CSM及TET，CS

.idea/Screen.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="C:\Users\PC20230606\Miniconda3" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.11" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

main_property.sh

@@ -0,0 +1,85 @@
+#!/bin/bash
+
+# ==========================================
+# 全流程自动化脚本 (直通筛选版)
+# ==========================================
+
+# 1. 环境初始化
+echo "============ Stage 0: Initialization ============"
+chmod -R u+w ../Screen
+source $(conda info --base)/etc/profile.d/conda.sh
+
+# 激活 screen 环境
+conda activate ~/anaconda3/envs/screen
+cd py/
+export PYTHONPATH=$(pwd):$PYTHONPATH
+
+# 2. 预处理与文件整理 (替代原 Step 1)
+echo "============ Stage 1: File Organization (Direct Pass) ============"
+# 运行预处理 (可选，确保 input 文件夹就绪)
+python pre_process.py
+
+# 运行直通版整理脚本
+# 功能: 读取 input, 识别阴离子, 按结构复制到 after_step1/Anion/ID/ID.cif
+# 跳过 check_basic 等耗时检查
+python step1_direct.py
+
+# 生成 Zeo++ 运行脚本
+# 功能: 遍历 after_step1, 生成 analyze.sh 和 sh_all.sh
+python make_sh.py
+
+# 3. 运行 Zeo++ 计算
+echo "============ Stage 2: Zeo++ Calculations ============"
+conda deactivate
+conda activate ~/anaconda3/envs/zeo
+
+# 进入数据目录
+cd ../data/after_step1
+if [ -f "sh_all.sh" ]; then
+    # 执行所有计算
+    source sh_all.sh
+    # 清理总脚本 (可选)
+    # rm sh_all.sh
+else
+    echo "Error: sh_all.sh not found! Please check Stage 1."
+    exit 1
+fi
+
+# 4. 数据提取与高级分析
+echo "============ Stage 3: Data Extraction & Analysis ============"
+# 切回 screen 环境
+conda deactivate
+conda activate ~/anaconda3/envs/screen
+cd ../../py
+
+# 3.1 提取 Zeo++ 基础数据
+# 输出: ../output/Anion/Anion.csv (含 Perc, Min_d, Max_node)
+python extract_data.py
+
+# 3.2 计算角共享 (Corner Sharing)
+# 输出: 更新 CSV, 增加 Is_Only_Corner_Sharing 列
+echo "Running Corner Sharing Analysis..."
+python analyze_cs.py
+
+# 3.3 联合筛选
+# 功能: 读取 CSV, 根据阈值筛选, 生成 ../data/after_screening 软链接/文件
+python step2_4_combined.py
+
+# 3.4 CSM 分析 (仅针对筛选后的材料)
+# 输出: ../output/CSM/Anion/ID.dat
+echo "Running CSM Analysis..."
+python analyze_csm.py
+
+# 3.5 统计四面体占据率
+# 输出: 读取 .dat, 更新 CSV, 增加 Tet_Li_Ratio 列
+echo "Updating Tetrahedral Li Ratio..."
+python update_tet_occupancy.py
+
+# 5. 结束
+echo "========================================================"
+echo "All tasks completed!"
+echo "Results stored in:"
+echo "  - CSV Data:    ../output/"
+echo "  - Screened:    ../data/after_screening/"
+echo "  - CSM Details: ../output/CSM/"
+echo "========================================================"

