtd1OyDAde0ODEQRGooiNKRsyUtNJc4bwRtC7ulJMfDFoYM8lVgL/UWyuRtsNr5IPcM1FguhNTS5p+gAC4LgUzKZ7GyjKBQaN8ZeWAizZxPSs6doGAiCIPgpuVaLunZt1LVrY2vUCPb/Q8g994jrtiD4iFynQ9+1Cxw6SOiDD1z0XJSfbWfVZHJfF0AQBEEQBEEQBEEQKoPoAAuCIAiCIAiCIAg1gugAC4LgVwICArj33nsJCAjwdVH8nqgrwVvcjS0RgwKIOKgoUV+Ct7gTWyL+RBZowQ/UhGxzgiAIgiAIguDvakK7XIwAC4LgV0wmE99++y0mk8nXRfF7oq4Eb3E3tkQMCiDioKJEfQne4k5sifgTHWBBEPyM1Wplz549WK1WXxfF74m6ErzF3dgSMSiAiIOKEvUleIs7sSXiT3SABUEQBEEQBEEQhBpC7AMs+JVu3bqhUCiu+P3JyckkJyd7rkCCIAiCIAiCIFQbogMs+JUtW7Zc1fs7derkmYIIgiAIgiAIglDtiCzQgs+dm23u5ptvFiPAgiAIgiAIguADNSELtBgBFvzK2rVrq+WJJgiCIAiCIAiC74kkWIIg+JXCwkImTJhAYWGhr4vi90RdCd7ibmyJGBRAxEFFifoSvMWd2BLxJzrAgiAIgiAIgiAIQg0hOsCCIAiCIAiCIAhCjSA6wIIgCIIgCIIgCEKNIDrAgiD4FbVazXXXXYdarfZ1UfyeqCvBW9yNLRGDAog4qChRX4K3uBNbIv7ENkiCH6gJ6dYFQRAEQRAEwd/VhHa5GAEWBMGvWK1Wdu/ejdVq9XVR/J6oK8Fb3I0tEYMCiDioKFFfgre4E1si/kQHWBAEP2Mymfjuu+8wmUy+LorfE3UleIu7sSViUAARBxUl6kvwFndiS8Sf6AALgiAIgiAIgiAINYToAAuCIAiCIAiCIAg1gugAC4IgCIIgCIIgCDWCyAIt+FxNyDYnuM9ut5OamkpiYiJKpdLXxfFroq4Eb3E3tkQMCiDioKJEfQne4k5sXe41NaFdLjrAgs/VhBNNEARBEARBEPxdTWiXiynQgiD4lZKSEpYuXUpJSYmvi+L3RF0J3uJubIkYFEDEQUWJ+hK8xZ3YEvEnOsCCIPgZu91OSkoKdrvd10Xxe6KuBG9xN7ZEDAog4qCiRH0J3uJObIn4A7HwQPAr3bp1Q6FQXPH7k5OTSU5O9lyBBEEQBEEQBEGoNkQHWPArW7Zsuar3d+rUyTMFEQRBEARBEASh2hEdYMGv3HzzzVc1AlynTh3PFUbwCblcTkhICHK5WKFxOaKuBG9xN7ZEDAog4qCiRH0J3uJObIn4E1mgBT9QE7LNCYIgCIIgCIK/qwnt8prb9RcEwS85nU4KCwtxOp2+LorfE3UleIu7sSViUAARBxUl6kvwFndiS8Sf6AALguBnDAYDs2fPxmAw+Loofk/UleAt7saWiEEBRBxUlKgvwVvciS0Rf6IDLAiCIAiCIAiCINQQogMsCIIgCIIgCIIg1AiiA1xFORwO9u7dy/vvv8+zzz7L9ddfj1qtRiaTIZPJKrwd0KlTp8re6+5PgwYNvPPlBEEQBEEQBEEQvEBsg1QFffvttzz++OOYTCZfF0UQPE6r1dKxY0e0Wq2vi+L3RF0J3uJubIkYFEDEQUWJ+hK8xZ3YEvEnOsBVUmFhoVc7v8HBwfTp0+eyr4uKivJaGYSaS6vVVngGQ00l6krwFndjS8SgACIOKkrUl+At7sSWiD/RAa7SYmJiuOGGG8p+1q1bx+zZs6/6uOHh4cydO9cDJRSEijObzWzbto327dvX6LuT7hB1JXiLu7ElYlAAEQcVJepL8BZ3YkvEn1gDXCV169aNlJQUMjMzWbVqFWPHjuWuu+4iNDTU10UThKtmNpvZvHkzZrPZ10Xxe6KuBG9xN7ZEDAog4qCiRH0J3uJObIn4EyPAVVJsbKyviyAIgiAIgiAIglDliBFgQRAEQRAEQRAEoUYQHWBBEARBEARBEAShRpBJkiT5uhCCZ4wfP54JEyYA0LFjR3755Re333vq1Cnq1q0LQEJCAh9++CE7duwgNzcXrVZLZGQk119/PW3btkWj0Xi03EajkaCgIABKSkoIDAz06PGFqsXpdGIwGNDr9cjl4h7dpYi6ErzF3dgSMSiAiIOKEvUleIs7sXW519SEdrlYAyxcIC0tjS5dupT7XFhYGIMHD2bEiBFlJ4cgeJJcLhcJ3dwk6krwFndjS8SgACIOKkrUl+At7sSWiD8xBVqooIKCAiZNmsT111/PkSNHfF0coRoyGAzMmjULg8Hg66L4PVFXgre4G1siBgUQcVBRor4Eb3EntkT8iRFg4RzBwcE8+OCDdOvWjVatWpGQkIBKpSI7O5tt27axcOFCNm7cCMDhw4fp1q0bf/75J1FRUR4rQ0pKCgEBAR473v8LDQ2t8Xe9/J3T6aSoqAin0+nrovg9UVeCt7gbWyIGBRBxUFGivgRvcSe2RPyJDrBwVlxcHOnp6eVOa05MTOShhx7ioYceYtGiRQwaNAhJkjh58iSvv/46S5Ys8Vg5mjdv7rFjlWfcuHGMHz/eq58hCIIgCIIgCIJ/Eh1gD5k7dy5z58716DHffPNNHnroIY8e82I0Go1bya2eeeYZUlJSmDx5MgBLly5l0qRJxMTEeKQc+/fv9/oIsCAIgiAIgiAINZPoAHtIbm4uhw8f9ugxCwsLPXo8T3n99deZOXMmpaWlOBwONmzYwBNPPOGRYyclJVXLbHOC+5RKJUlJSSiV4vJ0OaKuBG9xN7ZEDAog4qCiRH0J3uJObIn4Ex1g4QoEBQXRrl27sm2WDh486NsCCdVKUFAQycnJvi5GlSDqSvAWd2NLxKAAIg4qStSX4C3uxJaIP5EF2mPGjx+PJEke/enfv7+vv9ZFxcXFlf09NzfXhyURqhu73c6pU6ew2+2+LorfE3UleIu7sSViUAARBxUl6kvwFndiS8Sf6AALV8hoNJb9XUxZFjyppKSEZcuWUVJS4uui+D1RV4K3uBtbIgYFEHFQUaK+BG9xJ7ZE/IkOsHCFdu3aVfb3+Ph4H5ZEEARBEARBEATBPaIDLFTYxo0bOXPmTNm/O3Xq5LvCCIIgCIIgCIIguEl0gAWsVitWq9Wt1+bk5DBo0KCyfzdt2pTWrVt7q2iCIAiCIAiCIAgeIzrAAunp6dSvX59p06aRkpJS7mskSeKHH37ghhtu4Pjx4wDIZDJmzJiBXC7CSPCcgIAA7r33Xq/uB11diLoSvMXd2BIxKICIg4oS9SV4izuxJeIPZJIkSb4uhFBx3bt3Jz09/bzHMjMzycrKAlyJqRo0aHDB+9asWXPBmt1Tp05Rt27dsn/XqVOHa665hsjISFQqFTk5Ofz5558XfN706dN59dVXr/q7GI1GgoKCANfCfJFUSxAEQRAEQRAqX01ol4t9gKuoAwcOXHS0FlzBu2fPngsed2eq86lTpzh16tRFn09ISGD+/Pncc889bpVVECrCZDKxfv16unTpUqPvTrpD1JXgLe7GlohBAUQcVJSoL8Fb3IktEX9iCrQAJCUlsW/fPhYtWkRycjI33HADdevWRa/Xo1QqCQ8Pp3nz5iQnJ7Ny5UpOnjwpOr+C11itVvbs2eP2uvSaTNSV4C3uxpaIQQFEHFSUqC/BW9yJLRF/YgS4yrrUCG1FyWQyWrRoQYsWLRgwYIDHjisIgiAIgiAIguBPxAiwIAiCIAiCIAiCUCOIDrAgCIIgCIIgCIJQI4gs0ILP1YRsc4IgCIIgCILg72pCu1yMAAuCIAiCIAiCIAg1gkiCJfiVbt26oVAorvj9ycnJJCcne65AQqUrLCxk9uzZvPjii4SGhvq6OH5N1JXgLe7GlohBAUQcVJSoL8Fb3IktEX+iAyz4mS1btlzV+zt16uSZggiCIAiCIAiCUO2IDrDgV26++earGgGuU6eO5wojCIIgCIIgCEK1IjrAgl9Zu3ZttVxsLwiCIAiCIAiC74kkWIIg+BW1Ws11112HWq32dVH8nqgrwVvcjS0RgwKIOKgoUV//a+++46Oq8v6Bf6YkmUmZVAihhqIgHekIJghIkQ7SAkgR1Ees++y6z+Puyu6zz+Ouu/tbdXFVEJVeDFWa1ISi9I6A0gKBQEiZTCbTZ+7vj0muM5Ayk2SYydzP+/XKyyl37j1z/M7hfO899xzyFU9ii/HHZZAoAEhhunUiIiIiokAnhX45rwATUUCxWCw4ffo0LBaLv4sS8FhX5CuexhZjkADGgbdYX+QrnsQW448JMBEFGIPBgE2bNsFgMPi7KAGPdUW+4mlsMQYJYBx4i/VFvuJJbDH+mAATERERERGRRDABJiIiIiIiIklgAkxERERERESSwFmgye+kMNscec5msyE7OxuNGzeGUsmlyivDuiJf8TS2GIMEMA68xfoiX/EktqraRgr9cibA5HdS+KEREREREQU6KfTLOQSaiAKKXq/H119/Db1e7++iBDzWFfmKp7HFGCSAceAt1hf5iiexxfhjAkxEAcZmsyErKws2m83fRQl4rCvyFU9jizFIAOPAW6wv8hVPYovxxwSYiIiIiIiIJIIJMBEREREREUkCE2AiCihyuRzR0dGQy9k8VYV1Rb7iaWwxBglgHHiL9UW+4klsMf44CzQFACnMNkdEREREFOik0C/n4mMUUIYMGQKFQlHtz8+YMQMzZsyovQLRI+dwOKDT6aDRaCR9dtITrCvyFU9jizFIAOPAW6wv8hVPYovxxwSYAszBgwdr9PnU1NTaKQj5jU6nw0cffYQ33ngDMTEx/i5OQGNdka94GluMQQIYB95ifZGveBJbjD8mwBRg+vbtW6MrwMnJybVXGCIiIiIiCipMgCmg7NixIyjvNSAiIiIiIv+T5sBvIiIiIiIikhwmwEQUUFQqFVJSUqBSqfxdlIDHuiJf8TS2GIMEMA68xfoiX/Ekthh/XAaJAoAUplsnIiIiIgp0UuiX8wowEQUUk8mEjIwMmEwmfxcl4LGuyFc8jS3GIAGMA2+xvshXPIktxh8TYCIKMCaTCZmZmZJumD3FuiJf8TS2GIMEMA68xfoiX/Ekthh/TICJiIiIiIhIIpgAExERERERkSQwASYiIiIiIiJJ4CzQ5HdSmG2OPOdwOKDT6aDRaCCX8xxdZVhX5CuexhZjkADGgbdYX+QrnsRWVdtIoV/OBJj8Tgo/NCIiIiKiQCeFfjlPOxFRQNHpdPjwww+h0+n8XZSAx7oiX/E0thiDBDAOvMX6Il/xJLYYf0yAiSjAOBwOFBUVweFw+LsoAY91Rb7iaWwxBglgHHiL9UW+4klsMf6YABMREREREZFEMAEmIiIiIiIiSWACTEQBRalUolmzZlAqlf4uSsBjXZGveBpbjEECGAfeYn2Rr3gSW4w/zgJNAcB1trm+fftCoVBUe18zZszAjBkzaqlkRERERETSIYVZoKWb+lNAOnjwYI0+n5qaWjsFIb+x2WzIzs5G48aNJX120hOsK/IVT2OLMUgA48BbrC/yFU9ii/HHBJgCTE2vACcnJ9deYcgv9Ho9lixZgjfeeAMxMTH+Lk5AY12Rr3gaW4xBAhgH3mJ9ka94EluMPybAFGB27NgRlEMtiIiIiIjI/zgJFhEREREREUkCE2AiIiIiIiKSBCbARBRQwsPDMWrUKISHh/u7KAGPdUW+4mlsMQYJYBx4i/VFvuJJbDH+uAwSBQApTLdORERERBTopNAv5yRYRBRQDAYDdu7ciWeffVYyZycFqxV2rRa2gkLYCwthLyyAraAA9oJC2LVaOAwG8U8wGiHY7RBsNhgh4ETDhuielw91SAjkKhVkKlXpf8MgV6khj4xESFIDhDRsiJCGDaFs0ADysDB/f2UKcJ7+DqX4e6WHSTEOBEGAo7gY9oKC0rb7gXZbXwyHwfhL222zQbBZAZsdRgAnmzRG94JCqMPCIFeFQaZSu/xXBWX9+ghpmISQRo0QkpQEuUYDmUzm769NAc6T3+L1//1fHLx3D12zb0Nltz+8D5vN18X0OybARBRQLBYLzpw5g9TU1KDoSAmCAEdREax378J65w6sOTmw3rkDW85d5+N7d+Eo0lVr30a1Gtfat0fbEychMxo9/pyyXj2ENEyCMikJoU2bQdW+HdTt20MRHV2tclDw8fR3GGy/V6qeYIwDh8kE2927zrb79h1Y7+bAmpMD250cWO/ehT0/H0I1EwWjWo2rHTvgieMnAA/bbnlEROmJTGdSHNa6DdQdOyCkSRMmxiSq6rfoMBhQsH0Hrg0aiLYnT0FRTvxZy0mKgw0TYCKiGhAEAQ6dDtY7d2DJzobtzh1Yc0qT3dKkV/CkgyOXQxETA0VcLJSxcVDExUEZFwt5dDQUkZGQh4dDplZDrg6HLEQJyOXQWazAnt1I+p8/IUomg2Ayw2Eyuv3XXlTkTLRz7sB621kW2/37sN2/D5w561aE0GbNoOrYAeoOHaHq0B5hLVtCVoN1uYmIApXDbIat9ISkJTvbmdy6tN32/HyP9iOPiBDba0Vp262IjYEiSgN5uBryiAjnyJywMECugEypQJHJBOzejQZ/+H25bbfDYIDtXm7pSdM7sOflw1FSAvPPP8P8889ux1dER0PVoQPUHTtA1b4D1O3bQR6EQ1aplrjc+drwf/9c7jrAJUYj0Lv3IyzUo8cEmIioCg6TCdY7zo6S9fZt59+dO86/7Gw4Skqq3IciPh4hSUkISUqCMqlB6eOGCGmQCEVCAhQajdfJplWrBfbsRniXLojyYDF7QRCcQ61LO33WO3dgvnIVxnPnYL15E5asLFiysqD7dgsAQBauhrpjJ0SmPI3IlFSEJNb3qnxERP4iOByw3b9f2mb/0l6XtX223Nwq9yELV5e206W3kSQ1gLK07VbWS4AiNrZat5RYtFpg925E9OgBjQdtt8NkKk3Qc2C5fRvWm7dgunAepouXYC8qQsnBgyg5eLC00DKEtWqFiD59ENm/P1Tt2kIm55y39LCwNm2gTkx86HVP+jR1HRNgIpI8R0kJrPdyYcvNhe1e6dDkskT39h3Y8vKq3IciIV68z9bZUSodqpaUBGViIuQq1SP4JpWTyWRQxsZCGRsLVdu2bu/ZCgthOn8BxnNnYTp7DsYL5yEYjDAcPgzD4cPI/esHULVvj8jUVET1T0VocrJfvgMREQAIFgts+fmw3bsHa27uL1dzy9rtnBwIVmul+5CFqxHasBFCGjV0JraubXeDBpBHRwfE8GK5SoWw5s0R1rw5XK/tChYLTD/9DNO5szCeOw/j2TOw5dwVrxQXLFkCZb16iExJQWT//gjv+iRkISF++x5EgYKzQJPfSWG2OXr0BIsFNq0W9kKtc2IprRZ2bSFsBQXOIcBiwnsPDoOhyv3Jw8Odk5E0auTsJDVu7PxvI2fCGwgJbm0S7HaYr16F4YcfUJyRAdPZc27vhzZv7kyGBw6Aqk0bP5WSiIKJ4HA4J5YqLIStsGxSQOefLb+gNNm9B9v9+7DnF7gN5yyXUomQxET3trvscaOGUMTEBESCW5ts9+/DcOIk9JmZKDl40O3fN3lUFCL6PoWo/v0R0bcvJ0SUIEdJCX5+OgUA8Nihg+X2XaTQL2cCTH4nhR8aVcxhNsOh08FeXPzLf00mwO5wTjBit5XOnmmHYLcBNhscRpNzZs2Skl/+6/LYOQOn3qtyyCMjoUxMLJ0g6pfENqRRI4Q2ahQwVwL8xXb/PvT796N43z4Yjx13m/wlrE0bxIwZjaghQ6GI5O+XKNgJdjscej3sxcWwFxXBUVwMR0kJBKsNcNh/abNtVsDhgGC1QbBY3NvsB9pue3Ex7Fot4MUEPDKlEsr69Z1/DRLF9rqs7VbWrw+ZUrqDHR0WCwzHjkG/LwP6zEzYCwrE9+TRGkQ/NxzRY8YgrEVzP5aSaotgt8Nw4gQc+oqHMAsWM3Le/R0AJsBMgMmvpPBDkzrBYoH52jWYLl2C+dJlmC5fgi3nLuzFxRBMJrdt9Wo1tgwaiOG7diPSi5mNy1U2sVRsLBSxMVDGxEIRGwtlvQQo6yc6l5lIdHae5HVw5lKtVouPPvoIb7zxRrkTWfiKXa9HycFD0O/bC/3+AxAsFgCATKWCZvCziB4zFqr27SR9wqCu8zS2/BWD5HuCIMB27x5MFy/CfPkyTBcvwXL9ujPhfeAEY6222yidWCo29pe2OzYOivi40jY7UUx6FTExdfL+Vn/8bgS7HaZz51CckYHiXbthu3tXfE/duTOix45B1IABQTeaSUq0Gzfi6t//UeVvsez3+torryCu/sNze0ihXy7d02JE5DN2rRb6Q4dgPHUaposXYbl6tfJ7sWQyyKOioIiKQlhcLABA3akTwmWATKGETKmETKkAFErIFArI1CrIIyIgDw//5b8ujxXRMc5ZOKOj62TnKNApIiOhGTIYmiGDYddqUbRtG4rWb4Dl+nUUbdqMok2bEdaqFaLHjIFm2FAoNBp/F5mIqiDYbDCePo2SHw47k95LzgmWKiNTq6HQaBAWFwcAUHfsiHDglzZbqSxtwxWQhYQ42+gH2+yy55GRYtIrDw19BN9YWmQKBdSdO0PduTPqvfYaSn44jKL166E/eBDG06dhPH0auX/7OzTDhiFmzGiEPfaYv4tMXrLl3hcfq554AuoKRlPYSifclPLvjAkwEdUKS3Y29BmZ0O/PhPH0mYeGsck1GqjatEFYm9ZQtW6D0GZNodBoINdonMtElCaqWq0W+OijCqfnp8CiiIlB3JQpiJ08GaYzZ6BdvwHFu3fDfOUKcv/2N9xfsAAxY0YjNi0NIQ0a+Lu4ROTCUVIC/fffo2T/fugPHoJD98Ca5Eolwlq2hKpNa4S1boOwxx5zLs+m0UARGQlZaQdabLf/73/ZbtcBMoUCkX2fQmTfp2DNzYXu22+h3bABtpy70K5ZA+2aNQjv2RNx06cjvGcPjuapgxr97YMKf4tlv1cpYwJMRNVm+uknFO/cBX1mJizXrrm9F/b444jo0weqdm2hatMGyqQk/iMaxGQymXh1of5//gq67TugXb8OlitXUbhyFQrXfgPNs88ibvo0Xlkg8iN7URGKd+1CcUYGjMdPuI3OUcTEIKJvX6g7dYLqiTYIbdlS0leJpCCkfn3Ez56NuJkzYTh6FNr166HPyIThyBEYjhxBWJs2iJs2DVEDB0j6fmoKLoxkCihDhgyBwsu1UF3NmDEDM2bMqL0C0UMcBgN03+1E0YYNMF248MsbCgXCn3wSkakpiHz6aYQ0bFit/YeGhqJTp04IZaerSoFaVwqNBrETJyBmwvMwHDmCgq+XwHDsGHTbtkG3bRsi+vRB3IwXoH7ySZ4UCVCexlagxiC5EwQBxlOnULRhA4p37xHv2weAkKZNS9f6ToG6Y0ev1yMHGAfeCsT6ksnliOjVCxG9esGak4OC5ctRtHETzJcuIefdd5H3ySeInToV0aNG8j7hABZit+NxubzS2ArE+HvUOAkW+Z3rzfY19d5772H+/Pm1si/6hSAIMF+8CO2GjdDt2A7BUDqxglKJyH79EDVwACL69OG9nlQh048/omDJUhTv3Qs4HAAAVbt2iJs5A5GpqUyEiXzAVlgI3bdbULRxIyxZWeLrYY89hqghgxGZkoKw5pwBmMpn12pRuPYbaNescc7QDecogZiJExE7eRIUUVH+LSC5yVu4CPmff46Y8eOQ+F//Ve39SGESLCbA5HeuP7S+ffvyCnAAcVgsKN6xA4Vr1sJ86ZL4ekiTJogZMxqa4cOhjI+v1WNaLBb8+OOPaNu2raTPTnqiLtaV5dYtFC5fgaJvv4VgNgMAwlq3RsJLcxHx9NNMhAOEp7FVF2NQCoznL6BwxQro9+4VlyyTqdXQDB6M6DFjoGrXtlZ/a4wD79S1+nKYTCjavBmFy5bDeucOAOfSgbFpaYidMhmKWrqIQTWTt3AR7n7xBfJHjkDv3/ymwtiqKv6kkABzCDQFlB07dgTlD62usev1KFq3DoUrV8GWlwcAkIWEIPKZZxAzdgzUXbv6LFExGAzYtGkTkpOT60THwJ/qYl2FNmmCxP/6LeJfmovCVatQuHo1zJcv4/bbv0LYE22Q8NLLiOj7FBNhP/M0tupiDAYrQRBQcuh7FCxdAuOJk+LrqieecM7IPmQw5D7695Vx4J26Vl9ylQqxEyYgZuxYFO/eg/yvvoTlylXkf/45CletQtzUNMROmuSz+CLPmUNDkWG3o5PBUGFs1bX48wUmwEQkst7LReGqlShatx4OgwEAoKxXD7GTJyF61CgoOLsn1RJlXBzqvfoq4tLSULB8hTMRvngJt998E6p27ZDw8ksI792biTBRFQSLBbrvdqJg2TJYrl51vqhUQjN4MGKnTIaqTRv/FpCChkyphGbIYEQ9OwjFu3cjf+EiWK5fR96/P0XBihWImzYdsRMnQB4e7u+iElWKCTARwXLjBvK//hrF23eIw+VCW7RA3PRp0AweLC51QVTbFDExqDfvVcSmTUHhsmUoXLMWpgsXkP3a61B16ICEl19GRK+e/i4mUcBxGI3Qpq9D4cqVsOXmAgBk4WrEjB2H2MmTuOwY+YxMLofm2WcRNWAAinftRt7Cz2HNuom8BQtQuHw54l6YjpgJEzhZFgUsJsBEEma5cQN5X3yB4u92ihMTqZ98EnHTpyPiqT7i2rxEvqaMjUW9119HbFoaCpYshTY9HaZz55D96qsI794dCfPmQd2+nb+LSeR3DqMR2m/SUbB0KeyFhQAARUI8YidPRsy4cZyYiB4ZmULhvCI8cAB0332H/EVfwHrrFu5/9DEKV6xE/Nw5iB45ErKQEH8XlcgNJ8Eiv5PCzfaBxnz9OvIXL3ZLfCOe7of4WbOh7tDer2Wz2WzIzs5G48aNoeSag5UK5rqy3b+P/K+/RtG69eI6pZH9+yPhP15BWIsWfi5d8PM0toI5BgONM/H9BgVLl4mJb0ijRoibNROaYcP8ul4v48A7wVpfgs0G3bZtyFu4ELacuwCck2YmvPwyop4dxJPqPpa3cBFyFy2CafQodHznnQpjq6r4k0K/nAlwHXXjxg3s2rULmZmZOHfuHG7evAm9Xo+oqCg0btwYvXv3xpQpU5CSklKt/R87dgxfffUVMjIykJ2dDQBo3LgxUlNTMXPmTHTv3r3WvosUfmiBorzENzIlBfFz5/A+MQpI1pwc5H3+OXRbtzljVi6H5rlhSHjpJYQkJfm7eEQ+V27i27gx4l+cDc2QIby6RgHHYbGgaP0G5C9eDHtBAQAg7PHHkfDqq87RZZzbwSe4DJLnmADXMadOncLLL7+Mo0ePerR9amoqlixZgqZNm3q0vcViwa9//Wv861//QkWhIZPJ8MYbb+CDDz5ASC38wyuFH5q/WbJvI//zz6HbsSPgE1+9Xo/09HSMHz++1taHDlZSqivztWvI+/en0O/bB8A5K3n0uLGInz0byrg4P5cu+HgaW1KKwUfNYTZDm56Ogq++fiDxfRGaoUMgC6Arh4wD70ilvhwGAwpXrkTB0mVwlJQAANSdOyNh3qsI79LFz6ULPnkLFyH7669xdMhgTHn77Qpjq6r4k0K/PHBaT/LI5cuXH0p+H3/8cbRv3x4JCQnQarX4/vvvxau2GRkZ6N27Nw4cOIAWHgwbnDNnDpYuXSo+b9GiBXr16gUAOHz4MK5duwZBEPDhhx9Cp9Nh8eLFtfjtqLZZ791D/heLUbR5M1A6uVWgJr5lbDYbsrKyYCstL1VMSnUV1qIFGv39bzCeO4+8Tz6B4dgxaFevQdGmzc4lOKZO5VqUtcjT2JJSDD4qgsWCom+/Rf4Xi8XJrQI18S3DOPCOVOpLHh6O+BdfRMz48chfsgTaNWthPH0at16cg4i+fZHwH/8BVevH/V3MoGKXy5EjCJXGllTirzKB14qSR1q1aoUXX3wRU6dORaNGjdzeczgc+Prrr/Haa6/BYDDgzp07SEtLw/fff1/psJMvv/xSTH7lcjn+8Y9/4PXXX4e89J4Nh8OBjz/+GL/61a/gcDjw5ZdfIiUlBdOnT/fdF6VqsRUUoOCrr6BNXwfBYgEARPTpjYRXXoGqbVs/l46o+tQd2qPJZ5+i5PAR5C1YANPFi8hf9AW0a79B3MyZiHl+PGcepTpJsNuh274D+Z9/DuudOwAAZYMGiH/xRUQPf45DnanOUsTEoP4bbyB20mTkf/EFijZtQsnBgyg5dAhRg59FwiuvILRxY38XkySECXAdk5SUhK+++grTpk2DQqEodxu5XI5Zs2YhNjYWY8eOBeC8ertz504MHjy43M+YzWbMnz9ffP6b3/wGb7755kP7ffPNN3H37l389a9/BQD84Q9/wKRJkyS7kHagsRcVoWD5chSuWg3BaATgnNU54ZVXEP4khxtR8Ijo1RPhPXtAv3cv8v79KSw3buD+hx+icOVKxM+di+gRwwPyShnRgwSHwxnHn30Oy/XrAABFfDziZ81C9Ngxfp3ciqg2hSTWR4N3/xtxU9OQ99nnKN65E8U7vkPx7j2IGT0a8S/OhrJePX8XkySA07HVMSkpKZgxY0aFya+rMWPGoEePHuLzrVu3Vrjt5s2bcevWLQBAdHQ0fv/731e47R/+8AdoNBoAQFZWVqX7pUfDri9B3sJFuDZiJAq+/AqC0QhV27Zo/MkCNFn4eZ1KfuVyOaKjo8WRB1QxqdeVTCZD1IABSF6zGg3e+wOUDRrAlpuLe3/+M65PmADdrl0QSu95J+94GltSj8GaEAQB+v37kZU2FXfe+S0s169DrtEg4bV5aLFpI2InTawzyS/jwDtSr6/QZs3Q8P3/Q7MVyxHRpw9gs0Gbno5ro8fg/sf/gl2n83cR6yy5ICASqDS2pB5/ACfBCnpvv/02/vnPfwIARowYgc2bN5e7XVpaGlauXAkAmDt3Lj7//PNK9ztnzhx88cUXAIBp06a53TfsLSncbO8rDqMR2rVrkb9kCRxFzn8wwlq1QvzLLyEyNZUzLZKkOMxmaNetQ8HiL2HXagEAYW3aoN68VxHeqxd/DxQQBEGA4cgR5P37U5guXADgvFcydupUxKZN4b3sJDmGkyeRt2ABjGfOAgDkUVGImz4dsZMnQa5W+7l0dQdngfacdFN/iXDt8Nnt9gq321c6syrgnDm6Kv379xcf7927t3qFo2pzmM0oXLUK10aOwv2P/wVHkQ6hzZoh6f/+D81WrURU//51trPvcDig1Wrh4JW7KrGu3MnDwhA3ZQpabN6E+LlzIQ8Ph/nSJWTPew23XnoJxrNn/V3EOsPT2GIMesdw8iRuzZmL7FfnwXThAmQqFeJmzECLbzcj4aW5dTb5ZRx4h/XlLvzJJ9Fk8WI0+vCfCGvVCo7iYuR98gmujRqNwjVrxblMqGoOmQw6Qag0thh/TICD3rlz58THTZo0KXeboqIi5OTkiM+ffPLJKvfrus3t27eh43CVR0KwWKBNT8f10WOQ+/d/wF5QgJCGDdHgj39E8to10Ax+ts4vNK/T6fDRRx8xpjzAuiqfPCICCS/NRfPNmxCblgZZaCiMJ07i5sxZuP322zBfueLvIgY8T2OLMegZ47lzuPUfr+LWnLkwnjoFWWgoYqdMRovNm1DvtXlQxMT4u4g1wjjwDuvrYTKZDJH9+qHZqpVI+vP/IKRRI9jz85H7wQe4Pm48dNu2QajkQg45GVQqrLRaK40txh8nwQpqN2/edLs6O3DgwHK3u3z5sttzT9YMfnCby5cvo3v37tUoJXlCsNmg274d+QsXwnrHebJCmZjonB10xHDODkpUDmVsLOq//RZip0xG/sJFKPr2W+gz90O//wA0Q4ci4eWXEPLALPpEtcl06RLyPv0MJQcPAgBkSiWiR49G3KxZCEms7+fSEQUemVwOzdChiBowANqNG5G/eDGsd+4g5/d/QMGSpUh49T8Q0a9fnR3lRoGBCXAQe/vtt8Vhz02bNsWIESPK3S4/P198rNFooPbgfovw8HBERUWhuLgYAFBQUFALJaYHCQ4HinfuQt7Cz2HNugkAUCTEI37mLESPGQ15WJifS0gU+EIaNECDP/wesdOmIv+zz1C8ew9027aheOdOZzIycwZCGjTwdzEpiJivXkXe559Dv6f0JLRCgejhzyH+xRcR0rChfwtHVAfIQkMRO2ECokeMQOHqNShYsgTmK1dw+623oerYAQkvvYTwnj2ZCFO1MAEOUkuWLMG6devE5++//z7CKkiW9Hq9+NiT5Nd127IE2HUfNZGVlYXw8PBa2Vd5YmJiEFMHhpoJDgf0+/Yhb+FCWK5cBeBcRy/uhRcQM+F5rnNKVA1hzZuj4V//CtPFi7i/4BMYDh+GNj0dRRs3QjNqJOJnzkRIUpK/i0l1mOXGDeQtWoTi73YCggDIZIgaMhgJc+ci1IPRVUTkTq5WI37mDMSMG4uCJUtRuHo1TGfPIfvVeVB16ICEuXMQ3rs3E2HyChPgIHT8+HG8/PLL4vPJkydjypQpFW5vMpnEx96s5+uaUBtL15ytqXbt2tXKfiry3nvvua13HGgEmw3Fu3Yh/8uvYLl2DUDpbIjTpiJ20iTIg3AmvgepVCqkpKRAxSS/Sqyr6lE98QSafLIAhpMnkb9wEQzHjqFo3XroNm2GZsQIxM+cIfmh0Z7GFmPQyfTTTyj46msU79rlTHwBRA54BgkvvYSwli39XDrfYxx4h/XlPYVGg3qvzUPspIkoWLoM2vXrYTp3DtmvvQ5Vu3aInzMHEX2fknwiHGq1oqtcXmlsMf64DFKtWbBgARYsWFCr+/zzn/+M8ePHe/WZ69evo0+fPrh79y4AoGPHjjhw4IC4bm95vvnmG0yYMAEAkJiYKH62KomJicjNzQUApKenY9y4cV6VtYzrdOsXLlyQ5BVgwWJB0bZtKPjqa1izswEA8shIxE6ehNi0NCiiovxcQqLgZTh5CvmLFsFw9KjzBaUS0c8NQ9ys2QhtLO1EmCpnPH8BBV8uhj5zv/haZMrTiJ87F6o2bfxYMqLgZsvLQ8Gy5dCmp0MovZCjeuIJxM+dI9l7hLkMkud4BbiW5OXlPTSZVE1pS9ex9FROTg4GDRokJrAtWrTAjh07Kk1+AYhBDnh3Jdd128haWrqhWbNmQflDq4jDZELRps0oWLoUttL/b4roaMSmpSFm4oQ6uyRGTZhMJhw+fBi9evWS9NlJT7Cuakf4k10Q/um/YTx9GnkLF8Fw5AiKNm1G0Zat0Ax+FrHTpkH1+OP+LuYj5WlsSTUGDSdPIX/xYhgOH3a+IJMhatAgxM2cIblYAaQbB9XF+qo5ZUIC6r/1JuKmT0PBsmXQfpMO08WLuP3W2whr3Rpx06cjauAAyJTSSnUsSiWO2WwYaDJVGFuMPy6DFDTy8/MxaNAgXL3qvF80KSkJu3fvRpIH97PFx8eLj3U6nduQ6IoYDAbx/l8AiIuLq0appctWWIi8RYtwbcRI5H7wAWx370KREI96b72FFlu3IH72LEkmv4CzYc7MzPQoDqWOdVW71J07o8m/P0HTr75EeO9egN0O3bbtyJo8BbfmzUPJkaOQyqApT2NLSjEo2O0o3rsXWTNn4tacOc7kV6GAZvhwNE//Bg3f/z9JJr+AtOKgNrC+ao8yPh7133wTLb7djLgXpkOmVsN8+TJy3n0X10aPRuHq1XAYDP4u5iNjCQnBCYej0thi/PEKcK2ZP3++3+4t1el0GDx4MC5cuAAASEhIwO7du9G8eXOPPt+6dWu351lZWQ+99qCbN29Wug8qn+XmTRSuWImiLVvEITvKBg0Q98J0RI8axVmdiQKAumNHNFmwAKYff0TB0mUo3rMHhh8Ow/DDYYS1aYO4adMkeWVBqhxGI4o2f4vClSvFW1RkISHQjByB+BdekPz94kSBQBkXh3qvv47YadOg/SYd2jVrYMu5i9y//R15ny9EzPPjETtxIpQuF31Iuvivdx1XUlKCYcOG4cSJEwCA6Oho7NixA23btvV4H9HR0UhKSkJOjnN92VOnTlWZ0J48eVJ83KhRoyqHWUuZIAgwnTmDgmXLoc/MFCdIETvSA57hOr5EAUjVti0a/uV9WLKznSeuNm+G+dIl5Lz7LvI+WYDYtKmIHjkCch/OW0D+Y8vLQ+HatdCmp8NRpAMAyKM1iBk/HrETJkCZkODnEhLRg5SxsUiYOwdx06eh6NtvUbh8BazZ2ShY/CUKly2HZvhwxKVNQWhysr+LSn7EBLgOM5lMGDlyJA4dOgTAuTbv1q1b0bVrV6/31b9/f6xcuRIAkJGRgUmTJlW6fWZmpvj4mWee8fp4UuAwmVD83XfifSllIvr1Q9zUNKi7dpXkJA1EdU1o48ZIfOc3iJ87B9q130C7di2sd3KQ+7e/Ie+TT6AZPhwx48dJYrbfYFd2wrLwm2+g37MXgtUKAAhp3BixU6Y4T3h4sVwgEfmHXKVC7PPPI2bsWOgzMlCwZClMFy6gaP16FK1fj/CePREzfhwin36ao3kkiLNA11FWqxWjR4/Gtm3bADiXJNqyZQsGDhxYrf25zgQdExODO3fuVLgmsNFoRMOGDcVJutatW4exY8dW67hA8M02Z7lxA9p161D07RY4Su+TloWGQjNsGGLT0hDWwrOh6VLlcDig0+mg0Wggl3OagsqwrvzDYTKhaPNmFK5aDavL7SDqJ59EzPjxiOqfCpkXS8oFIk9jK1hi0FFSgqJt21CUvg7mK1fE11UdOyBu2jREpqRAplD4sYSBLVji4FFhfT16giDAeOoUCpYtQ8mBg+JoPGX9+ogeMwbRo0chpH59P5eyZvIWLsL9hQuhHDsGLX/72wpjq6r4C7Z+eXmYANdBdrsdkyZNQnp6OgBAqVRi3bp1GDlyZLX3aTab0apVK2SX3t/07rvv4s9//nO52/73f/833n//fQDOWZt/+uknr9YPflAw/NAEqxX6/fuhTV/3y1IqAEIaNkTM+HHQjBwJZWysH0tIRLVNcDhgOHoM2vR05+0NDgcAQBEfj+jRoxAzdixCGjTwcympMuaff4Z23XoUbd0CweBc2UCmUkEz+FnEjB8PlRe3ExFR3WC9fRva9RtQtGkT7IWFzheVSkSlpiBm/Hiou3WrkyP0uAyS55gA1zGCIGDmzJlYsmQJAEAul2PFihVVDln2xJdffonZs2eL+/3nP/+JefPmiWeHHA4HFixYgLfeeguO0o7ekiVLMH369Bodt67+0ARBgOnHH6Hbug3F330He9myVXI5Ivo+hZjx4xHRuzdkPLvrFZ1Ohy+//BKzZs3iveVVYF0FDuu9eyjasBFFGzbAlpfnfFEuR3iPHtAMG4qo/v3r1L3CnsZWXYxBW0EBir/7DkVbtsJ86ZL4ekizpogZPx7Rw4dDUUe+S6Coi3HgT6yvwOCwWKDfuxfab9JhPH1afD2kSRNohg6FZthQhDZp4r8Ceilv4SLcWrIEe58dhDlvvVVhbFUVf3W1X+4NDnqvYz799FMx+QWAli1b4uDBgzh48KBHn1+wYEGF782aNQsZGRlYtmwZHA4H3njjDXz88cfo1asXAODw4cPiMksAMHPmzBonv3WR9e5d6LZug27bNlhu3BBfV8THI3rUSOdVHw+Wn6LyORwOFBUViSdZqGKsq8ARkpiIhJdfQvzsWdBnZjpHgxw7BsPhwzAcPox7qvcRmZICzbBhiOjZI+AnvvM0tupKDDpMJugz90O3bRtKfvgBsNsBADKlEhEpTyNm/HiEd+9eJ6/6BIK6EgeBgvUVGOShodAMGQLNkCHO0SDp61C0bSust24hf+FC5C9cCFX79tAMG4aoZwfViZF8DpkMeqDS2GL8MQGuc3Jzc92e//zzz/j55589/nxlCTAAfPHFF4iOjsYnn3wCQRBw9epVt6QXAGQyGV577TX8/e9/97zgdZw1Nxf6jAwU79kD4/ET4usylQqRKSmIHv4cwnv04EQKRBInCwlB1MCBiBo4EJbsbOi2b4du23ZYb95E8Xffofi776CIjUXUs89CM3QoVO3bMenyEYfZDMPRoyjeuxf6PXvhKCkR36trnVoi8q2wxx5D4n/9FvXeeB36zEznybLDR2A6fx6m8+eR+49/IKJ3b2iGDkVkytOcDK+OY2+d3ISGhuJf//oXpk2bhi+//BIZGRm4ffs2AOdyR6mpqZg9eza6d+/u55L6niUrC8X79kG/LwOm8+fd3lN364ro555D5DMDoIgMvqEhRFRzoY0bI2HOHMS/+KLzdolt21D83U7YCwuhXbMG2jVroKxXDxH9+iHy6X4I794dcpXK38Wu0+x6PUoOHoJ+3z7ovz8k3tcLAMqkBtAMG4boYcO4BAoRlUseHu4c/jx0KGx5eSjetQu6rdtgungRJQcPouTgQcjCwhDerRsin+6HiH79EJKY6O9ik5eYANcx8+fPx/z5831+nB49eqBHjx4+P04gcVgsMJ09i5LDR6DPyIDl+nW391UdOyAqNRVRzz7LIc4+pFQq0axZMyh5Nb1KrKu6QSaTQd2uHdTt2qH+m2+i5OhR6LZtgz5zP2z374vLcshUKoT36I7Ivv0Q0a+vX2ck9TS2/B2DgiDAmpWFkqNHod+/H8ZjxyHYbL+ULzERkSkpiBo4AOouXTgng4/4Ow7qGtZX3aBMSEDs5MmInTwZ5uvXUbxjB3Tbd8B6+zZKDh1CyaFDwPt/QVjr1mIyrHriCb+2MwqHA0kyWaWxxfjjJFgUAPx1s71gt8P8008wHD2KkqPHYDx9GoLJJL4vUyqh7t4NUampiExJgbJevUdSLiKSBofZDOOJE9DvPwD9gQOw3b3r9n5YmzYI79oV6i6doe7cmUN1S1lzc533Vx85CsPx47Ddu+f2fmjz5ojsn4rI1FSo2rblEHMiqjWCIMBy9Sr0Bw6i5MB+GM+eE5dUAgBFQjwievSAunMXqLt0Rmjz5o+sDeIs0J5jAkx+96h+aPbiYpgu/AjTxYswnT8Pw8mTcOh0btso4uMR3r0bIvv1Q8RTT0ERFeWTslDFbDYbsrOz0bhxY0mfnfQE6yp4CIIA888/o+TAAej3H4DpwgW3ThUAhCYnQ92lC9SdOyO8S2coGzb0WcfK09jydQwKVivM16452+4ff4Tx1Cm3yQcB533X6k6dEN6rF6L6p3J4sx+wLfIO6yt42AoLUXLoe5Qc2I+S73+Aw2Bwe18RHQ11505iQqxq3dpn68TnLVyE3EWLYBo9Ch3feafC2Koq/qSQAPNXR0FHEATY8/NhycqC6fJlmC5cgOnHi7DevPnQtvKICKi7PomInj0R3r07Qlu04NUCP9Pr9ViyZAneeOMNxMTE+Ls4AY11FTxkMhlUjz8O1eOPI372bNjy82E4ehSGU6dgPH0GlqtXYblxA5YbN1C0YQMA5/C80MdaIaxlK4S1aomwli0R2rx5rUzO4mls1WYMOoxGWG7ehPnKFTHhNf/0EwSz2X1DmQyqJ55AeI8eCO/eHerOnXjvtJ+xLfIO6yt4KGNjET38OUQPfw6CxQLDqdMwnjrl/Dt/HvaiIugz90OfuR+Ac/LUsFatnO11q5YIa9ESYS1bQJGQUCv9T2NYGLbYbGih11cYW4w/JsBUBwmCAMFggK2wEPb8fFhzcmDJuglLVhYsN7Ngzbr50Bm4MiGNGkHVti1Ubds6z8Q98QRnbiaigKOMjxcnYgEAu1YL45kzMJw+DeOp0zBdvAhbXh5seXkw/HD4lw/KZAhp1AhhLVsgNLk5lImJUNar5/yrXw/KuDi/LcEkWK2wFRTCXlgAW24uLDdviW225eZN2B5Y5aCMPDJSbLdV7dshvGtXrtNLRAFHFhqKiJ49ENHTOYeOYLHAdPknZzJ8+jSMp0/DXlQkziztSh6tcZ7MbNECIY0aOtvshAQo69eHsl69OrWOfF3Anj8FlA/69kO4Qo4QAVAKApQQoBQAteBAlMP5F+kQEFLByP3QkBCEhIQAcjlCkpIQ1qqls9PUrh1UTzwBhUTPdBFR3aaIiUFkSgoiU1IAONe1NV+6BPPVazBfuwrL1WswX70Ke0EBrNnZsGZnA6VXHNzIZFDEx0GZUA/KuFjIQkIhCwmBLDTE+d/SP13pJC4Fq1bBrlZDplAC5Vyd0Fkszu1WrIDVbodgscBhsUAwWyCYTLBpC2HPL4CtIB+OIt1Dny/ve4YmJ0PV9gkx6Q1p0oSTVxFRnSMLDYW6Q3uoO7QHpk+D4HDAkpUF888/w3LtOsxXr8J89Qqst7LhKNLBePIkjCdPlrsveUSEeDJTHq6GLEwFWVgo5GEqyEJDIVOFwXT23CP+hnUXE2AKKAN0Oqg96OjYAZgEAQV2G+7ZbLhpsSLLYkHf58djxm//CyGNG0Huo3ssiIj8Ta5SQd3ZOTmWK1thISxXr8J89RosWVmw5d2HLfc+bPfvw5aXB9hssOflw56XD3P5uwYA6NVqYNBAFK5YCavRWPV2q1ZXup1IoYAiNgbKhHoIbdIEoc2aIqRpU4Q2bYbQpk2giI72rAKIiOoYmVyOsObNEda8udvrDrPZeYvL1aswX7sO2727sN3Pc7bbublwGAxwlJTAUlLy0BwID+H6xB5hAkwB5URkBBQKJWwyGawywAYZbDIZzDIZiuVytz+L/OGrEWF9+iCsRfNy9kx1RXh4OEaNGoVwDvepEuuKHqSMjYWyWzeEd+v20HuCwwG7Vgtbbi5s9/Ng12ohWK2//Fks4uNIiwWpRiPqDxkCpdUKwW4r52iACkAqgMTnnkNoaChkYWHOqxGhIZCrVFDExEIRFwtlXBwU8fFQREfzam4QYlvkHdYXuZKHhUHVujVUrVuX+76jpMSZDN+/D1t+PhwGIwSLGQ6TyTnaxmyGw+x8HCGX4blevSqNLcYfZ4GmACCF2eaIiIiIiAKdFPrlPA1LRAHFYDBg48aNMFQwkRn9gnVFvuJpbDEGCWAceIv1Rb7iSWwx/pgAE1GAsVgsOHPmDCylk+tQxVhX5CuexhZjkADGgbdYX+QrnsQW448JMBEREREREUkEE2AiIiIiIiKSBCbAREREREREJAmcBZr8TgqzzRERERERBTop9Mt5BZgCilar9XcRyM+0Wi3mz5/PWPAA64p8xdPYYgwSwDjwFuuLfMWT2PIm/oI1RpkAU0ApKirydxHIz7KzsyGTyZCdne3vogQ81hX5iqexxRgkgHHgLdYX+YonseVN/AVrv5wJMBEREREREUkCE2AiIiIiIiKSBCbAREREREREJAlMgIkooCiVSpw+fRpKpdLfRQl4rCvyFU9jizFIAOPAW6wv8hVPYovxxwSYiAKMSqXCxo0boVKp/F2UgMe6Il/xNLYYgwQwDrzF+iJf8SS2GH9MgIkowFitVnTu3BlWq9XfRQl4rCvyFU9jizFIAOPAW6wv8hVPYovxxwSYiAKM2WzG6NGjYTab/V2UgMe6Il/xNLYYgwQwDrzF+iJf8SS2GH+AdAd/U0B64YUXEBERUe3Pz5gxAzNmzKi9AhERERERUdBgAkwB5fjx4zX6fGpqau0UhIiIiIiIgg4TYAoo3bp1q9EV4OTk5NorDBERERERBRUmwBRQlixZgrZt2/q7GORHarUaX3/9NaZOnervogQ81hX5iqexxRgkgHHgLdYX+YonscX44yRYRBRgFAoFbty4AYVC4e+iBDzWFfmKp7HFGCSAceAt1hf5iiexxfhjAkxEAcZoNGLGjBkwGo3+LkrAY12Rr3gaW4xBAhgH3mJ9ka94EluMPybARBRg7HY7kpOTYbfb/V2UgMe6Il/xNLYYgwQwDrzF+iJf8SS2GH9MgImIiIiIiEgimAATERERERGRJDABJqKAIpPJoNVqIZPJ/F2UgMe6Il/xNLYYgwQwDrzF+iJf8SS2GH9MgIkowERERODDDz+s0XrQUsG6Il/xNLYYgwQwDrzF+iJf8SS2GH9MgIkowDgcDsTExMDhcPi7KAGPdUW+4mlsMQYJYBx4i/VFvuJJbDH+mAATUYAxGAx48803YTAY/F2UgMe6Il/xNLYYgwQwDrzF+iJf8SS2GH9MgImIiIiIiEgilP4uAJEgCOJjg8GAkpISP5aG/M1gMMBisTAWPMC6Il/xNLYYgwQwDrzF+iJf8SS2qtrG9TXXPnowkQnB+s2ozsjNzUViYqK/i0FERERERKWOHTuGbt26+bsYtY5DoImIiIiIiMhNdHS0v4vgE7wCTH7ncDiQl5cHAAgPD5f0umRERERERP4iCII4QVZCQgLk8uC7XsoEmIiIiIiIiCQh+FJ6IiIiIiIionIwASYiIiIiIiJJYAJMREREREREksAEmIiIiIiIiCSBCTARERERERFJAhNgIiIiIiIikgQmwERERERERCQJTICJiIiIiIhIEpgAExERERERkSQwASYiIiIiIiJJYAJMREREREREksAEmIiIiIiIKAgkJydDJpMhOTm5wm1kMhlkMhlSU1MfWbkCCRPgOqakpATr16/HvHnz0K1bNzRt2hQRERFQqVRo0KABOnXqhOnTp+Nf//oXbt68We3j9O3bV/xxyGQyrFmzxut9uH5eJpMhNDQUeXl5Xu2je/fuD+0nIyOj3G0zMjIe2rZLly5eHa+goABhYWEP7cdbX3zxhdvnhw4d6vU+gk1qaupD9bpp0yav9vHrX//6oX3Mnz/fNwUOIK515+n3PXv2LBITE8XPxcXF4dixY74tqMSwPf7l769//avbtmyPg1d12iMpqUn9zJ8/X/KJiRSV1z+qqG2tiGv/KCsrCwCg1Wqr/FxmZmal8VpePHvavwjkeGYCXEcYjUZ88MEHaN68OcaNG4dPPvkEJ06cwK1bt2AwGGA2m3Hv3j2cPXsWy5Ytw+uvv45mzZrhqaeewtatW7061pUrV3Do0CG315YsWVLj72C1WrFq1SqPt//xxx9x/PjxGh3z9OnTOH/+vMfbr169GhaLpUbHBB6ur127diEnJ6fG+w02S5cu9Xhbu92OFStW+LA0wePEiRPo378/cnNzAQD16tXDvn370L17dz+XLDiwPX6YJ2Vie0xE5B1P2tZH2T8Klv4FE+A6ICsrC71798Y777yD+/fvi6/Xq1cPgwcPRlpaGubMmYMxY8agR48eUCqV4jbff/89hg8fjn/+858eH6+8pGTnzp24e/duzb5IBfuujW0r401nsTaOefXqVRw8eNDtNbvdjuXLl9d438Fmy5YtKCws9Ghbdlo988MPP2DAgAEoKCgAACQlJSEzMxOdOnXyc8mCA9vj8l28eNGjEQZsj4mIPOdJ2/qo+ke3bt0Kmv4FE+AAd+3aNfTo0QNnzpwB4BzG9vzzz+P48eO4d+8eduzYgeXLl2PhwoVYv349jhw5goKCAmzYsAEDBgwQ91NSUuLR8QRBwLJly8TnarUaQM07DG3btgUAHD9+HBcvXqxye4fDIZ7Nql+/PuLj4706Xnx8POrXrw8AWLlyJex2e5Wf+emnn3DkyBG38laHa6etrP6A2rlqEyzK6tdisWD16tUefca1Xmvy/yeYZWZm4tlnn0VRUREAoEmTJti/fz+eeOIJP5csOLA9drbHrkl9WFiY+LiiNo7tMRFR9VXVXj2q/tHy5cuDpn/BBDiAGY1GjBs3ThxmEB4ejg0bNmDt2rXo2rVrhfdCRUVFYfTo0di9ezeOHDmCDh06eHzMzMxM3LhxQ9yP6/0ANekwTJs2TXzsyVn9PXv2IDs7GwAwZcoUtw6XJ5RKJSZPngwAuHPnDnbv3l3lZ1y/3/Tp0706XhlBENy+31/+8heEhoYCAC5cuIATJ05Ua7/BZtKkSQgJCQHgWTzodDps3LgRANC5c2evYloqdu3ahWHDhkGv1wMAWrRogQMHDqBVq1Z+LllwYHvsbI+fe+452Gw28b2XX35ZfLxq1apyhyyzPSYiqr6K2lbg0faPysoQDP0LJsAB7IMPPsDp06fF5ytWrMCoUaO82kePHj1w/PhxjBkzxqPtXTsd48aNw6xZs8RE5fz58zh58qRXxy/z3HPPISEhAYDzezgcjkq3d+20vPDCC9U6puvnqurkCYIgXlFJSEjAsGHDqnXM/fv3ix3WiIgIzJ49221fvOrgVK9ePXEimsOHD+Pnn3+udPtvvvkGRqMRQPXjIZht2bIFI0aMgMFgAAC0bt0a+/fvR7NmzfxcsuDB9tjJ9YovAIwYMQL16tUD4Jy0asuWLeXug+0xEZFnHhz1WFnb6uv+UX5+vtvzYOlfMAEOUCUlJfj444/F55MnT8bo0aOrta/Q0FC0a9fOo2Omp6eLz6dNm4aEhAQMGTJEfK26HYaQkBBMmjQJgPMegn379lW4rV6vx4YNGwAAHTp0QOfOnat1zC5duqB9+/YAgI0bN6K4uLjCbTMyMsRZWidPnix2Mr3lWj9jx45FRESE29WWVatWwWq1Vmvfwcb1qk5VHeKy95VKJaZMmeLTctU169evx9ixY2E2mwEA7dq1Q2ZmJho1auTnkgUPtsfO9rh9+/bYuXOn2/sKhULcV2VlYntMROSZCRMmPPRaRW2rL/tH69evx4ULF8Tn9erVC5r+BRPgAPXNN9+IN5kDwFtvveXzY65fv14cPtm4cWNxynLXDsPKlSur3WHwNOFZt26deI9cdYe+PXhMg8Hg1pl8kGt5qnvMB49RVm/Dhw9HbGwsACAvL8/rWWCD1YgRI8R6Wb58OQRBKHe7Gzdu4MCBAwCAwYMHi/cSkrMDP3HiRPE32aVLF2RkZCAxMdHPJQsubI+d7XG/fv1w7dq1h7ZxLdP27dvdJgcr75hsj4mIflF2Xy3gTGT/9Kc/ie1UmfLaVl/2j8r6F659sxkzZgRN/4IJcIByPSPfvHnzRzK9uOvZpbS0NMjlzvAYOXIkYmJiADg7DNu2bavW/rt37y7eLL9+/foKJ4IpK4dCoUBaWlq1jlUmLS0NCoUCQMWdPNeOUtu2bdGtW7dqHWv9+vXiVY2GDRuKk96EhoZi4sSJ4nYcdufkWi83btzA/v37y91u6dKlYgNc0xMiweSrr77C1KlTxfsxe/bsib1794pDW6n2sD12tscVrSnZvXt3tGnTBoBzeaWVK1eWux3bYyKih7nO6t+pUyckJCS4tVNA+W2rr/pHD/YvyoSHh9faMfyNCXCAKjujAzg7tr724DA41zP6YWFheP7558XntTH5il6vx/r168stR0ZGBgBg4MCBSEpKqvaxAPeOT2ZmpjiszpXrlRbX7+0t13qZMmWK2GF9cL9bt25FXl5etY8TTDy5ClU2C25MTAxGjhz5SMoV6D777DPMnj1bvHezX79+2LVrl5gYUe1iewz079+/0qulrmWsqExsj4mI3BmNRrcru2X/xpSX0D7Ytvqif/Rg/yI6OrpW9htovJtalx6ZW7duiY8fxRTjy5YtE4O9S5cuD92jNm3aNCxatAiAs8OQn5/v9dJEZfv53e9+B4fDgaVLlz7UwVm2bJl4Nqu2buZ/4YUXsHPnTnFJkXfffdft/bLESy6XY+rUqdU6RnZ2Nvbu3Ss+f/B79enTBy1btsTVq1dhtVqxatUqvPbaa9U6VjDp3bs3Hn/8cfz0009IT0/HggUL3JYq+f7773HlyhUAzntiVCqVv4oaMNasWYNLly6JzwcMGIDNmzcH1ZnZQMP22DnxSdnszTKZ7KFbFtLS0vC73/0OgiDg1KlT5Q6VBtgek7Rs27bNqxMsR48e9WFpKBBt2LDBbWm4smWMXPtHZU6dOoVz586hQ4cOPukflde/MJvN4lrqwRTPTIADkE6ncxt24MlVnW3btlU5FO5Pf/oT4uLiyn3P9axSeWfd+/bti+bNm+P69euwWCxYtWoV5s2bV2W5HtS4cWP0798fe/bswd69e3H79m23m+nLOj8ajabak8w8aMyYMYiKikJxcfFDHa7bt29jz549AIBnnnkGjRs3rtYxXDusHTt2RMeOHR/aZurUqfjjH/8IwFnf7HA5TZs2Db///e/FqfzLlksBaudewGDj+o9T+/btsWXLFp4Y8CG2x872+PLly+LrKpVKnHW0TLNmzfD0008jMzMTAPDdd9+Ve0y2xyQlx44dw7Fjx/xdDApgD17VLbtNBPilf/Tg9n//+9990j8qr3/hOvFiMMUzh0AHoAdnx4yIiKjyM0ePHsUnn3xS6Z9Opyv3s4cPHxbPMCkUCrcEpIxMJnM7G1+TYXdlP1SHwyEudQEAR44cETtZ48ePd7sSWBNqtRrjx48HAFy+fBlHjhwR31u+fLnYUapJA1JVh/XB10+cOOE2s56UTZs2TVxD1bVBN5vNWLNmDQCgZcuWeOqpp/xSvkB24cIFcQgU+QbbY2Do0KFuV1Qraptd29CK1vple0xE5HT79u1K10V37R+VWbFiBQwGg8/7R8Hev2ACHICioqLcnlc0OUltce0sDBo0CA0aNCh3O9cO1/Hjx/Hjjz9W63jjxo0TO5GuPy5fXu2r6F7TsuNHRkZi7Nix1dq3a0dRLpdXOA19y5Yt0adPH/E5J19xKrtyBAC7du0SJ4PYvHmzOOlOTe4FDDYvvvgiWrRoAcC5XupLL71U5TJSVH1sj533gJUlpm3atKlwWaLx48eLoxFcZ81+ENtjkor33nsPgiB4/Pfee+/5u8j0CLme9CuPa/+ozN27d/E///M/Pukflde/cJ2gK5jimQlwANJoNFAqfxmdXtHMm67mz5//UOBdv369ys+5XmUDKv8hPf74424TwFS3wxARESF2bi5cuIATJ07AYrGI5UhOTn7oB19TKSkp4qLda9asgcVicTvrX7ZGZHW41sOAAQPQsGHDCrd1rd/ly5e73fchZWUdYrvdjhUrVgD4pWMsk8mYALto1KgR9u7di6ZNmwJw/iM1a9Yst98x1R62x8luk4BVViaNRoNRo0ZVeUy2x0REnrXblU2GVdv9o/L6F663vwQTJsABqiz4AFT7zL4nNm/ejMLCQgDOs+5V3XdbWx0G1wmuli5dKk7kUnaMB4d81JRrI5Gfn4+tW7e6XXmo7oRbZrMZq1evFp9XNWnLxIkTERoaCgDIycnBrl27qnXcYPP888+LkzgtXboUubm52LFjBwDn/Y5lZyTJqVmzZti7d6/Yubfb7Zg6dSo2bNjg55IFJym3xwMGDMDFixcBONvRqpam86QzxvaYiKTu2LFjYttaGdf+UZmcnBwAvukfPdi/CFZMgANUv379xMe+nEXN9eyTXq9HREQEZDJZhX+uE63cuXOn0nsXKtO/f380adIEgHOx7cWLF4vv+WqyI9f9Ll68GKtWrQIANGnSBP3796/WPr/99luxwwo4O26V1V9cXBwsFou4PYfdOUVFRYmd/bNnz+Kdd94RJx7i5Ffla9myJfbu3SsuSm+z2TBx4sRKl6qh6pFye2w2m8XHgiAgOTkZ9+7dc/usa5mGDx/u0THZHhORlFXU3rzyyitu7ZRGo4HBYCh3W1/1jx7sX5RxnZG6rmMCHKBcOwDXr1/3Safr3r17Fc7U6anqdhjkcrl4JeH+/ftip713795o1apVjcpUkcceewy9evUC4Fw6pGzdtalTp1b7inNNO0wbN25EUVFRjfYRLFwb8q+//hqAc7ZZ1zVPyV3r1q2xZ88eJCQkAACsVivGjRvHK1m1TKrtcc+ePauczbq62B4TkVSVzd5fEzKZzKf9o7L+heucD2vXrg2a/gWXQQpQ48ePx69+9StxGNqHH36IlStX1uoxVqxYIV5li4iIQPv27T36nNFoxNmzZwE4Oww6nQ4ajcbr40+fPh1/+ctfHnrNl6ZPn47Dhw/XyjFdh+kCzuU2PJ25+vz58ygpKYHJZMLatWsxZ86capUhmAwcOBBJSUni0B4AGDVqVNAuwl5b2rVrh127duGZZ55BYWEhzGYzRo0ahe3btyMlJcXfxQsKUm2PO3XqJM7SrFQq0bVrVwDAyZMnYbVaATjXRX7weA6Hw22pDNdlpB48JttjIpKaLVu2iBMFKpVKqNVqccWB5OTkh668lq2vXtbulr127949n/aR2rVrh44dO+LEiRMAnLdbBU3/QqCANX/+fAGA+LdhwwavPn/9+nW3z1+/ft3t/Y4dO4rvvfbaax7v12KxCHFxceJnFy1aVO52rse+ePFiudt069ZN3CYsLEwoKCgod7vExERxu3379pW7zb59+8RtEhMTy92moKBACA0NFbfr3r17udtdvHjRrfzl+X//7/+J78fHxwsWi6Xc7cozb9488bNPPfWUx5+ry1JSUsTv/Omnn5a7zX/+53+61fvWrVvL3W7ixIniNu+9954PSx0YXOuuou979OhRQaPRiNtFRkYKhw4derQFDWJSbI+HDh0qPh8xYoS4nSftcfPmzcVtIiIiyt2G7XHd5El7JGU1qZ/33ntP/GxKSopPykf+N3LkSLe2tTr9IwDCu++++9B2rv2j6OjoCsvw4L4qilfXsnnbvwjkeOYQ6AD2zjvvoEuXLuLztLQ0bNq0qVb2ffr0afGqAVD1ZCGuQkJCMGHCBPF5TYadrV+/XlxY++TJk4iNja32vjwRGxuLU6dOicdct25dtffl+r0nTJhQ4dIg5XGt70OHDuHKlSvVLkcweffdd8X/N8eOHcPgwYP9XaQ6o3v37ti+fTsiIyMBOO8hHTp0aNAsWu9vUmuPd+/e7XZPsTdlApxLOJUxGo0QBOGhbdgeE5HU3L9/H9u3bxefe9q2lvWPXOd+WLZsWbltq6+UTRoYDP0LJsABTKVSYd26dahfvz4AwGAwYMyYMZg4cSJOnjxZYdA7HA5kZGRg7ty5Fe7btbPQqlUr9OjRw6uyuc4EevDgQVy9etWrz5dp0qQJunXrhm7duqFt27bV2oe32rZtKx6zbOIXb505cwZnzpwRn1c1M+qDevbsiZYtW4rPuY6rU0xMjPj/plu3blAoFP4uUp3Sp08fbNmyRRz6qdPpMHjwYJw+fdq/BQsCUmuPT5w4IQ63i4qKwogRI7za1zPPPCM+djgc2LdvX7nbsT0mIilZuXJltdrWsv7Rr3/9a3GehJs3b1bYtvrC5MmTg6Z/wQQ4wDVv3hxHjx5Fp06dADjH/K9duxZdu3ZFYmIihg4dimnTpuGVV17B9OnT8cwzz6B+/fro37+/243q/fv3R3x8PADn/Viu969521kAgKeeegrJycnic6l1GFw7rM2bN0efPn283odrvT/qs3gUvFJSUrBp0yaEhYUBAAoLCzFo0CBxjVWqPim1x65t3NixYz2+n7bMg/ew+XKGZbbH/vPZZ5+hc+fOHv9t3rzZ30Um8quatq1NmzZ1W5mgNtvW8n7Px48fF9/fsWMHkpKSxAS8LvcvmADXAc2aNcP333+P999/X5ztFXAOo9ixYweWL1+Ozz77DMuWLcO+ffvEiVpkMhn69euHjRs3Yu/evYiKigIAbN++Hbm5ueJ+vB3aVrbvKVOmiM+XLl0qmQ6DzWbDihUrxOdTpkyp1qylrvV+48YNZGZm1kr5iAYNGoR169aJw5Xy8vIwcODAoFrCwF+k0B6fO3cOp06dqlGZHrRu3Tro9foa7+dBbI/96969e+IVeE/+yib+IZKi2mpbXT9Xm21reb/nkpISt/evXbsGQRDEEXp1tX/BBLiOCA8Px29/+1vcuHED6enpeOWVV/Dkk0+icePGUKvVCAsLQ2JiItq3b4+pU6fio48+wpUrV7B//36MGjXKbV+uZ4t69OhR7WWHXM+YS6nDsGPHDrcOa3Wu2ADOZUC6d+8uPi9b+oeoNjz33HNYvXo1lErnZP93797FM888g2vXrvm5ZHVfsLfHrmVKSkpyG85cXSUlJUhPT6/xfh7E9piI6oraalvHjx8vjvLyVdtalXHjxtXp/oVMkMplOyIiIiIioiCWnJyMrKwsNGvWDDdu3Ch3m7KRMikpKcjIyHh0hQsQvAJMREREREREksAEmIiIiIiIiCSBCTARERERERFJAhNgIiIiIiIikgQmwERERERERCQJTICJiIiIiIhIEpgAExERERERkSRwHWAiIiIiIiKSBF4BJiIiIiIiIklgAkxERERERESSwASYiIiIiIiIJIEJMBEREREREUkCE2AiIiIiIiKSBCbAREREREREJAlMgImIiIiIiEgSmAATERERERGRJDABJiIiIiIiIklgAkxERERERESSwASYiIiIiIiIJIEJMBEREREREUkCE2AiIiIiIiKSBCbAREREREREJAlMgImIiIiIiEgSmAATERERERGRJDABJiIiIiIiIklgAkxERERERESSwASYiIiIiIiIJIEJMBEREREREUkCE2AiIiIiIiKSBCbAREREREREJAlMgImIiIiIiEgSmAATERERERGRJDABJiIiIiIiIklgAkxERERERESSwASYiIiIiIiIJIEJMBEREREREUkCE2AiIiIiIiKShP8PYVt0DqUu+nkAAAAASUVORK5CYII=",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "!dptb run band -i ./hbn_sp_model/sktb.json -stu ../structures/hBN.vasp -o band_hBN\n",
-        "\n",
-        "# display the band plot:\n",
-        "from IPython.display import Image, display\n",
-        "import os\n",
-        "image_path = f'./band_hBN/results/band.png'\n",
-        "display(Image(filename=image_path))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "5363a34e",
-      "metadata": {},
-      "source": [
-        "It can be clearly seen that the bands near 0 eV are missing. This is because for the hBN system, our built-in empirical model parameters only include sp orbitals. The d orbital parameters are all set to 0 just to maintain a consistent format."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "f0b4e111",
-      "metadata": {},
-      "source": [
-        "Similarly, we can obtain the corresponding model parameters for individual Si and GaAs systems. Readers are invited to explore this themselves."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "b81e0745",
-      "metadata": {},
-      "source": [
-        "<div style=\"color:black; background-color:#FFF3E9; border: 1px solid #FFE0C3; border-radius: 10px; margin-bottom:1rem\">\n",
-        "    <p style=\"margin:1rem; padding-left: 1rem; line-height: 2.5;\">\n",
-        "        Author: <a style=\"font-weight:normal\" href=\"mailto:guqq@ustc.edu.cn\">Gu, Qiangqiang : guqq@ustc.edu.cn</a>\n",
-        "    </p>\n",
-        "    <p style=\"margin:1rem; padding-left: 1rem; line-height: 2.5;\">\n",
-        "        Thank you for reading!\n",
-        "    </p>\n",
-        "</div>"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/docs/hands_on/tutorial1_c_chain.ipynb b/docs/hands_on/tutorial1_c_chain.ipynb
index 21aab86..1e13d65 100644
--- a/docs/hands_on/tutorial1_c_chain.ipynb
+++ b/docs/hands_on/tutorial1_c_chain.ipynb
@@ -154,20 +154,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[01;34m./\u001b[00m\n",
-      "├── chain.vasp\n",
-      "├── negf_chain_new.json\n",
-      "└── nnsk_C_new.json\n",
-      "\n",
-      "0 directories, 3 files\n"
+      "chain.vasp\tnegf_chain_new.json\tnnsk_C_new.json\n"
      ]
     }
    ],
    "source": [
     "import os\n",
+    "from pathlib import Path\n",
     "workdir='../../examples/atomic_chain_api/input_files'\n",
-    "os.chdir(workdir)\n",
-    "!tree -L 1 ./"
+    "wd = Path(workdir)\n",
+    "if not wd.is_dir():\n",
+    "    raise FileNotFoundError(f\"Workdir '{wd}' not found. Please adjust 'workdir'.\")\n",
+    "os.chdir(wd)\n",
+    "print(\"\\t\".join(sorted(os.listdir(\".\"))))"
    ]
   },
   {
@@ -188,8 +187,10 @@
     "from dpnegf.utils.loggers import set_log_handles\n",
     "import logging\n",
     "from pathlib import Path\n",
+    "\n",
+    "\n",
     "results_path = '../band_plot'\n",
-    "log_path = results_path+'/log'\n",
+    "log_path = os.path.join(results_path, 'log')\n",
     "log_level = logging.INFO\n",
     "set_log_handles(log_level, Path(log_path) if log_path else None)"
    ]
@@ -305,7 +306,6 @@
      "output_type": "stream",
      "text": [
       "DPNEGF ERROR   TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
-      "rm: cannot remove '../band_plot': Directory not empty\n",
       "DPNEGF WARNING Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: 3.0\n",
       "DPNEGF WARNING This is very dangerous, please make sure you know what you are doing.\n",
       "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
@@ -333,7 +333,8 @@
    ],
    "source": [
     "from dptb.postprocess.bandstructure.band import Band\n",
-    " \n",
+    "import shutil\n",
+    "\n",
     "task_options = {\n",
     "        \"task\": \"band\",\n",
     "        \"kline_type\":\"abacus\",\n",
@@ -348,9 +349,8 @@
     "\n",
     "       }\n",
     "\n",
-    "if os.path.exists(results_path):\n",
-    "    os.system('rm -r %s' % results_path)\n",
-    "\n",
+    "if os.path.isdir(results_path):\n",
+    "    shutil.rmtree(results_path, ignore_errors=True) \n",
     "\n",
     "band = Band(model, results_path)\n",
     "AtomicData_options = {\"r_max\": 3.0, \"pbc\": True}\n",
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "0f64a6b8",
    "metadata": {},
    "outputs": [
@@ -396,10 +396,12 @@
     "negf_input_file =  \"negf_chain_new.json\" \n",
     "structure =  \"chain.vasp\" \n",
     "output = \"../negf_output\"  \n",
-    "if os.path.exists(output):\n",
-    "    os.system('rm -rf %s' % output)\n",
-    "os.makedirs(output)\n",
-    "negf_json = json.load(open(negf_input_file))\n"
+    "if os.path.isdir(output):\n",
+    "    shutil.rmtree(output, ignore_errors=True)\n",
+    "os.makedirs(output, exist_ok=True)\n",
+    "\n",
+    "with open(negf_input_file, \"r\") as f:\n",
+    "    negf_json = json.load(f)\n"
    ]
   },
   {
@@ -493,13 +495,7 @@
       "DPNEGF INFO    ------ k-point for NEGF -----\n",
       "DPNEGF INFO    Gamma Center: True\n",
       "DPNEGF INFO    Time Reversal: True\n",
-      "DPNEGF INFO    k-points Num: 1\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "DPNEGF INFO    k-points Num: 1\n",
       "DPNEGF INFO    k-points: [[0 0 0]]\n",
       "DPNEGF INFO    k-points weights: [1.]\n",
       "DPNEGF INFO    --------------------------------\n",
@@ -566,8 +562,8 @@
     }
    ],
    "source": [
-    "if os.path.exists(output):\n",
-    "    os.system('rm -r %s' % output)\n",
+    "if os.path.isdir(output):\n",
+    "    shutil.rmtree(output, ignore_errors=True)\n",
     "os.makedirs(output)\n",
     "\n",
     "negf = NEGF(\n",
@@ -611,6 +607,8 @@
    "id": "0fd1a663",
    "metadata": {},
    "source": [
+    "### Results Analysis\n",
+    "\n",
     "We can inspect the outputs of the NEGF run by loading the results file (`negf.out.pth`). The output  contains:\n",
     "- `T_avg`: the total transmission as a function of energy,\n",
     "- `T_k`: k-point resolved transmission (if k-sampling is used),\n",
@@ -633,6 +631,14 @@
     "negf_out = torch.load(results_path,weights_only=False)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b32cf91e",
+   "metadata": {},
+   "source": [
+    "The result file is a dict containing all the results."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 13,
diff --git a/docs/index.rst b/docs/index.rst
index 5fc05f5..75cd183 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -34,11 +34,11 @@ For more details, see our papers:
    easy_install
    hands_on/index
 