py/CSM_reconstruct.py

@@ -0,0 +1,224 @@
+import os
+import sys
+import numpy as np
+import argparse
+from tqdm import tqdm
+from scipy.spatial import ConvexHull
+from pymatgen.core import Structure
+from pymatgen.core.periodic_table import Element
+from pymatgen.analysis.chemenv.coordination_environments.coordination_geometry_finder import LocalGeometryFinder
+
+# ================= 配置区域 =================
+# 建议使用绝对路径，避免找不到文件夹
+INPUT_DIR = "../../solidstate-tools/corner-sharing/data/1209/input"  # 请确保这里有你的 .cif 文件
+OUTPUT_DIR = "../output/CSM"
+TARGET_ELEMENT = 'Li'
+ENV_TYPE = 'both'
+
+
+# ===========================================
+
+class HiddenPrints:
+    '''用于隐藏 pymatgen 繁杂的输出'''
+
+    def __enter__(self):
+        self._original_stdout = sys.stdout
+        sys.stdout = open(os.devnull, 'w')
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        sys.stdout.close()
+        sys.stdout = self._original_stdout
+
+
+def non_elements(struct):
+    """
+    【关键修复】保留卤素(F, Cl, Br, I) 和其他阴离子，防止氯化物结构被清空。
+    """
+    # 这里加入了 F, Cl, Br, I, P, Se, Te 等
+    anions_to_keep = {"O", "S", "N", "F", "Cl", "Br", "I", "P", "Se", "Te", "As", "Sb", "C"}
+    stripped = struct.copy()
+    species_to_remove = [el.symbol for el in stripped.composition.elements
+                         if el.symbol not in anions_to_keep]
+    if species_to_remove:
+        stripped.remove_species(species_to_remove)
+    return stripped
+
+
+def site_env(coord, struct, sp="Li", envtype='both'):
+    stripped = non_elements(struct)
+
+    # 如果剥离后结构为空（例如纯金属锂），直接返回
+    if len(stripped) == 0:
+        return {'csm': np.nan, 'vol': np.nan, 'type': 'Error_NoAnions'}
+
+    with_li = stripped.copy()
+    # 插入一个探测用的 Li 原子
+    with_li.append(sp, coord, coords_are_cartesian=False, validate_proximity=False)
+
+    # 尝试排序，如果因为部分占据导致排序失败，则使用原始顺序
+    try:
+        with_li = with_li.get_sorted_structure()
+    except:
+        pass
+
+    tet_oct_competition = []
+
+    # ---------------- 四面体 (Tet) 检测 ----------------
+    if envtype == 'both' or envtype == 'tet':
+        for dist in np.linspace(1, 4, 601):  # 扫描距离 1A 到 4A
+            neigh = with_li.get_neighbors(with_li.sites[0], dist)
+            if len(neigh) < 4:
+                continue
+            elif len(neigh) > 4:
+                break
+
+            neigh_coords = [i.coords for i in neigh]
+            try:
+                with HiddenPrints():
+                    lgf = LocalGeometryFinder(only_symbols=["T:4"])
+                    lgf.setup_structure(structure=with_li)
+                    lgf.setup_local_geometry(isite=0, coords=neigh_coords)
+
+                site_volume = ConvexHull(neigh_coords).volume
+                # 获取 CSM
+                csm_val = lgf.get_coordination_symmetry_measures()['T:4']['csm']
+
+                tet_env = {'csm': csm_val, 'vol': site_volume, 'type': 'tet'}
+                tet_oct_competition.append(tet_env)
+            except Exception:
+                pass
+            if len(neigh) == 4: break
+
+    # ---------------- 八面体 (Oct) 检测 ----------------
+    if envtype == 'both' or envtype == 'oct':
+        for dist in np.linspace(1, 4, 601):
+            neigh = with_li.get_neighbors(with_li.sites[0], dist)
+            if len(neigh) < 6:
+                continue
+            elif len(neigh) > 6:
+                break
+
+            neigh_coords = [i.coords for i in neigh]
+            try:
+                with HiddenPrints():
+                    lgf = LocalGeometryFinder(only_symbols=["O:6"], permutations_safe_override=False)
+                    lgf.setup_structure(structure=with_li)
+                    lgf.setup_local_geometry(isite=0, coords=neigh_coords)
+
+                site_volume = ConvexHull(neigh_coords).volume
+                csm_val = lgf.get_coordination_symmetry_measures()['O:6']['csm']
+
+                oct_env = {'csm': csm_val, 'vol': site_volume, 'type': 'oct'}
+                tet_oct_competition.append(oct_env)
+            except Exception:
+                pass
+            if len(neigh) == 6: break
+
+    # ---------------- 结果判定 ----------------
+    if len(tet_oct_competition) == 0:
+        return {'csm': np.nan, 'vol': np.nan, 'type': 'Non_' + envtype}
+    elif len(tet_oct_competition) == 1:
+        return tet_oct_competition[0]
+    elif len(tet_oct_competition) >= 2:
+        return min(tet_oct_competition, key=lambda x: x['csm'])
+
+
+def extract_sites(struct, sp="Li", envtype='both'):
+    envlist = []
+    # 遍历所有位点寻找 Li
+    for i, site in enumerate(struct):
+        site_elements = [el.symbol for el in site.species.elements]
+        if sp in site_elements:
+            try:
+                # 传入结构副本以防修改原结构
+                singleenv = site_env(site.frac_coords, struct.copy(), sp, envtype)
+                envlist.append({
+                    'site_index': i,
+                    'frac_coords': site.frac_coords,
+                    'type': singleenv.get('type', 'unknown'),
+                    'csm': singleenv.get('csm', np.nan),
+                    'volume': singleenv.get('vol', np.nan)
+                })
+            except Exception as e:
+                # 捕捉单个位点计算错误，不中断程序
+                # print(f"  [Warn] Site {i} calculation failed: {e}")
+                pass
+    return envlist
+
+
+def export_envs(envlist, sp, envtype, fname):
+    with open(fname, 'w') as f:
+        f.write('List of environment information\n')
+        f.write(f'Species : {sp}\n')
+        f.write(f'Envtype : {envtype}\n')
+        for item in envlist:
+            # 格式化输出，确保没有数据也能看懂
+            f.write(f"Site index {item['site_index']}: {item}\n")
+
+
+# ================= 主程序 =================
+def run_csm_analysis():
+    # 1. 检查目录
+    if not os.path.exists(INPUT_DIR):
+        print(f"错误: 输入目录不存在 -> {os.path.abspath(INPUT_DIR)}")
+        return
+
+    cif_files = []
+    for root, dirs, files in os.walk(INPUT_DIR):
+        for file in files:
+            if file.endswith(".cif"):
+                cif_files.append(os.path.join(root, file))
+
+    if not cif_files:
+        print(f"在 {INPUT_DIR} 中未找到 .cif 文件。")
+        return
+
+    print(f"开始分析 {len(cif_files)} 个文件 (目标元素: {TARGET_ELEMENT}, 包含阴离子: F,Cl,Br,I,O,S,N...)")
+
+    success_count = 0
+
+    for cif_path in tqdm(cif_files, desc="Calculating CSM"):
+        try:
+            # 准备路径
+            rel_path = os.path.relpath(cif_path, INPUT_DIR)
+            rel_dir = os.path.dirname(rel_path)
+            file_base = os.path.splitext(os.path.basename(cif_path))[0]
+
+            target_dir = os.path.join(OUTPUT_DIR, rel_dir)
+            if not os.path.exists(target_dir):
+                os.makedirs(target_dir)
+
+            target_dat_path = os.path.join(target_dir, f"{file_base}.dat")
+
+            # 如果文件已存在且不为空，可选择跳过
+            # if os.path.exists(target_dat_path) and os.path.getsize(target_dat_path) > 0:
+            #     continue
+
+            # 读取结构
+            struct = Structure.from_file(cif_path)
+
+            # 检查是否含 Li
+            if Element(TARGET_ELEMENT) not in struct.composition.elements:
+                continue
+
+            # 计算环境
+            env_list = extract_sites(struct, sp=TARGET_ELEMENT, envtype=ENV_TYPE)
+
+            # 写入结果 (即使 env_list 为空也写入一个标记文件，方便debug)
+            if env_list:
+                export_envs(env_list, sp=TARGET_ELEMENT, envtype=ENV_TYPE, fname=target_dat_path)
+                success_count += 1
+            else:
+                with open(target_dat_path, 'w') as f:
+                    f.write(f"No {TARGET_ELEMENT} environments found (Check connectivity or anion types).")
+
+        except Exception as e:
+            print(f"\n[Error] File: {os.path.basename(cif_path)} -> {e}")
+            continue
+
+    print(f"\n分析完成！成功生成 {success_count} 个文件。")
+    print(f"输出目录: {os.path.abspath(OUTPUT_DIR)}")
+
+
+if __name__ == "__main__":
+    run_csm_analysis()

py/CS_catulate.py

@@ -0,0 +1,118 @@
+import os
+import pandas as pd
+from pymatgen.core import Structure
+# 确保你的 utils 文件夹在 py 目录下，并且包含 CS_analyse.py
+from utils.CS_analyse import CS_catulate, check_only_corner_sharing
+from tqdm import tqdm
+
+# 配置路径
+CSV_ROOT_DIR = "../output"
+DATA_SOURCE_DIR = "../data/after_step1"
+
+
+def get_cif_path(group_name, anion_name, material_id):
+    """
+    根据 CSV 的层级信息构建 CIF 文件的绝对路径
+    """
+    # 构建路径: ../data/after_step1/Group/Anion/ID/ID.cif
+    # 注意处理单阴离子情况 (Group == Anion)
+    if group_name == anion_name:
+        # 路径: ../data/after_step1/S/123/123.cif
+        rel_path = os.path.join(DATA_SOURCE_DIR, group_name, material_id, f"{material_id}.cif")
+    else:
+        # 路径: ../data/after_step1/S+O/S/123/123.cif
+        rel_path = os.path.join(DATA_SOURCE_DIR, group_name, anion_name, material_id, f"{material_id}.cif")
+
+    return os.path.abspath(rel_path)
+
+
+def process_single_csv(csv_path, group_name, anion_name):
+    """
+    处理单个 CSV 文件：读取 -> 计算角共享 -> 添加列 -> 保存
+    """
+    print(f"正在处理 CSV: {csv_path}")
+
+    # 读取 CSV，强制 ID 为字符串
+    try:
+        df = pd.read_csv(csv_path, dtype={'Filename': str})
+    except Exception as e:
+        print(f"读取 CSV 失败: {e}")
+        return
+
+    # 检查是否已经存在该列，如果存在且想重新计算，可以先删除，或者跳过
+    if 'Is_Only_Corner_Sharing' in df.columns:
+        print("  - 'Is_Only_Corner_Sharing' 列已存在，将覆盖更新。")
+
+    results = []
+
+    # 使用 tqdm 显示进度
+    for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=f"Analyzing {anion_name}"):
+        material_id = str(row['Filename']).replace('.0', '')
+        cif_path = get_cif_path(group_name, anion_name, material_id)
+
+        cs_result = None  # 默认值
+
+        if os.path.exists(cif_path):
+            try:
+                # 1. 加载结构
+                struct = Structure.from_file(cif_path)
+
+                # 2. 计算共享关系 (默认检测 Li 和常见阴离子)
+                # 你可以根据需要调整 anion 列表，或者动态使用 anion_name
+                target_anions = ['O', 'S', 'Cl', 'F', 'Br', 'I', 'N', 'P']
+                sharing_details = CS_catulate(struct, sp='Li', anion=target_anions)
+
+                # 3. 判断是否仅角共享 (返回 1 或 0 或 True/False)
+                # 根据你提供的截图，似乎是返回 0 或 1
+                is_only_corner = check_only_corner_sharing(sharing_details)
+
+                cs_result = is_only_corner
+
+            except Exception as e:
+                # print(f"计算出错 {material_id}: {e}")
+                cs_result = "Error"
+        else:
+            print(f"  - 警告: 找不到 CIF 文件 {cif_path}")
+            cs_result = "File_Not_Found"
+
+        results.append(cs_result)
+
+    # 将结果添加为新列
+    df['Is_Only_Corner_Sharing'] = results
+
+    # 保存覆盖原文件
+    df.to_csv(csv_path, index=False)
+    print(f"  - 已更新 CSV: {csv_path}")
+
+
+def run_cs_analysis():
+    """
+    遍历所有 CSV 并运行分析
+    """
+    if not os.path.exists(CSV_ROOT_DIR):
+        print(f"CSV 根目录不存在: {CSV_ROOT_DIR}")
+        return
+
+    for root, dirs, files in os.walk(CSV_ROOT_DIR):
+        for file in files:
+            if file.endswith(".csv"):
+                csv_path = os.path.join(root, file)
+
+                # 解析 Group 和 Anion (用于定位 CIF)
+                rel_root = os.path.relpath(root, CSV_ROOT_DIR)
+                path_parts = rel_root.split(os.sep)
+
+                if len(path_parts) == 1:
+                    group_name = path_parts[0]
+                    anion_name = path_parts[0]
+                elif len(path_parts) >= 2:
+                    group_name = path_parts[0]
+                    anion_name = path_parts[1]
+                else:
+                    continue
+
+                process_single_csv(csv_path, group_name, anion_name)
+
+
+if __name__ == "__main__":
+    run_cs_analysis()