-.. toctree::
-   :maxdepth: 2
-   :caption: INPUT TAG
+.. .. toctree::
+..    :maxdepth: 2
+..    :caption: INPUT TAG
    
-   input_params/index
+..    input_params/index
 
 .. toctree::
    :maxdepth: 2

From 86c2e1ee791908ee04ca329ad5512fd7fec5c611 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 14:54:01 +0800
Subject: [PATCH 133/152] add  example graphene

---
 docs/hands_on/tutorial2_2d_mat.ipynb          | 157 +++++++++++++
 examples/graphene/band.json                   |  18 ++
 .../extr_baseline/grap_spd_model/sktb.json    | 212 ++++++++++++++++++
 .../extr_baseline/input_templete.json         |  85 +++++++
 examples/graphene/graphene.vasp               |  13 ++
 examples/graphene/train/data/POSCAR           |  10 +
 .../train/data/kpath.0/band_structure.png     | Bin 0 -> 199740 bytes
 .../train/data/kpath.0/eigenvalues.npy        | Bin 0 -> 7408 bytes
 .../graphene/train/data/kpath.0/info.json     |  12 +
 .../graphene/train/data/kpath.0/kpoints.npy   | Bin 0 -> 968 bytes
 .../graphene/train/data/kpath.0/xdat.traj     | Bin 0 -> 381 bytes
 examples/graphene/train/input.json            |  71 ++++++
 12 files changed, 578 insertions(+)
 create mode 100644 docs/hands_on/tutorial2_2d_mat.ipynb
 create mode 100644 examples/graphene/band.json
 create mode 100644 examples/graphene/extr_baseline/grap_spd_model/sktb.json
 create mode 100644 examples/graphene/extr_baseline/input_templete.json
 create mode 100644 examples/graphene/graphene.vasp
 create mode 100644 examples/graphene/train/data/POSCAR
 create mode 100644 examples/graphene/train/data/kpath.0/band_structure.png
 create mode 100644 examples/graphene/train/data/kpath.0/eigenvalues.npy
 create mode 100644 examples/graphene/train/data/kpath.0/info.json
 create mode 100644 examples/graphene/train/data/kpath.0/kpoints.npy
 create mode 100644 examples/graphene/train/data/kpath.0/xdat.traj
 create mode 100644 examples/graphene/train/input.json

diff --git a/docs/hands_on/tutorial2_2d_mat.ipynb b/docs/hands_on/tutorial2_2d_mat.ipynb
new file mode 100644
index 0000000..4bc7898
--- /dev/null
+++ b/docs/hands_on/tutorial2_2d_mat.ipynb
@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "078cef78",
+   "metadata": {},
+   "source": [
+    "# Tutorial 2:  Quantum Transport in 2D materials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "026e2584",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "band.json\tgraphene.vasp\tlatest_dptb_b3.300_c2.600_w0.200.pth\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "workdir='../../examples/graphene'\n",
+    "wd = Path(workdir)\n",
+    "if not wd.is_dir():\n",
+    "    raise FileNotFoundError(f\"Workdir '{wd}' not found. Please adjust 'workdir'.\")\n",
+    "os.chdir(wd)\n",
+    "print(\"\\t\".join(sorted(os.listdir(\".\"))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "664ee3cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO                                      Version Info                                  \n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dpnegf.utils.loggers import set_log_handles\n",
+    "import logging\n",
+    "from pathlib import Path\n",
+    "\n",
+    "\n",
+    "results_path = '../band_plot'\n",
+    "log_path = os.path.join(results_path, 'log')\n",
+    "log_level = logging.INFO\n",
+    "set_log_handles(log_level, Path(log_path) if log_path else None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c4797d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'config'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[3], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdptb\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbuild\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m build_model\n\u001b[1;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m  \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlatest_dptb_b3.300_c2.600_w0.200.pth\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# the model for demonstration\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/dptb/nn/build.py:50\u001b[0m, in \u001b[0;36mbuild_model\u001b[0;34m(checkpoint, model_options, common_options, no_check)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     49\u001b[0m     f \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(checkpoint, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m, weights_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 50\u001b[0m     ckptconfig \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mconfig\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m     51\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m f\n\u001b[1;32m     53\u001b[0m \u001b[38;5;66;03m# init model from checkpoint\u001b[39;00m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'config'"
+     ]
+    }
+   ],
+   "source": [
+    "from dptb.nn.build import build_model\n",
+    "\n",
+    "model =  \"latest_dptb_b3.300_c2.600_w0.200.pth\" # the model for demonstration\n",
+    "\n",
+    "model = build_model(model,\n",
+    "                    model_options= model_json['model_options'],\n",
+    "                    common_options=model_json['common_options'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1041ae1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dptb.postprocess.bandstructure.band import Band\n",
+    "import shutil\n",
+    "\n",
+    "task_options = {\n",
+    "        \"task\": \"band\",\n",
+    "        \"kline_type\":\"abacus\",\n",
+    "        \"kpath\":[\n",
+    "                [0, 0, 0, 50],\n",
+    "                [0.5, 0, 0, 50],\n",
+    "                [0.3333333, 0.3333333, 0, 50],\n",
+    "                [0, 0, 0, 1]\n",
+    "                ],\n",
+    "        \"klabels\":[\"G\", \"M\", \"K\", \"G\"],\n",
+    "        \"emin\":-20,\n",
+    "        \"emax\": 20,\n",
+    "        \"nel_atom\":{\"C\": 4} \n",
+    "\n",
+    "       }\n",
+    "\n",
+    "if os.path.isdir(results_path):\n",
+    "    shutil.rmtree(results_path, ignore_errors=True) \n",
+    "\n",
+    "band = Band(model, results_path)\n",
+    "AtomicData_options = {\"r_max\": 3.0, \"pbc\": True}\n",
+    "band.get_bands(data = uni_cell_atoms, \n",
+    "               kpath_kwargs = task_options,\n",
+    "               AtomicData_options = AtomicData_options)\n",
+    "band.band_plot(emin = task_options['emin'],\n",
+    "               emax = task_options['emax'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dpnegf-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/graphene/band.json b/examples/graphene/band.json
new file mode 100644
index 0000000..160ed40
--- /dev/null
+++ b/examples/graphene/band.json
@@ -0,0 +1,18 @@
+{   
+    "structure":"train/data/POSCAR",
+    "task_options": {
+        "task": "band",
+        "kline_type":"abacus",
+        "kpath":[
+            [0, 0, 0, 50],
+            [0.5, 0, 0, 50],
+            [0.3333333, 0.3333333, 0, 50],
+            [0, 0, 0, 1]
+        ],
+        "nkpoints":151,
+        "klabels":["G", "M", "K", "G"],
+        "emin":-20,
+        "emax":20,
+        "nel_atom":{"C":4}
+    }
+}
\ No newline at end of file
diff --git a/examples/graphene/extr_baseline/grap_spd_model/sktb.json b/examples/graphene/extr_baseline/grap_spd_model/sktb.json
new file mode 100644
index 0000000..50ad48a
--- /dev/null
+++ b/examples/graphene/extr_baseline/grap_spd_model/sktb.json
@@ -0,0 +1,212 @@
+{
+    "version": 2,
+    "unit": "eV",
+    "model_options": {
+        "nnsk": {
+            "onsite": {
+                "method": "uniform"
+            },
+            "hopping": {
+                "method": "poly4pow",
+                "rs": {
+                    "C-C": 4.39
+                },
+                "w": 0.2
+            },
+            "soc": {},
+            "freeze": false,
+            "push": false,
+            "std": 0.01,
+            "atomic_radius": "cov"
+        }
+    },
+    "common_options": {
+        "basis": {
+            "C": [
+                "2s",
+                "2p",
+                "d*"
+            ]
+        },
+        "dtype": "float32",
+        "device": "cuda",
+        "overlap": true
+    },
+    "model_params": {
+        "onsite": {
+            "C-2s-0": [
+                -0.10686016082763672
+            ],
+            "C-2p-0": [
+                0.12751197814941406
+            ],
+            "C-d*-0": [
+                0.0
+            ]
+        },
+        "hopping": {
+            "C-C-2s-2s-0": [
+                -6.684751033782959,
+                4.326050281524658,
+                7.401242733001709,
+                -24.684650421142578,
+                7.83709192276001,
+                -0.5529974102973938
+            ],
+            "C-C-2s-2p-0": [
+                -6.9853434562683105,
+                -0.15866073966026306,
+                11.681811332702637,
+                -10.92180347442627,
+                -1.95439612865448,
+                1.0654785633087158
+            ],
+            "C-C-2s-d*-0": [
+                -0.00010646219016052783,
+                6.032549572410062e-05,
+                -0.00012444439926184714,
+                0.0001085955009330064,
+                -0.00013242202112451196,
+                0.822330117225647
+            ],
+            "C-C-2p-2p-0": [
+                6.175818920135498,
+                -0.6068210601806641,
+                -9.516790390014648,
+                14.982359886169434,
+                -2.222520351409912,
+                -0.3968461751937866
+            ],
+            "C-C-2p-2p-1": [
+                -2.7375760078430176,
+                1.839799165725708,
+                2.1155834197998047,
+                -7.809864044189453,
+                2.5414087772369385,
+                1.215492844581604
+            ],
+            "C-C-2p-d*-0": [
+                -8.977071411209181e-05,
+                4.293958772905171e-05,
+                0.0001835303846746683,
+                0.00033714837627485394,
+                0.00011189249926246703,
+                -0.8198558688163757
+            ],
+            "C-C-2p-d*-1": [
+                -0.00015152650303207338,
+                0.00021211523562669754,
+                2.822246460709721e-05,
+                -0.00032705828198231757,
+                0.0003069117374252528,
+                0.8305988311767578
+            ],
+            "C-C-d*-d*-0": [
+                9.454079554416239e-05,
+                -9.590329136699438e-05,
+                0.00011994513624813408,
+                -6.711072637699544e-05,
+                0.0002700634067878127,
+                0.8173061013221741
+            ],
+            "C-C-d*-d*-1": [
+                0.0001105186966015026,
+                -5.8947422076016665e-05,
+                0.00023174105444923043,
+                1.3896875316277146e-05,
+                0.00017471887986175716,
+                0.8142954707145691
+            ],
+            "C-C-d*-d*-2": [
+                -0.00010158663644688204,
+                0.0003293976478744298,
+                4.862011701334268e-05,
+                -7.672052015550435e-05,
+                0.00016208819579333067,
+                0.8196141719818115
+            ]
+        },
+        "overlap": {
+            "C-C-2s-2s-0": [
+                0.2466021627187729,
+                -0.20137032866477966,
+                -0.22465133666992188,
+                0.9475207924842834,
+                -0.3242596387863159,
+                0.35583510994911194
+            ],
+            "C-C-2s-2p-0": [
+                0.30228379368782043,
+                -0.19255711138248444,
+                -0.40023675560951233,
+                1.26386559009552,
+                -0.3918810188770294,
+                -5.578631680691615e-05
+            ],
+            "C-C-2s-d*-0": [
+                -9.066608618013561e-05,
+                -4.845444345846772e-05,
+                7.032023859210312e-06,
+                -0.0003356275847181678,
+                -3.039978037122637e-05,
+                -0.740434467792511
+            ],
+            "C-C-2p-2p-0": [
+                -0.31055670976638794,
+                0.0015619457699358463,
+                0.7435058951377869,
+                -1.2593721151351929,
+                0.22618918120861053,
+                -0.00022265399456955492
+            ],
+            "C-C-2p-2p-1": [
+                0.13884934782981873,
+                -0.12665610015392303,
+                -0.11550959944725037,
+                0.5953179597854614,
+                -0.22506600618362427,
+                0.875838041305542
+            ],
+            "C-C-2p-d*-0": [
+                0.00012514149420894682,
+                -6.299408414633945e-05,
+                0.0003052930405829102,
+                -0.00024931083316914737,
+                -3.0835799407213926e-05,
+                0.737001895904541
+            ],
+            "C-C-2p-d*-1": [
+                -9.066608618013561e-05,
+                -4.8454414354637265e-05,
+                7.032198482193053e-06,
+                -0.00033562741009518504,
+                -3.040030424017459e-05,
+                -0.740434467792511
+            ],
+            "C-C-d*-d*-0": [
+                -9.120674803853035e-05,
+                0.0001677007821854204,
+                1.4481425751000643e-05,
+                0.0002868282899726182,
+                0.00019680232799146324,
+                0.7440820932388306
+            ],
+            "C-C-d*-d*-1": [
+                5.5082375183701515e-05,
+                0.00013481048517860472,
+                6.67735148454085e-05,
+                0.00022058241302147508,
+                0.00018173769058194011,
+                0.741083025932312
+            ],
+            "C-C-d*-d*-2": [
+                0.00012257523485459387,
+                -0.0001119638400268741,
+                0.00022559240460395813,
+                0.00034283509012311697,
+                0.00016658403910696507,
+                0.7432126402854919
+            ]
+        }
+    }
+}
\ No newline at end of file
diff --git a/examples/graphene/extr_baseline/input_templete.json b/examples/graphene/extr_baseline/input_templete.json
new file mode 100644
index 0000000..1272931
--- /dev/null
+++ b/examples/graphene/extr_baseline/input_templete.json
@@ -0,0 +1,85 @@
+{
+    "common_options": {
+        "basis": {
+            "C": [
+                "2s",
+                "2p",
+                "d*"
+            ]
+        },
+        "device": "cpu",
+        "dtype": "float32",
+        "overlap": true,
+        "seed": 3982377700
+    },
+    "train_options": {
+        "num_epoch": 2,
+        "batch_size": 1,
+        "optimizer": {
+            "lr": 0.01,
+            "type": "Adam"
+        },
+        "lr_scheduler": {
+            "type": "exp",
+            "gamma": 0.999
+        },
+        "loss_options": {
+            "train": {
+                "method": "eigvals",
+                "diff_on": false,
+                "eout_weight": 0.001,
+                "diff_weight": 0.01
+            }
+        },
+        "save_freq": 1,
+        "validation_freq": 10,
+        "display_freq": 100,
+        "ref_batch_size": 1,
+        "val_batch_size": 1,
+        "max_ckpt": 4
+    },
+    "model_options": {
+        "nnsk": {
+            "onsite": {
+                "method": "uniform"
+            },
+            "hopping": {
+                "method": "poly4pow",
+                "rs": {
+                    "C-C": 4.39
+                },
+                "w": 0.2
+            },
+            "soc": {},
+            "freeze": [
+                "overlap"
+            ],
+            "push": false,
+            "std": 0.01,
+            "atomic_radius": "cov"
+        }
+    },
+    "data_options": {
+        "train": {
+            "root": "path/to/dataset",
+            "prefix": "prexfix_for_dataset",
+            "get_eigenvalues": true,
+            "type": "DefaultDataset",
+            "get_Hamiltonian": false
+        },
+        "validation": {
+            "root": "path/to/dataset",
+            "prefix": "prexfix_for_dataset",
+            "get_eigenvalues": true,
+            "type": "DefaultDataset",
+            "get_Hamiltonian": false
+        },
+        "reference": {
+            "root": "path/to/dataset",
+            "prefix": "prexfix_for_dataset",
+            "get_eigenvalues": true,
+            "type": "DefaultDataset",
+            "get_Hamiltonian": false
+        }
+    }
+}
\ No newline at end of file
diff --git a/examples/graphene/graphene.vasp b/examples/graphene/graphene.vasp
new file mode 100644
index 0000000..a003663
--- /dev/null
+++ b/examples/graphene/graphene.vasp
@@ -0,0 +1,13 @@
+graphite                                
+   1.00000000000000     
+     2.4600000381000000    0.0000000000000000    0.0000000000000000
+    -1.2300000191000000    2.1304225262999998    0.0000000000000000
+     0.0000000000000000    0.0000000000000000   20.0000000000000000
+   C 
+     2
+Direct
+  0.3333333429999996  0.6666666870000029  0.5000000000000000
+  0.6666666269999979  0.3333333129999971  0.5000000000000000
+ 
+  0.00000000E+00  0.00000000E+00  0.00000000E+00
+  0.00000000E+00  0.00000000E+00  0.00000000E+00
diff --git a/examples/graphene/train/data/POSCAR b/examples/graphene/train/data/POSCAR
new file mode 100644
index 0000000..22a41d8
--- /dev/null
+++ b/examples/graphene/train/data/POSCAR
@@ -0,0 +1,10 @@
+C 
+ 1.0000000000000000
+     2.5039999485000002    0.0000000000000000    0.0000000000000000
+    -1.2519999743000001    2.1685275664999999    0.0000000000000000
+     0.0000000000000000    0.0000000000000000   30.0000000000000000
+ C  
+   2
+Cartesian
+ -0.0000000012853333  1.4456850884267272 15.0000000000000000
+  1.2519999003653348  0.7228424780732728 15.0000000000000000
diff --git a/examples/graphene/train/data/kpath.0/band_structure.png b/examples/graphene/train/data/kpath.0/band_structure.png
new file mode 100644
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diff --git a/examples/graphene/train/data/kpath.0/eigenvalues.npy b/examples/graphene/train/data/kpath.0/eigenvalues.npy
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diff --git a/examples/graphene/train/data/kpath.0/info.json b/examples/graphene/train/data/kpath.0/info.json
new file mode 100644
index 0000000..cb9046c
--- /dev/null
+++ b/examples/graphene/train/data/kpath.0/info.json
@@ -0,0 +1,12 @@
+{
+    "nframes": 1,
+    "natoms": 2,
+    "pos_type": "ase",
+    "pbc": true,
+    "bandinfo": {
+        "band_min": 0,
+        "band_max": 8,
+        "emin": null,
+        "emax": null
+    }
+}
diff --git a/examples/graphene/train/data/kpath.0/kpoints.npy b/examples/graphene/train/data/kpath.0/kpoints.npy
new file mode 100644
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diff --git a/examples/graphene/train/data/kpath.0/xdat.traj b/examples/graphene/train/data/kpath.0/xdat.traj
new file mode 100644
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diff --git a/examples/graphene/train/input.json b/examples/graphene/train/input.json
new file mode 100644
index 0000000..4abbc5e
--- /dev/null
+++ b/examples/graphene/train/input.json
@@ -0,0 +1,71 @@
+{
+    "common_options": {
+        "basis": {
+            "C": [
+                "2s",
+                "2p",
+                "d*"
+            ]
+        },
+        "device": "cpu",
+        "dtype": "float32",
+        "overlap": true,
+        "seed": 3982377700
+    },
+    "train_options": {
+        "num_epoch": 5000,
+        "batch_size": 1,
+        "optimizer": {
+            "lr": 0.01,
+            "type": "Adam"
+        },
+        "lr_scheduler": {
+            "type": "exp",
+            "gamma": 0.9995
+        },
+        "loss_options": {
+            "train": {
+                "method": "eigvals",
+                "diff_on": false,
+                "eout_weight": 0.001,
+                "diff_weight": 0.01
+            }
+        },
+        "save_freq": 1,
+        "validation_freq": 10,
+        "display_freq": 100,
+        "ref_batch_size": 1,
+        "val_batch_size": 1,
+        "max_ckpt": 4
+    },
+    "model_options": {
+        "nnsk": {
+            "onsite": {
+                "method": "strain"
+            },
+            "hopping": {
+                "method": "poly4pow",
+                "rs": {
+                    "C-C": 4.39
+                },
+                "w": 0.2
+            },
+            "soc": {},
+            "freeze": [
+                "overlap"
+            ],
+            "push": false,
+            "std": 0.01,
+            "atomic_radius": "cov"
+        }
+    },
+    "data_options": {
+        "train": {
+            "root": "./data",
+            "prefix": "kpath",
+            "get_eigenvalues": true,
+            "type": "DefaultDataset",
+            "get_Hamiltonian": false
+        }
+    }
+}
\ No newline at end of file

From 1cdd53130a7047bf11aec24f10a5898016bae27f Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 15:02:34 +0800
Subject: [PATCH 134/152] add esk input.json

---
 examples/graphene/extr_baseline/c_spds.json | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 examples/graphene/extr_baseline/c_spds.json

diff --git a/examples/graphene/extr_baseline/c_spds.json b/examples/graphene/extr_baseline/c_spds.json
new file mode 100644
index 0000000..5da4b2c
--- /dev/null
+++ b/examples/graphene/extr_baseline/c_spds.json
@@ -0,0 +1,11 @@
+{
+    "common_options": {
+        "basis": {
+                "C": [                
+                    "2s",
+                    "2p",
+                    "d*"
+                    ]
+        }
+    }
+}
\ No newline at end of file

From c9ff0b59243f1a77ca21e1f5eece9b2d6571f64c Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 15:26:32 +0800
Subject: [PATCH 135/152] add example hBN

---
 docs/hands_on/tutorial2_2d_mat.ipynb          |  22 +
 .../train/train_out/checkpoint/nnsk.best.pth  |   1 +
 .../train_out/checkpoint/nnsk.ep4997.pth      | Bin 0 -> 7731 bytes
 .../train_out/checkpoint/nnsk.ep4998.pth      | Bin 0 -> 7731 bytes
 .../train_out/checkpoint/nnsk.ep4999.pth      | Bin 0 -> 7731 bytes
 .../train_out/checkpoint/nnsk.ep5000.pth      | Bin 0 -> 7731 bytes
 .../train_out/checkpoint/nnsk.iter4997.pth    | Bin 0 -> 7960 bytes
 .../train_out/checkpoint/nnsk.iter4998.pth    | Bin 0 -> 7960 bytes
 .../train_out/checkpoint/nnsk.iter4999.pth    | Bin 0 -> 7960 bytes
 .../train_out/checkpoint/nnsk.iter5000.pth    | Bin 0 -> 7960 bytes
 .../train_out/checkpoint/nnsk.latest.pth      |   1 +
 examples/hBN/band.json                        |  18 +
 examples/hBN/extra_baseline/bn_spds.json      |  16 +
 .../extra_baseline/hbn_spd_model/sktb.json    | 477 ++++++++++++++++++
 .../hBN/extra_baseline/input_templete.json    |  93 ++++
 examples/hBN/train/data/POSCAR                |  10 +
 .../hBN/train/data/kpath.0/band_structure.png | Bin 0 -> 163121 bytes
 .../hBN/train/data/kpath.0/eigenvalues.npy    | Bin 0 -> 31328 bytes
 examples/hBN/train/data/kpath.0/info.json     |  12 +
 examples/hBN/train/data/kpath.0/kpoints.npy   | Bin 0 -> 7328 bytes
 examples/hBN/train/data/kpath.0/xdat.traj     | Bin 0 -> 381 bytes
 examples/hBN/train/input.json                 |  79 +++
 .../train/train_out/checkpoint/nnsk.best.pth  |   1 +
 .../train_out/checkpoint/nnsk.ep4997.pth      | Bin 0 -> 10931 bytes
 .../train_out/checkpoint/nnsk.ep4998.pth      | Bin 0 -> 10931 bytes
 .../train_out/checkpoint/nnsk.ep4999.pth      | Bin 0 -> 10931 bytes
 .../train_out/checkpoint/nnsk.ep5000.pth      | Bin 0 -> 10931 bytes
 .../train_out/checkpoint/nnsk.iter4997.pth    | Bin 0 -> 11224 bytes
 .../train_out/checkpoint/nnsk.iter4998.pth    | Bin 0 -> 11224 bytes
 .../train_out/checkpoint/nnsk.iter4999.pth    | Bin 0 -> 11224 bytes
 .../train_out/checkpoint/nnsk.iter5000.pth    | Bin 0 -> 11224 bytes
 .../train_out/checkpoint/nnsk.latest.pth      |   1 +
 32 files changed, 731 insertions(+)
 create mode 120000 examples/graphene/train/train_out/checkpoint/nnsk.best.pth
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep4997.pth
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep4998.pth
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep4999.pth
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep5000.pth
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.iter4997.pth
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.iter4998.pth
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.iter4999.pth
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.iter5000.pth
 create mode 120000 examples/graphene/train/train_out/checkpoint/nnsk.latest.pth
 create mode 100644 examples/hBN/band.json
 create mode 100644 examples/hBN/extra_baseline/bn_spds.json
 create mode 100644 examples/hBN/extra_baseline/hbn_spd_model/sktb.json
 create mode 100644 examples/hBN/extra_baseline/input_templete.json
 create mode 100644 examples/hBN/train/data/POSCAR
 create mode 100644 examples/hBN/train/data/kpath.0/band_structure.png
 create mode 100644 examples/hBN/train/data/kpath.0/eigenvalues.npy
 create mode 100644 examples/hBN/train/data/kpath.0/info.json
 create mode 100644 examples/hBN/train/data/kpath.0/kpoints.npy
 create mode 100644 examples/hBN/train/data/kpath.0/xdat.traj
 create mode 100644 examples/hBN/train/input.json
 create mode 120000 examples/hBN/train/train_out/checkpoint/nnsk.best.pth
 create mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.ep4997.pth
 create mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.ep4998.pth
 create mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.ep4999.pth
 create mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.ep5000.pth
 create mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.iter4997.pth
 create mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.iter4998.pth
 create mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.iter4999.pth
 create mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.iter5000.pth
 create mode 120000 examples/hBN/train/train_out/checkpoint/nnsk.latest.pth

diff --git a/docs/hands_on/tutorial2_2d_mat.ipynb b/docs/hands_on/tutorial2_2d_mat.ipynb
index 4bc7898..aa5e1a8 100644
--- a/docs/hands_on/tutorial2_2d_mat.ipynb
+++ b/docs/hands_on/tutorial2_2d_mat.ipynb
@@ -8,6 +8,28 @@
     "# Tutorial 2:  Quantum Transport in 2D materials"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c514189b",
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dptb esk bn_spds.json -o hbn_spd_model\n",
+    "dptb config -m hbn_spd_model/sktb.json -tr -sk ./\n",
+    "\n",
+    "\"onsite\": {\n",
+    "    \"method\": \"strain\"\n",
+    "}\n",
+    "\n",
+    "dptb train input.json -i ../extra_baseline/hbn_spd_model/sktb.json -o train_out\n",
+    "dptb run band.json -i train/train_out/checkpoint/nnsk.best.pth -o band_plot"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.best.pth b/examples/graphene/train/train_out/checkpoint/nnsk.best.pth
new file mode 120000
index 0000000..7b032ac
--- /dev/null
+++ b/examples/graphene/train/train_out/checkpoint/nnsk.best.pth
@@ -0,0 +1 @@
+/personal/DeepTB/dptb_Zjj/dpnegf/examples/graphene/train/train_out/checkpoint/nnsk.ep5000.pth
\ No newline at end of file
diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.ep4997.pth b/examples/graphene/train/train_out/checkpoint/nnsk.ep4997.pth
new file mode 100644
index 0000000000000000000000000000000000000000..23cb188d64ea0285f0772b0f0c81b0efcdf22ada
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diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.ep4998.pth b/examples/graphene/train/train_out/checkpoint/nnsk.ep4998.pth
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diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.iter4998.pth b/examples/graphene/train/train_out/checkpoint/nnsk.iter4998.pth
new file mode 100644
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diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.iter4999.pth b/examples/graphene/train/train_out/checkpoint/nnsk.iter4999.pth
new file mode 100644
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diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.latest.pth b/examples/graphene/train/train_out/checkpoint/nnsk.latest.pth
new file mode 120000
index 0000000..4ec2f23
--- /dev/null
+++ b/examples/graphene/train/train_out/checkpoint/nnsk.latest.pth
@@ -0,0 +1 @@
+/personal/DeepTB/dptb_Zjj/dpnegf/examples/graphene/train/train_out/checkpoint/nnsk.iter5000.pth
\ No newline at end of file
diff --git a/examples/hBN/band.json b/examples/hBN/band.json
new file mode 100644
index 0000000..b60fe88
--- /dev/null
+++ b/examples/hBN/band.json
@@ -0,0 +1,18 @@
+{   
+    "structure":"train/data/POSCAR",
+    "task_options": {
+        "task": "band",
+        "kline_type":"abacus",
+        "kpath":[
+            [0, 0, 0, 50],
+            [0.5, 0, 0, 50],
+            [0.3333333, 0.3333333, 0, 50],
+            [0, 0, 0, 1]
+        ],
+        "nkpoints":151,
+        "klabels":["G", "M", "K", "G"],
+        "emin":-25,
+        "emax":20,
+        "nel_atom":{"B":3,"N":5}
+    }
+}
\ No newline at end of file
diff --git a/examples/hBN/extra_baseline/bn_spds.json b/examples/hBN/extra_baseline/bn_spds.json
new file mode 100644
index 0000000..3a372fc
--- /dev/null
+++ b/examples/hBN/extra_baseline/bn_spds.json
@@ -0,0 +1,16 @@
+{
+    "common_options": {
+        "basis": {
+                "B": [                
+                    "2s",
+                    "2p",
+                    "d*"
+                    ],
+                "N": [                
+                    "2s",
+                    "2p",
+                    "d*"
+                    ]
+        }
+    }
+}
\ No newline at end of file
diff --git a/examples/hBN/extra_baseline/hbn_spd_model/sktb.json b/examples/hBN/extra_baseline/hbn_spd_model/sktb.json
new file mode 100644
index 0000000..9ca6084
--- /dev/null
+++ b/examples/hBN/extra_baseline/hbn_spd_model/sktb.json
@@ -0,0 +1,477 @@
+{
+    "version": 2,
+    "unit": "eV",
+    "model_options": {
+        "nnsk": {
+            "onsite": {
+                "method": "uniform"
+            },
+            "hopping": {
+                "method": "poly2pow",
+                "rs": {
+                    "B-B": 4.22,
+                    "B-N": 4.04,
+                    "N-B": 4.04,
+                    "N-N": 3.85
+                },
+                "w": 0.2
+            },
+            "soc": {},
+            "freeze": false,
+            "push": false,
+            "std": 0.01,
+            "atomic_radius": "cov"
+        }
+    },
+    "common_options": {
+        "basis": {
+            "B": [
+                "2s",
+                "2p",
+                "d*"
+            ],
+            "N": [
+                "2s",
+                "2p",
+                "d*"
+            ]
+        },
+        "dtype": "float32",
+        "device": "cuda",
+        "overlap": true
+    },
+    "model_params": {
+        "onsite": {
+            "B-2s-0": [
+                -0.05371570587158203
+            ],
+            "B-2p-0": [
+                0.11017870903015137
+            ],
+            "B-d*-0": [
+                0.0
+            ],
+            "N-2s-0": [
+                -0.15561676025390625
+            ],
+            "N-2p-0": [
+                0.1445450782775879
+            ],
+            "N-d*-0": [
+                0.0
+            ]
+        },
+        "hopping": {
+            "B-B-2s-2s-0": [
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+                -1.199519157409668,
+                2.6829679012298584,
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+            ],
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+                -4.128350734710693,
+                -1.6135889291763306,
+                2.751228094100952,
+                -1.3629639148712158
+            ],
+            "B-B-2s-d*-0": [
+                0.0017082728445529938,
+                -0.00010188308078795671,
+                0.000718078576028347,
+                0.37305518984794617
+            ],
+            "B-B-2p-2p-0": [
+                3.9094669818878174,
+                1.5527981519699097,
+                -2.3236680030822754,
+                0.9059035181999207
+            ],
+            "B-B-2p-2p-1": [
+                -1.7056468725204468,
+                -0.2540814280509949,
+                1.0908770561218262,
+                -2.4367024898529053
+            ],
+            "B-B-2p-d*-0": [
+                -0.0017082728445529938,
+                0.00010188308078795671,
+                -0.000718078576028347,
+                -0.37305518984794617
+            ],
+            "B-B-2p-d*-1": [
+                0.0017082728445529938,
+                -0.00010188308078795671,
+                0.000718078576028347,
+                -0.37305518984794617
+            ],
+            "B-B-d*-d*-0": [
+                -0.0017082728445529938,
+                0.00010188308078795671,
+                -0.000718078576028347,
+                0.37305518984794617
+            ],
+            "B-B-d*-d*-1": [
+                -0.0017082728445529938,
+                0.00010188308078795671,
+                -0.000718078576028347,
+                -0.37305518984794617
+            ],
+            "B-B-d*-d*-2": [
+                -0.0017082728445529938,
+                0.00010188308078795671,
+                -0.000718078576028347,
+                -0.37305518984794617
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+                -5.041027069091797,
+                3.3467164039611816,
+                0.5421984195709229,
+                0.0005886993603780866
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+                -2.106684923171997,
+                3.2828798294067383,
+                -1.4983776807785034
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+                -0.0005914703942835331,
+                -0.0028732563368976116,
+                -0.4312877655029297
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+                2.0131304264068604,
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+}
\ No newline at end of file
diff --git a/examples/hBN/extra_baseline/input_templete.json b/examples/hBN/extra_baseline/input_templete.json
new file mode 100644
index 0000000..172ac9a
--- /dev/null
+++ b/examples/hBN/extra_baseline/input_templete.json
@@ -0,0 +1,93 @@
+{
+    "common_options": {
+        "basis": {
+            "B": [
+                "2s",
+                "2p",
+                "d*"
+            ],
+            "N": [
+                "2s",
+                "2p",
+                "d*"
+            ]
+        },
+        "device": "cpu",
+        "dtype": "float32",
+        "overlap": true,
+        "seed": 3982377700
+    },
+    "train_options": {
+        "num_epoch": 2,
+        "batch_size": 1,
+        "optimizer": {
+            "lr": 0.01,
+            "type": "Adam"
+        },
+        "lr_scheduler": {
+            "type": "exp",
+            "gamma": 0.999
+        },
+        "loss_options": {
+            "train": {
+                "method": "eigvals",
+                "diff_on": false,
+                "eout_weight": 0.001,
+                "diff_weight": 0.01
+            }
+        },
+        "save_freq": 1,
+        "validation_freq": 10,
+        "display_freq": 100,
+        "ref_batch_size": 1,
+        "val_batch_size": 1,
+        "max_ckpt": 4
+    },
+    "model_options": {
+        "nnsk": {
+            "onsite": {
+                "method": "uniform"
+            },
+            "hopping": {
+                "method": "poly2pow",
+                "rs": {
+                    "B-B": 4.22,
+                    "B-N": 4.04,
+                    "N-B": 4.04,
+                    "N-N": 3.85
+                },
+                "w": 0.2
+            },
+            "soc": {},
+            "freeze": [
+                "overlap"
+            ],
+            "push": false,
+            "std": 0.01,
+            "atomic_radius": "cov"
+        }
+    },
+    "data_options": {
+        "train": {
+            "root": "path/to/dataset",
+            "prefix": "prexfix_for_dataset",
+            "get_eigenvalues": true,
+            "type": "DefaultDataset",
+            "get_Hamiltonian": false
+        },
+        "validation": {
+            "root": "path/to/dataset",
+            "prefix": "prexfix_for_dataset",
+            "get_eigenvalues": true,
+            "type": "DefaultDataset",
+            "get_Hamiltonian": false
+        },
+        "reference": {
+            "root": "path/to/dataset",
+            "prefix": "prexfix_for_dataset",
+            "get_eigenvalues": true,
+            "type": "DefaultDataset",
+            "get_Hamiltonian": false
+        }
+    }
+}
\ No newline at end of file
diff --git a/examples/hBN/train/data/POSCAR b/examples/hBN/train/data/POSCAR
new file mode 100644
index 0000000..2671929
--- /dev/null
+++ b/examples/hBN/train/data/POSCAR
@@ -0,0 +1,10 @@
+N  B 
+ 1.0000000000000000
+     2.5039999485000002    0.0000000000000000    0.0000000000000000
+    -1.2519999743000001    2.1685275664999999    0.0000000000000000
+     0.0000000000000000    0.0000000000000000   30.0000000000000000
+ N   B  
+   1   1
+Cartesian
+ -0.0000000012853333  1.4456850884267272 15.0000000000000000
+  1.2519999003653348  0.7228424780732728 15.0000000000000000
diff --git a/examples/hBN/train/data/kpath.0/band_structure.png b/examples/hBN/train/data/kpath.0/band_structure.png
new file mode 100644
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diff --git a/examples/hBN/train/data/kpath.0/eigenvalues.npy b/examples/hBN/train/data/kpath.0/eigenvalues.npy
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diff --git a/examples/hBN/train/data/kpath.0/info.json b/examples/hBN/train/data/kpath.0/info.json
new file mode 100644
index 0000000..cb9046c
--- /dev/null
+++ b/examples/hBN/train/data/kpath.0/info.json
@@ -0,0 +1,12 @@
+{
+    "nframes": 1,
+    "natoms": 2,
+    "pos_type": "ase",
+    "pbc": true,
+    "bandinfo": {
+        "band_min": 0,
+        "band_max": 8,
+        "emin": null,
+        "emax": null
+    }
+}
diff --git a/examples/hBN/train/data/kpath.0/kpoints.npy b/examples/hBN/train/data/kpath.0/kpoints.npy
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diff --git a/examples/hBN/train/data/kpath.0/xdat.traj b/examples/hBN/train/data/kpath.0/xdat.traj
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diff --git a/examples/hBN/train/input.json b/examples/hBN/train/input.json
new file mode 100644
index 0000000..eb04861
--- /dev/null
+++ b/examples/hBN/train/input.json
@@ -0,0 +1,79 @@
+{
+    "common_options": {
+        "basis": {
+            "B": [
+                "2s",
+                "2p",
+                "d*"
+            ],
+            "N": [
+                "2s",
+                "2p",
+                "d*"
+            ]
+        },
+        "device": "cpu",
+        "dtype": "float32",
+        "overlap": true,
+        "seed": 3982377700
+    },
+    "train_options": {
+        "num_epoch": 5000,
+        "batch_size": 1,
+        "optimizer": {
+            "lr": 0.01,
+            "type": "Adam"
+        },
+        "lr_scheduler": {
+            "type": "exp",
+            "gamma": 0.9995
+        },
+        "loss_options": {
+            "train": {
+                "method": "eigvals",
+                "diff_on": false,
+                "eout_weight": 0.001,
+                "diff_weight": 0.01
+            }
+        },
+        "save_freq": 1,
+        "validation_freq": 10,
+        "display_freq": 100,
+        "ref_batch_size": 1,
+        "val_batch_size": 1,
+        "max_ckpt": 4
+    },
+    "model_options": {
+        "nnsk": {
+            "onsite": {
+                "method": "strain"
+            },
+            "hopping": {
+                "method": "poly2pow",
+                "rs": {
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+                "w": 0.2
+            },
+            "soc": {},
+            "freeze": [
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+            ],
+            "push": false,
+            "std": 0.01,
+            "atomic_radius": "cov"
+        }
+    },
+    "data_options": {
+        "train": {
+            "root": "./data",
+            "prefix": "kpath",
+            "get_eigenvalues": true,
+            "type": "DefaultDataset",
+            "get_Hamiltonian": false
+        }
+    }
+}
\ No newline at end of file
diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.best.pth b/examples/hBN/train/train_out/checkpoint/nnsk.best.pth
new file mode 120000
index 0000000..bf27b36
--- /dev/null
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\ No newline at end of file
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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.ep4998.pth b/examples/hBN/train/train_out/checkpoint/nnsk.ep4998.pth
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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.ep4999.pth b/examples/hBN/train/train_out/checkpoint/nnsk.ep4999.pth
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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.ep5000.pth b/examples/hBN/train/train_out/checkpoint/nnsk.ep5000.pth
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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.iter4997.pth b/examples/hBN/train/train_out/checkpoint/nnsk.iter4997.pth
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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.iter4998.pth b/examples/hBN/train/train_out/checkpoint/nnsk.iter4998.pth
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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.iter4999.pth b/examples/hBN/train/train_out/checkpoint/nnsk.iter4999.pth
new file mode 100644
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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.latest.pth b/examples/hBN/train/train_out/checkpoint/nnsk.latest.pth
new file mode 120000
index 0000000..1137c9d
--- /dev/null
+++ b/examples/hBN/train/train_out/checkpoint/nnsk.latest.pth
@@ -0,0 +1 @@
+/personal/DeepTB/dptb_Zjj/dpnegf/examples/hBN/train/train_out/checkpoint/nnsk.iter5000.pth
\ No newline at end of file

From 1974d1ecca9e2dae66af037610a0ef26e901aa80 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 15:55:42 +0800
Subject: [PATCH 136/152] update graphene data

---
 docs/hands_on/tutorial2_2d_mat.ipynb          |  99 +++++++++++++-----
 examples/graphene/graphene.vasp               |  13 ---
 .../train/data/kpath.0/band_structure.png     | Bin 199740 -> 165467 bytes
 .../train/data/kpath.0/eigenvalues.npy        | Bin 7408 -> 62528 bytes
 .../graphene/train/data/kpath.0/kpoints.npy   | Bin 968 -> 7328 bytes
 .../train_out/checkpoint/nnsk.ep4997.pth      | Bin 7731 -> 0 bytes
 .../train_out/checkpoint/nnsk.ep4998.pth      | Bin 7731 -> 0 bytes
 .../train_out/checkpoint/nnsk.ep4999.pth      | Bin 7731 -> 0 bytes
 .../train_out/checkpoint/nnsk.iter4997.pth    | Bin 7960 -> 0 bytes
 .../train_out/checkpoint/nnsk.iter4998.pth    | Bin 7960 -> 0 bytes
 .../train_out/checkpoint/nnsk.iter4999.pth    | Bin 7960 -> 0 bytes
 .../train_out/checkpoint/nnsk.iter5000.pth    | Bin 7960 -> 0 bytes
 .../train_out/checkpoint/nnsk.latest.pth      |   1 -
 13 files changed, 75 insertions(+), 38 deletions(-)
 delete mode 100644 examples/graphene/graphene.vasp
 delete mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep4997.pth
 delete mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep4998.pth
 delete mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep4999.pth
 delete mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.iter4997.pth
 delete mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.iter4998.pth
 delete mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.iter4999.pth
 delete mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.iter5000.pth
 delete mode 120000 examples/graphene/train/train_out/checkpoint/nnsk.latest.pth

diff --git a/docs/hands_on/tutorial2_2d_mat.ipynb b/docs/hands_on/tutorial2_2d_mat.ipynb
index aa5e1a8..35c01d6 100644
--- a/docs/hands_on/tutorial2_2d_mat.ipynb
+++ b/docs/hands_on/tutorial2_2d_mat.ipynb
@@ -40,7 +40,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "band.json\tgraphene.vasp\tlatest_dptb_b3.300_c2.600_w0.200.pth\n"
+      "band.json\tband_plot\textr_baseline\ttrain\n"
      ]
     }
    ],
@@ -67,7 +67,13 @@
      "text": [
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO                                      Version Info                                  \n",
-      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
       "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
       "DPNEGF INFO    ================================================================================\n",
@@ -89,31 +95,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5c4797d7",
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'config'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdptb\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbuild\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m build_model\n\u001b[1;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m  \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlatest_dptb_b3.300_c2.600_w0.200.pth\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# the model for demonstration\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/dptb/nn/build.py:50\u001b[0m, in \u001b[0;36mbuild_model\u001b[0;34m(checkpoint, model_options, common_options, no_check)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     49\u001b[0m     f \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mload(checkpoint, map_location\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m, weights_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 50\u001b[0m     ckptconfig \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mconfig\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m     51\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m f\n\u001b[1;32m     53\u001b[0m \u001b[38;5;66;03m# init model from checkpoint\u001b[39;00m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'config'"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    The ['overlap_param'] are frozen!\n",
+      "DPNEGF INFO    The ['overlap_param'] are frozen!\n"
      ]
     }
    ],
    "source": [
     "from dptb.nn.build import build_model\n",
     "\n",
-    "model =  \"latest_dptb_b3.300_c2.600_w0.200.pth\" # the model for demonstration\n",
+    "model =  \"./train/train_out/checkpoint/nnsk.best.pth\" # the model for demonstration\n",
     "\n",
-    "model = build_model(model,\n",
-    "                    model_options= model_json['model_options'],\n",
-    "                    common_options=model_json['common_options'])"
+    "model = build_model(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "31407e92",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Atoms(symbols='C2', pbc=True, cell=[[2.5039999485, 0.0, 0.0], [-1.2519999743, 2.1685275665, 0.0], [0.0, 0.0, 30.0]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from ase.io import read\n",
+    "structure =  \"./train/data/POSCAR\" \n",
+    "atoms = read(structure)\n",
+    "atoms"
    ]
   },
   {
@@ -121,7 +145,32 @@
    "execution_count": null,
    "id": "1041ae1c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF WARNING eig_solver is not set, using default 'torch'.\n",
+      "DPNEGF INFO    KPOINTS  klist: 300 kpoints\n",
+      "DPNEGF INFO    The eigenvalues are already in data. will use them.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF INFO    Fermi energy converged after 20 iterations.\n",
+      "DPNEGF INFO    q_cal: 8.000001001159495, total_electrons: 8.0, diff q: 1.001159494862236e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -4.0844590057224 based on the valence electrons setting nel_atom : {'C': 4} .\n",
+      "DPNEGF INFO    No Fermi energy provided, using estimated value: -4.0845 eV\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x560 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from dptb.postprocess.bandstructure.band import Band\n",
     "import shutil\n",
@@ -130,15 +179,16 @@
     "        \"task\": \"band\",\n",
     "        \"kline_type\":\"abacus\",\n",
     "        \"kpath\":[\n",
-    "                [0, 0, 0, 50],\n",
-    "                [0.5, 0, 0, 50],\n",
-    "                [0.3333333, 0.3333333, 0, 50],\n",
+    "                [0, 0, 0, 110],\n",
+    "                [0.5, 0, 0, 84],\n",
+    "                [0.3333333, 0.3333333, 0, 95],\n",
     "                [0, 0, 0, 1]\n",
     "                ],\n",
     "        \"klabels\":[\"G\", \"M\", \"K\", \"G\"],\n",
     "        \"emin\":-20,\n",
     "        \"emax\": 20,\n",
-    "        \"nel_atom\":{\"C\": 4} \n",
+    "        \"nel_atom\":{\"C\": 4},\n",
+    "        \"ref_band\": \"/personal/zjj/transiesta_cal/graphene_formal/dptb_train_input/data/data_graphene_siesta2/eigs.npy\"\n",
     "\n",
     "       }\n",
     "\n",
@@ -146,12 +196,13 @@
     "    shutil.rmtree(results_path, ignore_errors=True) \n",
     "\n",
     "band = Band(model, results_path)\n",
-    "AtomicData_options = {\"r_max\": 3.0, \"pbc\": True}\n",
-    "band.get_bands(data = uni_cell_atoms, \n",
+    "AtomicData_options = { \"pbc\": True}\n",
+    "band.get_bands(data = atoms, \n",
     "               kpath_kwargs = task_options,\n",
     "               AtomicData_options = AtomicData_options)\n",
     "band.band_plot(emin = task_options['emin'],\n",
-    "               emax = task_options['emax'])"
+    "               emax = task_options['emax'],\n",
+    "               ref_band = task_options['ref_band'],)"
    ]
   }
  ],
diff --git a/examples/graphene/graphene.vasp b/examples/graphene/graphene.vasp
deleted file mode 100644
index a003663..0000000
--- a/examples/graphene/graphene.vasp
+++ /dev/null
@@ -1,13 +0,0 @@
-graphite                                
-   1.00000000000000     
-     2.4600000381000000    0.0000000000000000    0.0000000000000000
-    -1.2300000191000000    2.1304225262999998    0.0000000000000000
-     0.0000000000000000    0.0000000000000000   20.0000000000000000
-   C 
-     2
-Direct
-  0.3333333429999996  0.6666666870000029  0.5000000000000000
-  0.6666666269999979  0.3333333129999971  0.5000000000000000
- 
-  0.00000000E+00  0.00000000E+00  0.00000000E+00
-  0.00000000E+00  0.00000000E+00  0.00000000E+00
diff --git a/examples/graphene/train/data/kpath.0/band_structure.png b/examples/graphene/train/data/kpath.0/band_structure.png
index e717fee4c965d9b574a453607a7dfdbaa42f2855..8d1e9a62c7ebab4cb7b88ad3fcdfdb12f997f232 100644
GIT binary patch
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diff --git a/examples/graphene/train/data/kpath.0/eigenvalues.npy b/examples/graphene/train/data/kpath.0/eigenvalues.npy
index ab387555e079ad69e6dc93a444a2b063b7c4074e..deee94bcd8baadcde28918599f9653c302ff7142 100644
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diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.ep4997.pth b/examples/graphene/train/train_out/checkpoint/nnsk.ep4997.pth
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diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.iter5000.pth b/examples/graphene/train/train_out/checkpoint/nnsk.iter5000.pth
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diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.latest.pth b/examples/graphene/train/train_out/checkpoint/nnsk.latest.pth
deleted file mode 120000
index 4ec2f23..0000000
--- a/examples/graphene/train/train_out/checkpoint/nnsk.latest.pth
+++ /dev/null
@@ -1 +0,0 @@
-/personal/DeepTB/dptb_Zjj/dpnegf/examples/graphene/train/train_out/checkpoint/nnsk.iter5000.pth
\ No newline at end of file

From 27f2c38f9149aee97083395a79803302fad4a5d9 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 21:51:10 +0800
Subject: [PATCH 137/152] refactor lead_property

---
 dpnegf/negf/lead_property.py | 162 +++++++++++++++++++++++++----------
 1 file changed, 117 insertions(+), 45 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index cf5258f..5e996e5 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -134,58 +134,128 @@ def self_energy(self, kpoint, energy,
         if not isinstance(energy, torch.Tensor):
             energy = torch.tensor(energy) # Energy relative to Ef
         
-        if save_path is None:
-            parent_dir = os.path.join(self.results_path, "self_energy")
-            if not os.path.exists(parent_dir):
-                os.makedirs(parent_dir)
-            if save_format == "pth":
-                save_path = os.path.join(parent_dir, 
-                                         f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
-            elif save_format == "h5":
-                if self.tab == "lead_L":
-                    save_path = os.path.join(parent_dir, "self_energy_leadL.h5")
-                elif self.tab == "lead_R":
-                    save_path = os.path.join(parent_dir, "self_energy_leadR.h5")
-                else:
-                    raise ValueError(f"Unsupported tab {self.tab} for saving self energy.")
-            else:
-                raise ValueError(f"Unsupported save format {save_format}. Only 'pth' and 'h5' are supported.")
+        save_path = self._get_save_path(kpoint, energy, save_format, save_path)
+
+        # Try load
+        if os.path.isfile(save_path):
+            if se_info_display:
+                log.info(f"Loading {self.tab} self-energy from {save_path}")
+            self.se = self._load_self_energy(save_path, kpoint, energy, save_format)
+            return
+        
+        # If not loaded, just compute
+        if se_info_display:
+            log.info(f"Computing {self.tab} self-energy (method={method}) "
+                    f"at k={kpoint}, E={energy.item():.6f}")
+
+        self.se = self.self_energy_cal( kpoint, 
+                                        energy,
+                                        eta_lead=eta_lead, 
+                                        method=method,
+                                        HS_inmem=HS_inmem)
+
+    def _get_save_path(self, kpoint, energy, save_format: str, save_path: str = None):
+        """
+        Generate the save path for self-energy files.
 
-        # If the file in save_path exists, then directly load it    
-        if os.path.exists(save_path):
-            if se_info_display: 
-                log.info(f"Loading self energy from {save_path}")   
+        Parameters
+        ----------
+        kpoint : array-like
+            The k-point (length 3).
+        energy : torch.Tensor or float
+            Energy value.
+        save_format : str
+            File format, supports "pth" or "h5".
+        save_path : str, optional
+            User-specified save path. If None, use default under results_path/self_energy.
 
+        Returns
+        -------
+        str
+            Full path to the save file.
+        """
+        # Ensure kpoint is array for string formatting
+        kx, ky, kz = np.asarray(kpoint, dtype=float).reshape(3)
+        energy_val = energy.item() if hasattr(energy, "item") else float(energy)
+
+        # Case 1: User provided save_path
+        if save_path is not None:
+            # If it's a directory, append default filename
             if os.path.isdir(save_path):
                 if save_format == "pth":
-                    save_path = os.path.join(save_path, f"se_{self.tab}_k{kpoint[0]}_{kpoint[1]}_{kpoint[2]}_E{energy}.pth")
+                    return os.path.join(save_path,
+                                        f"se_{self.tab}_k{kx:.4f}_{ky:.4f}_{kz:.4f}_E{energy_val:.6f}.pth")
                 elif save_format == "h5":
-                    save_path = os.path.join(save_path, f"self_energy_{self.tab}.h5")
+                    return os.path.join(save_path, f"self_energy_{self.tab}.h5")
                 else:
-                    raise ValueError(f"Unsupported save format {save_format}. Only 'pth' and 'h5' are supported.")
-                
+                    raise ValueError(f"Unsupported save_format {save_format}")
+            return save_path  # direct file path given by user
+
+        # Case 2: Default path under results_path
+        parent_dir = os.path.join(self.results_path, "self_energy")
+        os.makedirs(parent_dir, exist_ok=True)
+
+        if save_format == "pth":
+            return os.path.join(parent_dir,
+                                f"se_{self.tab}_k{kx:.4f}_{ky:.4f}_{kz:.4f}_E{energy_val:.6f}.pth")
+
+        elif save_format == "h5":
+            if self.tab == "lead_L":
+                return os.path.join(parent_dir, "self_energy_leadL.h5")
+            elif self.tab == "lead_R":
+                return os.path.join(parent_dir, "self_energy_leadR.h5")
+            else:
+                raise ValueError(f"Unsupported tab {self.tab} for h5 save.")
 
-            assert os.path.exists(save_path), f"Cannot find the self energy file {save_path}"
-            if save_path.endswith(".pth"):
-                # if the save_path is a directory, then the self energy file is stored in the directory
-                self.se = torch.load(save_path, weights_only=False)
-            elif save_path.endswith(".h5"):
-                try:
-                    self.se = read_from_hdf5(save_path, kpoint, energy)
-                    self.se = torch.from_numpy(self.se)
-                except KeyError as e:
-                    log.error(f"Cannot find the self energy for kpoint {kpoint} and energy {energy} in {save_path}.")
-                    raise e
+        else:
+            raise ValueError(f"Unsupported save_format {save_format}, only 'pth' and 'h5' are supported.")
+
+
+    def _load_self_energy(self, save_path: str, kpoint, energy, save_format: str):
+        """
+        Load self-energy from file.
+
+        Parameters
+        ----------
+        save_path : str
+            Path to the saved self-energy file.
+        kpoint : array-like
+            The k-point (length 3).
+        energy : torch.Tensor or float
+            Energy value.
+        save_format : str
+            File format, supports "pth" or "h5".
+
+        Returns
+        -------
+        torch.Tensor
+            Loaded self-energy tensor.
+        """
+        if save_format == "pth":
+            try:
+                se = torch.load(save_path, weights_only=False)
+            except Exception as e:
+                raise IOError(f"Failed to load self-energy from {save_path} (pth format).") from e
+
+        elif save_format == "h5":
+            try:
+                data = read_from_hdf5(save_path, kpoint, energy)
+                se = torch.as_tensor(data, dtype=torch.complex128)  # 自能一般是复数
+            except KeyError as e:
+                kx, ky, kz = np.asarray(kpoint, dtype=float).reshape(3)
+                ev = energy.item() if hasattr(energy, "item") else float(energy)
+                raise KeyError(
+                    f"Cannot find self-energy in {save_path} "
+                    f"for k=({kx:.4f},{ky:.4f},{kz:.4f}), E={ev:.6f}"
+                ) from e
+            except Exception as e:
+                raise IOError(f"Failed to read HDF5 self-energy from {save_path}.") from e
 
-            return
-            
         else:
-            if se_info_display:
-                log.info("-"*50)
-                log.info(f"Not find stored {self.tab} self energy. Calculating it at kpoint {kpoint} and energy {energy}.")
-                log.info("-"*50)
-        
-        self.self_energy_cal(kpoint, energy, eta_lead=eta_lead, method=method,HS_inmem=HS_inmem)
+            raise ValueError(f"Unsupported save_format {save_format}, only 'pth' and 'h5' are supported.")
+
+        return se
+
 
     def self_energy_cal(self, 
                         kpoint, 
@@ -197,12 +267,14 @@ def self_energy_cal(self,
         subblocks = self.hamiltonian.get_hs_device(kpoint, only_subblocks=True)
         # calculate self energy
         if not self.useBloch:
-            if not hasattr(self, "HL") or abs(self.voltage_old-self.voltage)>1e-6 or max(abs(self.kpoint-torch.tensor(kpoint)))>1e-6:
+            if  not hasattr(self, "HL") \
+                or abs(self.voltage_old-self.voltage)>1e-6 \
+                or max(abs(self.kpoint-torch.tensor(kpoint)))>1e-6:
+
                 self.HLk, self.HLLk, self.HDLk, self.SLk, self.SLLk, self.SDLk \
                     = self.hamiltonian.get_hs_lead(kpoint, tab=self.tab, v=self.voltage)
                 self.voltage_old = self.voltage
                 self.kpoint = torch.tensor(kpoint)
-
             
             HDL_reduced, SDL_reduced = self.HDL_reduced(self.HDLk, self.SDLk,subblocks)
             

From ddddca3b8ab711cdcd761215515e2357cc5165c8 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 22:46:34 +0800
Subject: [PATCH 138/152] remove self_energy_save as it must be saved

---
 dpnegf/negf/lead_property.py                  | 31 ++++++++++++-------
 dpnegf/runner/NEGF.py                         | 17 +++++-----
 .../tests/test_negf_negf_hamiltonian_init.py  |  3 +-
 dpnegf/utils/argcheck.py                      |  1 -
 4 files changed, 27 insertions(+), 25 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 5e996e5..24a709d 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -103,7 +103,6 @@ def __init__(self, tab, hamiltonian, structure, results_path, voltage,
     def self_energy(self, kpoint, energy, 
                     eta_lead: float=1e-5,
                     method: str="Lopez-Sancho",
-                    save: bool=False, 
                     save_path: str=None, 
                     save_format: str="h5",
                     se_info_display: bool=False,
@@ -429,37 +428,45 @@ def gamma(self):
 #     )
 
 
-def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, n_jobs=-1, batch_size=200):
+def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, 
+                            self_energy_save_path=None, n_jobs=-1, batch_size=200):
+
+    if self_energy_save_path is None:
+        if lead_L.results_path != lead_R.results_path:
+            log.warning("The results_path of lead_L and lead_R are different. "
+                        "Self energy files will be saved in lead_L's results_path.")
+        self_energy_save_path = os.path.join(lead_L.results_path, "self_energy")
+
 
     total_tasks = [(k, e) for k in kpoints_grid for e in energy_grid]
     if len(total_tasks) <= batch_size:
         Parallel(n_jobs=n_jobs, backend="loky")(
-            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+            delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, self_energy_save_path)
             for k, e in total_tasks
         )
-    
     else:
         for i in range(0, len(total_tasks), batch_size):
             batch = total_tasks[i:i+batch_size]
             Parallel(n_jobs=n_jobs, backend="loky")(
-                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R)
+                delayed(self_energy_worker)(k, e, eta, lead_L, lead_R, self_energy_save_path)
                 for k, e in batch
             )
 
-    save_path_L = os.path.join(lead_L.results_path, "self_energy", "self_energy_leadL.h5")
-    save_path_R = os.path.join(lead_R.results_path, "self_energy", "self_energy_leadR.h5")
 
-    merge_hdf5_files(os.path.join(lead_L.results_path, "self_energy"), save_path_L, pattern="tmp_leadL_*.h5")
-    merge_hdf5_files(os.path.join(lead_R.results_path, "self_energy"), save_path_R, pattern="tmp_leadR_*.h5")
+    save_path_L = os.path.join(self_energy_save_path, "self_energy_leadL.h5")
+    save_path_R = os.path.join(self_energy_save_path, "self_energy_leadR.h5")
+
+    merge_hdf5_files(self_energy_save_path, save_path_L, pattern="tmp_leadL_*.h5")
+    merge_hdf5_files(self_energy_save_path, save_path_R, pattern="tmp_leadR_*.h5")
 
 
 
 
 
-def self_energy_worker(k, e, eta, lead_L, lead_R):
+def self_energy_worker(k, e, eta, lead_L, lead_R, self_energy_save_path):
 
-    save_tmp_L = os.path.join(lead_L.results_path, "self_energy", f"tmp_leadL_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
-    save_tmp_R = os.path.join(lead_R.results_path, "self_energy", f"tmp_leadR_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
+    save_tmp_L = os.path.join(self_energy_save_path, f"tmp_leadL_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
+    save_tmp_R = os.path.join(self_energy_save_path, f"tmp_leadR_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
 
     seL = lead_L.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
     seR = lead_R.self_energy_cal(kpoint=k, energy=e, eta_lead=eta)
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 8887ca7..f1a39c5 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -49,7 +49,7 @@ def __init__(self,
                 sgf_solver: str,
                 e_fermi: float=None,
                 use_saved_HS: bool=False, saved_HS_path: str=None,
-                self_energy_save: bool=False, self_energy_save_path: str=None, se_info_display: bool=False,
+                self_energy_save_path: str=None, se_info_display: bool=False,
                 out_tc: bool=False,out_dos: bool=False,out_density: bool=False,out_potential: bool=False,
                 out_current: bool=False,out_current_nscf: bool=False,out_ldos: bool=False,out_lcurrent: bool=False,
                 results_path: Optional[str]=None,
@@ -80,7 +80,6 @@ def __init__(self,
         self.saved_HS_path = saved_HS_path
 
         self.sgf_solver = sgf_solver
-        self.self_energy_save = self_energy_save
         self.self_energy_save_path = self_energy_save_path
         self.se_info_display = se_info_display
         self.pbc = self.stru_options["pbc"]
@@ -524,9 +523,10 @@ def negf_compute(self,scf_require=False,Vbias=None):
 
         # self energy calculation
         log.info(msg="------Self-energy calculation------")
-        selfen_parent_dir = os.path.join(self.results_path,"self_energy")
-        if not os.path.exists(selfen_parent_dir): 
-            os.makedirs(selfen_parent_dir)
+        if  self.self_energy_save_path is None:
+            self.self_energy_save_path = os.path.join(self.results_path, "self_energy") 
+        os.makedirs(self.self_energy_save_path, exist_ok=True)
+        
         if scf_require and self.poisson_options["with_Dirichlet_leads"]:
             # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
             # In each iteration, the self-energy of the leads is not updated.
@@ -535,11 +535,11 @@ def negf_compute(self,scf_require=False,Vbias=None):
             #         self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
             #         self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
             compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
-                                    self.kpoints, self.density.integrate_range)
+                                    self.kpoints, self.density.integrate_range, self.self_energy_save_path)
         elif not self.scf:
             # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
             compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
-                                    self.kpoints, self.uni_grid)
+                                    self.kpoints, self.uni_grid, self.self_energy_save_path)
         log.info(msg="-----------------------------------\n")
 
 
@@ -627,7 +627,6 @@ def negf_compute(self,scf_require=False,Vbias=None):
                                         kpoint=k, 
                                         eta_lead=self.eta_lead,
                                         method=self.sgf_solver,
-                                        save=self.self_energy_save,
                                         save_path=self.self_energy_save_path,
                                         se_info_display=self.se_info_display
                                         )
@@ -641,7 +640,6 @@ def negf_compute(self,scf_require=False,Vbias=None):
                                         kpoint=k, 
                                         eta_lead=self.eta_lead,
                                         method=self.sgf_solver,
-                                        save=self.self_energy_save,
                                         save_path=self.self_energy_save_path,
                                         se_info_display=self.se_info_display
                                         )                                
@@ -736,7 +734,6 @@ def negf_compute(self,scf_require=False,Vbias=None):
                                         kpoint=k, 
                                         eta_lead=self.eta_lead,
                                         method=self.sgf_solver,
-                                        save=self.self_energy_save,
                                         save_path=self.self_energy_save_path,
                                         se_info_display=self.se_info_display
                                         )
diff --git a/dpnegf/tests/test_negf_negf_hamiltonian_init.py b/dpnegf/tests/test_negf_negf_hamiltonian_init.py
index 58b24ac..562919a 100644
--- a/dpnegf/tests/test_negf_negf_hamiltonian_init.py
+++ b/dpnegf/tests/test_negf_negf_hamiltonian_init.py
@@ -75,8 +75,7 @@ def test_negf_Hamiltonian(root_directory):
             energy=e, 
             kpoint=kpoints[0], 
             eta_lead=negf_json['task_options']["eta_lead"],
-            method=negf_json['task_options']["sgf_solver"],
-            save=False
+            method=negf_json['task_options']["sgf_solver"]
             )
     print("lead_L self energy:",deviceprop.lead_L.se)
     print("lead_R self energy:",deviceprop.lead_R.se)
diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index 25601d3..1c57032 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1047,7 +1047,6 @@ def negf():
         Argument("stru_options", dict, optional=False, sub_fields=stru_options(), doc=doc_stru_options),
         Argument("poisson_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[poisson_options()], doc=doc_poisson_options),
         Argument("sgf_solver", str, optional=True, default="Sancho-Rubio", doc=doc_sgf_solver),
-        Argument("self_energy_save", bool, optional=True, default=False, doc="whether to save the self energy"),
         Argument("self_energy_save_path", str, optional=True, default=None, doc="the path to save the self energy"),
         Argument("se_info_display", bool, optional=True, default=False, doc="whether to display the self energy information"),
         Argument("espacing", [int, float], optional=False, doc=doc_espacing),

From f575722c76242de44ae56cd131635ec21a4f7f84 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 22:57:33 +0800
Subject: [PATCH 139/152] add use_saved_se (default: False)

---
 dpnegf/negf/lead_property.py |  4 ++--
 dpnegf/runner/NEGF.py        | 34 +++++++++++++++++++---------------
 dpnegf/utils/argcheck.py     |  1 +
 3 files changed, 22 insertions(+), 17 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 24a709d..3bc9940 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -209,8 +209,8 @@ def _get_save_path(self, kpoint, energy, save_format: str, save_path: str = None
         else:
             raise ValueError(f"Unsupported save_format {save_format}, only 'pth' and 'h5' are supported.")
 
-
-    def _load_self_energy(self, save_path: str, kpoint, energy, save_format: str):
+    @staticmethod
+    def _load_self_energy(save_path: str, kpoint, energy, save_format: str):
         """
         Load self-energy from file.
 
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index f1a39c5..d6365fb 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -49,7 +49,7 @@ def __init__(self,
                 sgf_solver: str,
                 e_fermi: float=None,
                 use_saved_HS: bool=False, saved_HS_path: str=None,
-                self_energy_save_path: str=None, se_info_display: bool=False,
+                use_saved_se: bool=False, self_energy_save_path: str=None, se_info_display: bool=False,
                 out_tc: bool=False,out_dos: bool=False,out_density: bool=False,out_potential: bool=False,
                 out_current: bool=False,out_current_nscf: bool=False,out_ldos: bool=False,out_lcurrent: bool=False,
                 results_path: Optional[str]=None,
@@ -80,6 +80,7 @@ def __init__(self,
         self.saved_HS_path = saved_HS_path
 
         self.sgf_solver = sgf_solver
+        self.use_saved_se = use_saved_se
         self.self_energy_save_path = self_energy_save_path
         self.se_info_display = se_info_display
         self.pbc = self.stru_options["pbc"]
@@ -526,20 +527,23 @@ def negf_compute(self,scf_require=False,Vbias=None):
         if  self.self_energy_save_path is None:
             self.self_energy_save_path = os.path.join(self.results_path, "self_energy") 
         os.makedirs(self.self_energy_save_path, exist_ok=True)
-        
-        if scf_require and self.poisson_options["with_Dirichlet_leads"]:
-            # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
-            # In each iteration, the self-energy of the leads is not updated.
-            # for ik, k in enumerate(self.kpoints):
-            #     for e in self.density.integrate_range:
-            #         self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
-            #         self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
-            compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
-                                    self.kpoints, self.density.integrate_range, self.self_energy_save_path)
-        elif not self.scf:
-            # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
-            compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
-                                    self.kpoints, self.uni_grid, self.self_energy_save_path)
+
+        if self.use_saved_se:
+            log.info(msg="Using saved self-energy from {}".format(self.self_energy_save_path))
+        else:
+            if scf_require and self.poisson_options["with_Dirichlet_leads"]:
+                # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
+                # In each iteration, the self-energy of the leads is not updated.
+                # for ik, k in enumerate(self.kpoints):
+                #     for e in self.density.integrate_range:
+                #         self.deviceprop.lead_L.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+                #         self.deviceprop.lead_R.self_energy(kpoint=k, energy=e, eta_lead=self.eta_lead, save=True)
+                compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
+                                        self.kpoints, self.density.integrate_range, self.self_energy_save_path)
+            elif not self.scf:
+                # In non-scf case, the self-energy of the leads is calculated for each energy point in the energy grid.
+                compute_all_self_energy(self.eta_lead, self.deviceprop.lead_L, self.deviceprop.lead_R,
+                                        self.kpoints, self.uni_grid, self.self_energy_save_path)
         log.info(msg="-----------------------------------\n")
 
 
diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index 1c57032..23e9f63 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1047,6 +1047,7 @@ def negf():
         Argument("stru_options", dict, optional=False, sub_fields=stru_options(), doc=doc_stru_options),
         Argument("poisson_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[poisson_options()], doc=doc_poisson_options),
         Argument("sgf_solver", str, optional=True, default="Sancho-Rubio", doc=doc_sgf_solver),
+        Argument("use_saved_se", bool, optional=True, default=False, doc="whether to use saved self energy"),
         Argument("self_energy_save_path", str, optional=True, default=None, doc="the path to save the self energy"),
         Argument("se_info_display", bool, optional=True, default=False, doc="whether to display the self energy information"),
         Argument("espacing", [int, float], optional=False, doc=doc_espacing),

From ae8743656162e7915d8036d0cde7516e24fbae45 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 23:01:57 +0800
Subject: [PATCH 140/152] add log info for self_energy

---
 dpnegf/runner/NEGF.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index d6365fb..b6a37d3 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -531,6 +531,7 @@ def negf_compute(self,scf_require=False,Vbias=None):
         if self.use_saved_se:
             log.info(msg="Using saved self-energy from {}".format(self.self_energy_save_path))
         else:
+            log.info(msg="Calculating self-energy and saving to {}".format(self.self_energy_save_path))
             if scf_require and self.poisson_options["with_Dirichlet_leads"]:
                 # For the Dirichlet leads, the self-energy of the leads is only calculated once and saved.
                 # In each iteration, the self-energy of the leads is not updated.

From 38d9cb467a056ccc1bddcdc286309c3cb854f2b3 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Sun, 21 Sep 2025 23:58:02 +0800
Subject: [PATCH 141/152] fix types in self_energy file name

---
 dpnegf/negf/lead_property.py |  6 +++++-
 dpnegf/runner/NEGF.py        | 13 +++++++------
 2 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 3bc9940..34de3c4 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -134,6 +134,7 @@ def self_energy(self, kpoint, energy,
             energy = torch.tensor(energy) # Energy relative to Ef
         
         save_path = self._get_save_path(kpoint, energy, save_format, save_path)
+        # log.info(f"Self energy save path: {save_path}")
 
         # Try load
         if os.path.isfile(save_path):
@@ -185,7 +186,10 @@ def _get_save_path(self, kpoint, energy, save_format: str, save_path: str = None
                     return os.path.join(save_path,
                                         f"se_{self.tab}_k{kx:.4f}_{ky:.4f}_{kz:.4f}_E{energy_val:.6f}.pth")
                 elif save_format == "h5":
-                    return os.path.join(save_path, f"self_energy_{self.tab}.h5")
+                    if self.tab == "lead_L":
+                        return os.path.join(save_path, "self_energy_leadL.h5")
+                    elif self.tab == "lead_R":
+                        return os.path.join(save_path, "self_energy_leadR.h5")
                 else:
                     raise ValueError(f"Unsupported save_format {save_format}")
             return save_path  # direct file path given by user
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index b6a37d3..de38d4c 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -403,13 +403,13 @@ def compute(self):
             self.negf_compute(scf_require=False,Vbias=self.potential_at_orb)
         
         else:
-            # profiler = Profiler()
-            # profiler.start() 
+            profiler = Profiler()
+            profiler.start() 
             self.negf_compute(scf_require=False,Vbias=None)
-            # profiler.stop()
-            # output_path = os.path.join(self.results_path, "profile_report.html")
-            # with open(output_path, 'w') as report_file:
-                # report_file.write(profiler.output_html())
+            profiler.stop()
+            output_path = os.path.join(self.results_path, "profile_report.html")
+            with open(output_path, 'w') as report_file:
+                report_file.write(profiler.output_html())
 
     def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_iter=1000,
                          mix_method:str='linear', mix_rate:float=0.3, tolerance:float=1e-7,Gaussian_sigma:float=3.0):
@@ -529,6 +529,7 @@ def negf_compute(self,scf_require=False,Vbias=None):
         os.makedirs(self.self_energy_save_path, exist_ok=True)
 
         if self.use_saved_se:
+            # TODO: check if the saved self-energy exists or not
             log.info(msg="Using saved self-energy from {}".format(self.self_energy_save_path))
         else:
             log.info(msg="Calculating self-energy and saving to {}".format(self.self_energy_save_path))

From 97f27e015594afcaa1d5a5c568e05461629393f2 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 22 Sep 2025 10:22:10 +0800
Subject: [PATCH 142/152] add _has_saved_self_energy check and module
 prepare_self_energy func

---
 dpnegf/negf/lead_property.py | 15 ++++++++++-
 dpnegf/runner/NEGF.py        | 48 ++++++++++++++++++++++++++----------
 dpnegf/utils/argcheck.py     |  2 +-
 3 files changed, 50 insertions(+), 15 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 34de3c4..5c61f31 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -531,4 +531,17 @@ def merge_hdf5_files(tmp_dir, output_path, pattern, remove=True):
                 os.remove(path)
                 # log.info(f"Deleted tmp file: {path}")
             except Exception as e:
-                log.warning(f"Failed to delete {path}: {e}")
\ No newline at end of file
+                log.warning(f"Failed to delete {path}: {e}")
+
+
+def _has_saved_self_energy(root: str) -> bool:
+        from pathlib import Path
+        p = Path(root) if root is not None else None
+        if p is None or not p.exists():
+            return False
+        
+        patterns = ("*.h5", "*.pth")
+        for pat in patterns:
+            if any(p.rglob(pat)):
+                return True
+        return False
\ No newline at end of file
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index de38d4c..741a6d9 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -6,7 +6,7 @@
 from dpnegf.utils.elec_struc_cal import ElecStruCal
 from dpnegf.negf.density import Ozaki,Fiori
 from dpnegf.negf.device_property import DeviceProperty
-from dpnegf.negf.lead_property import LeadProperty, compute_all_self_energy
+from dpnegf.negf.lead_property import LeadProperty, compute_all_self_energy, _has_saved_self_energy
 from dpnegf.negf.negf_utils import is_fully_covered
 import ase
 from dpnegf.utils.constants import Boltzmann, eV2J
@@ -21,7 +21,6 @@
 from pyinstrument import Profiler
 import os
 
-
 log = logging.getLogger(__name__)
 
 
@@ -80,9 +79,9 @@ def __init__(self,
         self.saved_HS_path = saved_HS_path
 
         self.sgf_solver = sgf_solver
-        self.use_saved_se = use_saved_se
-        self.self_energy_save_path = self_energy_save_path
-        self.se_info_display = se_info_display
+        self.use_saved_se = use_saved_se # whether to use the saved self-energy or not
+        self.self_energy_save_path = self_energy_save_path # The directory to save the self-energy or for saved self-energy
+        self.se_info_display = se_info_display # whether to display the self-energy information after calculation
         self.pbc = self.stru_options["pbc"]
 
         if  self.stru_options["lead_L"]["useBloch"] or self.stru_options["lead_R"]["useBloch"]:
@@ -513,15 +512,28 @@ def poisson_negf_scf(self,interface_poisson,atom_gridpoint_index,err=1e-6,max_it
         # if iter_count <= max_iter: 
         #     profiler.stop()
         #     with open('profile_report.html', 'w') as report_file:
-        #         report_file.write(profiler.output_html())
-    
+        #         report_file.write(profiler.output_html())、
 
-    def negf_compute(self,scf_require=False,Vbias=None):
-        
-        assert scf_require is not None, "scf_require should be set to True or False"
-        self.out['k']=[];self.out['wk']=[]
-        if hasattr(self, "uni_grid"): self.out["uni_grid"] = self.uni_grid
+    def prepare_self_energy(self, scf_require: bool) -> None:
+        """
+        Prepares the self-energy for the NEGF calculation.
+
+        Depending on the calculation settings, this method either loads previously saved self-energy data
+        or computes and saves new self-energy values for the device leads. The computation method varies
+        based on whether self-consistent field (SCF) calculations are required and whether Dirichlet boundary
+        conditions are applied to the leads.
+
+        Args:
+            scf_require (bool): Indicates whether SCF calculations are required.
 
+        Side Effects:
+            - Creates the directory for saving self-energy data if it does not exist.
+            - Loads or computes self-energy data and saves it to disk.
+            - Logs the progress and actions taken during self-energy preparation.
+
+        Raises:
+            AssertionError: If `use_saved_se` is True but no saved self-energy is found at the specified path.
+        """
         # self energy calculation
         log.info(msg="------Self-energy calculation------")
         if  self.self_energy_save_path is None:
@@ -529,8 +541,9 @@ def negf_compute(self,scf_require=False,Vbias=None):
         os.makedirs(self.self_energy_save_path, exist_ok=True)
 
         if self.use_saved_se:
-            # TODO: check if the saved self-energy exists or not
+            assert _has_saved_self_energy(self.self_energy_save_path), "No saved self-energy found in {}".format(self.self_energy_save_path)
             log.info(msg="Using saved self-energy from {}".format(self.self_energy_save_path))
+            log.info(msg="Ensure the saved self-energy is consistent with the current calculation setting!")
         else:
             log.info(msg="Calculating self-energy and saving to {}".format(self.self_energy_save_path))
             if scf_require and self.poisson_options["with_Dirichlet_leads"]:
@@ -549,6 +562,15 @@ def negf_compute(self,scf_require=False,Vbias=None):
         log.info(msg="-----------------------------------\n")
 
 
+
+    def negf_compute(self,scf_require=False,Vbias=None):
+        
+        assert scf_require is not None, "scf_require should be set to True or False"
+        self.out['k']=[];self.out['wk']=[]
+        if hasattr(self, "uni_grid"): self.out["uni_grid"] = self.uni_grid
+
+        self.prepare_self_energy(scf_require)
+
         for ik, k in enumerate(self.kpoints):
 
             self.out['k'].append(k)
diff --git a/dpnegf/utils/argcheck.py b/dpnegf/utils/argcheck.py
index 23e9f63..14fa572 100644
--- a/dpnegf/utils/argcheck.py
+++ b/dpnegf/utils/argcheck.py
@@ -1048,7 +1048,7 @@ def negf():
         Argument("poisson_options", dict, optional=True, default={}, sub_fields=[], sub_variants=[poisson_options()], doc=doc_poisson_options),
         Argument("sgf_solver", str, optional=True, default="Sancho-Rubio", doc=doc_sgf_solver),
         Argument("use_saved_se", bool, optional=True, default=False, doc="whether to use saved self energy"),
-        Argument("self_energy_save_path", str, optional=True, default=None, doc="the path to save the self energy"),
+        Argument("self_energy_save_path", str, optional=True, default=None, doc="the directory to save the self energy or load the self energy"),
         Argument("se_info_display", bool, optional=True, default=False, doc="whether to display the self energy information"),
         Argument("espacing", [int, float], optional=False, doc=doc_espacing),
         Argument("emin", [int, float], optional=False, doc=doc_emin),

From 16b05ed1b9e11543f39047428701ab7f2018b2f0 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Mon, 22 Sep 2025 10:36:06 +0800
Subject: [PATCH 143/152] add docs

---
 dpnegf/negf/lead_property.py | 85 ++++++++++++++++++++++++++++++++++--
 dpnegf/runner/NEGF.py        | 11 +----
 2 files changed, 83 insertions(+), 13 deletions(-)

diff --git a/dpnegf/negf/lead_property.py b/dpnegf/negf/lead_property.py
index 5c61f31..b8b9e02 100644
--- a/dpnegf/negf/lead_property.py
+++ b/dpnegf/negf/lead_property.py
@@ -266,7 +266,33 @@ def self_energy_cal(self,
                         eta_lead: float=1e-5,
                         method: str="Lopez-Sancho",
                         HS_inmem: bool=True):
-        
+        """
+        Calculates the self-energy for a lead in a quantum transport calculation.
+        This method computes the self-energy matrix for a given k-point and energy, 
+        using either the standard or Bloch-based approach depending on the object's configuration.
+        Parameters
+        ----------
+        kpoint : array-like
+            The k-point in reciprocal space at which to calculate the self-energy.
+        energy : float or torch.Tensor
+            The energy value at which to evaluate the self-energy.
+        eta_lead : float, optional
+            Small imaginary part added to the energy for numerical stability (default: 1e-5).
+        method : str, optional
+            The method used for self-energy calculation (default: "Lopez-Sancho").
+        HS_inmem : bool, optional
+            If False, deletes Hamiltonian and overlap matrices from memory after calculation (default: True).
+            This is useful for large systems to save memory.
+        Returns
+        -------
+        se : torch.Tensor
+            The calculated self-energy matrix for the specified k-point and energy.
+        Notes
+        -----
+        - If `useBloch` is True, the calculation unfolds the k-points and applies Bloch phase factors.
+        - The method caches Hamiltonian and overlap matrices for efficiency unless `HS_inmem` is False.
+        - The shape of the returned self-energy matrix is consistent with the reduced Hamiltonian blocks.
+        """      
         subblocks = self.hamiltonian.get_hs_device(kpoint, only_subblocks=True)
         # calculate self energy
         if not self.useBloch:
@@ -429,12 +455,39 @@ def gamma(self):
     
 
 
-#     )
-
-
 def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid, 
                             self_energy_save_path=None, n_jobs=-1, batch_size=200):
+    """
+    Computes and saves self-energy matrices for all combinations of k-points and energy values
+    for left and right leads.
+
+    The self-energy calculations are performed in parallel batches, and results are saved as HDF5 files.
+    Temporary files are merged into final output files for each lead.
 
+    Parameters
+    ----------
+    eta : float
+        Small imaginary part added to energy for numerical stability.
+    lead_L : Lead
+        lead object containing Left lead Hamiltonian and results path.
+    lead_R : Lead
+        lead object containing Right lead Hamiltonian and results path.
+    kpoints_grid : array-like
+        List or array of k-points to compute self-energy for.
+    energy_grid : array-like
+        List or array of energy values to compute self-energy for.
+    self_energy_save_path : str or None, optional
+        Directory to save self-energy files. If None, uses lead_L's results_path.
+    n_jobs : int, optional
+        Number of parallel jobs to use. Default is -1 (use all available CPUs).
+    batch_size : int, optional
+        Number of (k, e) tasks per parallel batch. Default is 200.
+
+    Returns
+    -------
+    None
+        Results are saved to disk as HDF5 files.
+    """
     if self_energy_save_path is None:
         if lead_L.results_path != lead_R.results_path:
             log.warning("The results_path of lead_L and lead_R are different. "
@@ -468,6 +521,30 @@ def compute_all_self_energy(eta, lead_L, lead_R, kpoints_grid, energy_grid,
 
 
 def self_energy_worker(k, e, eta, lead_L, lead_R, self_energy_save_path):
+    """
+    Calculates the self-energy for left and right leads at a given k-point and energy,
+    and saves the results to HDF5 files.
+
+    Parameters
+    ----------
+    k : array-like
+        The k-point in reciprocal space, typically a 3-element array or list.
+    e : float
+        The energy value at which to calculate the self-energy.
+    eta : float
+        A small imaginary part added to the energy for numerical stability.
+    lead_L : object
+        The left lead object, which must implement a `self_energy_cal` method.
+    lead_R : object
+        The right lead object, which must implement a `self_energy_cal` method.
+    self_energy_save_path : str
+        Directory path where the self-energy HDF5 files will be saved.
+
+    Returns
+    -------
+    None
+        The function saves the calculated self-energies to files and does not return anything.
+    """
 
     save_tmp_L = os.path.join(self_energy_save_path, f"tmp_leadL_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
     save_tmp_R = os.path.join(self_energy_save_path, f"tmp_leadR_k{k[0]}_{k[1]}_{k[2]}_E{e:.8f}.h5")
diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 741a6d9..3be097e 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -523,16 +523,9 @@ def prepare_self_energy(self, scf_require: bool) -> None:
         based on whether self-consistent field (SCF) calculations are required and whether Dirichlet boundary
         conditions are applied to the leads.
 
-        Args:
+        Parameters:
+        ----------
             scf_require (bool): Indicates whether SCF calculations are required.
-
-        Side Effects:
-            - Creates the directory for saving self-energy data if it does not exist.
-            - Loads or computes self-energy data and saves it to disk.
-            - Logs the progress and actions taken during self-energy preparation.
-
-        Raises:
-            AssertionError: If `use_saved_se` is True but no saved self-energy is found at the specified path.
         """
         # self energy calculation
         log.info(msg="------Self-energy calculation------")

From f7c3855b8c7821251e27ef46945f811e53bc2550 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 10:55:27 +0800
Subject: [PATCH 144/152] rename extr_baseline as extra_baseline

---
 examples/graphene/{extr_baseline => extra_baseline}/c_spds.json   | 0
 .../{extr_baseline => extra_baseline}/grap_spd_model/sktb.json    | 0
 .../{extr_baseline => extra_baseline}/input_templete.json         | 0
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename examples/graphene/{extr_baseline => extra_baseline}/c_spds.json (100%)
 rename examples/graphene/{extr_baseline => extra_baseline}/grap_spd_model/sktb.json (100%)
 rename examples/graphene/{extr_baseline => extra_baseline}/input_templete.json (100%)

diff --git a/examples/graphene/extr_baseline/c_spds.json b/examples/graphene/extra_baseline/c_spds.json
similarity index 100%
rename from examples/graphene/extr_baseline/c_spds.json
rename to examples/graphene/extra_baseline/c_spds.json
diff --git a/examples/graphene/extr_baseline/grap_spd_model/sktb.json b/examples/graphene/extra_baseline/grap_spd_model/sktb.json
similarity index 100%
rename from examples/graphene/extr_baseline/grap_spd_model/sktb.json
rename to examples/graphene/extra_baseline/grap_spd_model/sktb.json
diff --git a/examples/graphene/extr_baseline/input_templete.json b/examples/graphene/extra_baseline/input_templete.json
similarity index 100%
rename from examples/graphene/extr_baseline/input_templete.json
rename to examples/graphene/extra_baseline/input_templete.json

From 3fe0d13a6807b3f3075adc4492544615d458f2a7 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 10:56:35 +0800
Subject: [PATCH 145/152] fix spds as spd

---
 examples/graphene/extra_baseline/c_spd.json | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 examples/graphene/extra_baseline/c_spd.json

diff --git a/examples/graphene/extra_baseline/c_spd.json b/examples/graphene/extra_baseline/c_spd.json
new file mode 100644
index 0000000..5da4b2c
--- /dev/null
+++ b/examples/graphene/extra_baseline/c_spd.json
@@ -0,0 +1,11 @@
+{
+    "common_options": {
+        "basis": {
+                "C": [                
+                    "2s",
+                    "2p",
+                    "d*"
+                    ]
+        }
+    }
+}
\ No newline at end of file

From b7d1ad3d39fa47b77b18faa71e0eacb3cd6b8205 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 11:26:21 +0800
Subject: [PATCH 146/152] add extraction of system-specific model