py/csm.py

@@ -0,0 +1,90 @@
+import os
+from pymatgen.core import Structure
+from pymatgen.core.periodic_table import Element
+# 导入你的CSM计算工具库 (根据 provided context [11])
+try:
+    from utils.analyze_env_st import extract_sites, export_envs
+except ImportError:
+    print("Error: 找不到 utils.analyze_env_st 模块，请检查 utils 文件夹。")
+    exit()
+
+from tqdm import tqdm
+
+# ================= 配置区域 =================
+# 输入目录：使用筛选后的目录，只计算符合要求的材料
+INPUT_DIR = "../../solidstate-tools/corner-sharing/data/1209/input"
+# 输出目录
+OUTPUT_DIR = "../output/CSM"
+# 分析参数
+TARGET_ELEMENT = 'Na'
+ENV_TYPE = 'both'  # 可选 'tet', 'oct', 'both'
+# ===========================================
+
+def run_csm_analysis():
+    """
+    遍历 after_screening 文件夹，计算 CSM 并生成 .dat 文件到 output/CSM
+    """
+    if not os.path.exists(INPUT_DIR):
+        print(f"输入目录不存在: {INPUT_DIR}，请先运行筛选步骤。")
+        return
+
+    # 收集所有需要处理的 CIF 文件
+    cif_files = []
+    for root, dirs, files in os.walk(INPUT_DIR):
+        for file in files:
+            if file.endswith(".cif"):
+                # 保存完整路径
+                cif_files.append(os.path.join(root, file))
+
+    print(f"开始进行 CSM 分析，共找到 {len(cif_files)} 个筛选后的材料...")
+
+    for cif_path in tqdm(cif_files, desc="Calculating CSM"):
+        try:
+            # 1. 确定输出路径，保持目录结构
+            # 获取相对路径 (例如: S/195819.cif 或 S+O/S/195819.cif)
+            rel_path = os.path.relpath(cif_path, INPUT_DIR)
+            # 获取所在文件夹 (例如: S 或 S+O/S)
+            rel_dir = os.path.dirname(rel_path)
+            # 获取文件名 (例如: 195819)
+            file_base = os.path.splitext(os.path.basename(cif_path))[0]
+
+            # 构建目标文件夹: ../output/CSM/S/
+            target_dir = os.path.join(OUTPUT_DIR, rel_dir)
+            if not os.path.exists(target_dir):
+                os.makedirs(target_dir)
+
+            # 构建目标文件路径: ../output/CSM/S/195819.dat
+            target_dat_path = os.path.join(target_dir, f"{file_base}.dat")
+
+            # 2. 如果已经存在，跳过 (可选，视需求而定，这里默认覆盖)
+            # if os.path.exists(target_dat_path):
+            #     continue
+
+            # 3. 读取结构
+            struct = Structure.from_file(cif_path)
+
+            # 检查是否包含目标元素 (Li)
+            if Element(TARGET_ELEMENT) not in struct.composition.elements:
+                # print(f"Skipping {file_base}: No {TARGET_ELEMENT}")
+                continue
+
+            # 4. 计算 CSM (引用 utils 中的函数)
+            # extract_sites 返回环境列表
+            env_list = extract_sites(struct, sp=TARGET_ELEMENT, envtype=ENV_TYPE)
+
+            # 5. 导出结果 (引用 utils 中的函数)
+            # export_envs 将结果写入 .dat 文件
+            if env_list:
+                export_envs(env_list, sp=TARGET_ELEMENT, envtype=ENV_TYPE, fname=target_dat_path)
+            else:
+                # 如果没有提取到环境（例如没有配位环境），生成一个空文件或记录日志
+                with open(target_dat_path, 'w') as f:
+                    f.write("No environments found.")
+
+        except Exception as e:
+            print(f"处理出错 {cif_path}: {e}")
+
+    print(f"CSM 分析完成，结果已保存至 {OUTPUT_DIR}")
+
+if __name__ == "__main__":
+    run_csm_analysis()

py/step1.py

@@ -1,69 +1,113 @@
 from pymatgen.core import Structure
-from pymatgen.core.periodic_table import Element, Specie
-from pymatgen.io.cif import CifWriter
 from crystal_2 import crystal
-import crystal_2
 import os
 import shutil
 
 
+def get_anion_type(structure):
+    """
+    判断阴离子类型。
+    仅识别 O, S, Cl, Br 及其组合。
+    其他非金属元素（如 P, N, F 等）将被忽略。
+    """
+    # 仅保留这四种目标阴离子
+    valid_anions = {'O', 'S', 'Cl', 'Br'}
+
+    # 获取结构中的所有元素符号
+    elements = set([e.symbol for e in structure.composition.elements])
+
+    # 取交集找到当前结构包含的目标阴离子
+    found_anions = elements.intersection(valid_anions)
+
+    if not found_anions:
+        return "Unknown"
+
+    # 如果有多个阴离子，按字母顺序排序并用 '+' 连接
+    sorted_anions = sorted(list(found_anions))
+    return "+".join(sorted_anions)
+
+
 def read_files_check_basic(folder_path):
-    file_contents = []
+    """
+    读取 CIF 文件，进行基础检查 (check_basic)，
+    通过筛选后按自定义阴离子规则分类并整理到 after_step1 文件夹。
+    """
+    # 输出基础路径
+    output_base = "../data/after_step1"
 
     if not os.path.exists(folder_path):
         print(f"{folder_path} 文件夹不存在")
-        return file_contents
+        return
 