---
 docs/hands_on/tutorial2_2d_mat.ipynb | 1302 +++++++++++++++++++++++++-
 1 file changed, 1264 insertions(+), 38 deletions(-)

diff --git a/docs/hands_on/tutorial2_2d_mat.ipynb b/docs/hands_on/tutorial2_2d_mat.ipynb
index 35c01d6..5a3f771 100644
--- a/docs/hands_on/tutorial2_2d_mat.ipynb
+++ b/docs/hands_on/tutorial2_2d_mat.ipynb
@@ -9,25 +9,46 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c514189b",
-   "metadata": {
-    "vscode": {
-     "languageId": "shellscript"
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "id": "e807a978",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "**DPNEGF** is a Python package that integrates the Deep Learning Tight-Binding (**DeePTB**) approach with the Non-Equilibrium Green's Function (**NEGF**) method, \n",
+    "establishing an efficient quantum transport simulation framework **DeePTB-NEGF** with first-principles accuracy. \n",
+    "\n",
+    "Based on the accurate electronic structure prediction in large-scale and complex systems, DPNEGF implements the\n",
+    "high-efficiency algorithm for high-throughput and large-scale quantum transport simulations in nanoelectronics.\n",
+    "\n",
+    "\n",
+    "### Learning Objectives\n",
+    "\n",
+    "In this tutorial, you will learn:\n",
+    "\n",
+    "1. how to extract model for specific systems from baseline model\n",
+    "2. what is principal layer and how to compute electrode self-energies with DeePTB model\n",
+    "3. how to compute the transmission spectrum of 2D materials with periodicity in the transverse direction\n",
+    "\n",
+    "As demonstrations, we will explore graphene, hBN, and MoS<sub>2</sub>.\n",
+    "\n",
+    "\n",
+    "### Requirements\n",
+    "\n",
+    "DeePTB and DPNEGF installed. Detailed installation instructions can be found in README.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8c712d9",
+   "metadata": {},
    "source": [
-    "dptb esk bn_spds.json -o hbn_spd_model\n",
-    "dptb config -m hbn_spd_model/sktb.json -tr -sk ./\n",
+    "## 1. Quantum Transport in Graphene\n",
+    "Two-dimensional (2D) materials have attracted significant attention in condensed matter physics and nanoelectronics owing to their unique electronic, optical, and transport properties. Their atomically thin nature enables ultimate device scaling, while their diverse electronic structures—ranging from metallic to semiconducting to insulating—provide a versatile platform for next-generation applications.\n",
     "\n",
-    "\"onsite\": {\n",
-    "    \"method\": \"strain\"\n",
-    "}\n",
+    "Graphene is the natural starting point for studying quantum transport in 2D materials. As the first experimentally realized 2D material, it combines a simple lattice structure with unique electronic features such as linear Dirac dispersion and high carrier mobility. These characteristics make graphene both historically significant and methodologically convenient as a benchmark system.\n",
     "\n",
-    "dptb train input.json -i ../extra_baseline/hbn_spd_model/sktb.json -o train_out\n",
-    "dptb run band.json -i train/train_out/checkpoint/nnsk.best.pth -o band_plot"
+    "In this section, we will use graphene to introduce the basic steps of quantum transport calculations, including the evaluation of electrode self-energies and transmission spectra. The same methodology will later be extended to hBN and MoS<sub>2</sub>."
    ]
   },
   {
@@ -40,7 +61,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "band.json\tband_plot\textr_baseline\ttrain\n"
+      "POSCAR\tband.json\tband_plot\tband_plot_api\textr_baseline\tnegf.json\tnegf_output\tnegf_output_k100\tnegf_output_k20\tnegf_output_k50\tstru_negf.xyz\tstruct.xyz\ttrain\n"
      ]
     }
    ],
@@ -67,14 +88,8 @@
      "text": [
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO                                      Version Info                                  \n",
-      "DPNEGF INFO    --------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DPNEGF INFO    DPNEGF               : 0.1.1.dev97+bccd946\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev143+4f25276\n",
       "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
       "DPNEGF INFO    ================================================================================\n",
       "\n"
@@ -87,12 +102,33 @@
     "from pathlib import Path\n",
     "\n",
     "\n",
-    "results_path = '../band_plot'\n",
+    "results_path = 'band_plot_api'\n",
     "log_path = os.path.join(results_path, 'log')\n",
     "log_level = logging.INFO\n",
     "set_log_handles(log_level, Path(log_path) if log_path else None)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e1e61d4c",
+   "metadata": {},
+   "source": [
+    "### 1.1 Extract system-specific model\n",
+    "Here we briefly outline the procedure for extracting a system-specific model from the baseline model. For more details, please refer to the  [DeePTB tutorial](https://www.bohrium.com/notebooks/15254953382).\n",
+    "\n",
+    "First, decide on the elements and basis set for the target system. For graphene, we choose the 'spd*' for carbon, specified in `extra_baseline/c_spd.json`. The command `dptb esk c_spd.json -o grap_spd_model` generates the extracted model `sktb.json`.\n",
+    "\n",
+    "To achieve higher accuracy, we further train the extracted model using `sktb.json` as the initialization.\n",
+    "The training input file `input_templete.json` can be automatically generated by: `dptb config -m grap_spd_model/sktb.json -tr -sk ./`.\n",
+    "\n",
+    "After switching to the `train` directory  and copying the input file here as `input.json` ,the training process can be started with:\n",
+    "`dptb train input.json -i ../extra_baseline/grap_spd_model/sktb.json -o train_out`.\n",
+    "We recommend that users carefully examine the `input.json` file to understand the meaning of each parameter. \n",
+    "For 2D materials, the onsite mode should be set to `strain`, which removes the degeneracy of onsite energies for orbitals with the same angular momentum. It is an essential adjustment for 2D systems. For example, the onsite energies for  $p_x,p_y,p_z$ in graphene should not be identical considering the geometry.\n",
+    "\n",
+    "Once training has converged, the resulting model can be loaded to plot and analyze the band structure.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -118,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "31407e92",
    "metadata": {},
    "outputs": [
@@ -128,12 +164,13 @@
        "Atoms(symbols='C2', pbc=True, cell=[[2.5039999485, 0.0, 0.0], [-1.2519999743, 2.1685275665, 0.0], [0.0, 0.0, 30.0]])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# read the structure\n",
     "from ase.io import read\n",
     "structure =  \"./train/data/POSCAR\" \n",
     "atoms = read(structure)\n",
@@ -142,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "1041ae1c",
    "metadata": {},
    "outputs": [
@@ -150,19 +187,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "DPNEGF ERROR   TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n",
+      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
       "DPNEGF WARNING eig_solver is not set, using default 'torch'.\n",
       "DPNEGF INFO    KPOINTS  klist: 300 kpoints\n",
       "DPNEGF INFO    The eigenvalues are already in data. will use them.\n",
       "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
-      "DPNEGF INFO    Fermi energy converged after 20 iterations.\n",
-      "DPNEGF INFO    q_cal: 8.000001001159495, total_electrons: 8.0, diff q: 1.001159494862236e-06\n",
-      "DPNEGF INFO    Estimated E_fermi: -4.0844590057224 based on the valence electrons setting nel_atom : {'C': 4} .\n",
-      "DPNEGF INFO    No Fermi energy provided, using estimated value: -4.0845 eV\n"
+      "DPNEGF INFO    Fermi energy converged after 21 iterations.\n",
+      "DPNEGF INFO    q_cal: 7.99999860016806, total_electrons: 8.0, diff q: 1.3998319401409276e-06\n",
+      "DPNEGF INFO    Estimated E_fermi: -3.582991372133437 based on the valence electrons setting nel_atom : {'C': 4} .\n",
+      "DPNEGF INFO    No Fermi energy provided, using estimated value: -3.5830 eV\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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PZCQ7wsJoXVHBgMICXJMeQ5PYAsPYsQRcd121S/J4Im+uSOBtvPevRGiUPPHNL8syeTNnkf/xxxjGj6fV/fcxNSenTubQZa5fj+PpZ2hltfJzbCx/h4WCLKO22z1iaFtoOmJiYtixY0fV+02WJCRZJqyinKiyMiSFAk3z5ki+vvi0aIEtMxNHXh6Sjw+auHh03bqh6969qsj06vnz2bBhA9jt6C0WYkpLiSsuoUtODldkZWPP+Yi8CguBN1xfq+kMjd2FToKPBQezs2dP7ujWjfxPFpH1wovkzZqF4b77CLzxRq9MAL25IoG38b6/DqFR87Q3v+xykfP22xR+/gVhkyYRMqZyZ4BLnUMnOxyYP/6YgpmzsPloWN62DcV+fmg8bGi7IQ0cONDdITRprVu3ZsWKFSDLtCwspKsph6jSUgq0Wv6MiuLayc/Rol8/Bh8+TEJiIs6SEsq3bqV88xbKN2+m6JtvACpXCnfvTpzdzjZZplyhIE+nI0+nY2d4OJIs07yiggE6PwqWLME8bx5+vXujv+MOdD17NIphy7pQnZNgXefO6Dp3xnLwIPkLFpD98iuYP/6E0AfuJ2DgwGovCPMEoiJBwxGJnxcaOXJktVZA7tmzpwGiqVue9OaXnU5Mr75K0bffYZz8HMEjRtTJcW0ZGWS9+CKW3XtI79qVb2QXFocDjYcNbQtNy4EDB4ivqODK46kYy8pIDwhgRWILjgYFodZoiM3Pp/1p91cGBBDQvz8B/fsDYDflUL51C+Wbt1Dy44/EZmfzgM1Glp8faYEBpAUEkuWnw6VQcFSnI+iZp2nRogUlP/1MwWefkTFxIj6JiYSMG0fAgOua1PzWc6nJSbC2VSuiXn8dy4EDmOfOJevF/8O8YGHlnuHXXec1ybS3ViTwNiLxEzyOJ7z5ZZuNrJemUvLjj0RMfYmgG2+sk+MWfbcK0+uvozIYiFvwEXsOH4Y1a8Dh8JihbXdZu3at2L3DTewnTmBYsoQRBw5i0vnyVds2nAgMrFyJ63IhSVLVScj52kltDCfoxhsJuvFGZFlm1fz5HFn5LdGFBXTOyeWKE1nYlQoy/APIDA7m+O+/065t28pixTfeQMXWreQvTibr+efJ/+QTQh96EL8+fbwmaalrtTkJ1rZuTfSMGVTs2YN5zhyynptMQfISwh6bVLUTjKfzxooE3kYkfl5o2bJlHreZfV0XXHbnm99ZWsaJp5+mYvt2oqZPI+Caay75mK7yckzTX6d49WoCbxqM8amnUPj5kWQ0etTQttC0yLJM0fJvyHn3HYJk+KllIv8EBKBUqapW1EPNT0IkSSJbktgdGcF2QwhKSSK8ooLYoiJii4q5IiMDzZy5HPl6edXKYl3PnsR88D4Vf/9N7qxZZD72OL6dLiP8qafQtm1bX78Cj1bbk2Df9u2J+eADyrduJffd90i//wH8+15F6MOP4JPQ/IKPFRo/kfgJl8yTCy7XlD0nh8z/TMJ+4gQxH36Arg72U7YcPEjWs89hz8kh4uWpZ/QeetLQtrtFR0e7O4QmxW4ykf3KK5Rv/IugoUMJvPMODr/+OuqTNTTPdxJS3XY6fY4aCgXmwEBy/PzYbDTip9EwpG1buvn6Ur55C6bXXwenE3VUZOV8vzFjCLlzDHkffkjqmLsIuvlmQic+hCokpF5+F57sUk6Cdd26Ebd4ESXrfiRv5kyO3347QUNvJvSBB1CFhtZxpJ7JU3eBcidJrkl1YsGttm/fTteuXdm2bZvH9PiZTCamTJlyzoLLarWaqVOnes2bzHr0KBmPPAouFzEfvI/PJQ47VvamLCfnrbfRxMcTNX0ammbNznlf8eEkNKTS9X+S9eKLKLRajC++gP/JpO5cJ3GnTkJqehJXk88GZ2kpFdu2UbZpM2V//ok9MxPJ1xdd9+4ofH0pS0kBIOzRR8ls1ZIV335LYWEhwcHBDB06lPbt258zBuF/XDYbhV99Re7cedjKy8m4/HIs111L7379Gu1nTV3+PdcHd32ni8TPi3hi4rd8+XLWrFmDxWI5o+Cy0+lEq9UyaNAgr5ivUb59B5mPP47KGE7M+++jvsQPQmdpGaZXX6Vk3TqCR44g7LHHztjHVPi3hQsXit076pnsdGKePx/z/I/w73sVES+9hDIw8Iz7XOwkpCbtVJsvXlmWsR07Runvf1C2/g8q/tmFLMso/f2x5eWR6evL982bkX/aArf+/fszbty4WvxGmpaUlBS+WPgxXdLSuCwriwq1mk3NmtHz8cdI6tPH3eHVKW/olHDXd7oY6hUuiScXXK6uolWrMb36KtrLOhL91lsoAwIu+pgLfTlaDhzkxNNP4ywoIHLaawQOGHCRowkAFRUV7g6hUXMWFXFi8vOUb95M6MSJhIy9+5wrZy82tFiTdqrNHDVJkvBJSMAnIQHDuLE4Cgoo+zOFzO++w5KXR3RpKffv2k1qYCDroyLJDAjg119/pUePHqLn7wKqdgJB5pfICLaF6OmTkcnVBw+S++RTZLz2KjF1MJ/ZU3j6LlDuJBK/JqquhhY9seBydckOB7nvvU/BZ58ROOQmjM89h0KjuejjLjSnsWNREdmvvoYmPp6YmR+iiYlpgFciCBdmy8gk49FHcBUWEfPBB/j16tlgz32pC7VUej1BNw3mw3/+5nB5Gc2Lirgy8wTxxcXEFRdT5OPDnlADP3/yCe3ffLMOI29czk6EKgIC+KFVS3YUFnH1iUwKH38CaeAAwh79D5oY759v2xg6JeqLSPyaoLpcjOFpBZery1lUxInnJlO+dSvhTz1F8G23VqtsRNVZ81nDBz5KJcdeeokQcz7BgwdjnPycGNqtoVGjRrk7hEapYtduMh97DIW/P3GLPrnkXTLc1U6FhYU4FQoO6/Uc1uuJLyri+mPHCbDZ6JZtQvnDGo6nZxB4/SACBg5EHRHhljg91fkSocwAf77o0IEbg/VctnsPx0eOJPj22zDce2+1Rj88VX11SjSG+dhNu0Kml/rll18wmUy1euzpiYvFYsFms2GxWLCf3CKspsc9tSpVrVaj1WrRaDRotVrUarXHrkq1HjlC6pi7sO7fT8yHH6C//bZq1wo7+6xZo9EQ6HQybM9e2mVlkzVwABFTXxJJXy0cPnzY3SE0OiW//Ub6hAlo4mKJ+3hhnWyN5q52Cg4OPuPn1KAgFnTswDajEYXLRUlQEKqICPLmzuPo4JtIe+ABCpd/g7O42C3xepqzEyGXy/W/REipRE7qTfPlXxNy7z0UfrWMY0OHUfDll8gOhzvDrrWkpCQUCgVqtRqn04nNZsPpdF5Sp0RKSgpTpkxhzZo1bNq0iTVr1jBlyhRSTi4+8hYi8fNCGzZsqPUf27kSF6VSid1ux+Vy1eqYSUlJTJ06lUGDBtGzZ08GDRrE1KlTPbK3r3jNGlLHjkXh60t88mL8evSo0ePPPmuOLi1l9J69BNpsLG/fjiMJCU224Oyl2rJli7tDaFSKVq/mxFNP439lEjGzZlXtoXup3NVOQ4cO/dd1dqWSX+Ni+bpVK0LUaiy7dxMxdSoRU/4PSaXGNG0aRwYMJPOJJyn9/Xfk03rpTzGZTCxfvpz58+ezfPnyWp9Ue7rqJEIKrZbQ+++n+TfL8buqDzlvvMnx20dR6mWJDdR9p0Rdd5q4kxjq9UJWq7Xqjy0xMbFGf8D1Ne/B06utuywWct54k6KVKwm4fhARkyej0OlqfJyqs2ZZpmPmCfqkp5Pl78d3zZvjCgigiwfPaRSajsKvv8Y0bTpBQ4ZgfH6yV+3Zej7t27enf//+/Prrr/+6LWHozbS6+Wayp75M1rPPEnzrrUTPeBtXSQnFP/5I8ervyXz8CZQGA4E33kDQTUPwSWjeqGqQXkxNaoaqw8OJnDIF/W23kfPOO2Q++h90V/QifNKkSy5z1ZDqcheoxrRYRCR+XuhUD11t/ti8eTFGbVmPHOHE5MnYMzKJ+L8XCRwypNa9cklJSfz8ww9cm5pGq7w8thmNrI+OQunjg9qD5zR6gw4dOrg7hEYh/9NPyZ3xDsG330b4E0/U+Z637myncePG0aNHD1asWHHOOn7R775D4Zdfkfvuu5Rv30bUq68SMno0IaNHYzlwgKJvv6Vo5UoKFiejaN2arWVlEByMRXZVPYdara7VSbU3qGkipG3Thtg5cyj9/Xdy332P46NGEzRsKKETJnhNMe266pRoTItFRB0/L3Kq5s+gQYMIDQ3F5XJRUFBAamrqOe+/Z8+ef13nDbWN6orsdFLw6WfkzZqFOjaWqOnT8GnR4pKOac/JYd+992E/fpyfExM5aAjxuKKg3qqiogLf02qzCTWXvziZ3PfeI2TcOEInPlQv0w68oZ2shw5x4oUXsKelEzZpEsG33lL1u3DZbJT98Qd7Z89Bs38/TkniUEgI+41G0vx0OF0ur6pB2lBkm42CZcswz5sPTich996DftQocgsLvX6xQ3XUR81aUcdPqLbTP8xtNluNHttUtgizZ2aS9dJUKnbsQH/HHbhGDGf11q2Yf/ml1h9Olv37yXzscfwkCe3cOSRmZ6Nv5B92DWnhwoVMnDjR3WF4rYLPP69M+u69h7CHHqq35/GGdvJp2ZL4RYvIfe99ct54g/JNm4j4vxdRBgej0GgIuPZa/jl2jD06XxKzs+lozqet2UyRjw+7DCEcioz0qh6chiBpNISMHk3QDTeQN38+ebPncGJxMmv0wRwKCcEly416qNxbK1ici0j8vJAkSWg0GtRqNTNmzKhxwlGX8x48jexwUPDZUvLmzkUVoid27hx2WCwsfvVVbDYbsiwjSRJr167lrrvuqvabteS338h6/gV8mjcn+p0ZqMLCEH0BgqcoXLaMnDffQj/mTkIffNDd4XgEhVaL8Zmn0fXsQfbLL3N89B1E/vcVdCd7VgwGA+W+vmyOjGRbdDSx5eW0zTbRKyubpBNZ2CoslLRsiX9SEpJa7eZX4zmUwcEYn3oK59VXs2XSY1y3/wDt/fz4LTaGbD+/RjtU3pg6TUTi54V8fHwuuVyKpy/GqA3L3r1kv/oa1oMH0d92K6EPPkhuaSkfP//8v3pGKyoq+Pjjjy/64STLMgXJS8h9/338r+5P5Msvi1Itgkcp+u47TNOmE3z7bYT95z9iVflZAvr1Q9u2LVkvvEj6+AkY7rsPw733/KsH57hWy/Fm8fzRvBntCgsZJEmceOJJlKEGgm4cTNDQm9HExV30+RpDnbfq+Cs1lTVt2xCek0O/jExG7z/AAUMIf0ZGYg0K8qrFDtXVWDpNROLnhXr37u11Zxj1yWE2kzdrNkUrV+LTqhXxiz5B264dAOuWLz/vcLjNZqsaljgX2WbD9PobFK1YQcg94wh98ME6nygv/I+nDx96ovSVKyma8hKmtm3Yk5BAUk5OvX8ueGM7qY1GYufMxrxwIeZ58ynfsoXI/77CmDFjWLx4MS6Xq2o0wKXR0OXpp2mZlFS5IGTFSgqXLyd/0SJ8u3QhaOjNBFxzzTlPAJvSKuFTix3SAgP5opOBdnl59ExNY5w5n4NhoZR50erfmmgMnSbiW8wLXX311SLpo3KSdv7ixRwbOoySX34h/MkniF+8qCrpAzhw4MAFj3G+251FRWQ88ijFq1YR8dJLhE2cKJK+erZmzRp3h+BV/lq8mJwXXuRgYACf63SsWbu2QYrJems7SUolofffT+y8udizsjg+ajTqnTs5e33j6T9rW7fG+MzTtFjzA5Gv/hdJoSD7/6ZwZOAgTNOmY9m/v+q+janOW3WcXiHC4XKxJyyMjy+7jN9iY4gtKqbzZ0s58cILWI8ec3OkwtnEN5ngdWS7ncJvVnBs2HByP5xJ4E03kfDNcvS3346kuvRObFtaGqnjxmE5dJCY2bMIumlwHUQtXMyRI0fcHYLXOLFpM4q3Z5Dt78d3cXFYTyYbDZFkeHs76Tp3ptlnn6Jo3x7NrNn0OXwY6WQB+1OrNM/+HSq0WgIHDSJ27hyar1xB8K23UPrbb6TecSfHR99BwRdfsvGnn+q8OL4nO1dBaCsye6Kj+axbNwIefpiK7Ts4fuutZD7+OOVbt/4ryRbcQwz1Ch7pXPNkwg0GStatI2/efOzp6QRcdx2G8Q/g07z5eY/TunVr0tLSLnj76cq3bSPzqadQBeuJX7SoTra4EoS6ZM/OJueJJyjx8eGbhARQq9F4cTFZd1AGBfF3/36kp6aSdOwYUcUlrG3Vklwfn4v+DjUxMYRNnEjo+PGUbdxI0TcryHn7bZrb7VwbGMDOkBByQkK8us5bdVxoscPoMWOIT0rCddcYir//noLPlpI+fgI+rVujHz2awAHXIWk07n4JTZZI/ASPc/Y8GbUsc+KTT+hbWoamqAi/Pn2Ien062rOStnMZMGAAv//++znn+Wk0GgYMGFD1c9G332J69TV8u3Qh6vXpKAMD6/R1CRd21VVXuTsEj+csLCRj4sM4ZJmVbVpjAzQNXEy2sbSTOT+fXcZwjmt9uOnYcW7dtZv1zeLZERxcrd+hpFLh36cP/n364MjN5c9p0zH+/ju35uZRqNWyLzycPYYQbCfbpzEWx7/YYgeFRkPw0KEE3Xwz5Zs2UfDpp2RPmULuB+8TNGQIQTffjCYm5oxjNpXFMe4kEj/Bo5w+T8a3tJROuXl0yMtD63RyICyMKz74gOjeV1T7eEajkXHjxrFo0SLsdnvVBG61Ws3dd9+N0WhEdrnI+3Am+YsWETR8OMannxLlG9wgxEt2AnAXl81G5uOP4ywqImvs3Vi2bAGLpcF34Gks7XRqjprZ15el7drRNyOd/kePERMczB+tW9Xod6gKC6PtM08zpayU8Px82mSb6J6RQc/0dFL1wRyIjKR3z561itPTE6HqLHaQJAm/Xr3w69UL69FjFH7xBYVffEn+wo/RdetG0LCh+Pfrx8Zt25rM4hh3Eomf4FFS1q8nLi+PtpknaF5cjE2pZG+ogW1hYViDglBkZ9W4ft6FzkpdFRVkvfh/lP72G2GPPYb+jtGiHIabrFixwitXjDYEWZYxvfoaln37iZ03j4DwMFZv2+aWYrKNpZ1OL+ditdtZFxPDMT8/BqSmcdvOv2l+5501Op7RaGTMXXeRnJxMtl7PnzYbbcxm2uXkMOTQYcruuZecG64ncND1+LRqWa3Pmca4StgnoTnG554l7LFJlPz8M0UrVpL1/Aug05GuVhOh13NM54t88vfTWOsCupNI/ASPYD10iOI1a4lfupSEoiJMOh0/t0jgcFgYdknCZrOhuYQhrHOdldpzcsh87HFsqalEz3gb/0YyhCU0PgVLPqV41SoiX/0vvh074AuNppisu5xrjlp6ZCRfGQzcVVxC6bPPYX7gfkLGjUNSKqt1zHOdZHZMSiKooICild9SvPJbChYno2nRgsBBgwi8fhDqyMhzHuv00Y+zt9dsDImQQqsl6MYbCbrxRmxpafz1+htE/bWRNtnZVKjVHDGEcECvJ02nE/NW65hI/AS3sWdmUrxmLcVr12I7cgRFYCCW1q35wWIhTa2q3A9RkuplCOvU9mtIEnELFqBt3apOjivUXmMZQqxrpX+mkPv++4SMHUvgoEFV17urmGxjaqfz/Q7DDQbMH31E3py5lG3ZQuTUqagjIqp1zHMOfRqNaNu0Ifzxxyj76y+Kf1iDecEC8mbOxPfyywm84XoCrr0WZVBQ1UNSUlLOWCWsbMQLeDRxcRy4vBObLBXoCwtpV1xMS7OZDqYcLEolqXo9BAXhvO46lAEB7g7X64nET6gT1ZmHIrtcWPbuo2z9H5T+/gfWQ4eQtFr8+/Yl7JGH8evZk8CCAvKmTEF98iy3PoawSn79lawXXjxj+zXB/UaNGuXuEDyO9egxsiZPxu/KJEIn/nv/XXcUk21s7XS+32HohAnounUj6/+mcPzW2wh/8gkCb7rpkqaCSGp11YIQV3k5pb/9RvEPP2B6/Q1y3ngTXe8rCLjmWvyv6lNVIBlA2cALeNzBYDCgUCox+fmRFxhISlwcYcXFNMsvILGkGOO6Hzn8x3p0l1+OrlcvdD26o23T5qK9sZ4+R9IdJFkU1vEa27dvp2vXrjRv3hxfX9+L3n/Pnj0NENW556GcGnLq2bYt5Vu2Ur55M6V/rseZZ0YRGIhfUm/8+1yFf58rUeh01T7epSR+lduvJZP7/gcEXHM1EVOniu3XPMjSpUsbXVJxKZxFRaTedTeS1of4hQtR+Pm5OySg6bWTs6SEnLfepnjVKvz69CHi+cl1frLoMJsp+fEnStato+Lvv5FUKgqio9ngdLBf54dN61PV4+d0OtFqtQwaNKjR9PhBZYI2ZcqUcw5tq9VqpkyciHb/fsrW/0n59u3IFRUoAgLQde2KrkcPdN27o2ne7IzEvL6+S+rKqe/0bdu20eXkHtINQfT4eaHQ0FDsdvt5tyJrSGfPQ/FxOIgpKSW+vAzrpMc45HSikCQ0zZsTOOh6/K/qg2+nThcstFwfQ1iyzYZp+usUrVwptl/zUPn5+e4OwWPIdjsnnn0OV0kJ8bNmekzSB02vnZQBAUROfYmAq/uT/eprHLvtNozPPEvgwAEXf3A1qQwG9Lffhv7227Dn5FD6628ofviB/lu20A9IDwjgUHAwh/TB2HW6el/A4w4Xqgs4ZswYIjt2hI4d0d9yC7LNhmXvXso2b6F8yxZyZ8xAdjhQhhrw7dQJ306dqIiJ4dNPP8V+cnj8lMYyR/JSiMTPCw1SKokNDaXzFVfQqnsPVIYQlKGhqKOjUVSzKGZddH/LLhebV66kZXY2oQWFRJWXE1ZeDkCRRsMJfTDOvn3p/9BDNT5DrsshLGdRESeefoaKnTuJmDqVoME31slxBaG+5Mx4h/Lt24mdNRN1dLS7wxEA/759aXbZZeS8/gZZkydT+ssvhD/7DCq9vk6fRx0ejv62W9Hfdisb1qxh6+w5JOTlcnV6Otekp5MTEIDhumsJKipCDg+v8dCzJw99VvekX9Jo8L38cnwvvxweuB9XRQUVO3dSvnUbFf/8Q97MWdjKyrjX5SJb50tWQAA5gYFkabUUyfIlzZGs6e9Pdjpx5OZiz8rCYcrBWVKMq6QUV2kp+ceO1vj564IY6vUip7qF57VqTTtfX7ROJzq1GsWpN75CgTo6Gk2zZvi0aolvhw5oO3RAddZk7Np0f8uyjCM7G+vBg1j2H6Bi1z9Ydu/Bkp+P0+kkX+uDKSCQ7KBA0gICMSskNBoNPXv25P7776/vX8152dLSyJg0CVdhEVFvvYWuS2e3xSJcWGZmJtEiyaHw668xvTYN4+TJBI/wvKE80U5QvHYdptenIymUhD/5JAEDB9RbGahTiUZRRgaxeXk0LyjEuXMHcnkFqogI/K+8Er+r+qDr1g2Fj88Fj+XpQ591RbbZWPrGG5g3bsRYWER0eRl+tspev3KVijx/PzSt25B0551o27VFZTTWurzO6b8/V1kZFbt2Y9m/D+vBQ1gPHMCWkQEOx/8OolSiDAhA4e/PnrIyhvz8kxjqFS5uRWIL1hsMqNVqBl93HTcmJeEwmbClpWM7fhzbsWMUfbOC/AULAVDHxuLXsye6K3pRFh9PcnIyNpsNx2l/jCqVqqr7O9TPD9vxVKyHDmE9eLDy30OHcJWWApXbHWk7dkR/x2i2Fhaxat9eih2OM1ae4XS6vVp9+datZD71NKqQEOIWL/pXhXjBs+Tn5zf5hKJ861ZMb7xJ8K23emTSB6KdAAIHDkDXtQs5b71N1vPPU7x6FcZnn0UdHV3nPWrnGv1w2WxUbN9B6fo/KPtjPYXLliFptfj17IHflX3wS+qN+qznbOzlYU4naTRo27VjV1oaWwwGlAoFgU4noSUlhJaWEm2xELl/PyeeegoARUAAPi1aoGmRgE+LRHxaJKBp0eKM3txTv7/TvzsVLhdxFRUcevllwvUhuI4erfzu0+nwadUKXY8eBN96K+roaNRRkaiNRiSdrirJLNy+Hbp2bfDfj0j8vJjdbic9JwdNbCya2Fh03bpV3SbLMo6sLCp27aZi+3bK/vqLwmXLsLtc3ODry9GAAPK1PqhcMnqrBb3FSojNRvaw4RRarZUHUSjQxMXh07IlfldcgU+rVvi0aonqtOGFziYTK6ZMQS1JDV5I9kKKVq4k+7Vp6MT2a17jjz/+oGPHju4Ow21sGZmcePoZdF26EP7E4+4O57yaejudogoNJWr6NEpvvAHT9Nc5duttFA+4jgWZJ3Ai12vBZYVGg1+vnvj16on85JPYjh2jbP16Sv9Yj2naNHC58ElMxC8pCb+k3vhedlmTKg8DZxbottvtFEgSBYGBHDcY+Fut5qWXXsIgKbDu34f18BGsR45g+WcXxSu/RT6Z2ClDQtDExqKOjeVAURHNsrIpkSDYaiW2pJTmRUX4OJ2UaTTkhYbR9pln8O3SGU18vEfPIReJn5crKSlBlmVkqxVXeTmusjJcZWU4C4tw5ptx5JmRfLX4XnYZSoOB0l27aFZQQOLJCdouwKpUkqfzpcBHi6NFC/qNGoUmPg5N8+YXXfV6sQm5DX0GWbn92ofkL1ostl8TvIaztIzMxx9HERhI1PRpF1z8JHgW/z590HXtSupbb6H64kuG++lYGxtHjl9ltYL67lGTJAmfhAR8EhIIuftunMXFlP31F2UpGyj67jvyFy1C4eeHwWCgtdPBYZ0Ou79/oy8Pc7HvpoiTdRnVxnD8+/atepxst2NLz8B65DC2o8ewZ1SOpAXv2sXgigokWUai8ruzXK0iV+dLvo+WAJcLV1kp1v37cWRloTg5nKsMCEAREICk0XjMrlDi08ULDT90mLYZmShkGe3uPRxc+S2c7Gk7m6TzRRViQGWovGTGRJNWWoZNIRFstRJXXEJoRQV+dgfHAn3IjIlmyKCBNYrHXYVkz+aqqCDrhRcp/f13wh5/DP1osf2aN2nRooW7Q3AL2ekk68UXcGRnE/fJJ2cU8fVETbWdLkSh07GjQwe279tP30OHuGP/fnZFGNkQFUV5A/eoKQMDCRwwgMABA5BdLqwHDlD6558UfbOCa1JTuUaWyfXzI1UfzNHAQDJ9fd0+Lae+1Oa7SVKr8Ulojk9Cc1xlZRR9/z2WgwdR2O3k+/iQGhiI2beyQ8TfbsfPbsffZic4NRXzRwtwlZWd58BSZfJ36qJWk1FSUh8v+6JE4ueFDgcHYQkKQlKqaNuhPfE9e6L080PS6VD6+aHw80MZFITSYPhXj93X77/P1q1bz7guvLycjrl5dM3JQbNqNVlKFfo7RqNt3braMbmjkOzp7KYcMh8X2695s0Gn7UrRlOTNmk3ZnylEv/sOPgnN3R3ORTXVdroYs9lMtp+OT9u0prs5n56ZGbTKM7M+KoqDUZFu6VGTFAq0bduibdsW55AhTJs8mci8POLy82mfbaJbRiZWlYqMED3d+vbDYTajamQJYG2+m+w5ORR8+hlFy5fjsljw79uXv5s148cTJ+A8nQndunXj0UcfxVVRgbO4GFdxMc6SUlylJThLSpAtVmS7Ddlmw2W1Itvt+B1PhfV/1MXLrBGR+Hmhf8LCMBmNaDQaRjz1FPoa9KzFxMTw999/nzG5N0en4+f4OP5qFs+tYeH4b9tG8erV+CUlYXjgAXw7tK+Pl1FnLPv3kznpMVAoxPZrXmzmzJlMnDjR3WE0qOIffiD/k08ImzQJfy9ZVdkU26k6DAYDCoUCl0LB1ggjB8JCueJ4KtelptLZbMbm5nmRRqORW+65h+TkZI4YjchOJxHl5TQrLKS7RkPFjBkcmTEDbdu2+F2ZhF9SEtp27aq9T3FjYMvIIP/jTyhevRrJx6dykdUtI1FHROC3fDnq1avP+O48Ra1WE3Ny8aDC1xeFry9U43tZv307vPlGnb+OixGJnxfy8fFBo9HUag7dqQmvsiz/a1WvrNHQ7qknCTcYKPnxR8wfLSDt7rvx692b0IceRNu2bV2/lEtWtf1aQgLRM94W268JXqNi126yX36FwJsGo7/zDneHI1yisxcTFAI/NItnd4SRfqlpNF+cTJbZTOgjj6AOD3dbjOcb+nSYzZRt/IuyDRso+OJLzPM/QhkUhO6KXvhfeSW6Xr3OWbPQk+sCVpfdZML80QKKVq5EGRyMYcJ4gkeOROnvX3WfC313qtVqryqHIxI/L9S7d+9aL5w4fcKrSqU672KMwOuvJ2DAAEp+/AnzRx+ReucYAm+4ntCJE6u9WfmluNiHidh+TfBmdlMOJ558Em3bNhgnTxZzURuB8y0mMOt06B55hKj8fHI/+JCSX38jZMwYQsbc+a/tKhsqznMNfaoMBoIG30jQ4BuRHQ4su3dTumEDZSkbyFqzFiQJbfv2+CUl4d+vLz4tW7Jhw4Z/1bWrj1XM9cVZXIz5owUUfvUVCl9fwh55hOBbRp7zu6S6353eQBRw9iJ1ua9fTc7SZIeDom+/I2/OHFylpehHjyZk7FiU/vWzjdTFimSesf3avfcQOmGCRy+dF6qnoqKiWntQezuXxUL6fffjKCggfvEir5tT1VTaqbYu9NnqLC3FvGABhZ9/gcLfH8P99xE8bJjHVx5w5OZStnEjZSkplG38C1dZGVJcHL9ZrezTB2M+bTj41N66U6dO9dhkSHY6KVqxgrxZs3HZrITcdRcho0dXa2vEuuzhdNdevSLx8yLu+iM5xVVWRn5yMvnJS1D4+hL68ESChgyp06Troht1P/449jffwrJrF8YXXhDbrzUiv//+O31PK6vQGMmyTNbzL1D6++/ELfgIbZs27g6pxppCO9U3e3Y2ebPnULx6NerYWMImPoT/Ndd4Rc+vbLNRtmkzu+bMQf3PP6icTrIC/DkQFs6+ED0VgFarZdCgQR5RF/DsRK1HYCCOhR9jPXCAwMGDCXt4otumCLnrO10M9XqhkSNHVuuMe8+ePXX6vAo/P0InTCBo2DDyPpyJ6ZX/UrxqNcbJkyn009XJWdCFioyGWa1k3/8AAWo1sXNmV+7TKDQau3fvbvQJRep771O6ejUHBg5k9969JOn1Htsrcj5NoZ3qmzoigsipL6G/YzR5H3zIiWeeRdu+PWGPPnJGIX5PJGk0+Pe5kt3797FNoyY2N5f2BYX0PXaMK1NT2afXs89Nq5jPdvrokdZi4arUNPLy8lAkJtLyk0/w7djB3SG6hUj8hBpTG41EvvIyQUNuIvu1aRy+5Rb+Cg9ja3Q0drikeR5msxmXywWAUqmsKjIaZ85n8PFj2AyhxCcvRh0ZWdcvSxDq1eZZs9DMn8+W2Fg25ZtRrFnjVfOhhLqnbdWKmA/ep3zLFnLfe5/08RPQde9O6ITxHn9iazAYcKnVHAgJ4XBYGEEOB21MJtrn5NLRbMZZUEhReDgB11+PQqNp8Piqtqiz2WiVbaJvRgYuSeLHFgkciTAyNTyMpjphQSR+XmjZsmVuGeo9m657d3Tvvcuv48fTOT2DBFMO6+LjyAwIqHW1+lMlEaCyojyyTKfME1yZnk5qiB75gfvpKZK+Rql79+7uDqHeZG7ciGL2HA6HhLA+LBRsNsA790ltzO3kLrru3YlLXkzpr79hnjePtHvvQ9erF6ETxlMcHu6Rq2bPXsWcL0lsiIhga0wMLUpKuSUkhOxX/kvurFnob7ud4JEjGnTrzJSUFPzKy7nq4CHiS0o4EBrKb7GxlCkktLLc6LaoqwkxI164JBu2bmVTbCyL27ahQq3mtoOH6JeZictqxeVykZKSUqPjJSUloVAoUKvVYLfT/9Ah+qSlsT0qkjXt2tH76qvr6ZUI7paYmOjuEOqFIz+f3GeepdDXl+/jYlGqVGg0GpRKJXa7vVbvE3dqrO3kbpIkEXB1f+I/+5So16fjyM3l2Ji72DxiJNu/WsamTZtYs2YNU6ZM8Yi/l1OrXNVqNVqtFo1Gg1arRaXRcNWk/5A4dw7Nl32F/1V9Mc+fz5EbbiTnrbewZ2fXe2yyy4Xq118ZtWMnIRYL37Vpzc+tW2HX+gA0yi3qakIkfsIlOTU0m+/ryzcd2rOhWTyXm3K4c99+DMUlNX5znfowCZDh1iNHaJtfwE+tWrKlRQvG3HWXR5zpCvVj6dKl7g6hzsk2GyeefgaXzcqq1q1wnJy3qlAoUJ5cCeltX0KNsZ08iaRQEHDttfi+9y7ft0jAv6KCkX//zY179hKSl4fdbic5ORmTyeTuUElKSmLq1KkMGjSInj17MmjQIKZOnVo1dUHTrBkRz08mYdV3hNx5B0Xff8+xm4dimv46dlNOvcTkyM0l45FHSfhjPfvDw1nUvh1HAwNxuVyVo0jQaLeoqy4x1CtcktOHZh0uF9sjIznq78+AI0e5ZfduiiMjkB2OGm063y08nLCsE1RICvbeegsJHTowxkOGNwShumRZxvT6G1h27yZ71CjK9+wBi6Xqy0d8CQkXsmHjRg4aDOzS6WhbWESPrCxuP3CQDH9/dsTFkfLnnwwfMcLdYVZrSzSVwUDohAmE3H03BV98Qf7ixRStXEnQ8GEYxo1DFRpaJ7GU/PYbppdfAbWK4OnT+HPlSmS7HafdXvV+U6vVKBSKJj2vViR+wiU5e56H0+kkx8eHrzp24IqsLHqvX0/6Aw8Q+d//oo6KuujxitesJfuVV9DExdF84UI6ifl8TUZjqw1X+MUXFK1YQcRLLxHUozvfTplyxvsEvPNLqLG1k6c6NZoiSxKHjeEcjTDSzGyma3oGN+3di2X2bEr0Ifj36+s1dUwVvr4Yxo4leOQtFH6+lPwln1K0YiX6224lZNw4lAEBtaqT56qoIGfGOxQtX45/374YX3wBlV7PmMDAfxXU9saCy3VN1PG7iAMHDrBu3Tq2bdvGtm3b2LdvH06nk1deeYUXXnjhgo/96aefmDFjBps3b6asrIz4+HhGjBjBc889h/9pW8FUl7vr+J3PhQoud/H358Tzz+MqKcX4/GQCBww45zFkh4PcDz6kYMkSAgYNJOLFF8VOHILXKvvrLzIeeRT9HaMJnzQJuHhhckE43fLly1mzZg0Wi+WM0lZOh4NEq5XrrVYCMzLRNG9OyLixBA4Y4PGFoM/mLCmhYMkS8pd8isLHh8Jrr2Hh8eM4oNrvEeuhQ5x45lnsJhPhjz9O0PBhZ9RD9OQt5UQBZw81adIk3nvvvX9df7HE75133uHxxx9HkiT69OmD0Whk/fr1ZGdn07p1a/78809Ca9i97amJH1ykWn1JCabXplGybh1BNw8h/KmnKjexPslRUEDWc5Mp376dsEn/QT9qlFcUMhXq1ooVKxg6dKi7w7hktuPHSR07Dt/LLiP6nRlnbHLvyV9C1dVY2snTXayY/dSpUwnMzsb88SeUrV+POioS/R13EDRkiFu2grsU9pwc0me8Q/F331Go1fJ7dBRHgoJAki64E0jRqtWYpk1DExtL1PRpaJo1c88LqCVRwNlDdejQgSeffJLOnTvTpUsXXnvtNZKTky/4mB07dvDEE0+gVCr57rvvuP766wEoLy9nyJAh/Pzzz0yYMIFly5Y1xEtoEBea56EMCCDytVfx69UT0xtvUr5zJ1GvvYa2TRss+/aR+eRTyFYrsbNmenzxUqH+ZGZmujuES+YsLCRj0mOowsKIfO3VM5I+qN58KE/XGNrJG5xv798zhiqNRmLefQfLgYPkJy8mZ8Y75M2ZS/DIkehvu9VtO1LUlDo8nJ09urMlPY1eR45y85GjZAYG8mtcLDlU1nQ9vfyKy2ol5823KPrmGwKH3ITxmWfECFENiMTvIu67774zflZUYy7FtGnTkGWZcePGVSV9ADqdjgULFpCQkMDXX3/N/v37aeOFWzbVhiRJBN18M76dOnHi+RdIu3ssgUNuonjVanwSE4l68w3UERHuDlMQak222ch8+mlcJSXEL16EshbTOQThdElJSSQmJl60l1jbuhVR//0v9ocfpmDp5xR+8UXltJnrBxFy5534tGjhpldQfWazmVxfX75umUjL8gr6pB7njt172BkWxtZm8VUr320ZmZx45hlsx44R8X8vEnTzzW6O3PuIxK+O2Ww2Vq9eDcDo0aP/dXt8fDxJSUmsX7+eb775hueee66hQ3QrTbNmxM6bS8bEhylY8im+nTsTM38eSnG21uQNHDjQ3SHUmizLmKa/juWfXcTMnoU6OtrdIdUbb24nb1STXmJ1RAThj03CcP99FC1fTsHSzyn+9jv8evdGP+ZOdN27e+w0mqoKEZLE0cAA0i+7jI4nTtAr8wRtCwooi46hYOlS8mbPQanXE/fxQrStW7s7bK/kHUuBvMjBgwcpLy8HoNt5hi1PXb9jx44Gi8tT2NLTSX9gPJY9e/Dv2xfL/v2YXn4Fl8Xi7tAEodYKlnxK0cqVGF94AV3nzu4OR2jilP7+hNx1FwkrVxDx8tTK2nYPPkTqnWNI/+ILln/1FfPnz2f58uUeUQ8Qzize73Q6sTgcbAkPZ/HlnUgL0RP13XeYpk3H76qriP90iUj6LoFI/OrYsWPHAAgODiYgIOCc94mNjT3jvk1F8Zq1pI6+A1d5OfGLPiF2zmyipk+j9PffSbv3vgap6C54rrVr17o7hFop/f13ct97j5Bx4wgafKO7w6l33tpOTZGk0RB0443EL/2MmJkfUuR0Yn75FcKnTcP2zTf8smqVx+0EolKpzihy7vLxoVXz5khKJZJOR8maNeS++x6OggJ3h+y1ROJXx0pKSgDw8/M7731OlXIpLi5ukJjczWWxkP3qa2Q9/zx+ffrQbEky2nbtAAgcMIC4hQtwFhWSOuYuyptgL6jgvSwHDnLi+Rfw79+f0IcedHc4gnBOkiRR2rw5s4ODWNqxA2l+/lyRmsbYzVtIOniQ7+bO9Ziev9MLjfjZbAz7Zxfq3XuIfvstWv7yM+FPPE7Jjz9ybMQICr/+GvlkTUyh+sQcPy80YsSISyqiOnHiRCZOnFiHEZ2/TIVl/36ynn8Be1YWxuefJ2jY0H/NMdG2bk18cjInnnmG9AkPEvH8ZCw9e3p92QuhZqK9bF6cIy+PzMcfRxMfT+TLU72miO6l8rZ2EiqlpKTgcrnI0mjISWzBX85mtM/KpmNODh1NOaQ++CB+TzyB3xVXuOVv2WQykZycXFmr0OkkrLycYYePgCSxtH07HmnfngCdDv2oUQQMHEjuBx9gem0ahd98g/GZZ/Ht2KHBY/ZWIvGrY6eGd8vKys57n9LSUgACAwNr9Rxff/21R9XxO1dh2h/XruU+YwT+a9fik5hI/JIl+CQ0P+8xVHo9sTNnYnr9DTL/bwp/RUTwV2wMLllGoVCwbt06Uei2kfOm2nCuigoyH38CnE6iZ8w4oy5lY+dN7ST8z6mdQKCyPIpFrWZLfBx/GcNpX1LClUXFZD76HzTx8QTfdhtBNw1u0HqApxJTu91OYnExNxw5Sr5WyzctEnDqdGeUc1GFhBA5ZQrBw4Zhev0N0saOJejmmwl95GFUen2DxeytmsYpagNqdrKAZGFhYdWw79nS09PPuK83O3WWZrfbsVgs2Gw21MXFDP7nH5Rff41m6FDiP154waTvFEmthvvu5Y+YaLqkp3P1vv04LRYsFotHbUwu1I+FCxe6O4RqkR0OTjw3Geuxo0S/+w5qY7i7Q2pQ3tJOwplO31fd6XTicrkqe9cUCg4ajZx46EFiP5qPT8tEct5+myPX30DOjHewN1DdRrPZjMvppIsphyGHDpMeHMzyDu0p02hwuVxV5VxO53vZZcQvXkT4M09T8ssvHBs+goIvvxTDvxchEr861rp1a3Qnz5K2bt16zvucut6Teu1q6/SzNKVCQafCQu7eu4/ACgsrOrRnR+tWSBpNtY+3YcMGtkdF8V1CcxILC7n18BH8Th7f5XJ5xCRkoX5UVFS4O4SLOlW2pWzjRqLfeANtE6nDeTpvaCfh385eNWuz2XA6nf/bL/rKK9F17kzU66+T8O1KgkeMoOi77zh681AyH3+Csr82IZ/sMawPhuBg+h1PpV96OtsiIljdqiXWk7cpFAoMBsM5HycplehvvZXm3ywnoH9/cl5/g9S77qbin3+Ays6J5cuXe9wqZncSQ711TKPRcOONN/LVV1/x2Wef0b9//zNuT01NZcOGDQAMGzbMHSHWqVPDB4FWKwPTM4gtKmJ/WCg/R0Uh63REn+MsrTrHO6TXY9HpuOnAQW7fu4/liS0o02jOedYnCA3F/NFHFH3zDREvvYTfFVe4OxxBqLZq7QRykjoigrBHH8HwwP0Ur/6egi+/IGPiRNSxsQQPH07gTYPrdEjVWVrGZWvXUmIy8UuLBHbq9XBym7qqxPQi03xUen1lQedhwzC9Pp20cfdQ3r07i21WylSqqtcrpg2JxK9ePPvssyxbtoyPP/6YESNGMGjQIKByy7Z7770Xp9PJiBEjGsWuHQa9nk7ZJnqlHseiUvNt2zYcCwjA6XSivcBZ2nmPd9pwxAmdji86tGfIvv2M2n+ANW3b1Ph4gvcYNWqUu0O4oKKVKzHPmUvoQw8SdNNgd4fjNp7eTsL5VXcnkFMUWi3BI4YTNHwYlr//pmDZMvJmzSJv1iz8r72G4BEjKI6MZMOGDbVeiOcwm8l49D/I6enYHpvEwS1b0J42X/xciemF+HbsQPyiRaQvWkzRe+9xuyyTEhXJ32FhyCf3/k1OTiYxMbHJLhgUid9FbN++nYceeqjq5yNHjgAwd+5cVq1aVXX9N998Q2RkJFA5hPv222/z+OOPc8MNN9C3b1/Cw8NZv349WVlZtG7dmjlz5jTsC6kH1kOH6LjyWxKOHWOXMZzfIiOxK5Vw+vBBDc+qkpKSWLduHWq1Grvdjlmh4LPWrbj52HGG7NlLWD0ONQjudfjwYXr06OHuMM6pNCWF7FdfI3jkCELuucfd4biVJ7eTcHG12S9akiR8L78c38svx/HEExR/t4rCr7/m+KrVZKvVZEYY2Wcw4NBoatSjZktLI+PhRyr3av9oPi1btaLZ4MGXXNFBUirZGhzEb1060/3oUa5Oz6CTOZ8/YmM45u//r71/mxqR+F1EcXExmzZt+tf1GRkZZGRkVP1stVrPuP2xxx6jY8eOvP3222zevJmysjLi4uJ47rnneO65585b3NkbOEtKyJszh8KvlqGJicH61JOk/PUXSpcLqZZnaaecazhC0mr5vmMHHnC6KJ/+OgWyjP6WW+rp1QnusmXLFo9KKE6VKLLt2kX7b79D17MH4U8/7bFbXjUUT2snoWGp9HpC7hqD7bpr+WzSJNqeOEHS0WNccew4h4OD2W80smTx4ov2qFn27iXj0f+gDAwkdu4c1Cc7TmqTmJ6L2WymXKnkx/h49kVG0ic1jWEHDpIaEMCG5s2a9LQhkfhdRL9+/c4oKFkT1157Lddee20dR+Q+sstF8XffkfvBh7isFsIenoj+9ttprtHQ7Prr66zu3vmGI8LDwsidMYOc6a/jNOdjGP9Ak/8SFurHqRJFIcXFDN29hzR/P7632xn9119Nem6QIJyyYeNG0oKCOOjjQ2BcHO3yC2ibk0Obffso9fHhwMuvoH/6KTTx8f96bNnGjWQ+9TQ+LVoQ/e479VKC5fRpQ1m+vnzdri3xZjNXpqUz6p9dlGs02Ifc3ORW5YNI/IRqKt+6ldz3P8CyZw8B1w8i7NFHUYf/7w1TV2dpFzte2BNPoDSEkvfhhzjMZozPPoOkVNbZ8wru06GDZxRgPVWiKKCoiJv27MXs48Py5s3B5Wryc4PAc9pJcK/T6wJafH3ZGevH9qhIQgoKuaywgA6bNnFs+Ah8O11GwIAB+PfrhzoiguLvvydr6sv49epF1PRp9VYD8+xpQ06nk0OBgaR1uoxOZjPXpaZxbOhQgkcMJ2TsWFShofUShycSiZ9wQRV79pA3cxblmzbh07YNsfPno+vivk3oJUnCMG4sKkMI2f99FWdBAZGv/heFj4/bYhLqhqcMH6akpBBQVsbQvfso9dGwqn07XJKE025v8nODwHPaSXCvs+sCAjhdLrL9/SgMNeB3zTVcE6ynePVqct95l5w330IVGYk9PZ3AG28kcupLlbVbz3K+XaBq6kKrmDs/8wyJnS6nYOlnFCz5lMJvVhA8ciQhd9+FKiTk0n4xXkAkfsI5WY8cIW/2HEp//RVNQgJRb76Bf//+HjO0GjRkCEq9nhPPPkfGw48QPeNtlF48b1KoLAxc11sJ1kbJ8ePcvHsPVqWSb9u1w67RoDxZ7PZ8hWSbEk9pJ8G9ztWjBv8rv9K7Xz8CjUYCBw7AWVqKado0ilZ+i6RQUPzjjzjz8/Hv3x//fn1Rn0zszrUL1KWUX7nYKubQ++9Hf/vtFHz6GQWffkrhsmUEjxyJftTtqCMi6u6X5WFE4ueFRo4cWa29evfs2XPe2853VlXx99/kL06m9PffUUdGEvHyVAIHDfLI4VT/Pn2InTWTjEmPkf7AeGJmftgkztaE+mM35XDZd99RKkksa9WSCoWiKumDCxeSFYSmpLp1AWVZJv/jTyhZs5bQCRMIvvUWyv74g5JffyNnxgxy3ngDbbt2yN278f3mzdg1GuwOR9XzXGr5lYtNQ1IGBBA6YTz6UbeTv2QJhV8to+Dzzwm49hpC7rwTbdu2Nf/leDiR+DVBZ59VqYAjn37GQNmFT2oammbNML7wPEE33FCjXTfcwbdTJ+LmzyN94kTS7ruP2FmzGvWZmlB/7FlZpI+fgE7jw2cdO2JVKXGeoydDLO4QhEoX61GTHQ5Mr02jaOVKwh5/jJA77gAgeORIgkeOxFlSQtmff1Ly66/kL1rMLRYLZSoVaUFBpOv1HA/wp7SBplgog4IImzgRw7hxFH37LQWffkbqnWPw7doF/e2j8O9zJZJaXWdD0e4kybVdsio0uO3bt9O1a1e2bdtW6+3eTCYTU6ZMwW6341taSse8PDrmmdE5HGQFBtLh2WeIuekmJIV37eZnS08n/cEHQYbY2bPQxMW5OyTBi9gyMkkfPx5JoSB23lw2Hz36ryGnUz0ZIvEThItzWa1kTX6e0vXrifi//yNo8I0XvP9Hs2eT8fMvROfn07y0hNCycgDMWi3ZgYHounRh8BOPo4qMrNGUo9omarLTSemvv5K/5FMsu3ahNBgo7tSJL0wmCrQ+dfK5UBff6bUhevyamL/WrKVDRiYtckxElpVjUyrZZzCwM9RAmV5PhdNJbC2SPnefBWliY4lbsICMBx8i7b77iZ35IT4tWzbY8wuXbs2aNVW73DQk2/HjpD/4EJJWS+yc2aiNRpIiI2u0w0FT4q52EryHq7yczMcep2LXLqLffgv/Pn0u+pgQo5HNIXqO6nzZoFQS4HQSVVBIVHExsaUlhP30E0d/+w1VeDjadu3Qtm2DT5u2aNu0Pu+K3EuZMygplQRcey0B116L5cBBspZ+huObFYxyODDpfDmo13NAr6fC39/rVvuLxK+Rk51OLPv3U5aygbKUFBJ27CDW5eJ4YABrWiRwJDgYh1KJ0+lEU8uJ63U9IRdql0iqjUZiP5pPxsOPkPbAeGI++ADfDu1r9fxCwzu1K05Dqti9h8z//AelXk/s7FmowsKqbqvrEkWNhTvaSfBcZ39W9+7cGdsr/8V66BAxH35Y7SoQZy8WKQQK9cEcCg9DrVYz5YknCMjKomLnTiz79pP/6We4iosBUIWGomnRAk18PJpmzdA0b0axnx/Jixdjdziwn9z3F2o3Z1DbuhU7LruMH9PTiTbl0LqggCtOZNEnI5M8X1/SDSFsX/Ipgx552OOnR4FI/LzSggUL8PHxoX37M5Ma2eXCYTJhPXIUy549VPzzD5Zdu3CVlaHw98evVy9MN9/M8rRUSk8f4T85h0mW5RpPXD9V88xut5/55lKp+OyTT0iIiCAsKAjZZsNltSLb7Mg2G7LNimy3I1utldfb7chWG7LNxpED+9mxaRMKpwuVy0mZLLN2zhzaX96Z+BYtkDQaJI0aSa1G4euLMjAQRWAgyqAglEFBxHz4ASeefJL0BycQ88476Lp1q/0vW2i0SlNSOPH0M/i0aknMO++gDA52d0iC4FXOPun3dbnwf+MNoiUFCfPm4dux+jUfL7ZYJLJlS2jZEv+rrgIqv68cWVlY9u/Hsn8/tmPHKd+6laLly5EdDuwOB/e7nJSo1ZT4aCnV+lCkVlOiVOLU6di+dCnXDB2KUq9HERBw0elNZrMZqyxzMETPwRA9aqeThKIimhcV08aUg/+iRRz6+mu0bduivawjvh064NOiBeqYGCSVZ6VaYo6fFzk1H+C/l19OCz8/okPDiA814CwuwVVcjCMvD/lk8qXQalHHxKCOiUYdE4s6KgpJqeBE5gl+XLcWCUAGCRkJUMgyClmmb+/ehAbrKxMxp6PyX7sd7P/7v+xwIFutyDYbZpOJkvx8FE4nKllGJbtQuGSULhdIEiqVCnUN/uhllYriigocCgVOqPxXklACKlkmPCQEhctZlUCeSlrPpggKwlVWhlxRge6KK5A7dmB/SQk5CiV+zZuR1Lev13TLNxW7du2iY8eODfJcRd+tIvuVV/C/MonI115DodU2yPM2Bg3ZToLnOn2+uN1ux9fuYMShQwTZbXzXsSOPvP12rT5jL3XakOxwYD9xgm/nzOHE3/+gKy8n2OEg0GYjwGpFa7cjAUqlEs2pOoIqVWUHgk6Hws8Pha8vCp0vCp0OSadD4aNlz4EDHE09jlOq/E5ySVLlvwoJlwyd4mJpG6zHnpGBLSOjqjcSpRKVIQRlUDCKgACUAQEo/HRIKjW78nIZOGeOmOMnXFzrgkLaVViQ88yUKhSVE10lqfKMRaGAk2dLtowM7BkZoNgCJ+/jslpJslo5le3LEshU/hG7FAqsf6ynzGBAUquRVCokjRrUaiTVyZ99fFD4+1f2uvloMAPpyFhlGUmlxqlU4pDA6nKBWkOLNq0ZOHgwkrry/pJGg0KjQfLxOdlzd9pFreabb75hzZo1WCwWlEolypPD0E6nE61Wy6BBg84YfpNtNpwlJTiLinEVF+EsLsZZUIAjNxd7djYlP/5EaUoK1pQUjLKMERkZif2+WrLatiUmKQmfVq3Rtm2DKiLCY+oUNkUhDVCKR3Y6yZs5i/xFiwgaOhTjc8963Nm4p2uIdhI8X0pKCi6XC7vdToDLxfDDh9DZ7XzRshWlOl2tV+Fe6hQLSaVCExeHols3dufl/eu7xOVwEKRSMaBnL67u3q3y+6KgAFdREa7yClzl5ZWXigqcpWW4cnKQrTYCsrNpUViE8mQnidLlqvz35M9qk4lStRpkufKiVILLBQ4H9qxs7Cey/ncbgCxTYrHU+nVeCvGJ54UWt2uLISQEGWjVujXPTZ4MpxLAi5g/fz6bNm3CZrOhOPkYWZZxuVxoNBp69uzJ/fffX+1YNixfzl8XSNQSe/as1sTeU07fBkipVJ5RGf5cxXMljQaVwYDqfEPUY8aw8Y47ic/P55e4WMy+voRYLOgtVsKOHMU38wQUFQGgCAxE27o1Pq1bo23XDt/LO1UVFhXq34oVK+q1MLCztIys55+nbMMGwh57DP0do0WiXwv13U6Cdzj1We1vs3HLocNoXC6Wt29HnkpV6/nidem8BaY1GmxqNd2HDUVXg8/3H+fPZ8OGDVXHOZ1SqaR3797/+u6UTyV6Z13kyhsp3boVrrzyUl5mrYjEzws5JQnnyYSooLi4RsWVT99mR5KkqkQNalec9mLV22u6uOOc2wBdQnw//vEHPzVvxkBJ4ur0DNY0i+ef0ybwX3fttdw+aBDWAwexHtiPZf8BSn/5mYIlSwBQRUTg26nTyctl+CQmIqlUbl/FLNSM7fhxMp98CkduLjHvvYtf797uDkkQvJrBYCDQbmfIgYMoga/btyNfrQan0yMKnVe3wHR1GQwG1Go1TqcTSZKqOk1kWUatVp/z9Z4ajfvX9Sf/VdbTPsUXIxI/LxdcwwnpdZ2o1fWbq67jO3DgALIksbZZPLIEg46nopBhT2jlm/TAwYOo77oLdXg4/n3+d+blyMuj4p9dVPz9NxV//03pzz8jOxxIOl8qYmPZWFRMRmAgJp0vklJ5yauYhUsfQjxXMh4eHk7xd99hev0N1BERxC/6BE2zZnUTcBMlhnoFgF4JCYTv2o1LkviiZSLFCgU4nR5V6PxiBaZreqzTv5tOLY/wpNdbXSLx83JDhw6t0f3rOlGDun1z1Ud8QGXyFx+PU5IYkJqKQpbZFXbu2k9QWR4g4Or+BFzdH6gsRmrZu5ec9X9y4Msv6FZUTC+nE5tSSUaAPyeCg/lh9mxaJCQQERlZ4/hEDyKMGjWq1o89V0mh377/nntk8N25k6CbhxD+1FMo3HSG3ZhcSjsJjYMtNZWK5yYTZDDwSUw0NrUaTR19Vte1uirLVF/fTe4gEj8v1r9//3+VdKmOukzUTqnLmmd1GV/r1q1JS0ur/EGS+CkuDpckcd3J5C/8uuuqdRyFjw+6zp3ZfuwYa9q1w1ZeTpTFQnxpKVFFRfRKS0eVmkbO0GE4r7wSXfdu6Lp2RdOixUXLBNRHHURvtHTp0lolFecqKdS8qIhr09KxO50Ynp9MxG231XW4TVZt20loHKxHj5I+4UGUQUG0nTWTp12uJnPSWh/fne4gEj8v1KxZMx566KFaJX2neHpx2rqKb8CAAfz+++/YbLbKKySJX2JjcUkS16Sno6/hqqpTE5pdCgU5wcHkhYSwxeXCabEQZ7XRKyiI4OJict99D9luRxkcjK5bV3y7dEXXpfO/EsHz1kG8xI3JvbEHMT8/v1aPO3t1Yb+0dFqazaQGBPBHq5YkqdXE1nGsTVlt20nwftYjR0gfPwFVaCgxs2eh0usxgkd/l9Q1T//urA6R+HmhH374gd9+++2i99uzZ0/9B+PhjEYj48aNY9GiRVXzMiRJ4q/ERC7v0hXLnLnk++oIufOOah3vfItPnAoFWfpgKgYNIm74cFwWCxV//0P5tq1UbN1K7owZyA4HiqBAdJd3xrdLF3RdOpOyZ09V0nL6quhTP9emJEJT60E0m80o7Ha6ZZvolZ2NS5JYl5jI7sAANBqN21cXCkJjUJX0hYURO3uWKHjuxUTiJzR65+ueDw8PJ2/mLHLfeQfZYccwdmy1jlWdxScKrRa/nj3w69kDAJfFgmXXLsq376Bi+3byZs1CtlppAQz28SHdT0deUBC5gYFYTtWbqkVJBG/pQTzX8Wo6XxUqi7UmHE+l3c6/8bXZ2BMexqa4OMpOTjT3hNWFjU1t2knwbqeGd0XS1ziIxM8LLVu2rEGrfDcG5+ueD534EJJKRd4HH4LDgeG++y56nNpM8FVotei6d0fXvTtQWXjasm8fmxYtgs2b6Z5tQnMiC4ACrZYsnQ5zcBDRFgsumw1FNfd/PH3Y01N7EM93vOuuu44RI0ZU6xgOs5miFSsoXP4NUVlZ7AkKYkN0FHlKZWWtLA9bXdiY5OfnEx0d7e4whAZiPXq0sqfPYCBm1kyR9DUCIvETmjRJkgidMB6UCvJmz0F2uggd/8AFH1MXE3wljQbfTp1o+8wzldse2WwElJQSUV5GRFkZUeUVtDl2HN+PP+FQ8hI0sbH4JLZAk9ACn8QW590DsqYFsC+mrnsQL3S8HTt2cOWVV573eK6yMso2bqTkl18p/flnUCoJHDSQ6NtvJz83l/LkZLSnJZPeuNrOG/zxxx9iy7Ymwnr0WGVPn8FQNadP8H4i8RMEIPT++5FUavI+/LDy54skf/VRIqBcH8zhoECOnkpabr+dtoZQrAcPYj1yBNuRIxR++SXOwkKgMnlUR0ejjoqq/DcmmvjcXFLLysmXXVglCdfJgqNQuwLYdd2DeKHjnbp9+PDhlRuwZ2dXvvZDhyjfuZOKrduQ7XY0iS0IffhhgobchDIoCICkVq0axWo7QfAUlUnfBFR6vUj6GhmR+AnCSYZxYwGqnfzVlYv1IPp27HDG/R35+VgPH8Z29Bj2jHRsmZmUb9uG/dtvia2oYKTVCoAMVKhUlKtU2NRq7Go1HQMCMB08VLkR+cmLpFJW9hwqVSf/VVT+K0nImzaTkJOL02FHo1SikCRwuXDZHWiUCtQ//0J+aSmy3V55sdn/9/9zXMIPH2ZovhnJ6USFhPLUnpdOJ4dlmeZr13FwxjvIdjs4HMDJrfTatiVs0n/w79MH9XmGGRvDajtv0KJFC3eHINQz67HTkr45s0XS18iIxE8QTmMYNxZkmbyZM0GSCH2g+vsWX4qaJC2qkBBUPXrg16PHGdfLsowzP5+tP/7IL8uX42u14mu14udwoHU6aRkTg6q4hIqsbFwlJTjLSpHLK5Adjv9tHH6W1g4HLU7dfvrWQ7KMrFCgysrGvHcvklr974tKhaTRnPF/ZVAgpWVl2FwuXEoFskKJA7ADirw8Cq/qQ3zXrkgqFerIKHxatUQVHi721PUggwYNcncIQj06lfQp9cEi6WukROInCGcx3DOuMvmbNQukymFgbyBJEiqDgV63307z/v2rehD9qjHsKbtclQmg04nscFQlg7n5+UybPh2bw4HV4UAGXJKEWqNBrVYzderUGg2nqkwmFk2Zcs45fpGRkdz8yCOEiOFZjzZz5kwmTpzo7jCEemA7frwy6QsKJna2SPoaK5H4CcI5GO69B5DJmzUbSZIuutrX09R02FNSKJDOsXI4MiSE28aNIzk5GXUdLJy40Kro4OBgMSdPENzEdvw46eNPJn1zZqMSezI3WiLxE4TzMNx7b2XP3+w5IEmVPzdBdb1N0fmOt2zZsjqOXBCEs52rhqbeYiF9/AQUgYEi6WsCROInCBdguO8+ZLmy5w+kkz2BTU9dL5w41/Huuadp/m69jWgn73WuGpqbVq5kzPFU/MLDRdLXRFx493hBEAi9/34M48eTN2sW5oUfuzucRmvz5s3uDkGoBtFO3un0GpoWiwWbzYa2sJAhf/9DXnk52ldeRiV2uWkSROInCNUQ+sD9GB54gLyZMzF//Im7w2mUdu/e7e4QhGoQ7eSdzq6hGeZycevBQ1gVCla0b8fGvXvdHaLQQMRQryBU06m6fnkffkhpaSnboqNEsWBBELzC6bv6hNjtDN+zF5tKyZctW+JQqWq8q4/gvUTi54VGjhyJr6/vRe+3Z8+eBoimaQkd/wCpqcepmDMHU3w826IiL3nvWqFS95P7GAueTbSTdzIYDCgUCoKsVoYdPIhNqeTr1q0pVyrR1mJXH8F7iaFeQagBk8nEfLOZLdFRXHH8OJelpWOxWLDb7SQnJ2MymdwdotdKTEx0dwhCNYh28k5JSUkEWW3cdvAQdknBl4ktKVYqUavVKBQKcdLahIgePy+0bNkyunTp4u4wmqSUlBRcssx6oxFZlrkqMxNJoWBLeFit9q4V/mfp0qWiMLAXEO3knUKcTu5OS8OsVPJt+/bY1Sq0l1CTU/BeIvEThBqomicjSWyOi0OpUNAnPR2X7GJXbKyYJyMIgsexZ2WRPn48usBADB9+QJ8DB8T85CZMJH6CUAOn5skAOF0uNsbE4HK66JuegUqtFvNkLkF15q0K7ifaybvYs7NJf2A8SApi585BHRHB8Pbt3R2W4EZijp8g1EBSUhIKhQK1Wo3T6cRmt7M+MoJt0VEkHTtO15wcd4fotURhYO8g2sl72E2myqQPiJ03F3VEhJsjEjyBSPwEoQZO7TWrVqvRarVoNBq0vr5sSUjAdcMNWOZ/RP5nn7k7TK+0YsUKd4cgVINoJ+9gN+WQPn4Cssspkj7hDGKoVxBq6Hx7zYaHh5P3wYfkvj0DgJDRo90cqXfJzMx0dwhCNYh28nz2nBzSH5yAbLcTN28u6shId4ckeBCR+AlCLZxv79rQRx4GEMmfIAhu4cjNJWPCg8gWK7Hz5qGOjnZ3SIKHEYmfINQhSZJE8ldLAwcOdHcIQjWIdvJcDrOZ9AkP4qqoIHb+PDQxIukT/k0kfoJQx0TyJwhCQ3Pk51cmfWVlxM6biyYmxt0hCR5KLO4QhHpwKvkLuftuct+eQf6nn7o7JI+3du1ad4cgVINoJ8/jKCgg/cEHcRYXETtnNpq4OHeHJHgw0eMnCPWkqudPksid8Q4AIXfc4eaoBEFoTBwFBWRMeBBnQSGxc+egadbM3SEJHk4kfoJQjyRJIvThyu2tRPJ3YdFiErpXEO3kOZyFhWQ8NBFHfj6xc+fi07y5u0MSvIBI/LzQyJEjq1U9f8+ePQ0QjXAxIvmrnqFDh7o7BKEaRDs1LJPJ9K/SUUajEWdREekTH8aRm0vs3Dn4JIikT6gekfgJQgMQyd/FLVy4UOwK4QVEOzWclJQUkpOTcblcuFwuFAoF69at464RI4hatAhHdjaxc2bj06KFu0MVvIhI/LzQsmXL6NKli7vDEGroX8mfDCF3iuTvlIqKCneHIFSDaKeGYTKZSE5Oxm63Y7fbq673kyTKXngRq6+W+Lnz8GnZ0o1RCt5IJH6C0ICqkj9JIvedkz1/IvkTBOEsKSkpuFwu7HY7SqWy8mK1cvO+/QTabBy+6y5at27l7jAFLyQSP0FoYJIkETrxIQCR/J1m1KhR7g5BqAbRTg3DbDbjcrkAUCqV+Lhc3HTwEHqrhRXt2pOgUbs5QsFbiTp+guAGp5K/kHHjyH3nHfKXiDp/hw8fdncIQjWIdmoYBoMBhaLyK1pptTJkz170FRUsa9kSc2AABoPBzREK3kokfvVo7NixSJJ0wYvFYnF3mIKbnJ78mWbM4NennmL+/PksX74ck8nk7vAa3JYtW9wdglANop0aRlJSEgqFAj9JYtj+AwRVVPBVq5bkBwejUChISkpyd4iClxJDvQ0gKSmJxMTEc96mVCobOBrBk0iSxMHLO3EwPIzO3/+Abc8e1sTEsG7dOsaMGSM+3AWhiTIajdw1YgRlL7xIoM3GinbtKA4MQK1QMGbMGIxGo7tDFLyUSPwawH333cfYsWPdHYbggUwmE8lLlmCPisJmt5N0PBXsdrbHxZGcnExiYmKT+YDv0KGDu0MQqkG0U8NwFhUR9ckirL5aDt99FwlqNd1Pq+MnCLUlEj9BcKOqlXsOB3/FxoJaTVJ6BkqXzPaE5qSkpDB8+HB3h9kgevTo4e4QhGoQ7VT/nIWFpD80EYfJRPy8ebRuJVbvCnVHzPETBDc6Y+WeSsW2uDg2xMXRKyuLK44fx5yX5+YIG87ChQvdHYJQDaKd6pejoID0Bx/CkZND7Nw5aEXSJ9Qx0ePXAH799Vd27dpFSUkJBoOBHj16cMMNN+Dj4+Pu0AQ3O33lntPpBGBLhBGr7KJ/egalm7cg33cfkkKcowlCY+coKCBjwoMn996dI3bkEOqFSPwawOLFi/91XWRkJAsXLmTQoEFuiEjwFElJSaxbtw61Wo3dbq9K/nZHR6P00TJgxw5Mr03DOPk5kfwJQiPmMJtJf/AhnEWFxM6dK/beFeqN+CapR506deK9995j9+7dFBcXYzKZWLduHb179yYrK4shQ4bw22+/uTtMwY2MRiNjxoxBrVaj1WrRaDRotVrUajVdn36KyJdeomjlSrKnvITscLg73Ho1ceJEd4cgVINop7rnyMsjffwEXEVFxImkT6hnkizLsruDaGpkWWbYsGGsXLmSTp06sXPnzmo9bvv27XTt2pVmzZrh6+tb6+efOHGi+PD2MCaTiZSUFMxmM4azVu4Vr1lL1v/9HwFXX03kKy8jqRtnxf41a9aIHnAvINqpbjlyc0mf8CCusjJi585BEx/v7pCEBnLqO33btm106dKlwZ5XDPW6gSRJTJ06lZUrV/L333+Tnp5ObGxstR//9ddfN+gfiVD/jEbjeVfvBg4aiKTRkPXcc5x45lkip09DodE0cIT178iRI+4OQagG0U51x56TQ/qECcgVFmLnzUUTF+fukIQmQAz1uknbtm2r/p+RkeHGSARvEHB1f6LeeouyjRs58cSTuMSOL4Lg1ezZ2aQ/MB7ZaiN2/jyR9AkNRiR+bmI2m6v+HxAQ4MZIBG/h3+dKot95h/Lt28n8zyRcZWXuDqlOXXXVVe4OQagG0U6XzpaRQdq994HTSdy8uWhiYtwdktCEiMTPTT7//HMAAgMDad26tZujEbyFX6+exHzwAZZ9+0if8CDOwkJ3h1RnQkJC3B2CUA2inS6N9egx0u+9D8lHQ+xH81FHR7s7JKGJEYlfPdm5cyfffvstjrNWYrpcLhYsWMDkyZMBePTRR1E30sn6Qv3QdelM7Ly52LOySLv/AeymHHeHVCdWrFjh7hCEahDtdGEmk4nly5czf/58li9fjslkqrrNcuAg6Q88gCI4iLj581GLrdcENxCLO+rJ8ePHGTZsGHq9ni5dumA0GiksLGT37t2kpaUBMGrUKKZMmeLmSAVvpG3ThrgFH5H+0ETS7r2H2Nmz0dRggZAgCHUvJSWF5ORkXC4XLpcLhULBunXrGDNmDF2Cgsl4+GHUMdHEfvghyuBgd4crNFGix6+edOrUiUmTJtG+fXv279/P8uXL+fnnnwEYOXIkq1ev5rPPPkOlErm3UDua+HjiFnyEQuND2r33YTlw0N0hXRIxhOgdRDudm8lkIjk5GbvdjsViwWazYbFYsNvt/PThh6SOH49PQnNi58wRSZ/gViLrqCfNmzfnnXfecXcYQiOnjoggdsFHZDz8COnjxxPz3rv4durk7rBqZdSoUe4OQagG0U7nlpKSgsvlwm63o1QqUSqVOJ1OIs1mbjxylOIWCbT68EMUOp27QxWaONHjJwheTqXXV27x1Kol6Q9NpPTPFHeHVCtLly51dwhCNYh2Ojez2YzL5QJAqVSiUChoUVTMsMNHyAwMZNeAASLpEzyCSPwEoRFQ+vsR8/77+PXsSebjj1PohRPw8/Pz3R2CUA2inc7NYDCgOLmfttPpJCEnlxsOHOBoUCA/tG2DPiLCzREKQiWR+AlCI6HQaol68w2Chw3D9Mp/yZs7D7EjoyA0jKSkJBQKBWq1mtamHAYeOsSBED1rW7cGlYqkpCR3hygIgEj8BKFRkZRKwp99htCJEzHPm4fplf8i2+3uDqtahg4d6u4QhGoQ7XRuRqOxcvVuTi6DUlPZFx7Ob23aoNJoGDNmTNXe24LgbmJxhxcaOXIkvr6+F73fnj17GiAawdNIkoTjxhtIT03FuvxrUv/+m/i33yKiWTN3h3ZB+fn5RItith5PtNO5ybJM6127CcnMJO+qPpR17szA0FCSkpJE0id4FJH4CUIjc3otsejERG44eIjdt93O0Zem0Pv6690d3nn98ccfdOzY0d1hCBch2unfZJeLnDffovDLLwl/9BHajh1LH3cHJQjnIRI/L7Rs2TK6dOni7jAED3R6LTG73c4RnY7PW7VkxOEjOF94kRMGA1E9erg7TEFoNGS7nayXXqJk7TqMzz9P8PBh7g5JEC5IzPEThEbk7FpiGo2G/IAAPm3dCrtCQd7DD1Oa4pnlXlq0aOHuEIRqEO30Py6LhcwnnqT0p5+Jmj5NJH2CVxCJnyA0IueqJaZUKinx8eGr9u0oioggc9JjFCxd6nErfgcNGuTuEIRqEO1UyVlSQsbEhynfvp3o994j4Npr3R2SIFSLSPwEoRE5u5aYy+XC6XRW/qzRkHvXXejvGE3OW29jem2aR634nTlzprtDEKpBtBM4zGbSHxiP9ehRYmfPwq9XT3eHJAjVJub4CUIjkpSUxLp161Cr1djt9qqkT61Wo1AoSOrTh3CjEZ/mzTG9Ng1bWipR06ej0uvdHLkgeAdbRiYZDz+MXFFB3Px5+CQmujskQagR0eMnCI3IqVpiarUarVaLRqNBq9WiVqvPqCUWdPPNxMyehe3IUVLvuJOKXbvdHLkgeD7L/v2k3XMPAHELF4ikT/BKosdPEBqZpKQkEhMTSUlJwWw2YzAYzllLTNelC/GfLuHEM8+Sft99hD/1JEEjRiBJklvivufkF6rg2ZpqO5Vt3Ejm00/j0zyB6PfeFb3kgtcSiZ8gNEJGo5Hhw4df9H5qo5G4eXPJeeddTNOmU/HPLoyTn0Oh1TZAlGfavHkzffv2bfDnFWqmKbZT0arVZL/yCn69ehE1fRqKahTQFwRPJYZ6BaGJkzQajM88TeR/X6Hk559JG3cP1qPHGjyO3bvFcLM3aGztZDKZWL58OfPnz2f58uWYTKaq22RZxrzwY7KnTCFo8GCi335LJH2C1xM9foIgABB4/fX4JCZy4tnnSB0zhvDHJrl16FcQ6tvpu9y4XC4UCgXr1q1jzJgx9O7Vq3I3jq++wvDAAxgeuF+8F4RGQfT4CYJQxadlS+I/XULQ4MGYpk0n8/HHcRQUNMhzd+/evUGeR7g0jaWdTt/lxmKxYLPZsFgs2O12PvvkE47+ZxKFX3+N8fnnCR3/gEj6hEZDJH6CIJxBodVifO5Zot+ZgWXXbo7fdjtlGzbU+/MmihWSXqGxtNO5drlRKpUoKyq4adduyjduJHrG22I3DqHREUO9XmjkyJH4VmOeyZ49exogGqGx8r/qKpp9vpTsqVPJeORRAofcRPikSSiDgurl+ZYuXcrEiRPr5dhC3Wks7XSuXW6CLBYG7z+Ar9PJ7pEj6Ninj5ujFIS6J3r8BEE4L1VoKNHvv4/xhRco/eVXjo28heJ16zxuuzdBqKmzd7kxFhZyy65dAHx9WUd82rd3Z3iCUG9Ej58XWrZsGV26dHF3GEITIUkS1t5XsLe4iOAVK7E89TTabt2InfwcPi1a1NnzVKcXW3C/xtJOp+9y0yLbxMDUVE74+fFDm9a4fH1JSkpyd4iCUC9E4icIwgWdsfLREEKCUsGVO3diveVWQm+9FcP4B+qkmG1TLQzsbRpLOxmNRsbceSf7Xn2Nbmlp7A8L5bfERFCpztjlRhAaGzHUKwjCeZ1r5eN+nY7kyzryR1QUBatWcWzIzeTOmoWzqOiSnmvFihV1E7RQrxpLO7lsNpqvWUOSyUThgOsoGT2aATfcwNSpU0Vvn9CoiR4/QRDO6+yVj0qlEqfTidXpZEdUJFG33coV+fkUfPoZhUs/J/i22wi+5RbUxvAaP1dmZmY9vAKhrjWGdnIUFHDiyaew7N1L9PRptB0wgH7uDkoQGojo8RME4bzOtfJRqVQC4HK5yLFYCP/Pf0j47luCRo6g4PPPOXrTTWQ+/TRlmzeLRSCCx7EdP07a2HHYUlOJnTeXwAED3B2SIDQokfgJgnBeZ698dLlcOJ1OABQKBQaDAQBVSAjh//kPLdasIfyJJ7AdPUbGgw9x9MbBmN54k/KtW5Edjgs+18CBA+v3xQh1wpvbqWzjRlLHjkPSaIhf9Am+HTu6OyRBaHBiqFcQhPM6feWj3W6vSvrUajUKheJfc6GU/n7ob7uV4FtvoWLHTkp++onSX3+l8IsvUOh0aDt0QNuxA9q2bVFHRaE2GlEEBYldEYR6JcsyBUs+Jff99/G74goiX3sVpb+/u8MSBLcQiZ8gCOdlNBoZM2YMycnJKJXKqv1MFQrFBVc+SpKErktndF06E/7kE1j27qN8yxYsu3ZR9M0K8hcs/N99tVoU/n6s6tyZm48cRfLRoPDRIvn4oNBqkbTas67zQfLRotD5otSHoAoLRWUwoDSEovDTiSSynq1du9ardu9wWSyYXn2V4u9/IGTsWEIfehDp5HQFQWiKROInCMIFJSUlkZiYSEpKCmazGYPBQFJSUrXLXUgKBb4d2uPbobIgrizLZO/bx46ffqIiI5MQZJpHRqIoK8OvZw9cVhuyxYJss+KyWHHl5+OyWJCtVlxWC/LJ213l5cg225nPpdWiCglBFR6OOjoadUwMmtgY1DExqKOjUer1IjFsQuwmEyeeeBLrsWNEvvoqgYO8d5haEOqKSPwEQbgoo9HI8OHD6+RYGzZs+F9dwFM9iMXFdOnShfAHHqj2cWRZxlVWhjMvD4fZjPnwYQ5v24YtJ5dAi4XQw4eRNmzAWVBQ9RiFToc6Pg6fFon4JCbi0zIRnxYtUIaGioSwmqKjo90dQrVU/P03mU89jaRWE7fgI7Rt2rg7JEHwCCLxEwShwZxeF9But1ddr1ar2b59OyaTqfo9iZKE0t8fpb8/WzIzSf7118pkUqVEoamcgzhm4kNccfnl2DIysWdmYM/IwHb8ONZDhyn56SdkiwUAZVAQmpaJaFu1RtuhA74dO2BWKNiwYUOtejkbs6FDh7o7hIsqXLGCnGnT0XboQNSbb6AKCXF3SILgMUTi54VGjhxZrW2T9uzZ0wDRCEL1na8uoN1ux2g0kpKSUuOexQslk8nJySQmJmJs3Qpt61ZnPE52ubBnZmI9dBjrkcPYDh+m9LdfKfjsM5xOJ0WAIsAfp58fuwMD+eWHHxg1dmyTL+67cOFCt+7eYTKZzjvtQLbbyZnxDoVffknQ8OEYn3oSSaNxW6yC4IlE4icIQoM5V11AqCwVo1AoMJvNNT7mhZJJpVJ53mRSUijQxMaiiY0l4Or+VddnHTjAJy+8QGhREeHFJXTOPIFPWjouhYK8Xbs5PmwoYX374dvpMhSNZN/amqioqHDbc5+xfeDJaQLr1q1jzJgx9GjZkqxnn6Nizx7Cn30G/S23uC1OQfBkIvHzQsuWLaNLly7uDkMQauzsuoCn/3vq9pq6UDLpcrlqnExu3LOHY3o9+3x9UUZHo1QoCCorI7KoiGblFRSt/Bbrl18hqVRo27dH170bul698O3YEUklPlLry4V6dn/+4ENCc3NQqTXEzZ2D7+WXuy9QQfBw4lNKEIQGc6G6gAUFBbUaRr1QMnl6kenqOlciWeTvT65Gw36NhoIePbjr2msp37qVim3bKPxqGeaPFqAIDMSvVy/8kpLw633FOeeVXWiY0luMGjXKLc97zp5dh4NO6en0OZFFcYsEOi9ciKoWJw+C0JSIxE8QhAZzobqAffr0qVUSVNMi0xdz0UQyNBSfhAR8EhLQ33orstOJZd9+ylJSKFu/nuwpU0CS0LZti9+VVxJwdX80iYnnXM18apjSm+YNHj58mB49ejT4856dkGtdLq4+coQEcz7bo6Nx3nAD3UXSJwgXJRI/QRAa1PnqAi5btqxWx6ttkekLxVeTRFJSKqvqFIaOfwCH2UzZho2UpaRQ8OmnmOfNQ4qM5KDDTnBQMCc0ajhZOuaMBShe0vO3ZcsWtyR+pyfkwSUl3HT4CL42G9+2aEFGZASDwsIaPCZB8EYi8RMEocHVZV1AuPQi02fHdimJpMpgIOimwQTdNBjZZqNsyxb+njefdjt30jktnWIfH44YDBzUB5MpyxdcgCL8T1JSEuvWrqVTQQF9jx2nQOvD123bUBYQgLoWPbuC0FQ1SOJ34sSJqhpdBQUF6PV6jEYjXbt2JTIysiFCEATBw3Xo0OGSHl+XyWRdJZKSRoN/UhJ79+5li1JBeH4+bYqLaZOXR+esLAp9NBwMC6f06NE6ibshXGo71VaYvz8TnC44eoy9xnD+iI/HpVajrmXPriA0VfWW+KWlpTF79mxWrFjBwYMHz3u/Vq1aMWzYMCZMmEBcXFx9hSMIgodzx/DhhdRlImkwGEClIi0wkEy9nt+bNyeyoIDWuXl0yTqBNnkJaXv3EnD99QRedx3K4OA6ed764I52suzfz4lnn8PPbEb7fy+iVavp6sULZATBneo88Tty5AjPPPMMK1eurJobExwcTNu2bTEYDAQGBlJUVITZbGbfvn0cOHCA6dOn8+abbzJ06FBef/11EhIS6josQRA83MKFC5k4caK7w6gX55o3eNzfn0y9ng0KBc9eey3SnynkvPkWuW+9jV+fPgQPH4auZ08kpfK8x3XHKuGGbCdZlin84kty330XTYsWxHzwPprYWGIb5NkFoXGq08Tv2Wef5b333sNqtdKpUyfGjh3LddddR7t27c55f1mW2bNnDz/++COLFi3i66+/ZtWqVUyaNIlp06bVZWiCIAhuc6F5g6PHjCE2KQluuw2H2Uzx2rUUr/yWjEceRRUZQfDQoQQOGYI6PPyMY16omHFjmO/mLCoi++VXKP3tN/Sjbif00UdRiF04BOGS1Wni98Ybb3DjjTcyderUahUYliSJDh060KFDBx577DG2bdvG//3f//HGG2+IxE8QhEalOvMGVQYDIaNHox81Csvu3RR98w3mjz8hb958/K+8kqDhw/G7ohc5eXkX36bOi4c/yzZvJvulqbgqKoh6+y0C+vVzd0iC0GhIsizLdXWwlJSUOjnTrKvjNDbbt2+na9eubNu2TezcIQhNhLO0lOIffqDomxVYDxxAHRVJaus2fJ1vptjhOGObOqfTiVarZdCgQV65Sthls5E3cxYFS5ag69aNiKkvoY6IcHdYglAv3PWdrqjLg9VVsiaSPkFoetasWePuEDyS0t8f/S23EP/pEuIWL8L38s4YfvqJsVu2cnVaGgabDYVCgfLkXMDabFNXE/XVTtbDh0m7624Kv/iCsEmTiJk9SyR9glAP6nSod9myZQwdOhSV2K+yXo0cORLfamwOv2fPngaIRhDqxpEjR9wdgkeTJAnf9u3xfeVlNie2IGfpUtpnZXN5bh6pwcFsN4ZzzM+vVtvU1URN2+liC1Bkl4vCL74g9/0PUMfGErdoEdrWreo6bEEQTqrTDO3WW28lLCyMO++8k3Hjxrmt3pMgCEJj1mvQIKZs3MiWyEgSTDl0yclh2P4DmHU6dsbF0rtnT3eHCFx8AYrdlEP2yy9T/tdflQs4HnkEhY+Pu8MWhEatTuf4hYWFYTabkU5uR9S9e3fuvfdebr/9dgICAurqaZosMcdPaMx27dpFx44d3R2G1zgjqXI6iSktpWvmCVqUlqKNjUF/5xiCbh6CQqut0+etbjuZTCamTJlyzgUoapWKyVdeifWjBSh8fYl4aQp+vXrVaZyC4Onc9Z1epz1+WVlZfPvttyxYsIB169axefNmtmzZwmOPPcbIkSO55557uOqqq+ryKQVBaCRCQkLcHYJXOdcq4R5JSQQVFpG/aBE5b72Fef589KNGEXzrLSjr6OS7uu2UkpKCy+XCbrefsQDFp6yMQRkZFP+1CcPQoYQ/8TjKwMA6iU0QhIur08UdKpWK4cOHs3r1atLT03nttddo2bIl5eXlLF68mP79+9OqVStef/11srKy6vKpBUHwcitWrHB3CF7n1O4i999/P8OHD8doNKJt3Yqo116l+TfLCbjmaswffcTRG24kd+ZMnEVFl/yc1W0ns9mMy+UCQKlUopAk2pvzuXvPXgylZey78QYip74kkj5BaGB1mvidLiIigmeffZb9+/fz559/Mm7cOPz8/Dh8+DCTJ08mPj6eIUOGnLHDhyAIglA3NDExGJ97joTvviVo5AgKPlvK0cE3kTd7Ns7i4vM+zmQysXz5cubPn8/y5csxmUy1en6DwYBCUfkVo62o4Ia9+7j2yBGOBAfzeZfOKLt1q9VxBUG4NPWW+J2ud+/eLFiwgOzsbBYsWEBSUhIOh4NVq1YxfPhwoqOjefrppxsiFEEQPJQY6q0fqtBQwv/zn8oEcPhw8pd8WpkAzpmLs6TkjPumpKQwZcoU1qxZw6ZNm1izZg1TpkwhJSWl6j7VbaekpCQUkkTn/HzG/LOLsNJSVrRI4OdWLbFrNKJslyC4SYMkfqfodDrGjRvHH3/8waFDh3juuecICQkhJyeHt99+uyFDaTBfffUV/fr1Q6/X4+fnR6dOnXjjjTfOmOwsCAKMGjXK3SE0aqqQEMIfm0TCyhUE3Xwz+YsXVyaA8+bjKivDZDJV7QZisViw2WxYLBbsdjvJyclVPX/VbafgsnIeyjPT/9hxjoaG8lnnyzkREYFarWbMmDFevbOIIHizBk38TrFarWzevJnNmzdTUFDgjhAaxKRJk7j11ltJSUmhR48eDBo0iLS0NJ555hmuvvpqKioq3B2iIHiMpUuXujuEJkEVGkr4E49XJoBDbiL/4485evNQ9sx4BxyOqsUYGo0GpVKJ3W7H5XJV9fpdrJ1cNht5c+eROmoU/i4Xoe/MwOeRh7k8KYlBgwYxdepU0dsnCG7UoJWWt27dysKFC/n8888pKipClmWUSiU33HAD9957b0OGUu9WrFjBe++9h7+/P7///nvVUu28vDyuvvpq/vzzT1588UXeeustN0cqCJ4hPz/f3SE0KaqwMMKfeAL9HXdgnjePsK+XM1qpZH1UJMeMxqr5eU6n84zdQC7UTuXbt2P676vYMzMJGTeWkHHjUPj4ENMgr0gQhOqo98QvLy+PJUuW8PHHH7N7924AZFkmISGBe+65h7FjxxIVFVXfYTS41157DYBnn332jPo8oaGhzJo1iz59+vDhhx/y4osvEhQU5K4wBUFo4tQREUT83/+xNTwcx6efcuPRY+SYctgYH8fxkyVgLrYbiKOggLwPPqBo5bf4drqMqDffwKdFi4Z6CYIg1EC9JH4ul4sffviBhQsXsnr1aux2O7Iso9VqGT58OPfeey/9+/evj6f2CJmZmWzZsgWA0aNH/+v2K6+8ktjYWNLT0/n+++/F3CZBAIYOHeruEJq07sOGMWXnTkLN+fROTeXmvftIDQxkQ7N4inS6quHZ09tJdjgo/Ho5ebNngyRhfO5ZgoYPR1K4ZRaRIAjVUKeJ38GDB1m4cCHJyclkZ2dzalOQyy+/nHvvvZc777yzSfRu7dixA6hc/da8efNz3qdbt26kp6ezY8cOkfgJApVDiNHR0e4Oo8kyGo2MGTOG5ORkVgQHEW82k5SWzqhdu5H79sWgVAL/a6fyHTvIeeNNrIcOETR0KKETH0Kl17v5VQiCcDF1mvi1bdsWqBzKDQ4OZvTo0dx777107ty5Lp/G4x07dgyAuLi4894nNjb2jPsKQlP3xx9/iC3b3CwpKYng4GBWrFhBYXAwGzp35mZ/fzTffcfRYcMwjB3H76UlhH75FcXff4+2fXviFn2Cb/v27g5dEIRqqtPET5Zl+vXrx7333suIESPQ1vEekd6i5GRtLD8/v/Pex9/fH4DiCxRSFQRBaEhn7P/rclFUVMQHCgV3PfssLXbuJPeDD7BffTUlmzZhfOEFgm4eIoZ1BcHL1Gnid/jwYRISEurykMI5jBgxAl9f31o/fuLEiUycOLEOIxKES9dCLAZwq9Pr+J1eZ1StUvHnvHmElleASkVUWSmuigpKfvoJXefL0TRr5r6gBUGosTpN/C6U9BUVFbFlyxZyc3OJj4+nd+/edfnUHiXg5Eq4srKy896ntLQUgMBa7FP59ddfn7FSWBAag0GDBrk7hCYtJSUFl8tVVcdPqVQSVljElelpRJeWUdCmDZd/+SVtEppTun49OW++xfHbbkd/xx0Y7rsXhU7n7pcgCEI11HsffUlJCffddx/h4eEMHDiQO++8k48++qjq9o8++oioqCg2bdpU36E0mGYnz4DT09PPe59TtzUTZ8uCAMDMmTPdHUKTZjabcblcABhsNm44eJDb9u1D43Tybds2/DNoID4JzZk5cyb+ffrQ7KsvCbnvXgo+/5xjI0ZSvGZt1YK+09XV3r+CINSNek38Kioq6NevHwsXLkSv13P99df/64Nh8ODBmEwmVqxYUZ+hNKhTi1nMZvN5F29s3boVQPTcCYLgEQwGA/4OB9empnHHzr8JLyllbUJzktu2JSMk5F91/BQ+PoTefz/Nl32Ftn17sp5/nvQJE7AePlx1n+rs/SsIQsOq18RvxowZVeVKjhw5wqpVq/51n4iICNq2bcuvv/5an6E0qJiYGLp37w7AZ5999q/b//zzT9LT0/Hx8eGGG25o6PAEQRDO4CwtpcvhI4zZtp3WhQX8ERPNgvbt2KPXo9ZoUCgU591mTR0VRfRbbxLz4Qc4cnM5PvoOct59l+zU1Grt/SsIQsOq18Tviy++ICIiggULFlxwhWurVq3IyMioz1Aa3OTJkwGYPn0627dvr7rebDbz0EMPAfDwww83ibqGglAd99xzj7tDaHKcpWWYP/qIozcNwbZiBdL1g/i0e3f2xMej1GrRarWo1WrGjBmD0WgEzt9OfldcQfPPPyd0wgQKv/yK7DF3EZNnvujev4IgNKx63bLtyJEjXHfddRct66LT6cjLy6vPUBrc0KFDefTRR3n//ffp1asX11xzDX5+fvz8888UFhaSlJTEK6+84u4wBcFjbN68mb59+7o7jCbBVVZGwedfkP/pEuTyCoKGDSNk7FjUxnCiTSZSUlIwm80YDAaSkpKqkj64cDtJGg2Ge8YRMOA6No+fwOB9/8/efYc3XXYNHP9mJ01H2nTv0rJBZI8iS5CKiggIoqKCG9wTBwLiFnGiqIBIERyAiIqAj4piAUGGArJa6G7TNt0z8/0D2pfRlrYkTVvuz3VxPdiM38lzl+TkHucc5qi3joR27ahUKIDze/8KgtC8nJr4VX+7u5D09PR6ZwRbq3fffZfY2FgWLVrE9u3bMZvNREdHM2vWLB599FGUSqWrQxSEFuPgwYMi8XMyW3k5BV99TUF8PLbycrzGjcNn2h0ozkjsAgICGD9+fJ3P0ZBxUoaGkjN9GjtWriT2xElu3f8P28PD+cf31D7BC/X+FQTBeZya+EVHR/PPP/9gsViQy2u/VGlpKf/++y9dunRxZiguM2nSJCZNmuTqMARBaIMMF5idq2YtLqbwmzUUrFqFrbQUr3HX4zNtGorAQKfFFjt4MFt+/pmTnp4MPJnMsBMn6JCTw9bodhSf0ftXEITm5dTEb+zYsbz00ku89NJLzJ07t9b7vPTSSxQVFXHDDTc4MxRBEFq46gNRQsOc22VDKpWyZcsWpk6dWpNUmQ05FKxeReHatWCx4jX2ulNLukFBTb5uQ8fpzN6/2zp15HhhACNOnGTKgYPYrrkGP52uyTEIgtB0Tk38Hn30UT777DPmz5/P/v37a2a+qus6ff3113zzzTdERkZy3333OTMUQRBauJiYGFeH0GrU2WVDoSA+Pp4olQrpxo0U/7gRiVqN9+Sb8L5pMnJf34u+dmPGKTY2lpiYmJpZyQIvLyLT0zF99TUpt9xK4JwX0Ij+zILQrJya+Ol0OjZt2sTYsWPZsGED33//PRKJhE2bNrFp0ybsdjsRERF8//33bXKPnyAIDbd69WrRSrCBauuyYbVY8M/Pp2+ekfw770ITGIjvzBl4jZ+AzN1x76+NHafa9gxWXX892XPnkTptOt4334zvjPuRXqK93QWhuTk18QPo0qULBw8eZPny5WzcuJETJ05gs9kICwvj6quv5p577sFNtPoRBEFosDO7bCglEjrm5nFZVia+pWUUajQkDR/GdW+8gbSFHiBTRUcT/tkyClatJm/xYkp//53AF2bj1ru3q0MThDbP6YkfgFqt5r777hPLuQ4yceJENBrNBe936NChZohGEByjIb/Twil6vR4Ps5lOGRlclmdEY7GQ7OXF7+3bY/D3I27IEKclfY4aJ4lcjs9tU3EfOoTsF+eTds+96CZOxO+hB5GKFSBBcJpmSfwEQRAuRBRwvjC7zUb57r/p8fsfRO3Zi0Ui4aBezz5/PwpPF1tWyGROPTHr6HFSRkQQ9uknFH6zhtz336f0zz8JnP082gEDHHodQRBOEYlfK7RmzRrR41doc9avX8+4ceNcHUaLZMnNpWjD9xR99x3mjAyUkZFYb57CihMnqZJKsNlsqKVSpFLpWV02nMEZ4ySRSvGePAn3wbFkv/Qy6TMfwOv6sfg9+igyDw+HXksQLnUOTfx69uzJq6++SlxcXJOfY+PGjTz33HPs27fPgZEJgtDSZWRkuDoEp2to3T0Au8VC2Y4dFH37LaV/JiBRKPAYOZLAeXPRXH45URIJYY14Pkdx5jgpQkII/XARReu/I/fttynbvoPAF2ajHTTIadcUhEuNQxO/vLw8rrnmGvr168edd97J5MmT8WjAt7Xi4mJWr17N0qVL2bNnD6GhoY4MSxAEweUaUnfPbrdTdfQoxT9tomTzZiy5uag6dsT/icfxvPrq82a/LtRlozWSSCTobhiHdtBAsl+cT/qDD+E1fjz+jzyMVKttVPIsCML5HJr4HT16lJdffpmFCxdy77338tBDD9G3b18GDhxI586d0ev1eHp6UlxcjNFo5L///mPHjh38/fffVFVVoVQqeeaZZ3j22WcdGZYgCK3A6NGjXR2C01yw7p67O6q//6b4p02YTp5E5u2Nx6hReI29DnXnzi6M/HzNNU6KgABCP3ifonXryHn7Hcp37iB3wgQ+27mz3uRZEIT6OTTxc3Nz4+WXX2bmzJksWrSIJUuWsG3bNrZt24ZEIjnv/na7HQA/Pz8ee+wxZsyYQXBwsCNDEgRBcLna6u5pKiqIysmhS2ER+bfdjtLdHffhw/B79BG0/fohUShcHbbLSSQSdBMm4NZ/AKnPPoPqzQUMCAzkj6BALFIp8P/Jc0xMjJj5E4QGcMrhjuDgYF5++WXmzp1LQkICv/76K/v27cNgMFBUVIROp8Pf359evXoxfPhwYmNjUYg3OUG4pG3evLnNdu+orrunq6ykU0kp0fn5BJSWYgPSdDqOjxrJ9S++iLQV1DR1xTgpQ0P4d+xYsoz5DEhJIaKwkJ/bx5Cl0dQk0wkJCW1u2VsQnMGpp3oVCgXDhg1j2LBhzryMIAhCi2S32ag6epQOBw7Q7p9/0ZeVYZHJSNV5sbldFIkeHkjc3YkbNKhVJH2uZCwoYF9QIMfdNIxJSeXGg4f4OziYBH8/bDYbRqPR1SEKQqsgyrkIgtAihISEuDqEszT1EIGloIDyv/6iLGE7ZTt3Ys3PJ8BNwwF3LTtDQ0jSas9aplRIpa1qf5qrxkmv1yOVSsnXaPiqcyf6GQz0TUsnMj+fXzt2QK/XuyQuQWhtROInCEKL0JJq+DXkBG41W1UVlQcPUr57N2UJ26k8fBjsdlQdO+I1diza2EFounfHuGsXv8THI7fZkJ5+zuaou+dorhqn2NhYtmzZgkKhwGw2k+Dvz3F3d65OTuHGf/7Fp39/7BYLErn4WBOE+oh/IYIgtAjLli1rEd07LnQCNzo0FI+sbCr27aV8z14qDx7EbjYj9fREO2AAukk3oh0wALmf31nPGxsbS0xMTKsvReKqcQoICGDq1KnEx8cjk8mw2WwUq9Ws8/HhTp2OqviVpO7dR9CL81CGhzd7fILQWojETxCEFqGiouKiHu+o+m5nncCVSvExm/ErKcG/pISQikryxt1AkVSKTKdD06sXfo88jKZnT1QxMUhksnqfuy3U3bvYcboY9SXPFf/+S9YLc0iecjN+Dz+E7sYba60mIQiXOpH4tUITJ05sUKP0Q4cONUM0gtB4tSVpF6MxS7N1sdtsmDMzse3aTb/kZPyKSwisKEdlsQJQpFSS4+FBVd8+jH7kEZRRUUhO79UTmk9dybPmssuIXL2K3HffI+f1Nyjd+juBc15A0cpmVAXB2UTiJwhCs6orSZswYUKTnu9CS7Pn1nezW61YDAaqTp7ElJREVWISVSeSMJ1Mxl5ZSSeLhWKJhCw3DXsDAsn19CBLraFMKkGtVhM3fDiq6OiL/v+htZoyZYqrQ6iTVKMhYNbTuA8dQva8F0mefBMBTz+FR1ycmP0ThNNE4tcKrVmzhl69erk6DEFotPqStJ9++onLLrus0cuz5xZHVkgkqCor0ZSVobfb+e+NN7Dr9ZjTMzBnZmLOygKLBQCJmwZVu2jUHTriefXVqKJjKPb04IN338VssZwXo7SVncB1hsTERPr16+fqMOqlHTiQyK+/wvD662Q9P5vSrVvxnzULube3q0MTBJcTiZ8gCM2mtg4WVosFe1UVWo2GnZs2MWb0aOwWC3azGSwW7FYrdpMJW1kZ1pISbGXl2EpLsZWVYSstwfuvv7guIxO3qiq0Fgvq00kddjtIJJCaSmWHjihCQnAfNhRFSAjKkBCUUVHIAwPPmwnSAlNvu+2sQwSt9QSuM+zevbvFJ34AMk9Pgl9+meKhQzG89hrJN91E4POzcb9isKtDEwSXEomfIAhOZ6uspPLwYZT/+4UhiYl4lFegM5txM5tRWK1gt/OLfwDt3lrIiffer//JJBKkbm5I3d2RurvjXllJvlxOnlJJhUpJhUpFiUxOiUyK2cODYddey/hGLiO3lRO4AnhedRVuPXuSPf8lMh55BK9x4/B/7FGkWq2rQxMEl3Bo4terVy+uv/565syZc95tqampuLu74+Pj48hLCoLQAtktFir+PUDZn39Stusvqo4ngsVCBKCRychXyEnU6qhQqTBJoBIJtopyMm+ewuChQ0EmQyJXIFHIkcjlSBSK04meB1I3zVmHKtwMBpbOmVPr8rFCoSB2cNNmeNrCCVxn6Natm6tDaDS5nx8h775D0bfryVm4kPJdfxE4bx5uvXo57DS4ILQWDk389u/fz+WXX17rbVFRUdxxxx0sXbrUkZcUBKGFsFutlO/+m+KNGyndtg1bcTEyb2+0AwegGzcOdbduFHp4sGj+/FqTNCVw97RpaBv5oVtbfTexNOs8rWGZtzYSiQTd+Btw69eP7DlzSLvnXsqGDmVpcTFmCU0+DS4IrU2zLfXa7XbsdntzXU4QBAeqb1bElJZG4dq1lPy0CUteHoqIcLwnT0I7+ArUXTqfNTsXCHUmadHR0U1O0sTSbPNZtmwZM2fOdHUYTaYMDSHsk49JXfwxZR99xAS1mo2REeSc7pVc12lwQWgrxB4/QRDqVWv5lc2bmd6rF8EHD1K2fQdSL088r74azzFjUHfpUm/pjLqStDVr1lxUnGJpVmgoiUzGnqBAdvW4jBFHj3Hz4SPsDgvlr8DAmoNHCQkJ4vdJaJNE4icIQp3OLb8itdnoWFDAAIMB5Z8JVHTrRuDcuXiMGolUrW7w84okTXA1o9FInkbDqk4dGZyTS7+0dCILCtkYEU6ZUonRaHR1iILgFCLxa4WWLl2KSqWia9eurg5FaOOqy6/YqqromV9An+xsPKqqOOHlyW8xMVw+aRLjr7vWIddqzcuHl5K2Mk56vR6pVIpNKiUhJJiT3jpGHU9k6n+H2REViV7U/BPaKNFvqBVKTk7m9ddf57PPPnN1KEIbl5+TQ9fMTO46cJDhKSkYPDz48rLurI+JIdPd3aGzIps2bXLYcwnO01bGKTY2FqlUikKhwGq1kqZSsaJzJ/7z9+eKEye57KdNmLOzXR2m0MIYDAbWrVvHp59+yrp16zAYDK4OqdEcPuO3adMmRowY0ejbJBIJv/zyi6PDadN+++03+vXr16SZP1HCQKiP3WajZPNmeq/+EkleHv/5+LA7JIQSdy1WqxWsVqRSKXq93mHXTEpKcthzCc7TVsaprtPg2zu0p+PNU/BYu47kyTfh/9STeI4ZI1q+CQ7pCd4SODzxy87OJruOb0n13Sb+UTXN+vXrG534tZVfXsHx7HY7ZQnbyfvgA6qOH8ezf3+WlhRjUCpPlV8xmQDRvkxoG+o7DW6dNAnDG2+Q/cIcSrf+TsCzz4iWb5ew6v3OJpMJS3V3IEAul7e6U+AOTfxqK9wsON6Nx44R46bFKpVQefAgK+NXUiCBbCATKJJIKAB+3b8fiVJ51mPb0i+v4FhViYnkLHyb8r/+QtOzJ+HLlqLp0YNrTn9RcHaNvCFDhjjsuQTnaWvjVNdBI5mnJ8EvvUTJsGEYXnmV5EmTCXz+OdyHDnVBlIKrJSQkYDabz/rcBLBYLEgkkppT4HaLBUt+Ptb8fCwGA1VJSZhSUrHk5WEtLMRWUY69ogJbZRWpRYUueS0i8WuFpHZQW63ILHa8bTZCAIkdqudM7XY7NuBo337I9HqUEeEoQ0NRhIfzT04ObsUlVMikp/qYnnbuL69w6bAWFZG3+GMK165FERJCyNsL0V5xRc0sfHPVyBNdfVqHS22cPEaORHP55WS/9DIZjz2O1/Vj8XvscWTuouXbpSQ9Pf2sovMasxn/igq8KyvxrqxC/+mnJH78CRaDAbvFAjbbqT9w6rNWIqnpQiRRKk9Nyshcc75WnOpthb7q2KFmb9XTTz9Nl86dsebnU5WcTNWRo1QdO0blkcNUnTiJNTeXitxcKg8fAZuNoPJybrPZsEil5KvV5Li5ka11I1urJU+tJj093cWvTmgudouFwrXryFu8GKxW/B58EO/Jk86bJYbmKb+yfv36NnNitC27FMdJ7utLyNsLKfruO3LeeouyXbsImjcPt969XR2a0AyshYW4HTnCwMxMAsrL8S+vwN1kqplssUkkKLBjlsqQKJWowsNRxsSg7twZdZcuKCPCUfj7n/feWrJ3L7jgd0gkfq3Y8OHDa/b3yX19kfv6ou3Tp+Z2u9WK6cQJyvfuo/yvnZTu2oW9HCplMowaDSaZDP/ycroYjUjtdixSKaUZmeQCmt690fS4vMHfasVhkdalbOdf5CxciOnECbyuvx7fGfcjd+BBDUFoayQSCbpx49D27UvWnLmk3Xsf3jffjO/MGUhVKleHJ9ShKZ9Nltxcyvfuo2LfPsr37cWUmER/k4kSqYQipYoyhRyZzYbGbMYilZLu6QFdujD6oYdQd+5c65fnlsShid+IESOIi4vjqaeeOu+29957jy5dujBy5Mjzbps5cyZffvmlKJjZQJGRkcyYMeOChzokMhmq9u1RtW+P9+RJ2M1m4mfPxrZzJ+0LCtGazRSpVOwKDCTHTYOXyUQ3tYbiHzeS//kKkEpRd+qEpk9vtP37o+nZs9Y3OHFYpPWw5OaSs/BtSrZsQdOzJxEr41F36uTqsIBLbwmxtbrUx0kREkLYx4spWLWavEWLKNuxg6AX56Hu3NnVoQnnaOhnk91iofLgQUoTEihL2E7V0aMAKCLCcevZC6+x13Po99+x/P03QWVllCsUHPfWccJLR6qnBxKVimuuuQZNjx6ueqmN4tDEb+vWrURGRtZ62yOPPMIdd9xRa+JXXl5OYWGhI0Np0+68884mlXCRKBS49+vHj7m5/GYyEVJaSlejkT4GAzK7nVSdF+aRI4l6/HEsaWmU/72H8r17KNn4EwUr4pGo1bj17Yt20EDcBw9GERx8XmeHaqLfpWud+y130IABqLb9Sd6iRUhUKoLmv4jH1Ve3qNP0U6ZMcXUIQgOIcTr1pdpn6q1oBw4ga85cUu6Yhv7OO9FPuwOJQuHq8ATO7zpUrfqzKTosDG1iIiX/+4WyHTuwFRcj8/JCGzsIn9umounZk6qjRylc9y1FGzbgp5Dzr5cXv0dGclLrhv30e6dcLkehULSqSQ6x1HuJiY2NZcuWLdjtdtI9PEj38ODXsDC6FBVxeU4uwau/JGX333hPmYLnddeimzAeu92OKTGRsu3bKU1IIOetheS8/gbKyEjSfX0JKCwkWalAJpcjk8mwWq2i36ULnfstN6C8HM/X3yC4qgqfSTfi98ADyDw9XR3meVavXi2SilZAjNP/U8XEEPHZMoxLl2JcsoSybdtQPPIwO1NSxLYXF6vuOlT9WSSTyZCYzITl5NK5uJjc8RMosttrVsS0sYNRd+mM3Wym6PvvSbvnXszp6ag6dyJg1iw840Zj3L+fX+PjUZ0xg+iMCgfOJhK/S8yZRUvlcnnNL+9xDw8GzJpFhFpNwapVGF5+GeOnn+Jz++14jbu+ZsnY5/bbsZaWUr5rF2V/JqD9aSM3FJdQLpdz0seHE7560jw8sAI2m00s3zezM7/lSioric3I5PLcXIxubnzVpTMz7ryzRSZ9APn5+a4OQWgAMU5nkyiV+N5/P9rBV5D42GOY7phGZkQE+wP8kcpkYtuLixiNRmw2G9jthJaV0yUvl5i8PBQWK0atlrRePRn53HMoIyIAsBQUYFyylMKvv8ZaXIzHlVcS9PLLaLr9/+pac1U4cDaR+F2CLvTL69anD1UnT5K/bBk5CxaQv2wZPndOR3fDDUgUCmTu7niMGIHHiBFs79KZfWvXEmEw0L6wiK45OZhkMpK8PEn298e3hSYZbVVCQgI2q5UoQw7D09NRWa1sCwtjj58vKo1GzMAKgpMU+/uxODKC/lYrg0+cIDwvl82RkVRqtWLbiwsEyOX0zcikg8GAd1UVJSolewMC+c9bR4VOR1xcHMqICCwFBeQv/5zCNWsA8Bo7Fu9bbkEZGlL78zZDhQNnE4nfJepCv7yqqCiC5s9Hf/fdGD9dQs4bb1L45Vf4PfwQ2iFD/r/G2xVXsOV//yPTzY0/TCZ8KypoX1hIh6IiOh85ivr9D8g6fBjPuDjc+vRBIhe/cs5UeuIE1/73H+EFhZzQ+/Bnu3YUKxTYTaYWPwM7btw4V4cgNIAYp9olJCRglkj4X2gISTovRp1M5vZD//FreBgngoPFl65mYLfbKf9rF4Vff02HP34npMrEcR9v/hceRpqHB0gkyOVylFIpgy6/nLzFH5P/xUokSPCZOhXd5EmXRHcW8Sks1EsZHk7Q/BfxvvUWct95l4zHHkfTpzf+jzyCunPn8/pdFqtU7PPx4R+plNtHjSI0N4/izZsp/v4HZD4+eIwciefoq1BfdhkSqdTVL6/NsJlMFMTH03P1lxTZbXwXE02yXl+z5xJweG9dR8vPzyckpPZv2ULLIcapdjVLi0CGjw9f6nRckXSCuJPJnCgupqhbdxdH2HZZS0sp/uEHCr/+BlNKCsqYaAJnzeK7vDx+2b797DubTFyv1lBy113YK6vQTZ6Ez223XRIJXzWR+AkNou7YkdAPF1G2fTu577xLyq1T8bzuWvwefpjY2Fh0Oh3r16+nsLAQnU7HuHHjak4e6++9h6ojRyjetJmSLVso/Ppr5IGBeFw1Cq8xY1C1b3/e9URdwIYr37MHwyuvYkpPx33CBD4+eYIKmw2r2VyT9LWG3rp//PEH3buLD8eWToxT7fR6PdLTX2atViuVMhmb2kVxzNODUalpRH/5JSXdu+MxYriLI207zNnZFHyxisL132I3mfEYNoyA559D07MnOTk57JgzB7lcfqrNmt1Oh4IChmZkojWbkY8fT+gDM1H4+7v6ZTQ7hyd+mzZtYsSIEY267fDhw44OQ3ACiUSCe2ws2v79KVq/ntwPFlH2xzYKrrmGpceOYrPbsdlsFBUV8f7779dsaJZIJKcqmHfujN/DD1Gxfz8lmzdT/N0GClbEo+rUCc9rxuA5ejRyvV7UBWwgS0EBue+8S/EPP6Dp0YPI115F1b49U5qpt64gCP+vumKCQqHAfMaXrmR/f77y8+MBtZrMJ5/EY+RI/J96UhRMr0NDvvRXJSaSvyKe4s2bkWo0eN90E7obbzwrias+1WuxWAisrGRYahrBJSUkeXmxs1tXBl7eg6hLMOkDJyR+2dnZZGdnN/q2llRPTKifRC5HN3Ei7sOHk/LyKyiWLeNaT09+DgulQK0G6q7jJ5FKcevVC7devfB//HFKt2+n+MeN5L37HrnvvIu8Vy+25+Zi8/Sg8vQbZ33Pdymy22wUfbeB3PfeAwkEzH4er7Fja5bOW+vJs+joaFeHIDSAGKfanbvt5cwvXROnTqXdoEGUbN5CzptvcvLGG/F//HE8x4wRn31nqO9L/6BBg6jYt5/8zz+n7M8/kQcE4PfQg+jGjUOqPb/DlNFoRFVVxRXJKXQ3Gilw0/Bd504kubmhVCpb9H5nZ5PY7Xa7o55s3rx5F/X4OXPmOCiStmnv3r307t2bqKgoNBrNBe9/6NAhp8e0bt06Dq3+kiGJibibzewODWVXYAAWmw21Wk1cXFyDNjRbCwsp3rKFpOWfI09JoUom5ZiPnqMB/mRoNFgb+XxtVdXx4xhefZWKf/7F89pr8Xvk4Utqb4ogtHQXmrGyFBSQs2ABJZs2ox00iIDnnkURGOjCiFsGg8HAnDlzzi+4LJcTWV7OTXIFtsOHUUZH43PbbXheNarO1mh2u51fXpiDx4YN2O12doaGcCgoCIvNhtVqbTGfJdWf6Xv27KFXr17Ndl2HzviJxO3SYzQaSfHy5POuXRhsyKF/WhqRBQVsjAinvBHfqmQ6Hd6TJrGvqIijW7fSPjubrvkFdM/NpUil4pCPD8cDAy7Zb2m2igryPvmEgi9WoQwLI+zjxbid0Ze5LVi0aBEzZ850dRjCBYhxqt+FKibIvb0JfvllSkePxvDqa5y88Ub8HnoI3YQJl/SBt9oKLgcWFDIgPZ3Q0lKKwsPp9M7baAcPrneWtOrkSQyvvkrI33s46OPN72FhFAOcTiZbw35nZxOHO1qhNWvWNOu3g/pUb2g2SaVsCw3hhI83o44nctt/h9keFYW+kX099Xo9xVotCSEh7AwLI6KsjI45OfQ1ZDMoKwtzcTGFfn54jByJzN3dSa+qZSnZupWcNxdgLSjA99578Zl6a4tvAi4IQv3chwxB06sXue+8S85rr1Oy5WcCZz+PMjzc1aG5xJmnokPKy08lfIVF5Gg0/NCxIwFxo+lzxRV1Pt5mMpH/2XLyP/sMeWAg4R9+SL7VgiU+HnUr77ThaCLxEy7KuRuaU1UqVnTuxLCsLIacOIH7T5swX3FFg09Onft8JzQaTkREoImKokNxMdd7+2B46WVy3ngT92HD8LxmDNr+/Vt9fcDalod8bDYMb7xB2R/b0A4ahP/TT6EMDXV1qIIgOIjM3Z3A55/D46pRGF56meSbpuB7371433xzq39Payy9Xo9/eTl9TyYTVVyM0U3DjzHRHPX0RK3R0MXXt87HVh4+TNacuZhTUvC543Z8pk1DqlYTC61yv7OzOXSPn+BcrtoPcCG1bciVSqVM690b3zVrsVssBL00H+3AgRf1fNWnes0GA8U//UTxjxsxnTiBTK/HMy4Oz2uuQd2xg5NfreOd+3rlQM+sbAbn5qDW++L/xOO4jxjR5jeBV1RUNGjvquBaYpycw1ZRQd5HiylYvRpVhw4EPvcs6i5dXB1WszBnZ5O24C2Kf/qJArWa7UGBHPX2BokEhUKBQqFg3rx55yVsdpMJ47LPMC5bhqp9e4Lmzqm1PFhL5arPdJH4tSItNfGDujc0WwoKyH5hDmXbt+Nzxx343n9fg77JNuRIv91up+roUYp/+IHiTZuxFhSgionB85pr8IgbfdYsY0utC3juhuaQkhJGpqbhU1nJgZBgrvr4YwKjolwdZrP4/fffGTp0qKvDEC5AjJNzVRw8hOHll6lKTEQ36UbsEyeyfd++Fvfe5QjW0lLyl31GwerVSD08KBgxgmUnTmDFXuuX/jNVHj1G9rx5VCUloZ8+Hf30aUgUChe9kqYRiV8bc6HZmcmTJ/Pll1826jlbcuJXH7vNRkF8PLmLPkTTvTtBr7yMwsFvXHazmbKdf1H844+U/v47dosFt/798Lr2Wg6oVMR/9VWdM4iutG7dOjZt2oSkpIRhmZl0zjOSrdXyc3gYJT4+LeLkWXMRhwZaBzFOzme3WChY/SVZ779HodnCtnZRJOp0SGWyFvPedTHsJhOF69Zh/ORTbFVV+Ey9FZ+pU5FqtRf8km63WMhfvhzjp0tQREYQNG8e6k6dXPhqmq5NnOoVznf77bfX+vP+/fs3cySuI5FK8bn9djSXX07ms8+RMuXmU0u/gwY57hoKBe5XDMb9isFYS0oo+fl/FP/4IxnPPofKYmGojzcHvL1Jd3evWT5oCXUBjXl5dM7KYmBKKhIJbI2O5qCfLyazGWUL760rCIJzSORyzFeN4rNf/sfg44nEHT5Cks6LX8PCqHR3bxHvXU1ht9sp27aNnIVvY87IwOu669Dfd+9ZqzP1nYo2paSQ9fxsKo8cwef229HfczdScdCt0UTi52TLly93dQgthqZHDyK/WEnWnLmkP/Qwvvffj8/0aQ7fuybz8EA3/gZ042/g+6VLyf5mDe0NBjrn5lGqVHJYr+eQtzfl3jqXNk6vPHqMnt9tQJ6czCEfHxIiwjGp1a2mt66j9e3b19UhCA0gxql5JCQkUKxUsq5dFB2L9QxLTWPaf4dJCAriv4hwl753NYUpOZmct96ibPsO3Pr3J2TBm6hiYhr0WLvdTvGGDRjeeBO5vz/hy5ah6d7NyRG3XSLxE5qVTKcj5O2FGD/5lLwPP6Ty6BGC5syptfK6I2TbbPwVGsI2P1/CTSY65+XRIyeHvpmZ5Li7Y5IrMMfGOnzpuT7W0lKMH39MwVdf4xkczOrLupPq5naqaKnJBFyataZiGvghILiWGKfmUVPeRCLhpJ8fmXo9/VNSGZKRQdeCAnLDw6EVJH7W0jKMS5dQuGo1cn8/ghecqshQ1xf+c5d6B152GfYlSyn5+We8rh+L/xNPIHVza+ZX0bY0e+KXn59PaWkp4ZdorSLh1NKv7333ourYgewX5pByxzRC3lrglPpVNY3TJRIy3NzIjorit9BQIvLz6VZYRMzOnZwYcw2anj3xuGoUHiNHIm9k7cGGsttsFP/wA7nvf4CtogLfGffjc/PNjNy9W/TWBVavXi32jrUCYpyaR817F2C1WqmSyfgtPIwDOi9Gp6XTfc1aDHI5vjNmIPPyclmcde3Js9tsFP/0E7nvvYettAz93XfhfeutSE+39azNuRUOQkpK8X/pZbxVKsJefQXPq65qxlfWdjV74vf4448THx+PxWJp7ku7xMKFC0lMTEQikRAeHs6oUaNa1cEMZ/IYPhzl55FkPPE4KVNvI/jVVxy67w/qb5yeERLCwKeeQvPfYUo2bybnrYXkvLkAt759TyWBw4c77A214tAhct54k8qDB/EYPRq/hx9GEeBfE6OoNSUIwpnqeu/K1+lYq/dlVu/eFH/xBSU//w/fmTPxGnc9EpmsWWOsq7fu7VdeSchPm6j45x88Ro3C75GHL9iWzmAwEB8fj9lsxmIyMSAriwFZ2WR5eLC+Y0ee7NEDz2Z6XW2dS5Z6L6WDxI8//vhZ/z1r1izi4uJYvny5+GAHVO2iiFixgqznZ5P+8CP4P/EE3pMnOez562ucPnXqVALbtYN27fC69hqshYWU/PobJVu2nCoS/epruA0YgMdVo3AfMgSZh0ejr2/Jzyfvg0UUbdiAqn17wj79FLdePWuNszXt13EGURuudRDj1Dzqe++6depUImJjsUycQO7772N45RUKv11HwNNPo+nevVniOzNRq+6tK7fZiDUYUP+ZQGV0O8IWf4RbA/eEVrdsU5WVMeFkMkGlpewMCWZnQAAqpaLV7WlsycQePye5+eabmTRpEpdffjkBAQGkpaWxZcsW5syZw6ZNmxg1ahS7du1CXc+096VC5u5OyFsLTrUueuMNTKkp2G+5he07dzpkBqyhM2oyna7mUIglN5eSX36lZMsWsl+YA3I5br174zFiOO5Dh2K02S5YcqBwzRryPloMUin+Tz2Jbvz4S64af2NMnz7d1SEIDSDGqflc6L1LrtcTNHcuuvHjMbz+Bql3TMPr+rH4PvCA07asVDu3t2674hKGnjiB1mxmd1gofrfcQodGHAQyGo2E5ucz6thxrDIZ33bvRqa7O3aTCZuocOBQzV7Hb9q0aaxYsaJm2rqleeqpp9iwYUOjH7dkyRIGDx58wfudPHmSnj17UlRUxNtvv80jjzzS4GtU1/yJjIy8qG/dM2fObLF7dAq++YbMV18jSevG5g4dqJJIXF53z2zIoXTrVkq3bqV8zx4sZjPpahVJPj4kentT6uZ2Vnzlu3djeHMBphMn8Bp/A77334/c27vZ425t1q9fz7hx41wdhnABYpxaJrvVStH69eQuWgRWG773349u4gSnfdn89NNP+euvv1CUljEiM5P2RiNpXl78HBpCuacn/fv35+67725w7H88/Ahev/1GsqcHm6OjMZ+ucGC1WlGr1W2ypqmo49dCZGZmcvTo0UY/rrS0tEH3i4qKYtq0abzzzjt8//33jUr8qq1du7bN7hM0DRnC+jVruOq/w4z751/Wx0RTolS6tO6eIsAf78mT8J48iazjx/nyqaeIyMmlX0oqg04mk6fRcMLHh10LFhCw/HOse/ei6XEZESvjW21hUVfIyMhwdQhCA4hxapkkMhm6CRPwuPJKcj/8kJwFCyj69lv8Hn0U7QDH143Ve3tzmcHAgOQULFIpW2JiOOytw2qzoW5EKSpLXh5Zzz1PwJ6/+T0ygl0BAZgtlku6woGzNXvi16lTJ4YMGdLcl22wlStXsnLlSqdeo3PnzgCkp6c79TqtUUJCAileXqzq1JHxiUncfOQoGzq0JwuQyWQu3+ex48ABDvr4sM/DA4XVSmRxMR3zC+iXmYHCaqNSk4JHr554TZqEIijIZXEKgnBpkul0BD77LLobbiBnwVukz5yJdvBg/B5+GFW7KIe0r6xKTKT72nW0O3GSg/7+/B4USJVcDjZboxK18r//JvO55wAIX7yYblVV7IuPRyaXX9IVDpyt2RO/p59+mqeffrq5L9uiVO9V8GjCYYG2rrp2lVGj4evu3bj26DFuPHyEH6KiSPdXunyfR3p6es1GZotUitpiIbS0FAsSDvv6og/wp31pKdnPPgdSKepuXdEOGIh20CDUXTo3+6m71mT06NGuDkFoADFOrYO6c2fClnxK6S+/kPvueyTfdBNlAwawoqyU8jMOi2zZsqXB22jsJhPG5Z+Tv3QpirAwKp9+ij+3b0dis6FsRKJmt9nI/2w5eYsX49arF0Evv4Tc15dYEBUOmoFY6m1mNpuNr7/+GoB+/fq5OJqW58zaVaUyGd926cyo48cZm5TEVonE5Z0sSkpKwG4nqqiYKzIy8K2o4D+9noSQYEqUSjp27MhVzz2H2WCgbMcOynfsoGD1aoyffILUy/NUEjhwAG59+ogZQUEQnEoikeAxciTaK64gbckSij/+hCnAzqBA9vn5YZNKG7yNpvLwYbLnvUjViRPop92Bz513EqVUEjV6dKMSNWtREVnPz6Zsxw70d92J/u67z/pCLCocOJ9I/Jzgiy++oE+fPnTs2PGsn+fk5PDwww+zf/9+FAoFDz74oIsibLnOrV1VDmyIimKkIoMRiYno0tKw2+0Ob/PWUCEVFfQ4dpzQkhLSPDz4onMnDGd0HamexVUEBKAbNw7duHHYLRYqDx2ibMcOyhK2k71lC9jtKIKD0PTujVvvPrj17XPBOldt3ebNm0VXiFZAjFPrI1Wp2BMUxO+9etLrZDJD0jO4PDePP8NCOerpWe82GltVFcZPl5C/YgWqmBgiVnx+1t7lxiRqlUePkvnEk9jKygh9/z20Awc67DUKDScSPyf45ptvuPXWW2nfvj1dunRBq9WSmprK/v37KS0txc3NjeXLl9fs9RP+X121qxI6tKfLoIFUrYgnp7IK/yceb9ZlU1NqKnmLFtFn02YMCgXfto/hpKcnnJGAKhQKQkNDz3usRC5H06MHmh498L3vPqxFRZTv20fFnj2U79lL8Q8/YrPZqPTwoCAwEFunjnS54QaCevRwWYIrCELbYjQaKZPL+SUinEMhwVyRkso1iUn01GrZERlR6zaain/+IfvF+ZgzMvC99x58brsNiULRpOsX//QT2S+9jDIykrCPF6MIDr7YlyQ0kUj8nOD222/Hw8OD/fv3k5CQQGFhIRqNhpiYGK688kpmzpxJVFSUq8NsseqrXVXYqzeG117Dmp9P4PwXkSqVTo3FYjRi/HQJhd9+i1yvx/PJJ1i0bRtVZjOc0X1GLpejUCgatE9G5uWFx7BheAwbBsD2LVvY9smnBBUWEnziBL4HD5KzZi353t7o+vRB070b6m7dUXftiszdOT2NW4KQkBBXhyA0gBin1unMbTS5KhXfde5ESH4+salpTDj0H5V2qBoxAlV0NLayMvIWL6Zg9Zeou3Qh4ouVqKKjm3Rdu9lM7rvvUrD6SzyvuYaAZ5+pt22b4HzNXsdPaDpX1fxpaUp++42sZ59Dc9llBL+1AJm7u8OvYS0to+CLL8hfGY9EJkc/7Q50kycjVatrbVPU1DqDBoOBOXPmnFX9Xm2xEFpVRUh5OUNDQrAfT8RWWgoSCcrodmi6dUfdpQuqjh1RxUSLN1FBEC6otvcaAIVcTufCIsZWVGDPy0Pbvz8VBw9iN5nwnTED7yk3NXl1xWI0kjnrGSr+/Rf/xx5DN+lGsYpxBlHHTxAayGP4cGQfLiLj0cdIu+deQj9432FV6m3l5RR8+RX5K+OxV1SimzQJ/fRpZ/XsdWRv3XOr38tkMswyGYlyOel6Pd5xcdwwbhym5BQqD/xLxYGDVBz4l6ING8BmA6kUZUQEqg4dUHfscCoZ7NDB6VX7nWHZsmWiK0QrIMapdareRrNixQpsNlvNXmm5QsHAxx8jukcPMh59jJJff0Xq5YnntdfiOfqqJid9FQcOkvnUU9itVsI++qjWVpWCa4jET2iV3Hr2JHzJp6TPfIDUu+4i7MMPL+pwhK2yksKvvyH/88+xlZbidcMN+EybhiLAv9b7O+rkWXX5GjhVp7B6KcZqtda0KZJIpajaRaFqF4XX9dfXxFuVlETV0WNUHTtG1bGj5P3xB/aKilNPrNNR7K6lSOuONDSUDkOHENCzJ/LAQCSnr9HSVFTHLrRoYpxat3MX+ex2O7KUVFLffgdzejp+Dz2EtaCA4k2bKPlxI7oJE/C5/Tbkvr4Nvkbhum/JeeMNVJ07Efz66yj8a38fFVxDJH5Cq6WKiSFs6RLS77+f1DvvIuyjD1GGh9d637qKltqqqihatw7jZ8uxFhXhdd116O+6s9lO