-    for filename in os.listdir(folder_path):
+    # 确保输出目录存在
+    if not os.path.exists(output_base):
+        os.makedirs(output_base)
+
+    cif_files = [f for f in os.listdir(folder_path) if f.endswith(".cif")]
+    print(f"在 {folder_path} 发现 {len(cif_files)} 个 CIF 文件，开始筛选与整理...")
+
+    count_pass = 0
+
+    for filename in cif_files:
         file_path = os.path.join(folder_path, filename)
 
-        if os.path.isfile(file_path):
-            try:
-                temp = crystal(file_path)
-                file_contents.append(temp)
-            except Exception as e:
-                print(e)
-                continue  # 如果出错跳过当前循环，避免temp未定义报错
-
-            print(f"正在处理{filename}")
+        # 1. 调用 crystal_2 进行基础筛选
+        try:
+            temp = crystal(file_path)
+            # 进行基础检查 (电荷平衡、化学式检查等)
             temp.check_basic()
 
-            if temp.check_basic_result:
-                # 获取不带后缀的文件名，用于创建同名文件夹
-                file_base_name = os.path.splitext(filename)[0]
+            if not temp.check_basic_result:
+                print(f"Skipped: {filename} (未通过 check_basic)")
+                continue
 
-                if not "+" in temp.anion:
-                    # 单一阴离子情况
-                    # 路径变为: ../data/after_step1/Anion/FileBaseName/
-                    base_anion_folder = os.path.join("../data/after_step1", f"{temp.anion}")
-                    target_folder = os.path.join(base_anion_folder, file_base_name)
+        except Exception as e:
+            print(f"Error checking {filename}: {e}")
+            continue
 
+        # 2. 筛选通过，进行分类整理
+        try:
+            print(f"Processing: {filename} (Passed)")
+            count_pass += 1
+
+            # 为了确保分类逻辑与 Direct 版本一致，重新读取结构判断阴离子
+            # (忽略 crystal_2 内部可能基于 P/N 等元素的命名)
+            struct = Structure.from_file(file_path)
+            anion_type = get_anion_type(struct)
+
+            # 获取不带后缀的文件名 (ID)
+            file_base_name = os.path.splitext(filename)[0]
+
+            # --- 构建目标路径逻辑 (Anion/ID/ID.cif) ---
+
+            if "+" in anion_type:
+                # 混合阴离子情况 (如 S+O)
+                # 分别复制到 S+O/S 和 S+O/O 下
+                sub_anions = anion_type.split("+")
+                for sub in sub_anions:
+                    # 路径: ../data/after_step1/S+O/S/123/123.cif
+                    target_folder = os.path.join(output_base, anion_type, sub, file_base_name)
                     if not os.path.exists(target_folder):
                         os.makedirs(target_folder)
 
-                    # 目标文件路径
-                    target_file_path = os.path.join(target_folder, filename)
-                    # 复制文件到目标文件夹
-                    shutil.copy(file_path, target_file_path)
-                    print(f"文件 {filename}通过基本筛选,已复制到 {target_folder}")
-                else:
-                    # 混合阴离子情况
-                    anions = temp.anion.split("+")
-                    for anion in anions:
-                        # 路径变为: ../data/after_step1/AnionCombination/Anion/FileBaseName/
-                        base_group_folder = os.path.join("../data/after_step1", f"{temp.anion}")
-                        base_anion_folder = os.path.join(base_group_folder, anion)
-                        target_folder = os.path.join(base_anion_folder, file_base_name)
+                    target_file = os.path.join(target_folder, filename)
+                    shutil.copy(file_path, target_file)
+            else:
+                # 单一阴离子或 Unknown: ../data/after_step1/S/123/123.cif
+                target_folder = os.path.join(output_base, anion_type, file_base_name)
+                if not os.path.exists(target_folder):
+                    os.makedirs(target_folder)
 
-                        if not os.path.exists(target_folder):
-                            os.makedirs(target_folder)
+                target_file = os.path.join(target_folder, filename)
+                shutil.copy(file_path, target_file)
 
-                        # 目标文件路径
-                        target_file_path = os.path.join(target_folder, filename)
-                        # 复制文件到目标文件夹
-                        shutil.copy(file_path, target_file_path)
-                        print(f"文件 {filename}通过基本筛选,已复制到 {target_folder}")
+        except Exception as e:
+            print(f"Error copying {filename}: {e}")
+
+    print(f"处理完成。共 {len(cif_files)} 个文件，通过筛选 {count_pass} 个。")
 
 
 if __name__ == "__main__":
-    read_files_check_basic("../data/input")
+    # 根据你的 readme，MP数据在 input_pre，ICSD在 input
+    # 这里默认读取 input，你可以根据实际情况修改
+    read_files_check_basic("../../solidstate-tools/corner-sharing/data/1209/input")

py/step1_direct.py

@@ -0,0 +1,103 @@
+import os
+import shutil
+from pymatgen.core import Structure
+
+
+def get_anion_type(structure):
+    """
+    判断阴离子类型。
+    仅识别 O, S, Cl, Br 及其组合。
+    其他非金属元素（如 P, N, F 等）将被忽略：
+    - Li3PS4 (含 P, S) -> 识别为 S
+    - LiFePO4 (含 P, O) -> 识别为 O
+    - Li3P (仅 P) -> 识别为 Unknown
+    """
+    # --- 修改处：仅保留这四种目标阴离子 ---
+    valid_anions = {'O', 'S', 'Cl', 'Br'}
+
+    # 获取结构中的所有元素符号
+    elements = set([e.symbol for e in structure.composition.elements])
+
+    # 取交集找到当前结构包含的目标阴离子
+    found_anions = elements.intersection(valid_anions)
+
+    if not found_anions:
+        return "Unknown"
+
+    # 如果有多个阴离子，按字母顺序排序并用 '+' 连接
+    sorted_anions = sorted(list(found_anions))
+    return "+".join(sorted_anions)
+
+
+def organize_files_direct(input_folder, output_base):
+    if not os.path.exists(input_folder):
+        print(f"输入文件夹不存在: {input_folder}")
+        return
+
+    # 确保输出目录存在
+    if not os.path.exists(output_base):
+        os.makedirs(output_base)
+
+    cif_files = [f for f in os.listdir(input_folder) if f.endswith(".cif")]
+    print(f"发现 {len(cif_files)} 个 CIF 文件，开始直接整理...")
+
+    count_dict = {}
+
+    for filename in cif_files:
+        file_path = os.path.join(input_folder, filename)
+
+        try:
+            # 读取结构分类
+            struct = Structure.from_file(file_path)
+            anion_type = get_anion_type(struct)
+
+            # 统计一下分类情况（可选）
+            count_dict[anion_type] = count_dict.get(anion_type, 0) + 1
+
+            # 获取不带后缀的文件名 (ID)
+            file_base_name = os.path.splitext(filename)[0]
+
+            # --- 构建目标路径逻辑 ---
+            # 目标: ../data/after_step1 / AnionType / ID / ID.cif
+
+            if "+" in anion_type:
+                # 混合阴离子情况 (如 S+O)
+                # 将文件复制到 S+O 下的各个子阴离子文件夹中 (S+O/S/ID/ID.cif 和 S+O/O/ID/ID.cif)
+                # 这样既保留了组合关系，又方便后续脚本按元素查找
+                sub_anions = anion_type.split("+")
+                for sub in sub_anions:
+                    # 路径: after_step1/S+O/S/123/123.cif
+                    target_folder = os.path.join(output_base, anion_type, sub, file_base_name)
+                    if not os.path.exists(target_folder):
+                        os.makedirs(target_folder)
+
+                    target_file = os.path.join(target_folder, filename)
+                    shutil.copy(file_path, target_file)
+
+                # print(f"整理: {filename} -> {anion_type} (Split)")
+
+            else:
+                # 单一阴离子或 Unknown: after_step1/S/123/123.cif
+                target_folder = os.path.join(output_base, anion_type, file_base_name)
+                if not os.path.exists(target_folder):
+                    os.makedirs(target_folder)
+
+                target_file = os.path.join(target_folder, filename)
+                shutil.copy(file_path, target_file)
+                # print(f"整理: {filename} -> {anion_type}")
+
+        except Exception as e:
+            print(f"处理 {filename} 失败: {e}")
+
+    print("整理完成。分类统计:")
+    for k, v in count_dict.items():
+        print(f"  {k}: {v}")
+
+
+if __name__ == "__main__":
+    # 输入路径
+    input_dir = "../../solidstate-tools/corner-sharing/data/1209/input"  # 如果是MP数据请改为 ../data/input_pre
+    # 输出路径
+    output_dir = "../data/after_step1"
+
+    organize_files_direct(input_dir, output_dir)

py/update_tet_occupancy.py

@@ -0,0 +1,129 @@
+import os
+import pandas as pd
+from tqdm import tqdm
+
+# ================= 配置区域 =================
+# CSV 所在的根目录
+CSV_ROOT_DIR = "../output"
+# CSM .dat 文件所在的根目录
+CSM_ROOT_DIR = "../output/CSM"
+
+
+# ===========================================
+
+def calculate_tet_ratio_from_dat(dat_path):
+    """
+    解析 .dat 文件，计算四面体位 Li 的占比。
+    返回: float (0.0 - 1.0) 或 None (如果文件不存在或为空)
+    """
+    if not os.path.exists(dat_path):
+        return None
+
+    tet_count = 0
+    total_count = 0
+
+    try:
+        with open(dat_path, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+
+            # 简单检查文件是否包含 "No environments found"
+            if len(lines) > 0 and "No environments found" in lines[0]:
+                return None
+
+            for line in lines:
+                # 根据截图，每行是一个位点的信息
+                # 简单字符串匹配，这比 eval 更安全且足够快
+                if "'type': 'tet'" in line:
+                    tet_count += 1
+                    total_count += 1
+                elif "'type': 'oct'" in line:
+                    total_count += 1
+                # 如果还有其他类型，可以在这里加，或者只要是位点行都算进 total
+
+        if total_count == 0:
+            return 0.0
+
+        return round(tet_count / total_count, 4)
+
+    except Exception as e:
+        print(f"解析出错 {dat_path}: {e}")
+        return None
+
+
+def process_single_csv(csv_path, group_name, anion_name):
+    """
+    读取 CSV -> 寻找对应的 CSM dat 文件 -> 计算比例 -> 更新 CSV
+    """
+    print(f"正在更新 CSV: {csv_path}")
+
+    # 读取 CSV，确保 ID 是字符串
+    try:
+        df = pd.read_csv(csv_path, dtype={'Filename': str})
+    except Exception as e:
+        print(f"读取 CSV 失败: {e}")
+        return
+
+    tet_ratios = []
+
+    # 遍历 CSV 中的每一行
+    for index, row in tqdm(df.iterrows(), total=df.shape[0], desc="Updating Occupancy"):
+        material_id = str(row['Filename']).replace('.0', '')
+
+        # 构建对应的 .dat 文件路径
+        # 路径逻辑: ../output/CSM/Group/Anion/ID.dat
+        # 注意: 这里的 Group/Anion 结构必须与 analyze_csm.py 生成的一致
+
+        if group_name == anion_name:
+            # 单一阴离子: ../output/CSM/S/123.dat
+            dat_rel_path = os.path.join(group_name, f"{material_id}.dat")
+        else:
+            # 混合阴离子: ../output/CSM/S+O/S/123.dat
+            dat_rel_path = os.path.join(group_name, anion_name, f"{material_id}.dat")
+
+        dat_path = os.path.join(CSM_ROOT_DIR, dat_rel_path)
+
+        # 计算比例
+        ratio = calculate_tet_ratio_from_dat(dat_path)
+        tet_ratios.append(ratio)
+
+    # 添加或更新列
+    df['Tet_Li_Ratio'] = tet_ratios
+
+    # 保存
+    df.to_csv(csv_path, index=False)
+    print(f"  - 已保存更新后的数据到: {csv_path}")
+
+
+def run_update():
+    """
+    主程序：遍历 output 目录下的 CSV
+    """
+    if not os.path.exists(CSV_ROOT_DIR):
+        print(f"CSV 目录不存在: {CSV_ROOT_DIR}")
+        return
+
+    for root, dirs, files in os.walk(CSV_ROOT_DIR):
+        for file in files:
+            if file.endswith(".csv"):
+                csv_path = os.path.join(root, file)
+
+                # 解析路径获取 Group 和 Anion
+                # root: ../output/S  --> rel: S
+                rel_root = os.path.relpath(root, CSV_ROOT_DIR)
+                path_parts = rel_root.split(os.sep)
+
+                if len(path_parts) == 1:
+                    group_name = path_parts[0]
+                    anion_name = path_parts[0]
+                elif len(path_parts) >= 2:
+                    group_name = path_parts[0]
+                    anion_name = path_parts[1]
+                else:
+                    continue
+
+                # 只有当 CSM 目录里有对应的文件夹时才处理（可选）
+                process_single_csv(csv_path, group_name, anion_name)
+
+
+if __name__ == "__main__":
+    run_update()