2J6576a6jWH1/0rrqX4vVavRdO2KpmvXmp/ZbTbMGRnsX/8dB378EV1BIbrMLLwO/Evejz9SrFAgd3NDGR6OMjISRUgIiqBAFMHByAODUAQHiWVjQWijDAYD8fHxNW3QAKQ2G70zM1G98gqWzp3P2svn+8BMClatpuCLLyhct+7/E8Bz3pPOfG/19fKix/5/MG/ejG7iBPwffxyJk/dhC43n0MRvxYoVxMTEMGjQoPNuKy4uRqlUoq7lg2X16tXs3r2bhQsXOjIc4RKgDA0lbMkS0mfMJPWuuwlb9AGq9u3Puk9te/J+2bSJaZFReP/xB5b8fDyvGYP+zrtQhjbvxvVzy9dUvyE3pU2RRCqlQKlk2aGDmEOCa/bxSOx2fGw2/MxmbhlxJap8I6bkZCoOHsCSkwtn9M2WeXujCDqVBMr9/ZH56JH76pH5+CDXn/5fb+8mn+yrz5QpUxz+nILjiXFqnc7dVuJXVcXI44n4lpfzd1gY+kk30v6MAxwyDw98770H7yk3UbBqFQVfrKJw7Vp0N07E57bbkPv4kJCQwIoVKzCZTGirqrg2MYmCigpst91GxyefcOGrFerj0MTvjjvu4I477qg18fP29uaOO+5g6dKl5922ZcsWVqxYIRK/Bpo4cSIajeaC9zt06FAzRON6ioAAwpZ8SvoDD5J6z72Evvcemu7dgP//llu9oVlhtdI1L48+hhzkCduRxMUR9fBDdc4UOltd5Wua2qaotj2DVqsVo9VKmVbLHn8/xt93b8397RYLltxczJmZmLOyMWdlYsnKwpyZRVViEhajEVtJCTa7HavVWrMvSOGtQ+nrh9TDHZm7B1IPD6TuWmTu7kjdT//dwwOJSoVEoUSiUCBRKZEoT/1dqjz1d2RyJHIZSKUcP3iQPr16I1UpnZJYCo6RmJgois+3QjXbSux2ehtyGJiWSrFKzepOHSnw9qZ/YWGtj5N5euJ73314T5lyKgFctZrCb9aguGYMXx49SoXNRkRxCVcnJ2OVSPiyQ3sKjh4h3GAQHTdaqGZb6rXb7eftLRAER5F7exP28WIyHn6EtPvvJ/Tthbj17VuTCEkrKhiYl0dPQw5Ki4XDeh/+CQ9nQOwg2rko6avmyMMiDdkzeCaJXH5qhq+eLiIJW7fy7fLPUVdVoTZVobVY0Jot9AkNIchLh620FKvRiCklBVtpKbaSEqxlZWeVu2mI7cOHoXv0sVOx63TIfPXIff1QRUej6tgBdYcOKKOikMjFDhVX2r17t0j8WiG9Xo+n2czwY8cJKy1lf1AQCcHBVGFHXc+2kmoyLy98778f7ylTyF+1isyly7i9qooClQq/igrSPDz4oV0UFQoFmEw1reCElke8g7ZCa9asuaTLudRF5u5O6KIPyHj8CdIfehjfBx+gIiWFASdP0j3bgBT4L8CfvUFBGCUSlErX9/6t5qjDIk3dM1gXg8FA/JdfYpZJMSvkoDj1lqFQKNhbWcm8Z+4nqJYE1W63Y6+qwl5Zic1kArMZm8mE3WQ+1e/TkM3BffspKSzA092dLh07IU88TtArr2ArL8eab8SSZ8SSk0Pp1q0UfPHFqdfg7o5bv35oBw5EO2jgJd/tRBAaqq/FQuj+fzBJpXzTPoY0T0/A3uhtJTKdDr8ZM1iclMSA338nsKycUoWcYqUCrdlyKvEDjh496sRXI1wMkfgJbYpUrSbk7YVkPfscuQvfpofZTIXdzj5/P/4JCqJKrT6VCFmtTUqEWjpH7hmEupeOq/+7rjZPEokEiVoNajXnFoNISEggfs2a/99zmZvLtykpxMbG4jn6qlrjsJaWUnXsGOV/76F85w4Mr74KNhuayy/Hc8wYPEaNRObp2ajXJjRNt27dXB2C0AjW0lJyXn+d8o0/oezTm5VIqJRJUV7EthJLXh6xu3fjVVlFQkgwcpuNjvkFdDX+R7KXJ38HBIBY4WuxROIntBl2u53y3bspWL2asj+2nSpToFSwPTiYf319Tx12MJmApidCLZ2j9ww2dun4Qs7dc1lNoVCwfft2rrrqqlpjlLm749arF269esE9d2MtLqbszz8p2rgRw2uvkfPmm3iMGoX3rbegPqdHtuBYYpm39Sjfu5es2S9gKykh6KX5eMTF8VxOzkVtK6k4dIjMJ57E22JldYcOZJ/uJvRncDAdCgrpYzAw8dhxzKVlFG/ciMeoUWLPbgsjEj+h1bNVVFC88ScKvvoKU1ISyuhoAue8gCI0lMK1axm58SesEikHvHX/X7RULm9SItQaOHLPoKOXjuubQQwKCqpzBvFcMk9PPMeMwXPMGCy5uRT/9BN5q1Zj3LCBopBgSocOpcfttxMoloIdbtmyZcycOdPVYQj1sJtM5H38Cfmff47m8ssJ+vSTmt64F7OtpOjHHzG89DKqDh0IfGsB+QsX1nyZtkmlHNH7cMTHm3YVldzq7U3W7BfI/WARuokT8bphHHJvb4e9RqHpROIntEh11d07kzkzk4Kvv6bouw3YSkpwHzoU/ycex61v35q2QJrLL8dQWMiIrb9ji4rk4OkipG39oJGj9gw6eum4vhnE6tsbS+7nx+HoaFaGhxPppqFHRiaBK+L599v1pNx9F/2mTxdtooRLRtWJk2TNnk1VYiK+M2fic9vUJnffqGa3WMh9730KvvgCz+uuJeCZZ5CqVEybNo3PP/8cs9n8/yf+FQpG3XcfMbGxVB0/Tv6qVRiXLMH4ySd4XHUVukmT0HTreuGLCk7j8MQvMTGRFStWNOq2xMRER4chtGK11d2rPiE2aMAAynbupGjtOkq3bUOq1eI17nq8b7wRRS3N43Nyc/nYYiE2wJ+RJ06C1cq/fn5IpVLi4+OJiYlpk7N+juLopeP6ZhCrb2+smuVjq4VD7u4c6tCe8JISrsjKQrXwbZK2/UnIk0+cVexaENoau91O4dffkPvuuyiCgoj4fDnqTp0u+nmtRUVkPvMs5X//jf+TT6CbPLnmi9SFVhdU7dsTNGcO/g8/TNGGDRR+8w3FP/6IuksXdJMn4zFqJFKV6qzrNeRLv3BxJHYHTn1IpdImfbOu/qZw5geAcD5XNXRuTnU1Eve22ehuzOcKmw3y8lDFxKCbdCOeY8Ygraem4bp169i0aROVFRVcmZ5Bj5wctkaEs9fXF7VaTVxcnENmxto6R70Z19koXqFAoVAwb968Rj9vzRhXVp61fGy1WOhYUUFcUTHuBQV4jb0O35kzz+s8IAitnSUvj+x5L1K2fTu6SZPwe/ghh3ThqUpKIuOxx7GWFBP86mto+1/c/k671UpZQgIFX39N+Y6dyLy88BxzNV7XX4+qfftav/RXf8lsa/uxwXWf6Q6d8QsPDxdLKsJFOXMPmFwqJbq4mC7ZBiKLirBKpeT17kWvt95C3bVLg37XapYWJRK2tYsCuYxhKanYrTYOhYW2mHIuLZ2jlo7rm0Hs16+fw2sXntTp2DtqFBN9fMj76CNK/vcL+vvuxXvy5Ite/rpUbdq0ibi4OFeHIZxWsnUrhvkvgUxGyLvv4j7YMQlSydatZM2ejTI4hNBFixzS1Ugik+E+ZAjuQ4ZgSkmhcN23FP/4IwWrv0TaPoY9FRVIdDoqz5iPUigUYnXGwRya+CUnJzvy6YRLkDEvD9/iYmIMBjoVFOJmNpOj1fK/8DBOBAbSa9AgYhuxP+SspUWbjW3h4VhsdoanpaGSy9pcOZfWoK7loTVr1jTp+S54AMXPD+/x4/EcNYq8xR+Tu/BtSn7aRMDs51F36OCYF3UJSUpKcnUIl5Q6+4yXl5Pz1kKK1q/HfehQAmY/75DDE3abDePSpRgXf4z7iBEEzZuL1M3NAa/kbMqICPwffQS/mTMo/TOB/z78kCsSkxgkkZDo48PhAH/StNoLlo4SGk8c7hBaBHNGBsU//UTvL7+ir8FAuVzOUb2eY/7+ZGtO1d5TKxSNTtRqO5zwe3AQNpmUQckpeGdkOukVCfVx1AwiNPwAikynI2DW03iOuZrs+S+RMvU2fG6/Df1ddyEVjeSFFqiu/c53XHEF/t+swZKbS8Bzz+F1wziHrLbZysvJmjuX0l9+RX/fvejvvBPJ6S9VziJRKvEYMZx/kxI5qFHTPjub7sZ8OhmNlCkUHPHWkRQQgDEvz6lxXEpE4ic4RFP2gJmzsynd+jslW7ZQ8c8/SDQadAMGsCotlWStFlN1yy+rtcmnSOtaWtwTFUWvPn2pXL4co0aN/q67mvrSBQcZMmRIkx7X2AMomssuI/KLlRiXf07+0qWUbfuToJfmozqjQb1Qt6aOk9A4tdW8lNjtDMrJQfVnAtZu3YhcvcphfcbNGRlkPPY4pswMgt9agMewYQ553obS6/VUaDTsCgpiT0gIwRUVtM/NpbMxn945udgNBvJsNjxGj0YVFdXg5xWHRc4nEj/hotV3CvfcRM2UmkrJr79S+utvVB46hEQux61fP4Jefgn3oUORajQMP/18Urn8ok+RQv0nz4ztosj7aDF2qw39PXeLPaou5OPj0+THNrZ2oUSpxPeeu3EfOoSs52eTcutU/B5+CN2kSU6f4WjtLmachIY7t+alt9nMVceOE1BWxt9hoegnjKe9g5K+sl27yJw1C5mHJxHLl7vkS9C5M/dpKhVpoaH8GRlJZFkZN0dGUbD6S4yfLkHZrh3uw4biPnQY6i6d6/w325jPpkuJSPxaoYkTJ6Kp5yRrtUOHDjk9lvo6McTHxxMdFYVXfgFlf/5J6W+/UZWYiEStRjtoEEFTbkI7eDAyD4+zntORBYir1bW0qL/rLpDJyfvgA7BZ0d93n0j+XGT9+vUXVRi4KcvH6o4diYhfQe7775Pz5gJK//yTwBdeQOHv3+Q42rqLHSehYWoOLdntdDMaGZqcTLlcwZcdO2D08aF/YWGTnvesGTAfH3rn51P56RLc+vQh+NVXkHl5OfaFNFB9M/fD77yTdrGx2KqqKNuxg9KtWylau478ZZ8h9/PDfehQ3IcPw61XLySnt21c6LPpUj4sIhK/S5Sjpr9r68SgqqwkNDubmNIyDOMnUGQ2I3V3R3vFYPT33oN20KALlhpw5B6wC9FPuwOJTEbuu+9it9vxvf9+kfxdQqRqNQFPPon74MFkzZ1L8k03ETTvRdyvGOzq0IRLmF6vR2O1MvrECdoXFnHYz4/fwsOoBNRN7DN+5gyYxGJh2MlkCnJysI0eTYc3Xkcid21KcKEv/VKVCo9hw/AYNgy7xULFP/9QunUrpVu3UrhmDVI3N9z69sGt/wB2FRdhO6OveEP7jF9IW1g6FolfKzRjxoyLajfmyOlvo9GI3GQisqiIiPIKIoqK8C0rA7udXHd3srp1Y8iDD6Dp3t3lbyr18bltKkgk5L7zDtjt+M6YIZK/ZubqJUTtwIFEfvkl2fNeJOORR1BNupG97dphLCxstW/wzuDqcbpU9NNoCNz/DzKble/bRXH89Indpu53PnMGTFlWztgTSfiXV/BzdDuSysuYZzS2iN/vhn7pl8jluPXujVvv3vg99hhVx45T9uc2yv76i5yFC4msqGCqXEayhwcZPj5k6HRUnE7+mtJnHNrO0nHL/SQW6rR9+3YSExOb9MvmiOlvS0EBFfv2U7FvL722/Eyf9HQkdjvlSiUZnp7sCfDnpLs7Ng8P4uLicOvZs0mvs7n5TL31VPL39ttgs+P7wEyR/DWjKVOmuDoE5N7ehCx8iz3z5lGxIh61pycH2sdQqVa3yjd4Z2gJ49SW2SoryfvgA8pWf4m2Y0c+16gpVShQXuR+5+rVGX1hIdcnnQAJfNO5E5kaDWqbrVWXS5FIJKg7dkDdsQP6O+/EVlbGz++/T87/fiEkP5/ueaeSvAK1mgytlhxvbwL5/+YRDdGWlo5F4tcKVVVVYTabm/TLVtvSbH3T39biYioPH6HqyGEqDx+h8vBhzOnpACiCg/G+7DI2aNSkumnJk0nh9D8ihUKBognfSl3N59ZbQAK5C98+NfP34AMi+Wsmq1evbhFJRU5uLksMBvy7dGb0sePc9M+//NAuCoOPT6t7g3eGljJObVHl0WNkzZ6NOS0N/yceRzd5Mk/n5jpkadFoNNLBYGB40glytVo2de5EqVwOJlOTZ8BaKqlWy+XTpjEnORmzORx1WRkhJaWElJYSWl5GN6MR1UeLSfrqazSXXYa6S2dUHTui6tARub9fre/5jf3sbMlE4tcKyWSyJv+y1dXlQGIyoS8ugR07yMvJxXTyBJWHj2DOyDh1u5sGdcdOuA+5AnXXrmguvxxFYCAAfRMSOBIfj7qWNjut8QPS55ZbkEil5Cx4C7vdht9DD4nkrxnk5+e7OgTg/9/gkzUaVnXrytUnTnLjseNsCwnmYEREq3qDd4aWMk5tid1mo+CLVeQtWoQyMpKI+BWoYmIAx+x3tlssdPr7bzwSkzio17M1KhLk8rMLnbexYvZnHhYxe3pywt2d5NOfTbdNmEB7rZaKf/6h4p9/yf9iFbbiYgBk3t6oOnRA1bED6g4dUISHowwPr7dDUGtLnEXi1wpV/8I15pfNbrNhycsjpLycDsZ81GWleJkt+FRVoauowKOqCiQS5IcPUxj4L8rISNyHDj31TahTJ5QREXUemXfGKVxX854yBSQSct5cAHY7fg8/LJK/S8SZb/BVGg3fd+tK/+QUhqRnEFhVRUEr2bogtA5mg4HsOXMp370b71tvxXfmDIcWFLcWFZH5zLP47dnL/6Lbsc/XF7PFAiYT0PQ9g63BhT6btIMGAaeWfC0GA5VHjlB19BhVx45R8vPPFKyIr3mufkol4XY7eQoFxRoNJW4aSmQyimUyzE1oLuBKIvFrhQKKigi22VApFERmZVG0YQO28nJsZWXYysqxlZdjLSrCWlCApSAfa34B1sJCsNmIstsJrqrCLJVSrFRSqFJx2NubInctJe4e3D//RYKaUMOpOU/hNhfvm24CiZScN94Amx2/Rx8RyZ8TjRs3ztUhALW0gJPJ2BYaQpZKyeiUVCQbvsc8fnzNjPelpqWMU1tQvGULhldeRarREPrhh2j793Po81clJZHx6GPYSksJ+3ARPc1mDsbHI3NQjdTWoCGfTRKJBEVgIIrAwLMKV1tLyzCnp2FKTiH/8H8c3fA9vuVlRBcVoa5uMHD6M0F95CgnV36B3M8PqZcnMncPpB4eyDzckbp7IFGrTiX0CgVSpRKJUknFsePOetn1EolfKzQsOYXOp3snqk4mk/3dBiQqFVI3t1N/tFpknp7IfHxQtmuH3Mcbmbc3cn9/5AEB7E1JYcXatWedTKr+x9+UpK8t8548CeBU8me34/fYoyL5c5L8/HxCQi6+EfzFqqsF3ImAANbrdEzNzSNl6m2EvPkGmssvd22wLtBSxqk1s5aWkfP66xRv3IjHqFEEPDPL4fXzSn77jawXXkAZHELYRx+iCAkhFtrc6owzydy1yDp1Qt2pE55xo8no16/mVK/UbMbDYsHDbCFu4ADCffRYcnOx5OVhLS7CYsjBVlKCtbQUW0kJ9tMzrGcylJe74FWJxK9V+qFrV3b5+3HjTTfRfVAsUrWqUaVSBnXoQPRll4l//A3kPXkSSCDn9TfAbsPv8cdF8ucEf/zxB927d3d1GPUWkr16+nSiO3cm86mnSbv3Pvyffhrd+BtcHXKzainj1FJdqM5b+b59ZL/wAtbCIgJfnIfnmDEOfT+xW63kLV5M/rLPcL9yBEFz5yI9PVEAbXN1prlczLYmu82G3WzGbjKd+mM2U7R/P1x9tfMDP4dI/FqhnsOGXvTUvPjH3zjep1t5GV59Dbvdjv8TT4jkrw270Bt82IeLyHnrLQwvv0zVsWP4P/4YEoXCxVELrlZfnbeBffti/PgT8lesQHPZZYR9/DGK4GCHXt9aXEzWc89RtvMvfB94AJ87bhfvUw7W1M9OiVSKRKUClarmZ3IXdQgSiV8rNGLECDE75wK6iRNBIsHwyquUl5Wxr3t3jPn5YsbUQaJb2DaD+t7gJUolAc88g6pDBwxvvIkpJYXg119D5unZzFE2v5Y2Ti1FfXXefvroI/zeWggZGfjefz8+t9+GRCZz6PUrjx0j84knsZaWEPr+e2gHDHDo8wtth0j8BKERdBMmcDwxEcWKeMr+/JO/IiKQymSiuK8DxMXFuTqERtNNmIAyIoKMJ58iddp0Qt59B2VoqKvDcqrWOE7NobY6bzaLhV6pqQzMyqYkOJiu8StQd+jg8GsXb9pM9vz5KMPDa/bzCUJdaq/PIQhCrQwGA0tPnuTXdlF0z8xicFISlRUVNQW1DQaDq0NstRYtWuTqEJrErU8fIpZ/ht1qJfW22ynft8/VITlVax0nZzu3zptPVRWTDx9mUGYWe4OD2Df+BocnfXaLhZyFb5P13HO4Dx9G+GfLRNInXJCY8WuFJk6ciEajueD9Dh061AzRXFqqv9Xv9/bG1i6KkSeTkUok/C80tNVVbxccRxkRQcTyz8h48inS759B4Auz8RwzxtVhCc2opgyQ3U73jAwGp2dQolTwZccOFPj6Eufg/VyWggKyZj1D+b59p7p83HST2M8nNIiY8ROERjjzW/2RwEB+i4nmspxcRqamYbNaW1X1dsGxZDodYYs+wCNuNFmzXyBv8WLsdrurwxKaSWxsLJ4mE5MSkxiaksq/ej0rOncmz9vb4QWSK//7j5Rbp1KVlETYh4vwnjJFJH1Cg4kZv1ZozZo19OrVy9VhXJLOLe57yM8Pi9XGyJMnkctkqF1wNL+tmD59uqtDuGgSpZLAOXNQRkSS98EHmFJSCZzzAlK12tWhOUxbGCdHs9vtqHfuZNrhIxSYzWzo1pVUT0/kDiiQfG55mD5lZVR++BGq9u0JfvMNFOJQmdBIIvEThEaorbjvAR9vkEoYlXQCzz17sY8fX2d7O6Fuu3btYujQoa4O46JJJBL00+5AGR5G1gtzSLvvfkLeWoBcr79gjbfWoK2Mk6OYs7PJfuklynfsxGfsdeinTiVn/36CHDDGZ5aHwWJhSEoq+dnZ2IcOIebtt5GeURpEEBpKJH6C0Ah1FfdNCg5m2LBhmFetxuDmRsBzz4rkr5EOHjzYphIKjyuvRB4YSMZjj5Fy++3k3H47n//yS6013lrTafC2Nk5NZbfZKFy7ltz33kPm7kHIO+/gfsVgAMa3a3fRz39meRh1WRnXJp0goLyc36IiOWq1Mq+wsNV9aRBaBpH4CUIj1Vfct6hjR7LnzAW7jYDnnxfJ3yVO07UrEZ9/TvLMB5DPf4mg9jEkabU1tysUCuLj44mJiREf4q2IKS2N7BfnU7F3L17jx+P38EPI3N0deo3qg2TBeUauST6JRSrj6y6dydRoUNvt4iCZ0GQi8ROEJqiruK/XNdcgkUjImjMXu91O4OzZIvlroL59+7o6BKdQBAZycOIEpIs+ZOyRo/weEc7B4GCsVmtNzbfW9CHeVsepIexWKwWrVpO3eDFyHx9CP/oQbb9+TrmWMTeXPikp9E3PIFWn438d2lMhk4HJhM1mEwfJhCYTiZ8gOJjnmDEglZI1+wWoTv4cXKW/LYqJiXF1CE6TV1rKro4dGHjiBMNSUtGbTPwREYHVam11H+JteZzqU5WURPaL86k8dAjvmybjO3Mm0gaU1WoKS0EBPTZtRpmeQUJwEH+HhCCTybBarQBIpVL0er1Tri20fSLxEwQn8IyLAyRkzZ4NNjuBc14Qyd8FrF69mpkzZ7o6DKfQ6/VIZDK2hoVR6ObGsJRUvMrK+T4qEola3ao+xNvaOF3owI3NZCJ/+efkL1uGIjiY8KVL0PTo4bR4Kv79l8xZz+BVXs433bpy0t0dq9mM1WQCTm0PcHR5GOHSIhI/QXASz7jRAKeSP7udwLlzRPJ3iTrzNPh+vR6jQsHYEye4+egxfurWVXyIu8iZp2ZrO3BTtmsXhtdew5yRic9tU9HfdZfTTtLa7XYKv/qKnLffQdO1C+GvvsrwxETSzzlIdrHlYQRBJH6C4ESecaNBcnrmDzuBc+eK5K8ODelG01qdexrcoFSyxsODaw8f4bbjiXikpUEr+SBvK+N05qlZs9lc83OFQsGapUsJWvct5q1b0fTsScibb6KKjnZaLLayMrLnv0TJzz/jffPN+D30IBKFgtiAgDoPkglCU4nETxCczHP0VSCVkPXc89htNoLmzUMiF//0ztXWCwPXdhq8w5zLsLy1kLSZDxDwzCx048a5OswLaivjVH1qtvqAjUwmw2qx0CkziyGZmZRqNES8MBvP665z6gGtymPHyJr1DJbcXIJffw2PkSPPur2ug2SC0FTi00cQmoHnqFFIJBIyn30O7BD0okj+zrV+/XrGtYLE52LU9iFuf+9dchYswDD/JUwnTuL38EMtela4rYzTme0XZTIZvhUVDE06QVBxMYf9/akcfwOXXX+9065vt9spWruWnLcWnur1vDIeZUSE064nCNXEJ08rNHHixAYttxw6dKgZohEaymPkSIKrkz9E8neujIwMV4fgEhKFAv9Zs1C2iyZnwQJMyckEvfIKMnfthR/sAm1lnKrbLyqsVgaeTKanwUCRSsXXHTuQ5+dHXGio065tLSnB8NLLlPzvf+gmTsTv0UfaVFs/oWUTnzqC0Iw8rryS4FclZD7zLNjtBM1/USR/AhKJBO/Jk1CGh5H59CxSp08n9O2FKEJCXB1amzVo4ECS41fS/+RJVBYLO4IC+TsgAKlKhcKJp2YrDh4i65lZWItLal3aFQRnE584rdCaNWvo1auXq8MQmshjxAiCX32VzGeeAZuNoJfmI1EoXB2Wy40ePdrVIbicduBAwpd/RsYjj5Jy2+0Ev/kmbr16ujqss7SFcao4dIiqN94k7uRJDuu8SIiIoFihQOHEU7N2m42CL1aR+8EHqDt2JGzxYpHYCy4hEj9BcAGPEcMJfv01sp6eRdbzzxP00ksi+RMAULVrR/jny8l8+mnS7r8f/0cfRTd5EhKJxNWhtXqWvDxyP/iA4u9/QNWhA5FLl6APDUXiwFOztdUF1CuVZM+ZS1lCAt5Tb8VvxgwkSqUDX5kgNJxI/ATBRTyGDUPyxutkPvU0Jx57nH+GXIGxsPCSLdmwefPmS7YrxLnk3t6ELVpE7nvvkfPmm1T+9x8Bzz7TIvaBtcZxslVWUvDll+QvXQYKOQHPzMLrhhuQyGS4gcNOzdZWF/Dg198wPisLtVxOyHvv4i5qNgouJhI/QXAh96FDKZx2B/IPFsE/+9kdEwNy+VlFZIVLk0ShwP/xx1F36UL2Sy9TlZREyII3UQQFuTq0VsNutVK8cSN5Hy3GYjSimzAB33vvQebl5fBrnVsXUGK30z8rm4FZWaR6edHzow9x79LF4dcVhMYSiZ8guJDBYGDZ/v2EdGjPmGPHueroUX6IikKmUhEfH09MTMwlM/MXIvY71crz6qtRtmtH5hNPknLLrQS98graAf2BC7cbcwZXj1NDXrPdbqds+3by3nufqsREPEaOxPeBmSjDwpwW15l1AXUWC1efOElgSQk7gwLZHxVF5ZEjjBeJn9ACiMSvHhs3bmTXrl3s2bOHPXv2kJWVBUBaWhqhFzjqbzKZePvtt1m1ahWJiYkolUp69OjBAw88wMSJE5sjfKEVqP6wOO7uzg/tY7jueCJjTyazISoSmUxGQkLCJVO8tS3UhnMWdceORKyMJ/O550h/4AF8pk/jWLduxK9aVWe7MWdx5ThdqMUaQOXhw+S++x7lu3ej6dWL8OXL0XTv5vTYqusCdjLmMzItDZNczrquXUhRq1Ha7RiNRqfHIAgNIRK/etx8880UFRU1+nHl5eWMGjWK7du3o9PpiIuLo7S0lF9//ZXff/+dxx9/nAULFjghYqG1ObOIbJpez0aZnDFHjnDdiZNs7tTxkvqwWLZsWZvpCuEMMi8vQt97j/zln5P74YeUKBSoottRcEZXCYVC4fSZYleNU30t1uLj44mUSmHdt5T++ivKdu0IeedttIMHN9uhGD+tltHHE2mfm8tRXz2/t2tHhUQCVitSqRS9Xt8scQjChTivD00bMH78eF555RU2bdpETk5Ogx/37LPPsn37drp3787x48dZu3YtmzdvZufOnbi7u/PWW2/xww8/ODFyobWoLiILYLVaSdZ5saF9eyKLixlz/Di+np4ujrD5VFRUuDqEFk8ilaKfPo2Tt96Ce2UlN/17gOiSEpRKJTKZDLPZjM1mIyEhwWkxuGqczm2xVv2avYqKuOq/wxTcdz9VR48SOHcOkatX4X7FFc2W9JXv3Ufnz1cQWVDA5vYx/BgZSanNhtVqRaFQIHViXUBBaCyR+NVj2bJlPPPMM4wePRo/P78GPaagoICPPvoIgI8++ghfX9+a23r37s3TTz8NwMsvv+z4gIVWJzY29lT3AIUCq9WKyWTihIc7P3TsQFhhEd03b8FWXu7qMIUWJl2r5cvLupPlruX6Y8e5IiUF5ekkx2aztcmZ4nNbrPlVVHBNYhK3/XcY39JSEkcMJ2rtGryuu67ZiqLbzWZyFy0i7d570YSFYXn5JU4GBaFWq1EqlajVahQKhVPqAgpCU4mlXgfbuHEjJpOJ8PDwWr/h3XzzzcyePZudO3eSmZlJcHCwC6IUWoqAgACmTp1KfHw8MpmsZt9Slpsbluuuw750GWkzZxL67rvI2vjs35QpU1wdQquh1+sxqVSsj46mT14eg9PSCcsv4KfICIrUaqcuK7pqnKpnxwPKyuh34iTtCwooVirZEhnBieBgrrriimathWlKSSHr+dlUHjuG73334XPH7YTLZLTr16/ZD9wIQmOIxM/B9u3bB0CfPn1qvb1du3b4+PiQn5/P/v37ReInEBsbS0xMTK0fFhV9+5L+4IOk3XsfoYs+QO7j4+pwnSYxMZF+/fq5OoxWITY2li1btqBQKvnbz4+T7u5cfTKZm48cZU94OIOc+P+jK8bJbrfTT6lEc/AQQUVFFKqUbIkI5z8fH2QqFQq5vNmWUu02G4Vff03u+x8g9/cnfNlSNF271tweEBBwyRzIElonsdTrYCdPngQgPDy8zvtUnwiuvq8gVH9Y3H333YwfP75mhkDTtSvhn3yCNT+f1Lvuwpyd7eJInWf37t2uDqHVqJ4pVigUqNVqSry8WHd5D/aGhzHQYKDi0ccoP/0l1NGac5xslZUUbdhA8uSbKJszlxB/fzZ37sTq3r05FhyM0s2tWZdSzZmZpN8/g5w3F+B1/fVErvrirKRPEFoDMePnYCUlJQBotdo67+Pu7g5AcXFxs8QktG6qmBjCli4h/f77Sb3zLsI++hBlPV8shEtDXTPFXoVFGF55hbS77sZr3Dj8HnrQKQWLncmUnkHh2jUUffcdtqJitFdcQcCsWWh6Xk5ATk6zL6Xa7XaK1n9HzsK3kHl6EfrRh2jF7LTQSrXJxO+pp55iw4YNjX7ckiVLGDx4sBMicqwJEyag0Wia/PiZM2cyc+ZMB0YkOJsyNJSwJUtInzGT1LvuJvSD91F36ODqsByqWzfn11pra2pdVgwIIHzZUgrXriXvg0WU/Por+unT0U2ehNQB/WEbO04NLTJtN5sp27mTwm/WULZ9O1J3d7zGjkV348SzCi8391KqOScHw0svU5aQgNf11+P32GPI3Ov+Yi8ILV2bTPwyMzM5evRoox9XWlp60df28PAAoKys7ILX8WziZv21a9fSq1evJj1WaL0UAQGELfmU9AceJO2eewl9/z003bu7OiyHEfv7HEcik+E9aRIeI0aQ98kn5L7/PoVff43vzBl4jBqFRCZr8nM3ZpwuVHDZbrdTdfQoxT/+SPGmzVjz81F16kTA88/hGRfn0t7Edrudks2bMbz+BhKlgpB33sb9iitcFo8gOEqbTPxWrlzJypUrXXLtyMhIAFJTU+u8T3p6+ln3FYSGknt7E7b4IzIefoS0+2cQsvCtNrPktGzZMjET7WByX18Cn30W75umkPv+e2Q99zx5iz/GZ+qteF57LVKV6rzHXGiGrqHjVF/B5Q0ff0Lovn3Ytm3DlJiEzMcHz7jReF5zDaqOHZut/l51nOe+Xr1cjuH11yn95Vc8Ro8m4Kknkel0zRaTIDhTm0z8XKl6Ju7vv/+u9fYTJ06Qn58PQM+ePZstLqHtkHl4ELroAzKeeJKMhx8h+PXXcB8yxNVhCS2Yql0UoW+/TcWhQxSsWIHh1dfI+/gTvMaOxfOaMaiiooCGtURrqLMKLkulhFRUEJVnJKqgAJ+qKooPHUI/ahR+DzyIdkD/Zi3FcmaMZ71eiYTk+JVcZTCgdnMj6LVX8Rw1qtnjEgRnEomfg40ZMwalUklqaioJCQnnvVmuWrUKgAEDBohSLkKTSTUaQt5eSNazz5HxxJMEzp6N13XXujosoYXTdO2K5vXXMaWlUfDFKgrXrCH/s89Qd+kCgwayMWE7ZqUCs8VS85imtIGzW61UHD1K18xMggsKCSstRW2xUCGXc8LLkx2REQSOHMmdM2Y466Ve0Lkzkh5VVYxKTSWyuITDfn4M/nARnm1sH60ggEj8HM7b25v777+fd999lxkzZvDrr7/WFFPdu3cvr7/+OgDPPfecK8MU2gCpUknwa69ieOVVsufOxZpvxPu225p1mcyRxDJv81GGhREw62n8HnuUsm3bKPrhBwo+XcKNVVWUy+VkeHqQr9ViVKowKhWYPTzYvnUr4yZNYubMmdjtduwmE7byciw5uViyszBnZWE6eZLKI0epSkykR2kpJquVLDc3/vX3I83bmww3Nyw2G2q1ms6BgS79/6BmRtJkopfRSGxaOlVyGd9GR5MVGIDs4EHGi8RPaINE4leP+fPn8+OPP57387Fjx6I8fTquV69efPjhh2fd/sorr7Br1y527NhB+/btGTFiBGVlZfzyyy+YzWYee+wxrr1WzM4IF08ilxMw+3lkvnpy33sfS54Rv0cfQSJtfSU6N23aRFxcnKvDuKRIlUo8rrwSjyuvZNNHH5H2668E5xcQVlZGWFY2aosF7HaQSJDt2cuxhW+zr2tXeh44AKfbp1WTKJUoQkNRdeyAx8iRlAf48+o331B5erkXAJutxfSuNRqNeJaVcf3xRELKyjgUGMCfYWGU2Wwo22jbO0EAkfjVKykpib/++uu8n+87ozCqupZTZ25ubmzdupWFCxfyxRdfsHHjRpRKJQMHDuSBBx7gxhtvdGrcwqVFIpHgN2MGcr0vOW++iSXfSNCcOUgcULqjOSUlJbk6hEuaT0AAu7y9OanRIJPJkMlkKCqr8CwvQyeR0Kd7d/p07UrO8eMEjLkaqZsbErUGuZ8viqAgZD4+Z33h8AFu1mrPa0colUpd3rvWbjbT/vBhev37LyUKBWs6dSTb2xur1QqAVCp1ats7QXAlkfjVY/ny5SxfvrxJj1UqlcyaNYtZs2Y5NihBqIP35EnIvHVkvzCHJIOBA1deSV5pqegXKjRITRs4hQKz2YzVasUklWDy9qZIoWD6/ffjHRCAbNEidBMnNvg562pH6CoV//6L4eVXCExKZGdwMDuCgqiw2cBkAmgxM5KC4Cwi8ROENsTzqqs4nJ6O9J13cT9wgF87daJKpWryyczmNEScTHap6jZwF5qha+w4tZTetdbiYnLf/4CidetQd+5M5MqVGI1GdsbHoz7jFHNLmJEUBGcSiZ8gtCEGg4HPdu1C16UzYw8fYcKBA6xp354Kd/dGn8xsbj4+Pq4O4ZLXkBm61jZOdrudkk2byFn4NvbKSvyfegrdxAlIZDJiocXNSAqCs4nETxDakOqTiplKJV916cz4o8e4+egx1sVEU+zjQ0JCQouYfanN+vXrxcneFuBCM3StaZxMKSkYXnud8l278Bg1Cr/HHkXh73/WfVrKjKQgNBeR+AlCG2I0GrGdPm1ZptWy7rLuXPPfYSYfPcamDu3FSUXhkmAzmcj/bDn5y5cj9/Ul9P330A4a5OqwBKFFEIlfKzRx4kQ0Gs0F73fo0KFmiEZoSfR6PdLTJyutVitlcjnfdOpIXGIS1x45SkELrkvW2pYQL1UtfZxKt20jZ8FbWLKz8b5tKvo773Rpz19BaGlE4icIbUhtJzMBfurUkaFp6fTZvIUc/wD8Hn6oxdX6mzJliqtDEBrA1eNUVy9hU2oqOQveoiwhAbf+/Ql5eyGqdu1cGqsgtEQi8WuF1qxZU9MTWBDOVN/JzHZz5xCYlkbOgrcwZ2YSNP/FFjUTsnr1apcnFcKFuXKcausl/OtPPzHd2xv3P7Yh89UT/OYbuA8f3mo72AiCs4nETxDamAudzFQEBZH57HOk3XsfIW8vRN5Clu7y8/NdHYLQAK4ap3N762K306mggKHpGditVpR33EH4zBkt6suMILREIvEThDaovpOK7kOHEv7pJ6Q/8iipt99ByHvvooqKauYIBaFxanrrms0EVFYxPDWV4JISjuu82BkdTWxUJJEi6ROEC2pZm3wEQWgW6i5diPh8ORKNhtRp0yn/+29Xh8S4ceNcHYLQAK4aJ6PRiKaykquSU7jl0CE0Fgvfde7E99HRFCmV4sS6IDSQSPwE4RKlCAoifOkS1F26kDbzAQrXrnVpPGKpt3VwxTjZKirocOAgU/ftJ7qwkK0R4ay+rDvJHh6A6K0rCI0hEj9BuITJPDwIffcddOPHY3jlVQyvvobdbHZJLH/88YdLris0TnOOk91qpWjDBk7eMJ6Av3ZyKCiIFT0vZ6+vL5UWC1arVfTWFYRGEnv8BOESJ1EoCHj6KVTt25Pz+utUJZ8k+LXXkHt7uzo04RJW9tcuct99l6qjR/G46irCH5iJMTmZHaK3riBcFJH4CYIAgG78DSgjI8l86ilSb7uNkIULUbVv32zXj46ObrZrCU3n7HGqSkwk94NFlG3bhvqy7oQv/wxN9+4AxIaEiN66gnCRROInCEINt149iYhfQcbjT5AybTpB81/EY/jwZrl2XFxcs1xHuDjOGidTegbGjxdT/NMmFMHBBL32Kh4jR55Xj0/01hWEiyP2+AmCcJbqQx/usYPIfOJJ8hYvxn66A4gzLVq0yOnXEC5eY8fJYDCwbt06Pv30U9atW4fBYDjrdktuLobXXid5wgTKd+0mYNbTRK1dg+eoUaIIsyA4gZjxEwThPFKNhqDXXkO17DPyFi+m4sBBgl6aL/b9CY1SW6eNLVu2MHXqVAZ0707+8s8p+OorJEolvjPuRzd5sijALAhOJhI/QRBqJZFI0N85HXXXrqTPmsXBsdfz3+irUHfpIvZVCRd0XqeN0zRSKYdefgV9STFSmx3vW27GZ+pUZKdLswiC4Fwi8WuFJk6ciEajueD9Dh061AzRCG3dfquFdVGRXHX4MJ2+/Io/oyKZs3kzU2+7zaElNKZPn+6w5xKcp6HjdGanDZlMhgronplFr6wsVDYbeQMHMOC115CL+nuC0KxE4icIQp1qZm2kUlbHxDA0PYMhSScIKiriq2WfERMT47CZv127djF06FCHPJfQdAaDod5Tsw0dJ6PRiM1mQ2m10icnl8uzslBarRzU69kXFkaXfv0YLJI+QWh2IvFrhdasWUOvXr1cHYZwCThr1kahICEmmixPT65MPsmkfXvZu2oVVz/6qEOudfDgQZH4NdKFkrTGqm9PXvXsbkPHyU+rpV9GBpdlZKKw2fjP359dgQEUyeWo1WrRaUMQXEQkfoIg1Kl61gZAJpMhlUo54e9HhpuGa5JTCFv+OXlqNfq77kKiULg42ktLdZImNZtRV1Whttk4+PXXxA0ZQsewMGwVFWAH7KfGT6JSI3VzQ6rVItPpUAQFIvPxqTk5W9eePIVCQXx8fINnd60lJRSsWk2nlfGEFRZxyM+Pnf5+lCqVNc8nOm0IguuIxE8QhDrp9Xqk0lNVn6ynS7pYrVZKlEq+696NKVotys+WU/bXLoLmv4gyLKzJ1+rbt69DYm5L7HY7ltxczOkZmDPSMaWlYcnKpiwjA/O//3JnVRWKM0vtSCRw4CCZKhUyhQJkslM/A+yVlec9v0SlQhEUhKpjR46aTAQWFJCqUCBTKJDJZFit1po9egkJCYwfP77OcTIbcij4cjVFa9Zit1rxmTCe/E6d2P7996eWfEWnDUFoEUTiJwhCnWJjY9myZQsKhQKz2VyT/CkUCiQyGZ2feQbPnByynnue5Mk34XvffXjfPAWJvPFvLTExMY4Ov1WwWyyYsw2Y09MxpaWeSvLS0zGlp2FOzzgrYZP7+6MICiS3opJUbx2FEgnlajVVSiXlUikVAFotw8aMYfyNN559HZsNe0UF1rIyrPn5mLOysGRnY0pNo+roEXz2/8O4qiqsEgkZnp6k+vhwQudFnlSKzWbDaDQC549T1YmT5MevoGTjT0hUKnQ33oj3zVOQ+/riD0T36SM6bQhCCyISP0EQ6hQQEMDUqVOJj49HJpPV3h81IIDIr74k76PF5L7/PiU//0zA7OdRd+jQqGutXr2amTNnOumVuJbdZMKcmYnhwAGOb/sTa0YGnhUVeJuqsOfmgcVy6o5yOYqgIBShIbj17IVi7FiUoaEowsJQBAfX1Ljb/Omn/PXXX5hMJpRKJdLTyZnJZEIpk2EsLDwvBolUikSrRarVovD3R92p01m3//3NN+xev56AvDzaFRUzMDmZwTYbme7uHA8MwE+rBf5/nCr27yd/xQpKf/8DuZ8fvjNn4jV+PDJ37VnPKzptCELLIhI/QRDqFRsbe8H+qFKNBv/HHsVj1Ciy588n5ZZb0Y0fj/6+ey+Zos+2ykrMGRmY0tIwp6VjTk/DlJaOOS0Vc7YBq9mMyWzGSyqlSKUiS6PmqEZDl3HX03n4cBShYSgCAxo0W1rXEjyAVCpt0sGJ2CFD2Lh5MxkBAewNCEBhtdKuqIguxnyGJJ1A8+57ZB4+jNXNjdTp06n451+UUVEEznkBz7g4JKf38AmC0LKJxE8QhAtq6KyNpns3IlfGU7BmDcZPPqV40yb0d92FbuIEpBeoPdmQ2pSuZDOZsBgMmDOzsGRnYc7KxpyVhTk7C3NaOpYzWpFJNBqUYWEowkLxGHUVFV6eLP/pJ4xyOQUSSc2+O4VCwfaUFOZFRTVq+bO+JXhHHZwwy2Qc9fHhqI8PfhYL93t6UvzdBqRDrqDy8BH0992Lz/TpSGWyi76WIAjNRyR+giA4lESpxOfmm/G8+mqMH39C7vvvk//ZZ+gmT0Y36cY6ZwBdWcDZVl6OJS/v1J/cPCzGPCy5uVjOSO6secazHiP39UUeGIgiKAjNZZedSvRCw1CGhSLz9T2rz+y6detI9/SksrISmUxW58GJhmrQEnwjJSQk1MwiSiQSJBIJnhUV9DTk0C0/n0qZDO9rrmFKTAwlBYUYF39MWcJ2fO+7D+2A/o2+niAIriESP0EQnELu7U3ArKfxmXor+V+sIn/FCvJXrMB9+DA8R4/GrU+fs2YB169fz7hx4y76unaTCWtZGbaiIqzFxViLirAWFWMtKsRaVISt6NTPLEbj6WQvF3t5xVnPIVGpQKejSCGnRKVG1rEjUbf0Rd+xI4rgIOQBAUgbsbRZW1kcOLU8e+bBicZoyBJ8Y9TEaLfTrrycy7MNRBQUUCGT8U9gIIqr47j90UdZv34916+Mp3zHDvI+/ZT0mTNxGzAAv4ceRN2xY5OuLQhC8xGJnyAITqUICSHgqSfxveduCteuo3jjRjJ+2oRELkfVoQPKdu2QeXqSWl5GXnY2SKTYLWbsFgtYLNgtVuzW0383W7BbrdgqyrGXV2ArL8dWUXHqT3k59vLyU4+rhUSjQeblVfNH7u+HukuXUzN3fr7IfHyQ+/oh9/Nl5z//EL9y5f8XMq6sRLo9ganR7YhtQskaZ+zJA8cenPBXqemdmUXnrCx0JhNGNzd+jorksE6H3M2NuIgIADIyMpBIJGgHDcJt4EBKt24l7/0PSLnlVjyvvhrfGfejCAqqeV5HF5kWBOHiiMRPEIRmIdPp0N85HZ/p08j6+2+OrF0LJ07iuXs3ngoFts6dKNqxE2w2kMuQyBVI5PJThx1kspq/S+TyU0mcXo8iNBSpmwapmxtlFitJGekUm0y46fV06dcPv6goZF5eSL28GjxDZzAYiF+58qILGZ+pOfbkNYXdZqN8998UrVtHx99+I7SigkQfbzb7+JDh7g4SSb0xSiQSPIYPx/2KKyj67jvyPv6Ekv/9D++pt6KfPp0de/ZcsBOIIAjNSyR+rdDEiRMbtBH+0KFDzRCNIDTO9u3b/z8Z8PRAqvNCKpVy9cCBRL/2WpOe87xWY6WlSNPSTiUYjawPeFabOgfsxwPn7Mm7GJb8fIq//57Cb9djTktDGRWF/yMPc8TXl9/Wrau34PLo0aPPez6JXI5uwgQ8r7761JL+8s8p+P57/nB3x+zp6bAEWhCEiycSP0EQmk19bcF+/fVXBgwY0OhkwFGtxqo5Yz8eOH5PHjRuGdVuNlO2cyfFP/xA6dbfQSrFY+SVBL7wApqelyORSBgERPfo0eQYpW5u+N53H55jxrD/kUe4+r/DJHl58kdkJGVubhedQAuCcPFE4tcKrVmzhl69erk6DEFotPpm0zw8PJqUDDh6hs5Z+/HAsXvyzpvlrGUZ1W63U3X0KMU//kjxps1Y8/NRtW+P70MP4nXNNch0ukbHuHnz5gt2WVGGh/NPXBx5djuDTyZz24GD7AoLY29Q4EUn0IIgXByR+AmC0Gzqm02rvt2Rz9mUBKOl7sc704VmOdt5e6P6ew/FP/5IVWIiMh8fPOPi8Lz2WtQdG9dRpan0vr7s9vMj0c2N2OxsBqSkEJ2by+aIcErU6otKoAVBaDqR+AmC0Gzqm00zm81NSgYcPUPX0vbj1aa2WU5lZSXRGZl0LirCOOVmlG5uuA8diu8DD6AdOKBJ/ZNrExIS0qD7VSfQVrWarSEhHNbpGJ2cwi1HjrIvNJRBffs6JB5BEBpHJH6CIDSb+mbTioqKmjSb5owZOmfsx3Ok6llOtcVCp4JCOhiNhBQVYQfSdV4kXjmC6+bNQ+bh4fBrN7TW4rkJdIFSyRpvb/pkZDIwJ4eKRx+jYs4LaLp3d3iMgiDUTSR+giA0m/pm0zp16tSkxMpZM3SO3I9XzRE17cyGHKJPnCDo8GGCC4uQABmenvwaGcExTy/sHu7EDR7slKQPYNmyZQ3uslJXAq0rLib7xfmkTpuO95Sb8J05E6la7ZR4BUE4m0j8BEFoVnUlA2vWrHH4c7aUGTpo2GGM2tjtdkwnT1L621ZKt26l8r//CJZKSVQq2RYZwWFPT8oVCuDULKfCyfsQKyoqLnynM9SaQAcEEP7ZMgpWf0neRx9RmpBA0Ivz0XTr6sBIBUGojUj8BEFods6YTXPGczpKY0vO2KqqqNi7l7KEBEoTtmNOTUWi0eAeOwjvm6egjY3FeOAAh08nknXV3GvJJHI5PlNvRTs4luzZL5A6fTr66dPQT5+OpBHt8ARBaByR+AmC0CJMmTLF1SE4TUNKzlzbrx9lCdspS0igfM8e7JWVyAMC0A4ahPtjj+HWry9SlarmOV01y+nocVJFRRH+2TKMny3HuHQppdv+JOjFeaiiox16HUEQThGJnyAILUJiYiL9+vVzdRg1HNljtraSMxqTicD8fCLLygk7/A4ny8uRyOVoevbE99570A4ahDI6GolEUufzumKW0xnjJFEo8L3nbtyvGEzW7BdIuXUqvjNm4H3zFCQymUOvJQiXOpH4CYLQIuzevbvFJH5N3Y9XF71ej8ZqJbyggIjSMsJKSvCpqAC7nUI3N6p69CB46q1o+/ZFqtU64RU5jjPHSd25MxEr48n78CNy332X0t9/J2jeXBQNLCEjCMKFicRPEAThDI5oAWe32TAlp1Dx7z9U/vsv3fbsoV1iEgBFSiWpHh7sDAwgy9sbs1bLvHnz8GgF+/Kag1Stxv+xR3EfOoSsOXM4edNN+D/2OF7jrq939lMQhIYRiZ8gCC1Ct27dLurxjlqabUoLOIvRSOXhI1Qe/o/Kg4eoOPAvtqJikEhQtW+P14CBFA0dxlf//kOxUtkii0I31MWOU0O59e5N5JdfkbtwIYaXXqJ061YCZz+P0Wpt0ae3BaGlE4lfKzRx4kQ0Gs0F73fo0KFmiEYQGq+2JO1ilg8duTRbbws4q5WS5GRKt/1J5eHDVB05TOXhI1hycgCQenqi7twZ78k3oenRA3W3bsjcTy3dBgDhDtw36CrNuRwvc9cS+MJs3IcNJXv+Sxy5fhw/BgWS5OPjkCV4QbgUicRPEIRmVVeSFhMTwxNPPNHo53PE0uyZ9Ho9UokEN7MZ/9JSfKuq8Ckrx6e8HN/KStz+3kPGF6uQenmi7tQZzzFjUHfuhLpzZ+TBwS3uMIajLVu2jJkzZzbrNd2HDEH7wfscvPseRh8+wiG9D7+FhVEpkzV5nAXhUiUSv1ZozZo19OrVy9VhCEKj1ZekFRYWYjAYGv3h3ZSlWTi1D8+Sl4c5PR1zWhqm9HTMael0T0oi7OhRFFYr2O1YpVKMajUFWjfS/HwZc++9BPbtizwwUOw5a0Y7Dh1iU8cORGdkMiItjfDSUjZHtSNV61bvOAuCcDaR+AmC0GzqS9Kqb2/sh/d5S7MSCWqbDXlFJZ6Vldj/2kWByYTZYMBiyMFiMGA2ZGPJzQOLpeZ55IGBKEND8ejRg6KuXdh88CB5Gg2FSiWS00u+U6dOJUwsKbqE0WjEZrdzyFdPto83VyUlMfHIEXYHBPB3ZARGo9HVIQpCqyASP0EQms25SZpMIsHTbEZSWYn9+HFMcjllwSHYLRbsFjN2sxksllP/bTJjq6rEVlqGrbwMW3k5ttIyLjt2jJCMDFQmExqLBY3FggTAbgeJBPnRY+T+8Qdyf3/kAf4ogoPQXH75qb8HBqIIDUURHHxWr9hAIKwN7MdzhuZe5q2m1+tr9lsWKhSs69KFy9MzGJieTrvSEir69nVJXILQ2ojETxCEZqPX6QgrLiY0N5ew0jJ8KyqQ2u1gt3Og5+V0+3Y96d//UPuDJRIkajUyrRaJ1g2pmxsyrTu+/n6k5uWR66ahVCqlQi6nQi7HpFZj1mh4+PnnCWzfvtHLsm1hP54zbNq0ibi4uGa/bmxsLFu2bEGhUGA2m7FarfwV4E+qt464pBPELF9OvrsH3rfcjOR0gigIwvlE4icIgtOZs7MpXLeOrmvW0s5goFSpJNXdnYN6HwrUaiwqFcrISK587DH8goJALkeiUCBRKJHIZaf+Xk8Hh+xaDoxUL80GdejQjK+07UtKSnLJdQMCApg6dSrx8fHIZLKacS5yc0P55hv47N13qujzn9sImjsXRVCQS+IUhJZOJH712LhxI7t27WLPnj3s2bOHrKwsANLS0ggNDa3zcZGRkaSkpNR5e//+/dm5c6fD4xWElqbyv/8wfrac0q1bkarV6K67joLQUFb+vhWb3X5Wkhbj7U1w795Nuo6r+tYKzavecR427FTLt7nzSJ58E/5PP4XnmDHiAI4gnEMkfvW4+eabKSoqavLjJ0yYgLu7+3k/jxbNx4U2rvLYMYyLP6b0999RRkTg/9STeI0Zg1SrJQCIunLEeR/eOadr4TWVWJptHkOGDHHp9esbZ7e+fYn8cjU5b7xJ9gtzKPvjDwKeeQaZTte8QQpCCyYSv3qMHz+e9u3b06tXL3r16oW/v3+jHr9gwQIiIyOdE5wgtEBmQw55H7xP8cafUISEEDT/RTxGjz5vmTYgIICrr76a4uJiysrKKC4uBuD48eOuCFtoJFeOk0wmQ6vV4unpWWshe5mHB0HzX0Q75AoMr75K8uSbCJzzAtpBg1wQrSC0PCLxq8eyZctcHYIgtAo2k4mCL1ZhXLoUqVpNwLPP4DV2LBKFotb7FxUVkZmZedbPKioqkMvFW1JL5+pxslgsVFVVkZ+fT3BwMF5eXrXez3PUKDQ9epA970XSH3wI3Y034vfIw2ed3haES5F4lxUE4YLq6oNrt9sp27aNnLcWYs7KwnvyZPT33I3Mw6PO56qoqDgv6ZPL5Wi1WpH4tQKuHifLGbUXMzMzUalUqOtI5hT+/oR+8D6FX39D7nvvUbbrL4JenI+mW9fmClcQWhzxLutEn332Gfn5+VgsFoKDgxk6dKjL98cIQmPV1WLttmuuIfyXXynbtg23/v0JeXshqnbtLvh81cu6AJ6engQGBiKTyWqSSqFlc/U4Wa1WsrOza36PioqK6kz8ACQSCd6TJ6Ht34+s2S+QOn06+rvuQj99GhLxRUO4BInfeid68cUXz/tZ3759WbVqFTExMS6ISBAap9YWa3Y7vfILUDz3PGUBAQS/+Qbuw4c3+PRkWVlZzd+rkz5AJH2thKvHSSaTERgYWJP4nfn7VB9lZCThy5ZiXPYZxiVLKPvzT4Lmv0iBWi1OgwuXFJH4OcE111zD4MGD6devHyEhIWRnZ7Nt2zaef/55du/ezbBhw9i7d2+jD4sIQnM7t8Wa3mRieGISISUl/BcYiNu0O+g4YkSjntNqtQKnlndlZxz6cPVMktAwLWGcZDIZcrkci8VS8/vUEBKFAt9770E7aBBZL7zA8Rtv5NfAIA4E+GOz22tms6dOnUqsaM0ntFFtMvF76qmn2LBhQ6Mft2TJEgYPHnzR11+0aNFZ/x0ZGUlkZCTXXHMNvXv3Jjk5mVdeeYV33nmnSc8/YcKEWk+zNdTMmTNd1nZJaF2qW6xJbTb65ebSLz2dUoWSbzp0wOCrp38DZ1saojEf4ILrtIVx0nTvhts7b7Pjzju5IimJ0NxctkREUKZUoFAoiI+PJyYmRsz8CW1Sm0z8MjMzOXr0aKMfV1pa6oRo/p+Pjw+PPPIIjzzyCN9//32TE7+1a9fSq1cvxwYnCLXQ6/X4l5cz7Nhx/Coq2B8cxPagYKqwo5ZKXT7zIwhNtX3PHn5v146jWi1xKancfvgwv0RGcNTLC5lMRkJCgqgLKbRJbbKh4cqVK7Hb7Y3+0xz9Jzt37gxAenq6068lCBfDVllJz2PHuPHfA0glElZ16shvQUFUYUehUCCVSh26HKYTRXZbhbYyTtWz2cleXqzqcRmZXp5ck5hE3Mlk5CYTRqPR1SEKglO0yRm/lqz6zcSjnnIXguBq5Xv3kf3SfCyZWdhuGMc6gwELoDynD64jl8IsFgtKpdJhzyc4R1sZJ71ej1R6au6jTCplY/v2dPDKYVhyCuH/HqDksstcHKEgOEebnPFryb788ksA+vXr5+JIBOF81tIyDK++RtrddyPX6YhYtYre8+czZ/584uLi6N+/P3FxccybN8/hm9+dvdWiJUlOTkYikZz1x83NjeDgYK688kpeeOEFkpKSmj2urVu3nheXWq2mXbt23H333SQnJ1NaWsrcuXPPu199f+bOnQvAHXfccd5tcrmcwMBArr/+erZt29ZsrzU2NhapVIpCocBqtWIymzno7c2qHpdRolYR9cUqct59F5vJ1GwxCUJzEDN+Dvbdd98RGhpK73OazZeUlDB79uyaQyePPfaYK8IThDqVbvsTw6uvYi0pxv+pp9DdOBHJ6RkR0QfXOaKjo7n11lsBqKqqIicnh127djF//nxeeeUVnnrqKV5++eUGl8pxlN69e3PttdcCUFhYyNatW1myZAlr165l48aNDBs27LzH7N+/n++++46hQ4eed/u5/33nnXcSGhoKnCroffjwYTZu3MgPP/zAt99+y9ixY53xss4SEBDA1KlTiY+PRyaT1dSorJRK8Xz9dfxPJpP34YeUb99B4PwXUXfo4PSYBKE5iMSvHvPnz+fHH3887+djx46tWero1asXH374Yc1tv/32G++++y7h4eF0794dnU5HZmYm+/fvp6CgALlczoIFCxg5cmSzvQ5BqI+loICcBQso2bQZ7aCBBDz7LIqgoGaPQ6VSNfs1XS0mJqZmNuxMf/75J1OnTuXVV19FJpMxf/78Zo2rT58+Z8Vlt9u5/fbbiY+P57333mPVqlXnJXPLly/nu+++Y9iwYbW+pjPddTPEpPYAAChCSURBVNddDBgw4KyfffPNN0yaNIkFCxY0S+IHp2b9YmJiaq/jd8UVuA3oT/bsF0i97XZ8Z9yP9y23nNd3WhBaG5H41SMpKYm//vrrvJ/v27ev5u/nVowfN24cpaWl7N27l7///pv8/HyUSiXh4eFMnjyZGTNm0L17d6fHLggXYrfbKdm8hZw338RutxE0/0U8rr662WeXqtXVc/VSNHjwYDZt2kSPHj144403uOeeewgLC6u5/bvvvuO9995j7969VFRUEBMTwx133MGjjz56Vm3Ept7/XBKJhJkzZxIfH88///zj0NdarfpwXV5enlOevy71zWarO3QgPH4FeR99RO5771O67U+C5s1FERzcrDEKgiOJPX71WL58+QVPAm/duvWsxwwbNowlS5awd+9esrOzMZlMlJaW8t9///HRRx+JpE9oEczZ2WQ8/AhZzz2HW//+RK1Zg+eYMS5L+gBycnJcdu2WqGPHjkyaNAmTycT69etrfv7MM88wbtw4jh49yvjx45kxYwYajYYnn3ySm2666bznaez9L8RZdfy2bNkC0OJKVUmVSvwffpiwjxdjzsrk5KTJFHz1NXabzdWhCUKTiBk/QbiE2G02Cr9ZQ+4H7yNz9yBk4Vu4Dx3q6rCEOgwbNoz4+Hh2794NwM8//8xrr73G6NGjWbt2LVqtFjg1eztjxgwWL17M2rVrmTBhQpPuXxe73c5HH30EQM+ePS/6dS1ZsoRNmzYBp/b4HT16lI0bN9KrVy9eeeWVi35+Z3Dr3Zuor74i9/0PyHnjDUp+/pnAF2ajDA93dWiC0Cgi8ROENshgMJy3b0lXVkb2/PlU/nsA3cSJ+D74IDJ3ratDPYutshJTcrKrw6iXMjIS6TlbPJwl+PSSYvXy5wcffADAJ598UpPEwaml2Ndee42PP/6Y1atX1yRyjb1/tb///rtmn15RURFbt25l//79+Pj48Oijj17061q6dOl5P/P19WXKlCk1r7klkmq1BMx6Go9RI8l+cT7JN03B97778L7lZrH3T2g1ROInCG1MQkIC8fHx2Gw2bDYbciD/0yUMzM3FLTycsCWf4uaAWRtH8/X1xXTsGCm33OrqUOoV8cVK1J06ueTaO3fuRKvVsmzZslpv12g0HDlypMn3r7Znzx727NkDgFKpJCQkhLvvvpvnnnvurL2GTbVjx46awx0mk4nk5GTeffddnnzySXbs2MHatWsv+hrO5Na7N5FffUneog/Jfe89Sv73PwLnvIAqOrrWL12i9ZvQkojErxWaOHFig3r1Hjp0qBmiEVoSg8FAfHw8ZrMZs9lMUGkpV6Wk4l1VxV+hoYx5523cWujSVFlZGdrISCK+WOnqUOqljIxstmtlZmYC4OfnB0B+fj4Wi4V58+bV+ZiyM/onN/b+1e69914WL15c6/1LSkocWoBeqVTSoUMHFi1axD///MO6detISEhweJ1IR5Oq1fg//tip2b95L5Jy8y2UjBrJ0pwcLFBTHmbLli1MnTq1xb8e4dIhEj9BaEMSEhKw2WxQWcmIrCwuN+Rg0LqxslNHSn180P39N+NbaOJXUVGBh4eHy2bTWqLqw2N9+/YFwNPTE4lE0uCTr429f0NUj5Mz9O/fn4SEBHbv3t1qEiXNZZcRseoLUt95B2n8SsZrNGwJDyP79NK6QqEgPj6emJgYMfMntAgi8WuF1qxZ0+JOvgktg9FoJMxoZFjSCTRWKwlRkewPCKDKbEZps4n+o63IsWPH+Prrr1GpVNxwww3AqcTop59+4vjx47Rv3/6Cz9HY+7taQUEBcGq2rDWRqlTsbd+ePZd1Z+ix49x85CgHAgP4MySECrMZmUxGQkKCKIIutAiinIsgtBHm7Gy6//w/rjtylHy1mvju3dgXGIjl9IeoVCpFr9e7OMq6nXn44FKXkJDA6NGjqaqqYtasWYSEhADw0EMPATB9+vRak/js7GwOHz5c89+NvX9DOGuckpOTWbduHQBDhgxxyjWcyWg0YnBz44vOnfgzIoLOObncduAAMQUF2KxW8aVLaDHEjJ8gtHJ2s5mC1V+S98kneKvVbOjUkSNeXpgtFjjdZ1ShUCCVSlv08tml2LkjMTGx5vSsyWSqadl24MABZDIZzz//PHPmzKm5f1xcHLNnz2b+/PnExMQQFxdHREQERqORxMREtm3bxksvvUTnzp2bdP+GcMQ4nVnOxWw2k5yczPr16ykvL+eee+6hT58+F32N5qbX65FKpdglEvYE+JOo92HoiROMTTpBcmEh0sGDXR2iIAAi8ROEVq1i/34Mr75G1YkT6CbdiO/9Mxj0z36S4uORyeU1G8ylUilTp05t0XuM8vPz8ff3d3UYzSopKanm4IVGo0Gn09GpUydmz57N7bffTnR09HmPefHFFxkyZAjvvfcev/zyC4WFhej1eqKiopg7dy633HLLRd3/QhwxTmeWc5FIJOh0Ovr168edd95Z07u4tYmNjWXL/7V372FR13nDx98zwwwzwMwwIAfREJDKzFY00jZNETXNzDzgrZuWpuluPXrnba6WZZu17daa2dpT3rcF5XHLXC1N8/ZR00oqz62aEnhESeQMMxzm+PyBsKGgiMjMMJ/XdXGh8zvw4foO8/38vsetW1Gr1dhsNgoVCtbHxdGpzEzSmTOEvP8+hRoNpjFjUPhJ1SvcR+FyuVzuDkI0zoEDB7j77rvZv3+/jPHzcY7iYvIWv0PJ55+jvfNOIuY+X2dShCcvKZGZmYndbsfPz6/OuLOLFy/6XOLnjTylnBp6H7nT5Usp1T50pYwmfv9+ij/9FP/bbyfiuTnoZBcnn+euOl0eO4TwIi6nk5ING8hf/A4uh4OI55/DOGLEFYvHXm3/UU+lVMqQY28g5dSwXr16ER8fX/9D14D+GB56iNy//pWzE5/A8PBQwqZPx8+Dx92K1kkSPyG8RMWhQ+S++SZVx45jGPIgYTNmtKpKo02bNu4OQTSClNPVXe2hS9flTjosX0bJZ5+R9+67mHd8Regffo9p9GgUanULRyp8lTy6CeHhbLm55LzwAmcnPwlAdFoqbV99tVUlffDvpTyEZ5NyujEKlYrgUaOIW7cOw4MPkrfobU6PG49lzx53hyZ8hLT4CeEB6huTF2Y0UrRyJQUffoQyIIDIl+ZhePhhFK20q81ms7k7BNEIUk7NQxUcfGmoxnAu/m0B5556Gv2AAYT91wzUkZHuDk+0YpL4CeFmVwwIVyjIWr2aBwuL0FgsmMaOJXTKk6iCgtwdqhCimWk7deKW1A8o+/JLLv7975walULIhAmEPDYepU7n0RO1hHeSxE8IN7p8b93w8nL6Zp/jFrOZUyYTie+9S/jdd7s7zBZhMBjcHYJoBCmn5qdQKDAMGUJgn74UpH5AYVoaJevWUTigP2mZmThcLtn7VzSb1tlnJISXqNlbV2c289Dp04w/dpwgu5318R35ovMdfH/mjLtDFEK0EFVQIOHPPEPsP9dCp06oP/yIUQcOEn7xIlarlcrKSmw2GytWrCA3N9fd4QovJS1+XiglJQWdTnfN844ePdoC0YgbUXz+PPedPMVdFy5Q5efHzo5xHA0L88m9dUtLS9Fqte4OQ1yDlNPNp27Xjh/73M/B3Av89sRJUjKzOBMczNft25MHsvevuCGS+AnhBs6qKoo/+YTElauwVpTzQ2QkB6Pa4tJocDgcgOfvrSuEuHkKCgr4JTCQj2+/jc5mM/edPcv4I0f4KTSEfZe23ROiKSTx80Jr166VnTu8lMtqpWTjRgpS07Dn56MfPJjF57IpVSiqZ0t60d66zU0t65h5BSmnllGz9y8KBRnBwZwymej8ywXuycnhjoOHKNfqsI8ahV9IiLtDFV5GxvgJ0QJcdjslG7/g1KgUcv/6OrqEBGI/XUPsK/NJmTwZtVqNVqtFo9Gg1WpRq9Uev7duczOZTO4OQTSClFPL6NWrF0qlErVajcPhoMJuZ39YG5Z3S2BfdDRtjh/j5LBHyHvvPRxlZe4OV3gRafET4iZyOZ2Ubf1/FCxdivXMGYL69aPdWwvx/9Xeolfd5smH5Ofny64QXkDKqWVERETw2GOPsWLFClQqVe2sXpRK7nxhLrd26ULhsuUUrVpN8adrCXn8cUz/MRplYKC7QxceThI/IW4Cl9OJeccOCt7/gKqsLALvu4+2f34VbefO9Z7vjXvrNjen0+nuEEQjSDm1nGs9FIb953RMvxtLQWoaBf/93xSuWE7IuHEEjxkj636KBkniJ0QTNLSoqstup2zrVgrSPsR66hQBPXoQnZaKrmtXd4csfExmZiazZ8/m+++/Jzc3F4PBQHFxsbvDEtfpWg+FfmFhRDw3h5CJEyhctpyC9z+gcMVKTI8+iul3Y1Hp9S0YrfAGkvgJcZ2u2GlDqWTbli08ER9P6Le7sZ0/T2Dv3kS+NA/db37j7nC9RogPDVI/ffo0sbGxdV7z8/MjPDycXr16MXv2bBITE5t8f4fDwfDhw8nKyuKxxx6jffv2zbYEiy+VkzdRR0YSMWc2IRMnUrRiOYUffUTRypWYHv0dprFjUQUHuztE4SEk8RPiOly+04afw0HnggJ65F5EvTsdV//+dPjbG2g7dXJ3qF6nqqoKPz/f+kjq2LEj48ePB8BisbB//34+/fRTPvvsM7Zt20afPn2adN9Tp07x008/MWXKFJYuXdqcIftkOXkTdUQ44bNmETJxIoUrVtZ+BY8YjmncOAqVSp8fT+zr5K9XiOtQs9OGurycnvn5dL2Yh8Zu53iIiUMdOtMzqS8dJelrEovFQqCPDUyPj4/n5ZdfrvPa66+/zvPPP8+8efPYtWtXk+6bk5MDQFRU1I2GeAVfLCdv5NemDeH/NYOQiRMo/mQNRWs+IW/1P/jJYOBAuyjydDrZAs5HyXIuQlyH8p8zScrMZMrhI3S/kEtGeBjLu3Xjy9hYCrRaWVTVg+Tm5rJu3Tref/991q1b5zVbXE2ePBmA/fv313ndarXy1ltv0b17dwIDA9Hr9dx///1s2LChznkxMTH07dsXgPnz56NQKFAoFFckmMI3+JlMtPnD79F/+CE727WjbUkJYw4eYujRo4RdvIjNapUt4HyMtPgJcQ0ul4vyvXspWrWahJ07KVEqSY9qy9GICOz+/tU7bTgcstPGDWrMNoSNVd84TG9r2fh1d2pVVRWDBw9m586dJCQkMHnyZGw2G5s2beKRRx7hnXfeYdq0aQDMmDGDQ4cOsWzZMvr27UtSUhJA7fcb1ZzlJFpO+v79/Ng2kr1GA51KS0n85QKjM7PI0+k40i6K9J07GTFmjLvDFC1AEj8hGuAwWyjdtIniTz/FeuoU/vHxGGbP5r1dO6lyOHx+p43m1lzdh5ePw6yhVqtZsWIF8fHxHj2m6YMPPgCgd+/eta+98sor7Ny5k3nz5tW24gGUlZWRnJzMs88+y8iRI4mKimLGjBns3LmTZcuWkZSU1OwtfdLN650KCgqqH4SUSrLCwzkZEUG7oiLuysmhX9YJXAvfIi8vn+DRKag9+O9D3DhJ/IS4TNWJExSv+ZSSzZtwVVnR90si4rk56O6+G4VCwbiYDlcsqqpUKn1up43mlp+fT3h4+A3fp2Ycps1mQ6VSoVKpcFxK1D1tc/usrKzaxKxmcsdXX31FREQECxYsAKrXzVuyZAkdO3ask/QB6PV6XnrpJYYNG8a6detqW/1upuYqJ9GyareAo3rWNyoVp/V6TsTHE+FyMUyno/iTTyhcvhx9cjKmsWPQdu1a5/0mWgdJ/LxQSkpKo7pbjh492gLRtA4umw3zrl0UffopFfv2owoNJWTcOIwjRlzx9Cs7bXi2mpYNAJVKVaeyczqdHjUO88SJE8yfP7/Oa5GRkXzzzTfEx8cDkJGRQVFREVFRUVecC5CXlwfA8ePHb37Awmv16tWLrVu3olarsdls1ckf1S3hZWo1t82fT1hQECVfbKLok485O/lJNB07EjxyBIYhQ1AZDG7+DURzkcRP+ISGFly2ZmdTsmEDpRu/wJ6Xh65rV9q+9hr65H4oNJoG7yc7bXiuK1o2fvXd08ZhDho0iC1btgDVCdyyZcuYM2cOw4YNY8+ePQQFBVFYWAhUP8hd7WHOYrG0SMzCOzW0BdzlvRWmMf9B8OgUyvfspWT9ei4uepu8xe+g798f48gR6BISpBXQy0ni54XWrl1L9+7d3R2G17h8oL/a5eL0ipX091OhPXkKZVAQhsGDMI4chfb229wdrs9qru7Dq7VsePI4zLCwMGbNmkVJSQl//vOfefHFF3n77bcxXGppGTVqFGvXrnVzlM1XTqLlNba3QqFUEnhvTwLv7Ym9oICSjRspWf8ZpZs3o4mNxThyBMaHHkJlNDb4UC08lyR+olWrHehvtRJaXEKXgnw6FRahcTo5bzTSZdYs2o8YjrKZdjUQTVdSUoLRaLzh+zS2ZcNTzZ07l7S0NN577z1mzJjBHXfcgcFgYN++fdhsNtRqtVvja65yEu5xvb0VfqGhhE6cSMjjj1O+dx8l69eT//fF5L/zfym/oxNflpk5YzRgB6+cPe+LJPETrdoPmzfT9Ww2t+XmElpZiVmj4WBkJEdCTFQZjVT6a4iWpM8jVFVVNdu9vHkcpk6nY86cOTzzzDO8+uqrpKam8tRTT/HGG28wa9Ys3nzzzSuSvyNHjhAeHt4irXHNWU7CeyiUSgJ79iCwZw/shYWc/8fHZC9bxoMWC+V+fmSYTPwUGkKh0egVs+d9mSR+otWxFxVh3r6d0i3/S+yePbRzucgyGtgdG8N5kwmHy4XVakXjYQP9RfPy5nGYU6dO5Y033mD58uXMnTuX+fPnc+DAARYvXsymTZvo06cP4eHhnD9/nsOHD/Pjjz/y3XffSTesaBF+ISHsbxvJloSu6AsLubOoiNsLCumWl0ehvz+ZERH8sHkzw554wt2hinpI4idaBWd5OeZduyj9cguWH34Al4vAnj3IHTGC9eeyKatZ2sPl8tiB/r4uKCjI3SF4DK1Wy/PPP8/06dOZP38+y5cv58svvyQ1NZXly5fzz3/+k6qqKiIiIujcuTN/+MMfuOuuu1okNiknAf+ePZ8XEEB6cDA/xMURVVTEbbm53H3uHP6L3ubs7nT0AwYQ1K8f6gh5KPEUkvgJr+W0WDB/uxvzV19h/uYbXJWV6Lp2JXzmTPQDB+AXEoI6N5e1f/oTavCqgf6+6Nc7VbR2MTExuFyuq54zbdq0OuvyqVQqpk6dytSpU695/6SkpGvev6l8qZxEwxpcFzAggG/Uaka2b4+puIS8RYu4+Oab6H7zG/QDBxCU3F+SQDeTv2DhkRqaKWYvKsK8axfmr3ZS/sMPuGw2/Dt1InTyZAyDB6G+bFN6bx/o70uKi4ulq9ILSDkJuPrseadazV1PP01ERASOsjLMO3dRtm0beW//nYtvLkTX9TcEDRiAPjkZdWRknfvKLOGbTxI/4XEuX37FYLPxy4cf0Vvrj/b0GXC50HXrRpv/nI6+Xz/Ubdte9X7ePNBfCCE8UWMfqlV6PcaHh2J8eGh1Erjra8q2bSN/8TvkLXwL/06dCLq/N4H338+BwkJWrFrl1XtsewNJ/IRHyc3NZdWyZYQXFnFLYSGxJSW0qajAqVRyNjiYrtP+D1EPP4xfSMh13debB/r7CpVK5e4QRCNIOYka1/tQrdLrMQ59COPQh3CYzVi+3Y35m68p+mQNeUvfR+ly0cdoJNOg54xej1Wl8po9tr2JJH7CI9hyL2JJT+fUJx/zxE/HUNvtlKvVnDUFs7ddFCf0epSBgdj0ekZeZ9InvENNV5HwbFJO4tea+lCturRwvmHwIFx2O1sWLyb3yy1EFxTQOS8Ph0JBtsHAKb2eC2Ft2P3tt4wcNeom/Aa+RxI/4RYOs4WKgwcp37eP8u+/pyorC5RKnCYTP0a1JTMoiGKjEeWlLgRZfqX1Ky8vd3cIohGknERzU/j5cU6v54cO0VjbRhLmcBBXUkKHgkL6nD+P6tw5nCdO8MuhHwno2ZOAe+6RCSI3QBI/0SyuNSDXWVlJxaEfKd+7l/J9+6g8dgwcDvwiIghITCRk0iQC7+3J0e3b2b9lC5WVlaicTlAoZPkVHyFdiN5BykncDL+eJVyo0VASGcm+sDAUNhuxVVaS2oRSdfIkpZs3A6CJiyOgxz0EJCaiS0jAz2Sq974yWeRKkvh5oZSUFHQ63TXPu9qG7s3p8skYSqWSbzd+wdju3YmxWqn417+oOn4cl92OKiSEgMREjMOGEdDjHtTt29fZ8Ntb91kVQgjRdA1+9vv7cz4oiNtefrl2ZYfyPXsp37MH865dFH/8CQCaDh3QJSSg69YNXbcE1O3akZ6efkXdJJNFJPETNyg3N5d/fPQRoSUlhJaWEmW2EGWxYLBaYc8eiqNvIah7dwxDhhCQeDeauLg6id7lZPkV32WxWNwdgmgEKSdxMzT2s9/PZMIw6AEMgx4AwHbhAhWHDlFx8BAVhw5RsmEDuFwQHEye1UpCQAA5Wn9yAwKpUPvJZBEk8fNKa9eupXv37jd0j6Y2fzvKyqjKzKIq4ziVPx3jl+/SmfzLBRQuFw6lkvzAAE6YTJwLDKAwJJT7Hxl23QN/ZfmV1k2lUmG327Hb7Tgcjtquw5puHuHZPKGcHA4HdrsdkK7n1qQpn/3qyEjUgwdjGDwYAEdpKRX/OsyBf6zG79Ahuv2Sw70OJwClGg0XAgIoMBo59NEykic8jl9ExFUbIy7XGrqOJfHzQjt27KBdu3ZNfrPV1zV7efO3o6SEqpMnsZ48hfXUSapOnsJ68iT2vDwAFBoN/rfeSmF4BP/SBZDjr6HUYAA/v39PxlD7NXkyhiy/0noFBgZSVVUFwIULF4iMjESlUjVq+IJwP3eXk8Ph4MKFC7X/DwwMdGM0ornd6Ge/ymAgqHcvMo79xA9VVVirqghzOoksLyesrIywsjLisrPxX7WKk2vWoAwKwr9jR/xvjUcT1xFNbAya6Gj8wsNRXPaQ05i60xtI4ueF0tPTycrKatKbLTc3lxXLl6OorCSgvAK91UpwVRUmu53cuXPJDI+AvDycZWXVFyiVqG9pj39cRwzDHsY/Lg5NbBz+cbEo1Gr2r1vH8ZrJGC4XKqdTJmOIqzIYDBQWFgJQWlpKaWkpfn5+VFZWUllZ6eboxLW4u5xqWvpqGI1GN0UiPFntZBGFonqyiE7HT8HBOBwOtP7+DL33XpJvvZWqrBNUZWVRcehHSj77HNel95dCo0Hdrh3q9u3R3NKeCoORb7b+L8FKFSUKBVVqP1wKhVd2HUvi543Ky1FbLKz74ANig4JoExyMy2bDabHgMJtxmi04zWacFnP1/0tKsRcUYC/Ip/DESaYUFaFyOOBS87YTKNNoKAvQcTEoiE5DhqBuF4V/bCzqDh1QajQNhiKTMcT10ul0REVFkZOTU/ua3W6nuLiYNm3auDEy0RieVE5RUVFotVp3hyE80FXrJpWKnkOHEhQRQVCfPrXXuGw2bL/8gjU7G1v2OazZZ7Fln8OyOx3zqVMMtlqrxw8qFLgAi0ZNmZ+aSq2WrNlzUCQkoDLoUer1qAzG6n8HBqLQaK78UmtwuukBShK/Bly8eJEtW7awZcsW9u7dS3Z2NkqlkujoaB544AFmzpxJTExMg9dbrVYWLVrE6tWrycrKQqPR0LVrV6ZNm0ZKSsoNxTb650zuOJtd/STz+ATK6ts0XaFAGRiIMigIlcGAX5tQNB06kIuCny/mUqpQYA0IwKLVUqpWU2WzodFo6NmzJ72emNjoWGQyhmgKo9GIv78/JSUlWCwWHA4HmZmZRF62b6fwPO4uJ5VKRWBgIEajUZI+0aCm1E0KtRpNdDSa6Ogrjr3/P//D4d270VjKMbmcBNnsBFRVEVBZgd7hQJmTg7msFGdpGY7SUmjEQudn3bQmpiR+DZg5cyarVq1CqVTSpUsXhg0bhsViYe/evbzzzjukpaWxfv16Bg4ceMW15eXlDBw4kPT0dIKDgxk8eDBms5kdO3awa9cunn32Wd58880mx7a7QzQZej1KjYZOnTszdPhwFGo1yoCA6kQvKAhFQEC9A1a/W7eOIzVdsyoVKpXqhrtmZTKGaAqtVlun4l6yZAmTJk1yY0SiMaSchLdozropNCyMSq2WYqDgV3Wnw+FAq9UyePBgfntpbKLL5cJVXo6jtBRneTkumw2X1Vr75bz0Pffnn2H69Ob9pRtBEr8GhISEMH/+fCZPnky7du1qXzebzUyZMoWPP/6YsWPHkpWVhemyhSPnzp1Leno6d911Fzt27KjtFtm/fz9JSUksXLiQpKQkhg4d2qTYzur1lAYHo9Vq6ZKQQMB1zPC9WV2zMhlDCCGEp2muuul66k6FQoEiMBDlNSYeBblpyIT75+V7qMWLF/PSSy/VSfoAgoKCSE1NRa/XU1hYyKZNm+ocLyoqYsmSJUD1k/Gvx8LcfffdzJkzB4DXXnutybE5HI4mJ2o1zd9qtRqtVotGo0Gr1aJWq6VrVgghhKhHa6o7pcWvCQICArj99tvZt28f2dnZdY5t3rwZq9VKdHR0vUnZo48+yrx58/j+++/JyckhKirqun++v7//Db3ZpGtWeKIdO3a4OwTRCFJOwle1lrpTEr8msNlsnD59GoC2bdvWOXbw4EEAEhMT6702Li6OkJAQCgsLOXToUJMSv/vuu++GnzCka1Z4mvj4eHeHIBpBykn4stZQd0pXbxOkpqaSn5+PTqfjwQcfrHPs1KlTAETXMyuoRvv27euce72Sk5O97glDiGu52t+M8BxSTkJ4N2nxu06HDx/mj3/8IwDz5s27IgEru7Tw8dVWkw8KCgKqF6+9HhUVFQAcO3bsuq4Twhvk5uZy4MABd4chrkHKSYjmUVOX19TtLaVVJn6zZ89mw4YN133dBx98QO/evRs8fu7cOR5++GHMZjPDhg3jueeeu5Ewr1tN9/L48eNb9OcK0VJWr17t7hBEI0g5CdF8Tp8+3aKbHbTKxC8nJ4eMjIzrvs5sNjd47MKFC/Tv358zZ84waNAg1qxZU+86eXq9HgCLxXLNn2MwGK4rvkGDBrFy5UpiYmLcvl+mEEIIIZquoqKC06dPM2jQoBb9ua0y8Vu5ciUrV65stvtdvHiR5ORkfv75ZwYMGMBnn32Gv79/vefW7OZx9uzZBu937ty5Ouc2Vps2bRg3btx1XSOEEEIIz+SObU1lcsc15OXlkZyczLFjx+jfvz8bNmy46jZB3S8tprxv3756j588ebJ2g/pu3bo1f8BCCCGEEA2QxO8q8vPzSU5O5ujRo/Tv35+NGzdes4t1yJAhaDQazp49y+7du684XjM25t57723SUi5CCCGEEE0liV8DCgsL6d+/P0eOHGHAgAGNSvoATCYTTz31FABPP/00BQUFtccOHDjAG2+8AcALL7xwcwIXQgghhGiAwuVyudwdhCcaOXIk69evR6FQMHr06AaTvuHDhzN8+PA6r5WXlzNgwAC+++47TCYTycnJWCwWtm/fjs1mY+bMmSxcuLAFfgshhBBCiH9rlZM7mkPNODyXy8WaNWsaPC8mJuaKxC8gIICdO3fy1ltvsWrVKjZv3oxGo+G3v/0t06ZNY/To0TczdCGEEEKIekmLnxBCCCGEj5AxfkIIIYQQPkISPw+3c+dOpkyZQufOnTGZTKjVakJDQ+nRowfTpk1j27ZtSKOt8BYxMTEoFAoUCgXPPPPMVc9dsGBB7bl+fjIqxR1qyuujjz6q93heXh6JiYkoFAruvPNOzp8/37IBCuGF3F2vS1evh8rPz2fcuHFs3boVgHbt2pGQkIDRaKSkpIQjR45w5swZoHo9QNk7U3iDmJiY2vdtaGgoOTk5aDSaes+94447OH78OAAqlQq73d5icYpqNeX14YcfMnHixDrHsrOzGThwIBkZGfTo0YPNmzcTGhrqnkCF8AKeUq/LY7QHKi4upnfv3mRkZNCpUyfee+89+vXrd8V5R44cYdGiRXz88cduiFKIpktMTGTfvn18/vnn9U52Sk9P5/jx49xzzz3s3bvXDRGKq8nIyGDgwIFkZ2czYMAA1q9fT1BQkLvDEsJjeVK9Ll29Hmj69OlkZGQQFxdHenp6vW8OgC5dupCamspXX33VwhEKcWMmTZoEQFpaWr3HU1NT65wnPMeBAwe4//77yc7OZtSoUWzatEmSPiGuwZPqdUn8PMyJEydqd/dYtGgRJpPpmtf06NHjZoclRLO66667SExMZOvWrVeMCzObzaxZs4b27dvzwAMPuClCUZ+vv/6afv36kZeXx5QpU1izZk2DXfVCiGqeVq9L4udhvvjiC5xOJyaTiaFDh7o7HCFumkmTJuF0Oq+YOLBmzRrMZjMTJkxAqZSPKE/xxRdfMGjQIEpLS5kzZw5Lly6V8hGiETytXpe/Wg+zf/9+ALp37y4fqqJVe/TRR9HpdFckfmlpaSgUCunm9SBr165lxIgRVFZWsmDBAl5//XV3hySE1/C0el0md3iY/Px8AMLCwuo9/uOPP7Jo0aIrXn/yySfp3bv3TY1NiOZkNBoZOXIkq1atYteuXfTt25eMjAx2795NUlIScXFxnD592t1hCmDTpk0AjBgxglmzZrk5GiG8i6fV65L4eZns7GyWLVt2xetJSUmS+AmvM2nSJFatWkVaWhp9+/atnewhrX2epU+fPnz99desX7+ev/zlL8ydO9fdIQnRarR0ve7+NkdRR5s2bYDqhVHrM3ToUFwuV+1X//79WzI8IZpVv379iI2NZe3atRQVFbF8+XIMBgMpKSnuDk38yhNPPMG7776LQqHghRde4NVXX3V3SEJ4DU+r1yXx8zDdu3cHqpdMcDqdbo5GiJtLoVAwceJEysvLmTBhAhcuXGDs2LHodDp3hyYu8/TTT7NkyRIUCgUvvfQS8+fPd3dIQngFT6vXJfHzMEOHDkWpVFJUVMTmzZvdHY4QN93EiRNRKpVs3LgRkG5eT/b73/+epUuXolAoePnll/nTn/7k7pCE8HieVq9L4udh4uPjGTNmDAAzZ86kpKTEzREJcXNFR0fzyCOPEBoayr333kvPnj3dHZK4iieffJLU1FSUSiWvvPIKL774ortDEsKjeVq9LomfB3r33XeJj48nMzOT++67j127dtV73unTpzl37lwLRydE81u3bh35+fl899137g5FNMITTzxBWloaSqWS1157jeeff97dIQnh0TypXpdZvR7IZDKxe/duHn30UbZv305SUhLt27cnISGB4OBgKioqyMzM5PDhw7hcrtpdEIQQoqVMmDABlUrFxIkTef3113E4HPztb39zd1hCeCRPqtcl8fNQ4eHhbNu2je3bt7N69Wp2797N119/TXl5OXq9ntjYWKZOnUpKSgrJyckesSikEMK3jB8/HpVKxWOPPcaCBQtwOBwsXLjQ3WEJ4ZE8pV5XuFwu1025sxBCCCGE8CjSTCSEEEII4SMk8RNCCCGE8BGS+AkhhBBC+AhJ/IQQQgghfIQkfkIIIYQQPkISPyGEEEIIHyGJnxBCCCGEj5DETwghhBDCR0jiJ4QQQgjhIyTxE0IIIYTwEZL4CSGEEEL4CEn8hBBCCCF8hCR+QgghhBA+QhI/IYQQQggfIYmfEEIIIYSPkMRPCCGEEMJHSOInhBBCCOEjJPETQgghhPARkvgJIYQQQvgISfyEEEIIIXyEJH5CCCGEED5CEj8hhBBCCB8hiZ8QQgghhI/4//o8hFDm56eLAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x560 with 1 Axes>"
       ]
@@ -180,15 +220,15 @@
     "        \"kline_type\":\"abacus\",\n",
     "        \"kpath\":[\n",
     "                [0, 0, 0, 110],\n",
-    "                [0.5, 0, 0, 84],\n",
-    "                [0.3333333, 0.3333333, 0, 95],\n",
+    "                [0.5, 0, 0, 62],\n",
+    "                [0.3333333, 0.3333333, 0, 127],\n",
     "                [0, 0, 0, 1]\n",
     "                ],\n",
     "        \"klabels\":[\"G\", \"M\", \"K\", \"G\"],\n",
     "        \"emin\":-20,\n",
-    "        \"emax\": 20,\n",
+    "        \"emax\": 15,\n",
     "        \"nel_atom\":{\"C\": 4},\n",
-    "        \"ref_band\": \"/personal/zjj/transiesta_cal/graphene_formal/dptb_train_input/data/data_graphene_siesta2/eigs.npy\"\n",
+    "        \"ref_band\": \"./train/data/kpath.0/eigenvalues.npy\"\n",
     "\n",
     "       }\n",
     "\n",
@@ -204,6 +244,1192 @@
     "               emax = task_options['emax'],\n",
     "               ref_band = task_options['ref_band'],)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cca83808",
+   "metadata": {},
+   "source": [
+    "- 说明dpnegf的结构文件需要两个Principal Layer倒置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "558867cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    Numba is available and JIT functions are compiled.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    from dpnegf.runner.NEGF import NEGF\n",
+    "except ImportError as e:\n",
+    "    raise ImportError(\"dpnegf not found. Please install firstly.\") from e\n",
+    "\n",
+    "import json\n",
+    "negf_input_file =  \"negf.json\" \n",
+    "structure =  \"stru_negf.xyz\" \n",
+    "\n",
+    "with open(negf_input_file, \"r\") as f:\n",
+    "    negf_json = json.load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ceffb8de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gamma point calculation\n",
+    "negf_json['task_options'][\"stru_options\"]['kmesh'] = [1,1,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3530f93e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.05, -10, 10)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Energy step and range for transmission calculation\n",
+    "negf_json['task_options']['espacing'], negf_json['task_options']['emin'], negf_json['task_options']['emax']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4322d59f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'gamma_center': False,\n",
+       " 'time_reversal_symmetry': True,\n",
+       " 'nel_atom': {'C': 4},\n",
+       " 'kmesh': [1, 1, 1],\n",
+       " 'pbc': [False, True, False],\n",
+       " 'device': {'id': '32-64', 'sort': True},\n",
+       " 'lead_L': {'id': '0-32',\n",
+       "  'voltage': 0.0,\n",
+       "  'kmesh_lead_Ef': [1, 50, 20],\n",
+       "  'useBloch': False},\n",
+       " 'lead_R': {'id': '64-96',\n",
+       "  'voltage': 0.0,\n",
+       "  'kmesh_lead_Ef': [1, 50, 20],\n",
+       "  'useBloch': False}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Structural information for device and electrodes\n",
+    "negf_json['task_options'][\"stru_options\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eafa1b12",
+   "metadata": {},
+   "source": [
+    "- 提示：自能计算对于非自洽NEGF计算几乎可认为是最为关键的环节\n",
+    "- Princpal Layer的选择应当大于max[r_max, er_max, oer_max]\n",
+    "- 以下计算在cpu with 8 cores 需要约2min"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5c9f9038",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    ------ k-point for NEGF -----\n",
+      "DPNEGF INFO    Gamma Center: False\n",
+      "DPNEGF INFO    Time Reversal: True\n",
+      "DPNEGF INFO    k-points Num: 1\n",
+      "DPNEGF INFO    k-points: [[-0. -0. -0.]]\n",
+      "DPNEGF INFO    k-points weights: [1.]\n",
+      "DPNEGF INFO    --------------------------------\n",
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": {\n",
+      "                       \"C-C\": 4.99\n",
+      "                   },\n",
+      "                   \"er_max\": null,\n",
+      "                   \"oer_max\": 6.3\n",
+      "               }\n",
+      "DPNEGF INFO    The structure is sorted lexicographically in this version!\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732061e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    The coupling width of lead_L is 72.\n",
+      "DPNEGF WARNING WARNING, the lead's hamiltonian attained from diffferent methods have slight differences   RMSE = 0.0000006.\n",
+      "DPNEGF INFO    The coupling width of lead_R is 72.\n",
+      "DPNEGF WARNING WARNING, the lead's hamiltonian attained from diffferent methods have slight differences   RMSE = 0.0000019.\n",
+      "DPNEGF INFO    The Hamiltonian is block tridiagonalized into 3 subblocks.\n",
+      "DPNEGF INFO       the number of elements in subblocks: 61128\n",
+      "DPNEGF INFO                   occupation of subblocks: 73.69791666666666 %\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
+      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0\n",
+      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 4}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 4}\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 535 kpoints\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 57 iterations.\n",
+      "DPNEGF INFO    q_cal: 128.0000000009015, total_electrons: 128.0, diff q: 9.015082014229847e-10\n",
+      "DPNEGF INFO    Estimated E_fermi: -3.5829886198043823 based on the valence electrons setting nel_atom : {'C': 4} .\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 535 kpoints\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 57 iterations.\n",
+      "DPNEGF INFO    q_cal: 128.00000000090498, total_electrons: 128.0, diff q: 9.04975649973494e-10\n",
+      "DPNEGF INFO    Estimated E_fermi: -3.582987666130066 based on the valence electrons setting nel_atom : {'C': 4} .\n",
+      "DPNEGF INFO    -------------------------------------------------\n",
+      "DPNEGF INFO    Zero bias case detected.\n",
+      "DPNEGF INFO    Fermi level for lead_L: -3.5829886198043823\n",
+      "DPNEGF INFO    Fermi level for lead_R: -3.582987666130066\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -3.5829886198043823\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -3.582987666130066\n",
+      "DPNEGF INFO    Reference energy E_ref: -3.5829886198043823\n",
+      "DPNEGF INFO    =================================================\n",
+      "\n",
+      "DPNEGF INFO    ------Self-energy calculation------\n",
+      "DPNEGF INFO    Calculating self-energy and saving to ./negf_output/self_energy\n",
+      "DPNEGF INFO    Merging 400 tmp self energy files into ./negf_output/self_energy/self_energy_leadL.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Merging 400 tmp self energy files into ./negf_output/self_energy/self_energy_leadR.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    -----------------------------------\n",
+      "\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,-0.0000,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output = \"./negf_output\"  \n",
+    "if os.path.isdir(output):\n",
+    "    shutil.rmtree(output, ignore_errors=True)\n",
+    "os.makedirs(output, exist_ok=True)\n",
+    "\n",
+    "negf = NEGF(\n",
+    "    model=model,\n",
+    "    structure=structure,\n",
+    "    results_path=output,\n",
+    "    use_saved_se=False, # whether to use the saved self-energy  \n",
+    "    se_info_display=False,\n",
+    "    **negf_json['task_options']\n",
+    ")\n",
+    "   \n",
+    "negf.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4aef7816",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "results_path = os.path.join(output, 'negf.out.pth')\n",
+    "if os.path.exists(results_path) is False:\n",
+    "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
+    "negf_out = torch.load(results_path,weights_only=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f8801738",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'./negf_output'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8cb138b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg'])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negf_out.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8abf7fa4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0., -0., -0.]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negf_out['k']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b5fd4d8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('Transmission')\n",
+    "plt.title('Transmission vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f1f6ee6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Increase k-point sampling for better accuracy\n",
+    "negf_json['task_options'][\"stru_options\"]['kmesh'] = [1,20,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e9f643b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'task': 'negf',\n",
+       " 'scf': False,\n",
+       " 'block_tridiagonal': True,\n",
+       " 'ele_T': 300,\n",
+       " 'unit': 'eV',\n",
+       " 'stru_options': {'gamma_center': False,\n",
+       "  'time_reversal_symmetry': True,\n",
+       "  'nel_atom': {'C': 4},\n",
+       "  'kmesh': [1, 20, 1],\n",
+       "  'pbc': [False, True, False],\n",
+       "  'device': {'id': '32-64', 'sort': True},\n",
+       "  'lead_L': {'id': '0-32',\n",
+       "   'voltage': 0.0,\n",
+       "   'kmesh_lead_Ef': [1, 50, 20],\n",
+       "   'useBloch': False},\n",
+       "  'lead_R': {'id': '64-96',\n",
+       "   'voltage': 0.0,\n",
+       "   'kmesh_lead_Ef': [1, 50, 20],\n",
+       "   'useBloch': False}},\n",
+       " 'density_options': {'method': 'Fiori', 'integrate_way': 'direct'},\n",
+       " 'poisson_options': {'solver': 'fmm', 'err': 1e-05},\n",
+       " 'sgf_solver': 'Sancho-Rubio',\n",
+       " 'espacing': 0.05,\n",
+       " 'emin': -10,\n",
+       " 'emax': 10,\n",
+       " 'eta_lead': 1e-05,\n",
+       " 'eta_device': 0.0,\n",
+       " 'out_dos': True,\n",
+       " 'out_tc': True,\n",
+       " 'out_ldos': True,\n",
+       " 'out_current_nscf': False}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negf_json['task_options']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0bb746b",
+   "metadata": {},
+   "source": [
+    "For kmesh=[1,20,1], it takes ~23 mins in cpu8 from scratch. However, if you use the saved self-energies, it takes ~6 mins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e058e426",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    ------ k-point for NEGF -----\n",
+      "DPNEGF INFO    Gamma Center: False\n",
+      "DPNEGF INFO    Time Reversal: True\n",
+      "DPNEGF INFO    k-points Num: 10\n",
+      "DPNEGF INFO    --------------------------------\n",
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": {\n",
+      "                       \"C-C\": 4.99\n",
+      "                   },\n",
+      "                   \"er_max\": null,\n",
+      "                   \"oer_max\": 6.3\n",
+      "               }\n",
+      "DPNEGF INFO    The structure is sorted lexicographically in this version!\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732061e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    The coupling width of lead_L is 72.\n",
+      "DPNEGF WARNING WARNING, the lead's hamiltonian attained from diffferent methods have slight differences   RMSE = 0.0000006.\n",
+      "DPNEGF INFO    The coupling width of lead_R is 72.\n",
+      "DPNEGF WARNING WARNING, the lead's hamiltonian attained from diffferent methods have slight differences   RMSE = 0.0000019.\n",
+      "DPNEGF INFO    The Hamiltonian is block tridiagonalized into 3 subblocks.\n",
+      "DPNEGF INFO       the number of elements in subblocks: 61128\n",
+      "DPNEGF INFO                   occupation of subblocks: 73.69791666666666 %\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
+      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0\n",
+      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 4}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 4}\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 535 kpoints\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 57 iterations.\n",
+      "DPNEGF INFO    q_cal: 128.0000000009015, total_electrons: 128.0, diff q: 9.015082014229847e-10\n",
+      "DPNEGF INFO    Estimated E_fermi: -3.5829886198043823 based on the valence electrons setting nel_atom : {'C': 4} .\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 535 kpoints\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 57 iterations.\n",
+      "DPNEGF INFO    q_cal: 128.00000000090498, total_electrons: 128.0, diff q: 9.04975649973494e-10\n",
+      "DPNEGF INFO    Estimated E_fermi: -3.582987666130066 based on the valence electrons setting nel_atom : {'C': 4} .\n",
+      "DPNEGF INFO    -------------------------------------------------\n",
+      "DPNEGF INFO    Zero bias case detected.\n",
+      "DPNEGF INFO    Fermi level for lead_L: -3.5829886198043823\n",
+      "DPNEGF INFO    Fermi level for lead_R: -3.582987666130066\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -3.5829886198043823\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -3.582987666130066\n",
+      "DPNEGF INFO    Reference energy E_ref: -3.5829886198043823\n",
+      "DPNEGF INFO    =================================================\n",
+      "\n",
+      "DPNEGF INFO    ------Self-energy calculation------\n",
+      "DPNEGF INFO    Using saved self-energy from ./negf_output_k20/self_energy\n",
+      "DPNEGF INFO    Ensure the saved self-energy is consistent with the current calculation setting!\n",
+      "DPNEGF INFO    -----------------------------------\n",
+      "\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0250,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0750,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1250,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1750,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2250,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2750,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3250,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3750,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4250,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4750,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -10.000\n",
+      "DPNEGF INFO    computing green's function at e = -7.995\n",
+      "DPNEGF INFO    computing green's function at e = -5.990\n",
+      "DPNEGF INFO    computing green's function at e = -3.985\n",
+      "DPNEGF INFO    computing green's function at e = -1.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 2.030\n",
+      "DPNEGF INFO    computing green's function at e = 4.035\n",
+      "DPNEGF INFO    computing green's function at e = 6.040\n",
+      "DPNEGF INFO    computing green's function at e = 8.045\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output = \"./negf_output_k20\"\n",
+    "\n",
+    "negf = NEGF(\n",
+    "    model=model,\n",
+    "    structure=structure,\n",
+    "    results_path=output, \n",
+    "    use_saved_se=True,   # use the saved self-energy to speed up calculation\n",
+    "    **negf_json['task_options']\n",
+    ")\n",
+    "   \n",
+    "negf.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f98b0fe4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_path = os.path.join(output, 'negf.out.pth')\n",
+    "if os.path.exists(results_path) is False:\n",
+    "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
+    "negf_out = torch.load(results_path,weights_only=False)\n",
+    "\n",
+    "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('Transmission')\n",
+    "plt.title('Transmission vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "0d4308ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['k', 'wk', 'E', 'T_k', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "\n",
+    "output = 'negf_output_k100'\n",
+    "results_path = os.path.join(output, 'negf.out.pth')\n",
+    "if os.path.exists(results_path) is False:\n",
+    "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
+    "negf_out = torch.load(results_path,weights_only=False)\n",
+    "\n",
+    "negf_out.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c18e1cff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([600])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negf_out['T_avg'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "10b5dffb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "Erange = np.linspace(-15,15,int((15-(-15))/0.05))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0782f323",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sisl \n",
+    "tbt_k = sisl.get_sile(os.path.join(output, 'siesta.TBT.nc'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a99b01cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-39.99464308, -39.98464317, -39.97464326, ..., -10.02491054,\n",
+       "       -10.01491063, -10.00491072])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbt_k.E"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "43609801",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(Erange, negf_out['T_avg'], label='DeePTB-NEGF')\n",
+    "\n",
+    "Ef_TS = -24.878582000732422\n",
+    "plt.plot(tbt_k.E - Ef_TS, tbt_k.transmission(),'--', label=r'DFT-NEGF')\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('Transmission')\n",
+    "plt.title('Transmission vs Energy')\n",
+    "plt.grid()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1e0899",
+   "metadata": {},
+   "source": [
+    "For kmesh=[1,50,1], it takes ~40 mins in cpu8."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d2dc226c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    ------ k-point for NEGF -----\n",
+      "DPNEGF INFO    Gamma Center: False\n",
+      "DPNEGF INFO    Time Reversal: True\n",
+      "DPNEGF INFO    k-points Num: 25\n",
+      "DPNEGF INFO    --------------------------------\n",
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": {\n",
+      "                       \"C-C\": 4.99\n",
+      "                   },\n",
+      "                   \"er_max\": null,\n",
+      "                   \"oer_max\": 6.3\n",
+      "               }\n",
+      "DPNEGF INFO    The structure is sorted lexicographically in this version!\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
+      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732061e-10 (threshold: 1.000000e-05)\n",
+      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
+      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
+      "DPNEGF INFO    The coupling width of lead_L is 72.\n",
+      "DPNEGF WARNING WARNING, the lead's hamiltonian attained from diffferent methods have slight differences   RMSE = 0.0000006.\n",
+      "DPNEGF INFO    The coupling width of lead_R is 72.\n",
+      "DPNEGF WARNING WARNING, the lead's hamiltonian attained from diffferent methods have slight differences   RMSE = 0.0000019.\n",
+      "DPNEGF INFO    The Hamiltonian is block tridiagonalized into 3 subblocks.\n",
+      "DPNEGF INFO       the number of elements in subblocks: 61128\n",
+      "DPNEGF INFO                   occupation of subblocks: 73.69791666666666 %\n",
+      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
+      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
+      "DPNEGF INFO    ================================================================================\n",
+      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
+      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0\n",
+      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0\n",
+      "DPNEGF INFO    Number of electrons in lead_L: {'C': 4}\n",
+      "DPNEGF INFO    Number of electrons in lead_R: {'C': 4}\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 535 kpoints\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 57 iterations.\n",
+      "DPNEGF INFO    q_cal: 128.0000000009015, total_electrons: 128.0, diff q: 9.015082014229847e-10\n",
+      "DPNEGF INFO    Estimated E_fermi: -3.5829886198043823 based on the valence electrons setting nel_atom : {'C': 4} .\n",
+      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
+      "DPNEGF INFO    KPOINTS  kmesh sampling: 535 kpoints\n",
+      "DPNEGF INFO    Getting eigenvalues from the model.\n",
+      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
+      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 57 iterations.\n",
+      "DPNEGF INFO    q_cal: 128.00000000090498, total_electrons: 128.0, diff q: 9.04975649973494e-10\n",
+      "DPNEGF INFO    Estimated E_fermi: -3.582987666130066 based on the valence electrons setting nel_atom : {'C': 4} .\n",
+      "DPNEGF INFO    -------------------------------------------------\n",
+      "DPNEGF INFO    Zero bias case detected.\n",
+      "DPNEGF INFO    Fermi level for lead_L: -3.5829886198043823\n",
+      "DPNEGF INFO    Fermi level for lead_R: -3.582987666130066\n",
+      "DPNEGF INFO    Electrochemical potential for lead_L: -3.5829886198043823\n",
+      "DPNEGF INFO    Electrochemical potential for lead_R: -3.582987666130066\n",
+      "DPNEGF INFO    Reference energy E_ref: -3.5829886198043823\n",
+      "DPNEGF INFO    =================================================\n",
+      "\n",
+      "DPNEGF INFO    Merging 5000 tmp self energy files into ./negf_output_k50/self_energy/self_energy_leadL.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Merging 5000 tmp self energy files into ./negf_output_k50/self_energy/self_energy_leadR.h5\n",
+      "DPNEGF INFO    Merge complete.\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0100,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0300,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0500,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0700,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0900,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1100,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1300,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1500,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1700,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1900,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2100,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2300,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2500,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2700,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2900,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3100,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3300,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3500,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3700,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3900,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4100,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4300,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4500,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4700,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n",
+      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4900,-0.0000]\n",
+      "DPNEGF INFO    computing green's function at e = -5.000\n",
+      "DPNEGF INFO    computing green's function at e = -3.995\n",
+      "DPNEGF INFO    computing green's function at e = -2.990\n",
+      "DPNEGF INFO    computing green's function at e = -1.985\n",
+      "DPNEGF INFO    computing green's function at e = -0.980\n",
+      "DPNEGF INFO    computing green's function at e = 0.025\n",
+      "DPNEGF INFO    computing green's function at e = 1.030\n",
+      "DPNEGF INFO    computing green's function at e = 2.035\n",
+      "DPNEGF INFO    computing green's function at e = 3.040\n",
+      "DPNEGF INFO    computing green's function at e = 4.045\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "negf_json['task_options'][\"stru_options\"]['kmesh'] = [1,50,1]\n",
+    "negf_json['task_options']['emin'] = -5\n",
+    "negf_json['task_options']['emax'] = 5\n",
+    "\n",
+    "output = \"./negf_output_k50\"\n",
+    "if os.path.isdir(output):\n",
+    "    shutil.rmtree(output, ignore_errors=True)\n",
+    "os.makedirs(output)\n",
+    "\n",
+    "negf = NEGF(\n",
+    "    model=model,\n",
+    "    structure=structure,\n",
+    "    results_path=output,  \n",
+    "    **negf_json['task_options']\n",
+    ")\n",
+    "   \n",
+    "negf.compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "97a8217d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "results_path = os.path.join(output, 'negf.out.pth')\n",
+    "if os.path.exists(results_path) is False:\n",
+    "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
+    "negf_out = torch.load(results_path,weights_only=False)\n",
+    "\n",
+    "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
+    "plt.xlabel('Energy (eV)')\n",
+    "plt.ylabel('Transmission')\n",
+    "plt.title('Transmission vs Energy')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {

From d105cc568e38a0b8d7297756ac3009f967cb91c8 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 13:32:48 +0800
Subject: [PATCH 147/152] add update_atomicdata_options

---
 dpnegf/runner/NEGF.py | 49 ++++++++++++++++++++++++++++++++-----------
 1 file changed, 37 insertions(+), 12 deletions(-)

diff --git a/dpnegf/runner/NEGF.py b/dpnegf/runner/NEGF.py
index 3be097e..0c31a29 100644
--- a/dpnegf/runner/NEGF.py
+++ b/dpnegf/runner/NEGF.py
@@ -132,18 +132,8 @@ def __init__(self,
             else:
                 assert self.stru_options[lead_tag]["voltage"] == 0, f"{lead_tag} voltage should be 0 in non-scf calculation"
 
-        if AtomicData_options is None:
-            from dptb.utils.argcheck import get_cutoffs_from_model_options
-            # get the cutoffs from model options
-            r_max, er_max, oer_max  = get_cutoffs_from_model_options(model.model_options)
-            AtomicData_options = {'r_max': r_max, 'er_max': er_max, 'oer_max': oer_max}
-        else:
-            log.warning(msg="AtomicData_options is extracted from input file. " \
-                            "This may be not consistent with the model options. " \
-                            "Please be careful and check the cutoffs.")
-        formatted = json.dumps(AtomicData_options, indent=4)
-        indented = '\n'.join(' ' * 15 + line for line in formatted.splitlines())
-        log.info("The AtomicData_options is:\n%s", indented)
+        # preparing AtomicData_options, including cutoffs
+        AtomicData_options = self.update_atomicdata_options(model,AtomicData_options)
 
         # computing the hamiltonian
         self.negf_hamiltonian = NEGFHamiltonianInit(model=model,
@@ -833,6 +823,41 @@ def get_nel_atom_lead(self, struct_leads, charge:float=None):
                 nel_atom_lead[lead_tag] = {elem: nel_atom_lead[lead_tag][elem] + charge[lead_tag] for elem in nel_atom_lead[lead_tag]}
 
         return nel_atom_lead  
+
+    @staticmethod
+    def update_atomicdata_options(model,AtomicData_options: dict=None) -> dict:
+        """
+        Updates or initializes the AtomicData_options dictionary based on the provided model.
+
+        If AtomicData_options is not provided, it extracts cutoff values from the model's options
+        using `get_cutoffs_from_model_options` and constructs the dictionary. If AtomicData_options
+        is provided, a warning is logged to indicate potential inconsistency with the model options.
+
+        The function logs the resulting AtomicData_options in a formatted and indented manner.
+
+        Parameters:
+        ----------
+            model: The model object containing model_options used to extract cutoff values.
+            AtomicData_options (dict, optional): Dictionary of atomic data options. If None, it will be generated.
+
+        Returns:
+        -------
+            dict: The updated or initialized AtomicData_options dictionary.
+        """
+        if AtomicData_options is None:
+            from dptb.utils.argcheck import get_cutoffs_from_model_options
+            # get the cutoffs from model options
+            r_max, er_max, oer_max  = get_cutoffs_from_model_options(model.model_options)
+            AtomicData_options = {'r_max': r_max, 'er_max': er_max, 'oer_max': oer_max}
+        else:
+            log.warning(msg="AtomicData_options is extracted from NEGF input file. " \
+                            "This may be not consistent with the model options. " \
+                            "Please be careful and check the cutoffs.")
+        formatted = json.dumps(AtomicData_options, indent=4)
+        indented = '\n'.join(' ' * 15 + line for line in formatted.splitlines())
+        log.info("The AtomicData_options is:\n%s", indented)
+
+        return AtomicData_options
     
     def fermi_dirac(self, x) -> torch.Tensor:
         return 1 / (1 + torch.exp(x / self.kBT))

From 047880d53058fc9278ed1f7db0eb46ad58cd1568 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 15:12:13 +0800
Subject: [PATCH 148/152] add fig and PL instructions

---
 docs/hands_on/stru_negf_PL.png       | Bin 0 -> 117097 bytes
 docs/hands_on/tutorial2_2d_mat.ipynb | 103 +++++++++++++++++++++++----
 2 files changed, 91 insertions(+), 12 deletions(-)
 create mode 100644 docs/hands_on/stru_negf_PL.png

diff --git a/docs/hands_on/stru_negf_PL.png b/docs/hands_on/stru_negf_PL.png
new file mode 100644
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diff --git a/docs/hands_on/tutorial2_2d_mat.ipynb b/docs/hands_on/tutorial2_2d_mat.ipynb
index 5a3f771..4352ae5 100644
--- a/docs/hands_on/tutorial2_2d_mat.ipynb
+++ b/docs/hands_on/tutorial2_2d_mat.ipynb
@@ -44,9 +44,9 @@
    "metadata": {},
    "source": [
     "## 1. Quantum Transport in Graphene\n",
-    "Two-dimensional (2D) materials have attracted significant attention in condensed matter physics and nanoelectronics owing to their unique electronic, optical, and transport properties. Their atomically thin nature enables ultimate device scaling, while their diverse electronic structures—ranging from metallic to semiconducting to insulating—provide a versatile platform for next-generation applications.\n",
+    "Two-dimensional (2D) materials have attracted significant attention in condensed matter physics and nanoelectronics owing to their unique properties. \n",
     "\n",
-    "Graphene is the natural starting point for studying quantum transport in 2D materials. As the first experimentally realized 2D material, it combines a simple lattice structure with unique electronic features such as linear Dirac dispersion and high carrier mobility. These characteristics make graphene both historically significant and methodologically convenient as a benchmark system.\n",
+    "Graphene is the natural starting point for studying quantum transport in 2D materials. As the first experimentally realized 2D material, it combines a simple lattice structure with unique electronic features such as linear Dirac dispersion and high carrier mobility. \n",
     "\n",
     "In this section, we will use graphene to introduce the basic steps of quantum transport calculations, including the evaluation of electrode self-energies and transmission spectra. The same methodology will later be extended to hBN and MoS<sub>2</sub>."
    ]
@@ -61,7 +61,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "POSCAR\tband.json\tband_plot\tband_plot_api\textr_baseline\tnegf.json\tnegf_output\tnegf_output_k100\tnegf_output_k20\tnegf_output_k50\tstru_negf.xyz\tstruct.xyz\ttrain\n"
+      "POSCAR\tband.json\tband_plot\tband_plot_api\textra_baseline\tnegf.json\tnegf_output\tnegf_output_k100\tnegf_output_k20\tnegf_output_k50\tstru_negf.xyz\tstruct.xyz\ttrain\n"
      ]
     }
    ],
@@ -89,8 +89,14 @@
       "DPNEGF INFO    ================================================================================\n",
       "DPNEGF INFO                                      Version Info                                  \n",
       "DPNEGF INFO    --------------------------------------------------------------------------------\n",
-      "DPNEGF INFO    DPNEGF               : 0.1.1.dev143+4f25276\n",
-      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n",
+      "DPNEGF INFO    DPNEGF               : 0.1.1.dev148+0e0863a\n",
+      "DPNEGF INFO    DeePTB               : 2.1.2.dev53+5b97981\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "DPNEGF INFO    ================================================================================\n",
       "\n"
      ]
@@ -123,10 +129,18 @@
     "\n",
     "After switching to the `train` directory  and copying the input file here as `input.json` ,the training process can be started with:\n",
     "`dptb train input.json -i ../extra_baseline/grap_spd_model/sktb.json -o train_out`.\n",
+    "\n",
     "We recommend that users carefully examine the `input.json` file to understand the meaning of each parameter. \n",
-    "For 2D materials, the onsite mode should be set to `strain`, which removes the degeneracy of onsite energies for orbitals with the same angular momentum. It is an essential adjustment for 2D systems. For example, the onsite energies for  $p_x,p_y,p_z$ in graphene should not be identical considering the geometry.\n",
+    "For 2D materials, the onsite mode should be set to `strain`, which removes the degeneracy of onsite energies for orbitals with the same angular momentum. It is an essential adjustment for 2D systems. For example, the onsite energies for  $p_x,p_y,p_z$ in graphene should not be identical considering the geometry. Once training has converged, the resulting model can be loaded to plot and analyze the band structure.\n",
+    "\n",
+    "**SUMMARY:**\n",
     "\n",
-    "Once training has converged, the resulting model can be loaded to plot and analyze the band structure.\n"
+    "In `./extra_baseline`: \n",
+    "- `dptb esk c_spd.json -o grap_spd_model`\n",
+    "- `dptb config -m grap_spd_model/sktb.json -tr -sk ./`\n",
+    "\n",
+    "In `./train`:\n",
+    "- `dptb train input.json -i ../extra_baseline/grap_spd_model/sktb.json -o train_out`"
    ]
   },
   {
@@ -154,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "31407e92",
    "metadata": {},
    "outputs": [
@@ -164,7 +178,7 @@
        "Atoms(symbols='C2', pbc=True, cell=[[2.5039999485, 0.0, 0.0], [-1.2519999743, 2.1685275665, 0.0], [0.0, 0.0, 30.0]])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -179,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "1041ae1c",
    "metadata": {},
    "outputs": [
@@ -250,12 +264,20 @@
    "id": "cca83808",
    "metadata": {},
    "source": [
-    "- 说明dpnegf的结构文件需要两个Principal Layer倒置"
+    "### 1.2 Principal layer and self-energy calculation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4dea8b3",
+   "metadata": {},
+   "source": [
+    "Now we can pay attention to quantum transport simulaitons."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "558867cd",
    "metadata": {},
    "outputs": [
@@ -281,6 +303,63 @@
     "    negf_json = json.load(f)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6471bf68",
+   "metadata": {},
+   "source": [
+    "A critical component is `AtomicData_options`, which specifies the cutoff parameters for the model:\n",
+    "- `r_max`: the cutoff value for bond considering in TB model\n",
+    "- `er_max`: the cutoff value for environment correction, should set for nnsk+env correction model\n",
+    "- `oer_max`: the cutoff value for onsite correction in nnsk model, need to set in strain mode\n",
+    "\n",
+    "In other words, `AtomicData_options` determines the **locality** of the model, i.e., how far atomic interactions are considered in the calculation.\n",
+    "\n",
+    "The code would determine the `AtomicData_options` automatically with DeePTB v2.2. At the same time, it can be visiualized conviently\n",
+    "by `NEGF.update_atomicdata_options`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1c95296d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DPNEGF INFO    The AtomicData_options is:\n",
+      "               {\n",
+      "                   \"r_max\": {\n",
+      "                       \"C-C\": 4.99\n",
+      "                   },\n",
+      "                   \"er_max\": null,\n",
+      "                   \"oer_max\": 6.3\n",
+      "               }\n"
+     ]
+    }
+   ],
+   "source": [
+    "AtomicData_options = NEGF.update_atomicdata_options(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8890a34b",
+   "metadata": {},
+   "source": [
+    "The largest cutoff value determines the maximum interaction distance, which is critical for defining the electrode geometry in NEGF simulations.\n",
+    "\n",
+    "A key requirement of the self-energy algorithm implemented in `DPNEGF` is to define two **principal layers** (PL) for each electrode. The length of each PL must exceed the maximum interaction distance to ensure correct treatment of interactions. In other words, the PLs are restricted to **nearest-neighbor** interactions only.\n",
+    "\n",
+    "Additionally, the algorithm requires that the PLs within the same electrode be identical, **differing only by a translation** along the transport direction.\n",
+    "\n",
+    "<div style=\"text-align: center;\">\n",
+    "    <img src=\"./stru_negf_PL.png\" alt=\"jupyter\" style=\"width:80%;\"/>\n",
+    "</div>"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,

From 73e309a5944eee5cb0f5b1753cdc2d6c558ff498 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 16:22:29 +0800
Subject: [PATCH 149/152] graphene is done

---
 docs/hands_on/tutorial2_2d_mat.ipynb | 747 ++++-----------------------
 1 file changed, 109 insertions(+), 638 deletions(-)

diff --git a/docs/hands_on/tutorial2_2d_mat.ipynb b/docs/hands_on/tutorial2_2d_mat.ipynb
index 4352ae5..d8b089f 100644
--- a/docs/hands_on/tutorial2_2d_mat.ipynb
+++ b/docs/hands_on/tutorial2_2d_mat.ipynb
@@ -264,7 +264,7 @@
    "id": "cca83808",
    "metadata": {},
    "source": [
-    "### 1.2 Principal layer and self-energy calculation"
+    "### 1.2 Principal layers in leads"
    ]
   },
   {
@@ -349,53 +349,82 @@
    "id": "8890a34b",
    "metadata": {},
    "source": [
-    "The largest cutoff value determines the maximum interaction distance, which is critical for defining the electrode geometry in NEGF simulations.\n",
+    "The largest cutoff value determines the **maximum interaction distance**, which is critical for defining the lead geometry in NEGF simulations.\n",
     "\n",
-    "A key requirement of the self-energy algorithm implemented in `DPNEGF` is to define two **principal layers** (PL) for each electrode. The length of each PL must exceed the maximum interaction distance to ensure correct treatment of interactions. In other words, the PLs are restricted to **nearest-neighbor** interactions only.\n",
+    "In `DPNEGF`, a key requirement of the self-energy algorithm is to define two **principal layers (PLs)** for each lead. Each PL must be longer than the maximum interaction distance to ensure proper treatment of interactions. That is, the PLs only include **nearest-neighbor interactions**.\n",
     "\n",
-    "Additionally, the algorithm requires that the PLs within the same electrode be identical, **differing only by a translation** along the transport direction.\n",
+    "Additionally, the algorithm requires that:  \n",
+    "\n",
+    "1. The PLs within the same lead be **identical, differing only by a translation** along the transport direction.  \n",
+    "2. In the input structure file, the atomic coordinates of the **left lead** should be ordered with the 1st PL first, followed by the 2nd PL. See `stru_negf.xyz` for reference.\n",
+    "\n",
+    "The following figure illustrates the device/lead region and principal layers in the lead:\n",
     "\n",
     "<div style=\"text-align: center;\">\n",
-    "    <img src=\"./stru_negf_PL.png\" alt=\"jupyter\" style=\"width:80%;\"/>\n",
-    "</div>"
+    "    <img src=\"./stru_negf_PL.png\" alt=\"Geometry region in DPNEGF and Principal Layers in lead\" style=\"width:75%;\"/>\n",
+    "</div>\n",
+    "\n",
+    "In this case, the length of PL should be larger than 6.3 $\\AA$. However, too long PL is unnecessary for the computing cost. Considering both the translation requirement and nearest-neighbor interaction, we set the length of PL as 8.67 $\\AA$.\n",
+    "\n",
+    "Now we can strat the NEGF calculation!"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "ceffb8de",
+   "cell_type": "markdown",
+   "id": "02d1f0b0",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Gamma point calculation\n",
-    "negf_json['task_options'][\"stru_options\"]['kmesh'] = [1,1,1]"
+    "### 1.3 NEGF calculation for graphene"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "975c31d7",
+   "metadata": {},
+   "source": [
+    "For NEGF calculation on systems with leads having periodicity along the transverse direction, it's natural to calculate the $k$-resolved transmission $T(k,E)$ and sum up for the total transmission $T(E)$:\n",
+    "$$\n",
+    "T(E) = \\sum_k w_k T(k,E)\n",
+    "$$\n",
+    "where $w_k$ is the weight for the specified $k$.\n",
+    "\n",
+    "Firstly, we only consider the Gamma point calculation for graphene."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "3530f93e",
+   "execution_count": 17,
+   "id": "ceffb8de",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(0.05, -10, 10)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Energy range: -10 to 10 eV, step: 0.05 eV\n"
+     ]
     }
    ],
    "source": [
+    "# Gamma point calculation\n",
+    "negf_json['task_options'][\"stru_options\"]['kmesh'] = [1,1,1]\n",
     "# Energy step and range for transmission calculation\n",
-    "negf_json['task_options']['espacing'], negf_json['task_options']['emin'], negf_json['task_options']['emax']"
+    "print(f\"Energy range: {negf_json['task_options']['emin']} to {negf_json['task_options']['emax']} eV, step: {negf_json['task_options']['espacing']} eV\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ff34393",
+   "metadata": {},
+   "source": [
+    "Here `kmesh` specify  the $k$-point mesh for transmission calculaiton.\n",
+    "`k_mesh_lead_Ef` is the $k$-point mesh for lead Fermi level determination.\n",
+    "For leads with long PLs, a sparse `kmesh_lead_Ef` is enough."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "id": "4322d59f",
    "metadata": {},
    "outputs": [
@@ -418,7 +447,7 @@
        "  'useBloch': False}}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -433,14 +462,12 @@
    "id": "eafa1b12",
    "metadata": {},
    "source": [
-    "- 提示：自能计算对于非自洽NEGF计算几乎可认为是最为关键的环节\n",
-    "- Princpal Layer的选择应当大于max[r_max, er_max, oer_max]\n",
-    "- 以下计算在cpu with 8 cores 需要约2min"
+    "Now we can run the NEGF calculation ( ~2 mins in a 8-core CPU ):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "id": "5c9f9038",
    "metadata": {},
    "outputs": [
@@ -554,9 +581,17 @@
     "negf.compute()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e22ee1b1",
+   "metadata": {},
+   "source": [
+    "Read the results through the dict `negf_out`:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "id": "4aef7816",
    "metadata": {},
    "outputs": [],
@@ -571,28 +606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "f8801738",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'./negf_output'"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "id": "8cb138b4",
    "metadata": {},
    "outputs": [
@@ -602,7 +616,7 @@
        "dict_keys(['k', 'wk', 'uni_grid', 'DOS', 'T_k', 'LDOS', 'T_avg'])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -613,34 +627,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "8abf7fa4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-0., -0., -0.]])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "negf_out['k']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 58,
    "id": "b5fd4d8f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -652,72 +645,28 @@
    "source": [
     "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
     "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Transmission')\n",
-    "plt.title('Transmission vs Energy')\n",
+    "plt.title('Gamma-Point Transmission')\n",
     "plt.grid()\n",
     "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "f1f6ee6b",
+   "cell_type": "markdown",
+   "id": "7c006fad",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Increase k-point sampling for better accuracy\n",
-    "negf_json['task_options'][\"stru_options\"]['kmesh'] = [1,20,1]"
+    "Furthermore, we increase the $k$-point mesh for transmission."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "e9f643b3",
+   "execution_count": 27,
+   "id": "f1f6ee6b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'task': 'negf',\n",
-       " 'scf': False,\n",
-       " 'block_tridiagonal': True,\n",
-       " 'ele_T': 300,\n",
-       " 'unit': 'eV',\n",
-       " 'stru_options': {'gamma_center': False,\n",
-       "  'time_reversal_symmetry': True,\n",
-       "  'nel_atom': {'C': 4},\n",
-       "  'kmesh': [1, 20, 1],\n",
-       "  'pbc': [False, True, False],\n",
-       "  'device': {'id': '32-64', 'sort': True},\n",
-       "  'lead_L': {'id': '0-32',\n",
-       "   'voltage': 0.0,\n",
-       "   'kmesh_lead_Ef': [1, 50, 20],\n",
-       "   'useBloch': False},\n",
-       "  'lead_R': {'id': '64-96',\n",
-       "   'voltage': 0.0,\n",
-       "   'kmesh_lead_Ef': [1, 50, 20],\n",
-       "   'useBloch': False}},\n",
-       " 'density_options': {'method': 'Fiori', 'integrate_way': 'direct'},\n",
-       " 'poisson_options': {'solver': 'fmm', 'err': 1e-05},\n",
-       " 'sgf_solver': 'Sancho-Rubio',\n",
-       " 'espacing': 0.05,\n",
-       " 'emin': -10,\n",
-       " 'emax': 10,\n",
-       " 'eta_lead': 1e-05,\n",
-       " 'eta_device': 0.0,\n",
-       " 'out_dos': True,\n",
-       " 'out_tc': True,\n",
-       " 'out_ldos': True,\n",
-       " 'out_current_nscf': False}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "negf_json['task_options']"
+    "# Increase k-point sampling for better accuracy\n",
+    "negf_json['task_options'][\"stru_options\"]['kmesh'] = [1,20,1]"
    ]
   },
   {
@@ -725,12 +674,15 @@
    "id": "d0bb746b",
    "metadata": {},
    "source": [
-    "For kmesh=[1,20,1], it takes ~23 mins in cpu8 from scratch. However, if you use the saved self-energies, it takes ~6 mins."
+    "For $k$-point mesh [1,20,1], the computation from scratch takes ~23 mins in a 8-core CPU.\n",
+    "However, if directly loading the saved self-energies, it takes ~6 mins. \n",
+    "\n",
+    "We have prepared the self-energies in `negf_out_k20/self_energy` and you can just run the cell with `use_saved_se=True`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "id": "e058e426",
    "metadata": {},
    "outputs": [
@@ -936,13 +888,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 59,
    "id": "f98b0fe4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -952,6 +904,7 @@
     }
    ],
    "source": [
+    "output = \"./negf_output_k20\"\n",
     "results_path = os.path.join(output, 'negf.out.pth')\n",
     "if os.path.exists(results_path) is False:\n",
     "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
@@ -959,116 +912,57 @@
     "\n",
     "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
     "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Transmission')\n",
-    "plt.title('Transmission vs Energy')\n",
+    "plt.title('Total Transmission kmesh=[1,20,1]')\n",
     "plt.grid()\n",
     "plt.show()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0d4308ff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['k', 'wk', 'E', 'T_k', 'T_avg', 'BIAS_POTENTIAL_NSCF', 'CURRENT_NSCF'])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "\n",
-    "output = 'negf_output_k100'\n",
-    "results_path = os.path.join(output, 'negf.out.pth')\n",
-    "if os.path.exists(results_path) is False:\n",
-    "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
-    "negf_out = torch.load(results_path,weights_only=False)\n",
-    "\n",
-    "negf_out.keys()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c18e1cff",
+   "cell_type": "markdown",
+   "id": "2099a5b5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([600])"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "negf_out['T_avg'].shape"
+    "If we further increase the $k$-point mesh density, we can get a smoother transmission curve for graphene. Considering the time, we directly plot the total transmission corresponding to $k$-point mesh [1,100,1], and compare it with DFT-NEGF (TranSIESTA + TBtrans). "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "10b5dffb",
+   "execution_count": 51,
+   "id": "0d4308ff",
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "Erange = np.linspace(-15,15,int((15-(-15))/0.05))"
+    "import matplotlib.pyplot as plt\n",
+    "output = 'negf_output_k100'\n",
+    "results_path = os.path.join(output, 'negf.out.pth')\n",
+    "if os.path.exists(results_path) is False:\n",
+    "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
+    "negf_out = torch.load(results_path,weights_only=False)\n",
+    "Erange_dpnegf = np.linspace(-15,15,int((15-(-15))/0.05))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "0782f323",
+   "execution_count": 52,
+   "id": "bc38754a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sisl \n",
-    "tbt_k = sisl.get_sile(os.path.join(output, 'siesta.TBT.nc'))"
+    "tbt_trans_E = np.load(os.path.join(output, 'trans_tbt_E.npy'))\n",
+    "tbt_trans = np.load(os.path.join(output, 'trans_tbt_Tavg.npy'))\n",
+    "Ef_TS = -24.878582000732422"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "a99b01cf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-39.99464308, -39.98464317, -39.97464326, ..., -10.02491054,\n",
-       "       -10.01491063, -10.00491072])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tbt_k.E"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "43609801",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1078,437 +972,14 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "plt.plot(Erange, negf_out['T_avg'], label='DeePTB-NEGF')\n",
-    "\n",
-    "Ef_TS = -24.878582000732422\n",
-    "plt.plot(tbt_k.E - Ef_TS, tbt_k.transmission(),'--', label=r'DFT-NEGF')\n",
+    "plt.plot(Erange_dpnegf, negf_out['T_avg'], label='DeePTB-NEGF')\n",
+    "plt.plot(tbt_trans_E - Ef_TS, tbt_trans,'--', label=r'DFT-NEGF')\n",
     "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Transmission')\n",
-    "plt.title('Transmission vs Energy')\n",
+    "plt.title('Total Transmission')\n",
     "plt.grid()\n",
     "plt.legend()\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9b1e0899",
-   "metadata": {},
-   "source": [
-    "For kmesh=[1,50,1], it takes ~40 mins in cpu8."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d2dc226c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "DPNEGF INFO    ------ k-point for NEGF -----\n",
-      "DPNEGF INFO    Gamma Center: False\n",
-      "DPNEGF INFO    Time Reversal: True\n",
-      "DPNEGF INFO    k-points Num: 25\n",
-      "DPNEGF INFO    --------------------------------\n",
-      "DPNEGF INFO    The AtomicData_options is:\n",
-      "               {\n",
-      "                   \"r_max\": {\n",
-      "                       \"C-C\": 4.99\n",
-      "                   },\n",
-      "                   \"er_max\": null,\n",
-      "                   \"oer_max\": 6.3\n",
-      "               }\n",
-      "DPNEGF INFO    The structure is sorted lexicographically in this version!\n",
-      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732051e-10 (threshold: 1.000000e-05)\n",
-      "DPNEGF INFO    Lead principal layers translational equivalence error (on average): 1.732061e-10 (threshold: 1.000000e-05)\n",
-      "/opt/mamba/envs/dpnegf-dev/lib/python3.10/site-packages/torch/nested/__init__.py:107: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. (Triggered internally at ../aten/src/ATen/NestedTensorImpl.cpp:178.)\n",
-      "  return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None)\n",
-      "DPNEGF INFO    The coupling width of lead_L is 72.\n",
-      "DPNEGF WARNING WARNING, the lead's hamiltonian attained from diffferent methods have slight differences   RMSE = 0.0000006.\n",
-      "DPNEGF INFO    The coupling width of lead_R is 72.\n",
-      "DPNEGF WARNING WARNING, the lead's hamiltonian attained from diffferent methods have slight differences   RMSE = 0.0000019.\n",
-      "DPNEGF INFO    The Hamiltonian is block tridiagonalized into 3 subblocks.\n",
-      "DPNEGF INFO       the number of elements in subblocks: 61128\n",
-      "DPNEGF INFO                   occupation of subblocks: 73.69791666666666 %\n",
-      "DPNEGF INFO    --------------------------------------------------------------------------------\n",
-      "DPNEGF INFO    The Hamiltonian has been initialized by model.\n",
-      "DPNEGF INFO    ================================================================================\n",
-      "DPNEGF INFO    -------------Fermi level calculation-------------\n",
-      "DPNEGF WARNING No doping detected in lead_L, fixed_charge = 0\n",
-      "DPNEGF WARNING No doping detected in lead_R, fixed_charge = 0\n",
-      "DPNEGF INFO    Number of electrons in lead_L: {'C': 4}\n",
-      "DPNEGF INFO    Number of electrons in lead_R: {'C': 4}\n",
-      "DPNEGF INFO    -----Calculating Fermi level for lead_L-----\n",
-      "DPNEGF INFO    KPOINTS  kmesh sampling: 535 kpoints\n",
-      "DPNEGF INFO    Getting eigenvalues from the model.\n",
-      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
-      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 57 iterations.\n",
-      "DPNEGF INFO    q_cal: 128.0000000009015, total_electrons: 128.0, diff q: 9.015082014229847e-10\n",
-      "DPNEGF INFO    Estimated E_fermi: -3.5829886198043823 based on the valence electrons setting nel_atom : {'C': 4} .\n",
-      "DPNEGF INFO    -----Calculating Fermi level for lead_R-----\n",
-      "DPNEGF INFO    KPOINTS  kmesh sampling: 535 kpoints\n",
-      "DPNEGF INFO    Getting eigenvalues from the model.\n",
-      "DPNEGF INFO    Calculating Fermi energy in the case of spin-degeneracy.\n",
-      "DPNEGF WARNING Fermi level bisection did not converge under tolerance 1e-10 after 57 iterations.\n",
-      "DPNEGF INFO    q_cal: 128.00000000090498, total_electrons: 128.0, diff q: 9.04975649973494e-10\n",
-      "DPNEGF INFO    Estimated E_fermi: -3.582987666130066 based on the valence electrons setting nel_atom : {'C': 4} .\n",
-      "DPNEGF INFO    -------------------------------------------------\n",
-      "DPNEGF INFO    Zero bias case detected.\n",
-      "DPNEGF INFO    Fermi level for lead_L: -3.5829886198043823\n",
-      "DPNEGF INFO    Fermi level for lead_R: -3.582987666130066\n",
-      "DPNEGF INFO    Electrochemical potential for lead_L: -3.5829886198043823\n",
-      "DPNEGF INFO    Electrochemical potential for lead_R: -3.582987666130066\n",
-      "DPNEGF INFO    Reference energy E_ref: -3.5829886198043823\n",
-      "DPNEGF INFO    =================================================\n",
-      "\n",
-      "DPNEGF INFO    Merging 5000 tmp self energy files into ./negf_output_k50/self_energy/self_energy_leadL.h5\n",
-      "DPNEGF INFO    Merge complete.\n",
-      "DPNEGF INFO    Merging 5000 tmp self energy files into ./negf_output_k50/self_energy/self_energy_leadR.h5\n",
-      "DPNEGF INFO    Merge complete.\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0100,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0300,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0500,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0700,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.0900,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1100,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1300,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1500,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1700,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.1900,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2100,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2300,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2500,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2700,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.2900,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3100,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3300,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3500,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3700,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.3900,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4100,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4300,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4500,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4700,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n",
-      "DPNEGF INFO    Properties computation at k = [-0.0000,0.4900,-0.0000]\n",
-      "DPNEGF INFO    computing green's function at e = -5.000\n",
-      "DPNEGF INFO    computing green's function at e = -3.995\n",
-      "DPNEGF INFO    computing green's function at e = -2.990\n",
-      "DPNEGF INFO    computing green's function at e = -1.985\n",
-      "DPNEGF INFO    computing green's function at e = -0.980\n",
-      "DPNEGF INFO    computing green's function at e = 0.025\n",
-      "DPNEGF INFO    computing green's function at e = 1.030\n",
-      "DPNEGF INFO    computing green's function at e = 2.035\n",
-      "DPNEGF INFO    computing green's function at e = 3.040\n",
-      "DPNEGF INFO    computing green's function at e = 4.045\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "negf_json['task_options'][\"stru_options\"]['kmesh'] = [1,50,1]\n",
-    "negf_json['task_options']['emin'] = -5\n",
-    "negf_json['task_options']['emax'] = 5\n",
-    "\n",
-    "output = \"./negf_output_k50\"\n",
-    "if os.path.isdir(output):\n",
-    "    shutil.rmtree(output, ignore_errors=True)\n",
-    "os.makedirs(output)\n",
-    "\n",
-    "negf = NEGF(\n",
-    "    model=model,\n",
-    "    structure=structure,\n",
-    "    results_path=output,  \n",
-    "    **negf_json['task_options']\n",
-    ")\n",
-    "   \n",
-    "negf.compute()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "97a8217d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import matplotlib.pyplot as plt\n",
-    "results_path = os.path.join(output, 'negf.out.pth')\n",
-    "if os.path.exists(results_path) is False:\n",
-    "    raise FileNotFoundError(f\"Results file {results_path} not found. Please check if the NEGF calculation was successful.\")\n",
-    "negf_out = torch.load(results_path,weights_only=False)\n",
-    "\n",
-    "plt.plot(negf_out['uni_grid'], negf_out['T_avg'])\n",
-    "plt.xlabel('Energy (eV)')\n",
-    "plt.ylabel('Transmission')\n",
-    "plt.title('Transmission vs Energy')\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
   }
  ],
  "metadata": {

From 10b846d678b8ebb47162a5ee1fc83ef04252389c Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 16:27:09 +0800
Subject: [PATCH 150/152] update related files

---
 examples/graphene/extra_baseline/c_spds.json  |  11 --
 examples/graphene/negf.json                   |  52 ++++++++++
 .../graphene/negf_output_k100/negf.out.pth    | Bin 0 -> 281518 bytes
 .../graphene/negf_output_k100/trans_tbt_E.npy | Bin 0 -> 24128 bytes
 .../negf_output_k100/trans_tbt_Tavg.npy       | Bin 0 -> 12128 bytes
 examples/graphene/stru_negf.xyz               |  98 ++++++++++++++++++
 examples/graphene/struct.xyz                  |  50 +++++++++
 examples/graphene/train/input.json            |   4 +-
 .../train/train_out/checkpoint/nnsk.best.pth  |   2 +-
 .../train_out/checkpoint/nnsk.ep3000.pth      | Bin 0 -> 7731 bytes
 .../train_out/checkpoint/nnsk.ep5000.pth      | Bin 7731 -> 0 bytes
 11 files changed, 203 insertions(+), 14 deletions(-)
 delete mode 100644 examples/graphene/extra_baseline/c_spds.json
 create mode 100644 examples/graphene/negf.json
 create mode 100644 examples/graphene/negf_output_k100/negf.out.pth
 create mode 100644 examples/graphene/negf_output_k100/trans_tbt_E.npy
 create mode 100644 examples/graphene/negf_output_k100/trans_tbt_Tavg.npy
 create mode 100644 examples/graphene/stru_negf.xyz
 create mode 100644 examples/graphene/struct.xyz
 create mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep3000.pth
 delete mode 100644 examples/graphene/train/train_out/checkpoint/nnsk.ep5000.pth

diff --git a/examples/graphene/extra_baseline/c_spds.json b/examples/graphene/extra_baseline/c_spds.json
deleted file mode 100644
index 5da4b2c..0000000
--- a/examples/graphene/extra_baseline/c_spds.json
+++ /dev/null
@@ -1,11 +0,0 @@
-{
-    "common_options": {
-        "basis": {
-                "C": [                
-                    "2s",
-                    "2p",
-                    "d*"
-                    ]
-        }
-    }
-}
\ No newline at end of file
diff --git a/examples/graphene/negf.json b/examples/graphene/negf.json
new file mode 100644
index 0000000..afc353a
--- /dev/null
+++ b/examples/graphene/negf.json
@@ -0,0 +1,52 @@
+{   
+    "structure":"./struct.xyz",
+	"task_options":
+        {
+            "task": "negf",
+            "scf": false,
+            "block_tridiagonal": true,
+            "ele_T": 300,
+            "unit": "eV",
+            "stru_options":{
+                "gamma_center": false,
+                "time_reversal_symmetry": true,
+                "nel_atom": {"C": 4},
+                "kmesh":[1,1,1],
+                "pbc":[false, true, false],
+                "device":{
+                    "id":"32-64",
+                    "sort": true
+                },
+                "lead_L":{
+                    "id":"0-32",
+                    "voltage":0.0,
+                    "kmesh_lead_Ef":[1,50,20],
+                    "useBloch": false
+                },
+                "lead_R":{
+                    "id":"64-96",
+                    "voltage":0.0,
+                    "kmesh_lead_Ef":[1,50,20],
+                    "useBloch": false
+                }
+            },
+            "density_options": {
+            "method": "Fiori",
+            "integrate_way": "direct"
+            },
+            "poisson_options": {
+            "solver": "fmm",
+            "err": 1e-5
+            },
+            "sgf_solver": "Sancho-Rubio",
+            "espacing": 0.05,
+            "emin": -10,
+            "emax": 10,
+            "eta_lead":1e-5,
+            "eta_device":0.0,
+            "out_dos": true,
+            "out_tc": true,
+            "out_ldos": true,
+            "out_current_nscf": false
+    }
+}
diff --git a/examples/graphene/negf_output_k100/negf.out.pth b/examples/graphene/negf_output_k100/negf.out.pth
new file mode 100644
index 0000000000000000000000000000000000000000..c886417e84d1b57fc9690a2152e68358509aacf7
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diff --git a/examples/graphene/negf_output_k100/trans_tbt_E.npy b/examples/graphene/negf_output_k100/trans_tbt_E.npy
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diff --git a/examples/graphene/negf_output_k100/trans_tbt_Tavg.npy b/examples/graphene/negf_output_k100/trans_tbt_Tavg.npy
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diff --git a/examples/graphene/stru_negf.xyz b/examples/graphene/stru_negf.xyz
new file mode 100644
index 0000000..5befc43
--- /dev/null
+++ b/examples/graphene/stru_negf.xyz
@@ -0,0 +1,98 @@
+96
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diff --git a/examples/graphene/struct.xyz b/examples/graphene/struct.xyz
new file mode 100644
index 0000000..dec88c7
--- /dev/null
+++ b/examples/graphene/struct.xyz
@@ -0,0 +1,50 @@
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+C   15.00000000  3.12999994  19.87816936
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+C   15.00000000  3.12999994  21.32385440
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diff --git a/examples/graphene/train/input.json b/examples/graphene/train/input.json
index 4abbc5e..818d7f1 100644
--- a/examples/graphene/train/input.json
+++ b/examples/graphene/train/input.json
@@ -13,7 +13,7 @@
         "seed": 3982377700
     },
     "train_options": {
-        "num_epoch": 5000,
+        "num_epoch": 3000,
         "batch_size": 1,
         "optimizer": {
             "lr": 0.01,
@@ -41,7 +41,7 @@
     "model_options": {
         "nnsk": {
             "onsite": {
-                "method": "strain"
+                "method": "uniform"
             },
             "hopping": {
                 "method": "poly4pow",
diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.best.pth b/examples/graphene/train/train_out/checkpoint/nnsk.best.pth
index 7b032ac..3fe8729 120000
--- a/examples/graphene/train/train_out/checkpoint/nnsk.best.pth
+++ b/examples/graphene/train/train_out/checkpoint/nnsk.best.pth
@@ -1 +1 @@
-/personal/DeepTB/dptb_Zjj/dpnegf/examples/graphene/train/train_out/checkpoint/nnsk.ep5000.pth
\ No newline at end of file
+/personal/DeepTB/dptb_Zjj/dpnegf/examples/graphene/train/train_out/checkpoint/nnsk.ep3000.pth
\ No newline at end of file
diff --git a/examples/graphene/train/train_out/checkpoint/nnsk.ep3000.pth b/examples/graphene/train/train_out/checkpoint/nnsk.ep3000.pth
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From e540e47e0348f1997fd7aced278ebb05c49449bc Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 16:33:42 +0800
Subject: [PATCH 151/152] remove hbn train_out

---
 .../train/train_out/checkpoint/nnsk.best.pth    |   1 -
 .../train/train_out/checkpoint/nnsk.ep4997.pth  | Bin 10931 -> 0 bytes
 .../train/train_out/checkpoint/nnsk.ep4998.pth  | Bin 10931 -> 0 bytes
 .../train/train_out/checkpoint/nnsk.ep4999.pth  | Bin 10931 -> 0 bytes
 .../train/train_out/checkpoint/nnsk.ep5000.pth  | Bin 10931 -> 0 bytes
 .../train_out/checkpoint/nnsk.iter4997.pth      | Bin 11224 -> 0 bytes
 .../train_out/checkpoint/nnsk.iter4998.pth      | Bin 11224 -> 0 bytes
 .../train_out/checkpoint/nnsk.iter4999.pth      | Bin 11224 -> 0 bytes
 .../train_out/checkpoint/nnsk.iter5000.pth      | Bin 11224 -> 0 bytes
 .../train/train_out/checkpoint/nnsk.latest.pth  |   1 -
 10 files changed, 2 deletions(-)
 delete mode 120000 examples/hBN/train/train_out/checkpoint/nnsk.best.pth
 delete mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.ep4997.pth
 delete mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.ep4998.pth
 delete mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.ep4999.pth
 delete mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.ep5000.pth
 delete mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.iter4997.pth
 delete mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.iter4998.pth
 delete mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.iter4999.pth
 delete mode 100644 examples/hBN/train/train_out/checkpoint/nnsk.iter5000.pth
 delete mode 120000 examples/hBN/train/train_out/checkpoint/nnsk.latest.pth

diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.best.pth b/examples/hBN/train/train_out/checkpoint/nnsk.best.pth
deleted file mode 120000
index bf27b36..0000000
--- a/examples/hBN/train/train_out/checkpoint/nnsk.best.pth
+++ /dev/null
@@ -1 +0,0 @@
-/personal/DeepTB/dptb_Zjj/dpnegf/examples/hBN/train/train_out/checkpoint/nnsk.ep5000.pth
\ No newline at end of file
diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.ep4997.pth b/examples/hBN/train/train_out/checkpoint/nnsk.ep4997.pth
deleted file mode 100644
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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.ep4998.pth b/examples/hBN/train/train_out/checkpoint/nnsk.ep4998.pth
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HcmV?d00001

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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.iter4999.pth b/examples/hBN/train/train_out/checkpoint/nnsk.iter4999.pth
deleted file mode 100644
index 0acb1df589ef9012848b89b79f7438cecff074e2..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/examples/hBN/train/train_out/checkpoint/nnsk.latest.pth b/examples/hBN/train/train_out/checkpoint/nnsk.latest.pth
deleted file mode 120000
index 1137c9d..0000000
--- a/examples/hBN/train/train_out/checkpoint/nnsk.latest.pth
+++ /dev/null
@@ -1 +0,0 @@
-/personal/DeepTB/dptb_Zjj/dpnegf/examples/hBN/train/train_out/checkpoint/nnsk.iter5000.pth
\ No newline at end of file

From cd9953258bae25b745bf5e226b08e4def4f225a5 Mon Sep 17 00:00:00 2001
From: AsymmetryChou <181240085@smail.nju.edu.cn>
Date: Tue, 23 Sep 2025 16:44:58 +0800
Subject: [PATCH 152/152] ignore self-energy

---
 .gitignore | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.gitignore b/.gitignore
index 1c5c5fd..d2fed56 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,6 +27,10 @@ dpnegf/data/try_test.ipynb
 dpnegf/negf/check.ipynb
 dpnegf/tests/data/test_negf/show.ipynb
 dpnegf/tests/data/test_tbtrans/show.ipynb
+# tutorial files
+dpnegf/examples/graphene/negf_output_k20/self_energy/self_energy_leadL.h5
+dpnegf/examples/graphene/negf_output_k20/self_energy/self_energy_leadR.h5
+
 run_config.json
 dpnegf/nnet/__pycache__/
 dpnegf/sktb/__pycache__/