py/utils/CS_analyse.py

@@ -0,0 +1,356 @@
+from typing import List, Dict
+
+from pymatgen.core.structure import Structure
+from pymatgen.analysis.local_env import VoronoiNN
+import numpy as np
+
+def check_real(nearest):
+    real_nearest = []
+    for site in nearest:
+        if np.all((site.frac_coords >= 0) & (site.frac_coords <= 1)):
+             real_nearest.append(site)
+
+    return real_nearest
+
+def special_check_for_3(site, nearest):
+    real_nearest = []
+    distances = []
+    for site2 in nearest:
+        distance = np.linalg.norm(np.array(site.frac_coords) - np.array(site2.frac_coords))
+        distances.append(distance)
+
+    sorted_indices = np.argsort(distances)
+    for index in sorted_indices[:3]:
+        real_nearest.append(nearest[index])
+
+    return real_nearest
+
+
+def CS_catulate(
+        struct,
+        sp: str = 'Li',
+        anion: List[str] = ['O'],
+        tol: float = 0,
+        cutoff: float = 3.0,
+        notice: bool = False
+) -> Dict[str, Dict[str, int]]:
+    """
+    计算结构中不同类型阳离子多面体之间的共享关系（角、边、面共享）。
+
+    该函数会分别计算以下三种情况的共享数量：
+    1. 目标原子 vs 目标原子 (e.g., Li-Li)
+    2. 目标原子 vs 其他阳离子 (e.g., Li-X)
+    3. 其他阳离子 vs 其他阳离子 (e.g., X-Y)
+
+    参数:
+        struct (Structure): 输入的pymatgen结构对象。
+        sp (str): 目标元素符号，默认为 'Li'。
+        anion (list): 阴离子元素符号列表，默认为 ['O']。
+        tol (float): VoronoiNN 的容差。对于Li，通常设为0。
+        cutoff (float): VoronoiNN 的截断距离。对于Li，通常设为3.0。
+        notice (bool): 是否打印详细的共享信息。
+
+    返回:
+        dict: 一个字典，包含三类共享关系的统计结果。
+              键 "sp_vs_sp", "sp_vs_other", "other_vs_other" 分别对应上述三种情况。
+              每个键的值是另一个字典，统计了共享2个(边)、3个(面)等情况的数量。
+              例如: {'sp_vs_sp': {'1': 10, '2': 4}, 'sp_vs_other': ...}
+              共享1个阴离子为角共享，2个为边共享，3个为面共享。
+    """
+    # 初始化 VoronoiNN 对象
+    voro_nn = VoronoiNN(tol=tol, cutoff=cutoff)
+
+    # 1. 分类存储所有阳离子的近邻阴离子信息
+    target_sites_info = []
+    other_cation_sites_info = []
+
+    for index, site in enumerate(struct.sites):
+        # 跳过阴离子本身
+        if site.species.chemical_system in anion:
+            continue
+
+        # 获取当前位点的近邻阴离子
+        try:
+            # 使用 get_nn_info 更直接
+            nn_info = voro_nn.get_nn_info(struct, index)
+            nearest_anions = [
+                nn["site"] for nn in nn_info
+                if nn["site"].species.chemical_system in anion
+            ]
+        except Exception as e:
+            print(f"Warning: Could not get neighbors for site {index} ({site.species_string}): {e}")
+            continue
+
+        if not nearest_anions:
+            continue
+
+        # 整理信息
+        site_info = {
+            'index': index,
+            'element': site.species.chemical_system,
+            'nearest_anion_indices': {nn.index for nn in nearest_anions}
+        }
+
+        # 根据是否为目标原子进行分类
+        if site.species.chemical_system == sp:
+            target_sites_info.append(site_info)
+        else:
+            other_cation_sites_info.append(site_info)
+
+    # 2. 初始化结果字典
+    # 共享数量key: 1-角, 2-边, 3-面
+    results = {
+        "sp_vs_sp": {"1": 0, "2": 0, "3": 0, "4": 0},
+        "sp_vs_other": {"1": 0, "2": 0, "3": 0, "4": 0},
+        "other_vs_other": {"1": 0, "2": 0, "3": 0, "4": 0},
+    }
+
+    # 3. 计算不同类别之间的共享关系
+
+    # 3.1 目标原子 vs 目标原子 (sp_vs_sp)
+    for i in range(len(target_sites_info)):
+        for j in range(i + 1, len(target_sites_info)):
+            atom_i = target_sites_info[i]
+            atom_j = target_sites_info[j]
+
+            shared_anions = atom_i['nearest_anion_indices'].intersection(atom_j['nearest_anion_indices'])
+            shared_count = len(shared_anions)
+
+            if shared_count > 0 and str(shared_count) in results["sp_vs_sp"]:
+                results["sp_vs_sp"][str(shared_count)] += 1
+                if notice:
+                    print(
+                        f"[Li-Li] Atom {atom_i['index']} and {atom_j['index']} share {shared_count} anions: {shared_anions}")
+
+    # 3.2 目标原子 vs 其他阳离子 (sp_vs_other)
+    for atom_sp in target_sites_info:
+        for atom_other in other_cation_sites_info:
+            shared_anions = atom_sp['nearest_anion_indices'].intersection(atom_other['nearest_anion_indices'])
+            shared_count = len(shared_anions)
+
+            if shared_count > 0 and str(shared_count) in results["sp_vs_other"]:
+                results["sp_vs_other"][str(shared_count)] += 1
+                if notice:
+                    print(
+                        f"[Li-Other] Atom {atom_sp['index']} and {atom_other['index']} share {shared_count} anions: {shared_anions}")
+
+    # 3.3 其他阳离子 vs 其他阳离子 (other_vs_other)
+    for i in range(len(other_cation_sites_info)):
+        for j in range(i + 1, len(other_cation_sites_info)):
+            atom_i = other_cation_sites_info[i]
+            atom_j = other_cation_sites_info[j]
+
+            shared_anions = atom_i['nearest_anion_indices'].intersection(atom_j['nearest_anion_indices'])
+            shared_count = len(shared_anions)
+
+            if shared_count > 0 and str(shared_count) in results["other_vs_other"]:
+                results["other_vs_other"][str(shared_count)] += 1
+                if notice:
+                    print(
+                        f"[Other-Other] Atom {atom_i['index']} and {atom_j['index']} share {shared_count} anions: {shared_anions}")
+
+    return results
+
+def CS_catulate_old(struct, sp='Li', anion=['O'], tol=0, cutoff=3.0,notice=False,ID=None):
+    """
+    计算结构中目标元素与最近阴离子的共享关系。
+
+    参数:
+        struct (Structure): 输入结构。
+        sp (str): 目标元素符号，默认为 'Li'。
+        anion (list): 阴离子列表，默认为 ['O']。
+        tol (float): VoronoiNN 的容差，默认为 0。
+        cutoff (float): VoronoiNN 的截断距离，默认为 3.0。
+
+    返回:
+        list: 包含每个目标位点及其最近阴离子索引的列表。
+    """
+    # 初始化 VoronoiNN 对象
+    if sp=='Li':
+        tol = 0
+        cutoff = 3.0
+    voro_nn = VoronoiNN(tol=tol, cutoff=cutoff)
+    # 初始化字典，用于统计共享关系
+    shared_count = {"2": 0, "3": 0,"4":0,"5":0,"6":0}
+    # 存储结果的列表
+    atom_dice = []
+
+    # 遍历结构中的每个位点
+    for index,site in enumerate(struct.sites):
+        # 跳过阴离子位点
+        if site.species.chemical_system in anion:
+            continue
+        # 跳过Li原子
+        if site.species.chemical_system == sp:
+            continue
+        # 获取 Voronoi 多面体信息
+        voro_info = voro_nn.get_voronoi_polyhedra(struct, index)
+
+        # 找到最近的阴离子位点
+        nearest_anions = [
+            nn_info["site"] for nn_info in voro_info.values()
+            if nn_info["site"].species.chemical_system in anion
+        ]
+
+        # 如果没有找到最近的阴离子，跳过
+        if not nearest_anions:
+            print(f"No nearest anions found for {ID} site {index}.")
+            continue
+        if site.species.chemical_system == 'B' or site.species.chemical_system == 'N':
+            nearest_anions = special_check_for_3(site,nearest_anions)
+        nearest_anions = check_real(nearest_anions)
+        # 将结果添加到 atom_dice 列表中
+        atom_dice.append({
+            'index': index,
+            'nearest_index': [nn.index for nn in nearest_anions]
+        })
+
+
+
+
+
+        # 枚举 atom_dice 中的所有原子对
+    for i, atom_i in enumerate(atom_dice):
+        for j, atom_j in enumerate(atom_dice[i + 1:], start=i + 1):
+            # 获取两个原子的最近阴离子索引
+            nearest_i = set(atom_i['nearest_index'])
+            nearest_j = set(atom_j['nearest_index'])
+
+            # 比较最近阴离子的交集大小
+            shared_count_key = str(len(nearest_i & nearest_j))
+
+            # 更新字典中的计数
+            if shared_count_key in shared_count:
+                shared_count[shared_count_key] += 1
+                if notice:
+                    if shared_count_key=='2':
+                        print(f"{atom_j['index']}与{atom_i['index']}之间存在共线")
+                        print(f"共线的阴离子为{nearest_i & nearest_j}")
+                    if shared_count_key=='3':
+                        print(f"{atom_j['index']}与{atom_i['index']}之间存在共面")
+                        print(f"共面的阴离子为{nearest_i & nearest_j}")
+
+    # # 最后将字典中的值除以 2，因为每个共享关系被计算了两次
+    # for key in shared_count.keys():
+    #     shared_count[key] //= 2
+
+    return shared_count
+
+
+def CS_count(struct, sharing_results: Dict[str, Dict[str, int]], sp: str = 'Li') -> float:
+    """
+    分析多面体共享结果，计算平均每个目标原子参与的共享阴离子数。
+
+    这个函数是 calculate_polyhedra_sharing 的配套函数。
+
+    参数:
+        struct (Structure): 输入的pymatgen结构对象，用于统计目标原子总数。
+        sharing_results (dict): 来自 calculate_polyhedra_sharing 函数的输出结果。
+        sp (str): 目标元素符号，默认为 'Li'。
+
+    返回:
+        float: 平均每个目标原子sp参与的共享阴离子数量。
+               例如，结果为2.5意味着平均每个Li原子通过共享与其他阳离子
+               （包括Li和其他阳离子）连接了2.5个阴离子。
+    """
+    # 1. 统计结构中目标原子的总数
+    target_atom_count = 0
+    for site in struct.sites:
+        if site.species.chemical_system == sp:
+            target_atom_count += 1
+
+    # 如果结构中没有目标原子，直接返回0，避免除以零错误
+    if target_atom_count == 0:
+        return 0.0
+
+    # 2. 计算加权的共享阴离子总数
+    total_shared_anions = 0
+
+    # 处理 sp_vs_sp (例如 Li-Li) 的共享
+    # 每个共享关系涉及两个目标原子，所以权重需要乘以 2
+    if "sp_vs_sp" in sharing_results:
+        sp_vs_sp_counts = sharing_results["sp_vs_sp"]
+        for num_shared_str, count in sp_vs_sp_counts.items():
+            num_shared = int(num_shared_str)
+            # 权重 = 共享阴离子数 * 涉及的目标原子数 (2) * 出现次数
+            total_shared_anions += num_shared * 2 * count
+
+    # 处理 sp_vs_other (例如 Li-X) 的共享
+    # 每个共享关系涉及一个目标原子，所以权重乘以 1
+    if "sp_vs_other" in sharing_results:
+        sp_vs_other_counts = sharing_results["sp_vs_other"]
+        for num_shared_str, count in sp_vs_other_counts.items():
+            num_shared = int(num_shared_str)
+            # 权重 = 共享阴离子数 * 涉及的目标原子数 (1) * 出现次数
+            total_shared_anions += num_shared * 1 * count
+
+    # 3. 计算平均值
+    # 平均每个目标原子参与的共享阴离子数 = 总的加权共享数 / 目标原子总数
+    average_sharing_per_atom = total_shared_anions / target_atom_count
+
+    return average_sharing_per_atom
+def CS_count_old(struct, shared_count, sp='Li'):
+    count = 0
+    for site in struct.sites:
+        if site.species.chemical_system == sp:
+            count += 1  # 累加符合条件的原子数量
+
+    CS_count = 0
+    for i in range(2, 7):  # 遍历范围 [2, 3, 4, 5]
+        if str(i) in shared_count:  # 检查键是否存在
+            CS_count += shared_count[str(i)] * i  # 累加计算结果
+
+    if count > 0:  # 防止除以零
+        CS_count /= count  # 平均化结果
+    else:
+        CS_count = 0  # 如果 count 为 0，直接返回 0
+
+    return CS_count
+
+
+def check_only_corner_sharing(sharing_results: Dict[str, Dict[str, int]]) -> int:
+    """
+    检查目标原子(sp)是否只参与了角共享（共享1个阴离子）。
+
+    该函数是 calculate_polyhedra_sharing 的配套函数。
+
+    参数:
+        sharing_results (dict): 来自 calculate_polyhedra_sharing 函数的输出结果。
+
+    返回:
+        int:
+        - 1: 如果 sp 的共享关系中，边共享(2)、面共享(3)等数量均为0，
+             并且至少存在一个角共享(1)。
+        - 0: 如果 sp 存在任何边、面等共享，或者没有任何共享关系。
+    """
+    # 提取与目标原子 sp 相关的共享数据
+    sp_vs_sp_counts = sharing_results.get("sp_vs_sp", {})
+    sp_vs_other_counts = sharing_results.get("sp_vs_other", {})
+
+    # 1. 检查是否存在任何边共享、面共享等 (共享数 > 1)
+    # 检查 sp-sp 的共享
+    for num_shared_str, count in sp_vs_sp_counts.items():
+        if int(num_shared_str) > 1 and count > 0:
+            return 0  # 发现了边/面共享，立即返回 0
+
+    # 检查 sp-other 的共享
+    for num_shared_str, count in sp_vs_other_counts.items():
+        if int(num_shared_str) > 1 and count > 0:
+            return 0  # 发现了边/面共享，立即返回 0
+
+    # 2. 检查是否存在至少一个角共享 (共享数 == 1)
+    # 运行到这里，说明已经没有任何边/面共享了。
+    # 现在需要确认是否真的存在角共享，而不是完全没有共享。
+    corner_share_sp_sp = sp_vs_sp_counts.get("1", 0) > 0
+    corner_share_sp_other = sp_vs_other_counts.get("1", 0) > 0
+
+    if corner_share_sp_sp or corner_share_sp_other:
+        return 1  # 确认只存在角共享
+    else:
+        return 0  # 没有任何共享关系，也返回 0
+
+# structure = Structure.from_file("../raw/0921/wjy_001.cif")
+# a = CS_catulate(structure,notice=True)
+# b = CS_count(structure,a)
+# print(f"{a}\n{b}")
+# print(check_only_corner_sharing(a))

py/utils/analyze_env_st.py

@@ -0,0 +1,210 @@
+#!/usr/bin/env python
+# This code extracts the lithium environment of all of lithium sites provided in a structure file.
+import os, sys
+import numpy as np
+import scipy
+import argparse
+from scipy.spatial import ConvexHull
+from itertools import permutations
+from pymatgen.core.structure import Structure
+from pymatgen.core.periodic_table import *
+from pymatgen.core.composition import *
+from pymatgen.ext.matproj import MPRester
+from pymatgen.io.vasp.outputs import *
+from pymatgen.analysis.chemenv.coordination_environments.coordination_geometry_finder import LocalGeometryFinder
+from pymatgen.analysis.chemenv.coordination_environments.structure_environments import LightStructureEnvironments
+from pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies import SimplestChemenvStrategy
+from pymatgen.analysis.chemenv.coordination_environments.coordination_geometries import *
+
+__author__ = "KyuJung Jun"
+__version__ = "0.1"
+__maintainer__ = "KyuJung Jun"
+__email__ = "kjun@berkeley.edu"
+__status__ = "Development"
+
+'''
+Input for the script : path to the structure file supported by Pymatgen
+Structures with partial occupancy should be ordered or modified to full occupancy by Pymatgen.
+'''
+parser = argparse.ArgumentParser()
+parser.add_argument('structure', help='path to the structure file supported by Pymatgen', nargs='?')
+parser.add_argument('envtype', help='both, tet, oct, choosing which perfect environment to reference to', nargs='?')
+args = parser.parse_args()
+
+
+class HiddenPrints:
+    '''
+    class to reduce the output lines
+    '''
+
+    def __enter__(self):
+        self._original_stdout = sys.stdout
+        sys.stdout = open(os.devnull, 'w')
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        sys.stdout.close()
+        sys.stdout = self._original_stdout
+
+
+def non_elements(struct, sp='Li'):
+    """
+    struct : 必须是一个有序结构
+    sp : the mobile specie
+    returns a new structure containing only the framework anions (O, S, N).
+    """
+    anions_to_keep = {"O", "S", "N","Br","Cl"}
+    stripped = struct.copy()
+    species_to_remove = [el.symbol for el in stripped.composition.elements
+                         if el.symbol not in anions_to_keep]
+
+    if species_to_remove:
+        stripped.remove_species(species_to_remove)
+
+    return stripped
+
+
+def site_env(coord, struct, sp="Li", envtype='both'):
+    '''
+    coord : Fractional coordinate of the target atom
+    struct : structure object from Pymatgen
+    sp : the mobile specie
+    envtype : This sets the reference perfect structure. 'both' compares CSM_tet and CSM_oct and assigns to the lower one.
+    'tet' refers to the perfect tetrahedron and 'oct' refers to the perfect octahedron
+    result : a dictionary of environment information
+    '''
+    stripped = non_elements(struct)
+    with_li = stripped.copy()
+    with_li.append(sp, coord, coords_are_cartesian=False, validate_proximity=False)
+    with_li = with_li.get_sorted_structure()
+    tet_oct_competition = []
+    if envtype == 'both' or envtype == 'tet':
+        for dist in np.linspace(1, 4, 601):
+            neigh = with_li.get_neighbors(with_li.sites[0], dist)
+            if len(neigh) < 4:
+                continue
+            elif len(neigh) > 4:
+                break
+            neigh_coords = [i.coords for i in neigh]
+            with HiddenPrints():
+                lgf = LocalGeometryFinder(only_symbols=["T:4"])
+                lgf.setup_structure(structure=with_li)
+                lgf.setup_local_geometry(isite=0, coords=neigh_coords)
+            try:
+                site_volume = ConvexHull(neigh_coords).volume
+                tet_env_list = []
+                for i in range(20):
+                    tet_env = {'csm': lgf.get_coordination_symmetry_measures()['T:4']['csm'], 'vol': site_volume,
+                               'type': 'tet'}
+                    tet_env_list.append(tet_env)
+                tet_env = min(tet_env_list, key=lambda x: x['csm'])
+                tet_oct_competition.append(tet_env)
+
+            except Exception as e:
+                print(e)
+                print("This site cannot be recognized as tetrahedral site")
+            if len(neigh) == 4:
+                break
+    if envtype == 'both' or envtype == 'oct':
+        for dist in np.linspace(1, 4, 601):
+            neigh = with_li.get_neighbors(with_li.sites[0], dist)
+            if len(neigh) < 6:
+                continue
+            elif len(neigh) > 6:
+                break
+            neigh_coords = [i.coords for i in neigh]
+            with HiddenPrints():
+                lgf = LocalGeometryFinder(only_symbols=["O:6"], permutations_safe_override=False)
+                lgf.setup_structure(structure=with_li)
+                lgf.setup_local_geometry(isite=0, coords=neigh_coords)
+            try:
+                site_volume = ConvexHull(neigh_coords).volume
+                oct_env_list = []
+                for i in range(20):
+                    '''
+                    20 times sampled in case of the algorithm "APPROXIMATE_FALLBACK" is used. Large number of permutations
+                    are performed, but the default value in the function "coordination_geometry_symmetry_measures_fallback_random"
+                    (NRANDOM=10) is often too small. This is not a problem if algorithm of "SEPARATION_PLANE" is used.
+                    '''
+                    oct_env = {'csm': lgf.get_coordination_symmetry_measures()['O:6']['csm'], 'vol': site_volume,
+                               'type': 'oct'}
+                    oct_env_list.append(oct_env)
+                oct_env = min(oct_env_list, key=lambda x: x['csm'])
+                tet_oct_competition.append(oct_env)
+
+            except Exception as e:
+                print(e)
+                print("This site cannot be recognized as octahedral site")
+            if len(neigh) == 6:
+                break
+
+    if len(tet_oct_competition) == 0:
+        return {'csm': np.nan, 'vol': np.nan, 'type': 'Non_' + envtype}
+    elif len(tet_oct_competition) == 1:
+        return tet_oct_competition[0]
+    elif len(tet_oct_competition) == 2:
+        csm1 = tet_oct_competition[0]
+        csm2 = tet_oct_competition[1]
+        if csm1['csm'] > csm2['csm']:
+            return csm2
+        else:
+            return csm1
+
+
+def extract_sites(struct, sp="Li", envtype='both'):
+    """
+    struct : structure object from Pymatgen
+    envtype : 'tet', 'oct', or 'both'
+    sp : target element to analyze environment
+    """
+    envlist = []
+
+    # --- 关键修改：直接遍历原始结构，即使它是无序的 ---
+    # 我们不再调用 get_sorted_structure()
+    # 我们只关心那些含有目标元素 sp 的位点
+
+    # 遍历每一个位点 (site)
+    for i, site in enumerate(struct):
+        # 检查当前位点的组分(site.species)中是否包含我们感兴趣的元素(sp)
+        # site.species.elements 返回该位点上的元素列表，例如 [Element Li, Element Fe]
+        # [el.symbol for el in site.species.elements] 将其转换为符号列表 ['Li', 'Fe']
+        site_elements = [el.symbol for el in site.species.elements]
+
+        if sp in site_elements:
+            # 如果找到了Li，我们就对这个位点进行环境分析
+            # 注意：我们将原始的、可能无序的 struct 传递给 site_env
+            # 因为 site_env 内部的函数 (如 LocalGeometryFinder) 知道如何处理它
+
+            # 为了让下游函数（特别是 non_elements）能够工作，
+            # 我们在这里创建一个一次性的、临时的有序结构副本给它
+            # 这可以避免我们之前遇到的所有 'ordered structures only' 错误
+            temp_ordered_struct = struct.get_sorted_structure()
+
+            singleenv = site_env(site.frac_coords, temp_ordered_struct, sp, envtype)
+
+            envlist.append({'frac_coords': site.frac_coords, 'type': singleenv['type'], 'csm': singleenv['csm'],
+                            'volume': singleenv['vol']})
+
+    if not envlist:
+        print(f"警告: 在结构中未找到元素 {sp} 的占位。")
+
+    return envlist
+
+def export_envs(envlist, sp='Li', envtype='both', fname=None):
+    '''
+    envlist : list of dictionaries of environment information
+    fname : Output file name
+    '''
+    if not fname:
+        fname = "extracted_environment_info" + "_" + sp + "_" + envtype + ".dat"
+    with open(fname, 'w') as f:
+
+        f.write('List of environment information\n')
+        f.write('Species : ' + sp + "\n")
+        f.write('Envtype : ' + envtype + "\n")
+        for index, i in enumerate(envlist):
+            f.write("Site index " + str(index) + ": " + str(i) + '\n')
+
+
+# struct = Structure.from_file("../raw/0921/wjy_475.cif")
+# site_info = extract_sites(struct, envtype="both")
+# export_envs(site_info, sp="Li", envtype="both")