147 lines
5.1 KiB
Python
147 lines
5.1 KiB
Python
import os
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import pandas as pd
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import math
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# ================= 配置区域 =================
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# 定义各阴离子的筛选阈值
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# perc: Percolation diameter (对应 Step 2, 大于此值)
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# min_d: Minimum of d (对应 Step 3, 小于此值)
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# node: Maximum node length (对应 Step 4, 大于此值)
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THRESHOLDS = {
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"O": {"perc": 0.50, "min_d": 3.0, "node": 2.2},
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"S": {"perc": 0.55, "min_d": 3.0, "node": 2.2},
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"Cl": {"perc": 0.45, "min_d": 3.0, "node": 2.0},
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"Br": {"perc": 0.45, "min_d": 3.0, "node": 2.0}
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}
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# 路径配置
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CSV_ROOT_DIR = "../output" # CSV 所在的根目录
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DATA_SOURCE_DIR = "../data/after_step1" # 原始 CIF 文件所在的根目录 (用于创建链接源)
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TARGET_DIR = "../data/after_screening" # 筛选后放置软链接的目标目录
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# ===========================================
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def check_requirements(row, anion_type):
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"""
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检查单行数据是否符合要求
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"""
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# 获取该阴离子类型的阈值配置
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config = THRESHOLDS.get(anion_type)
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if not config:
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print(f"Warning: 未知的阴离子类型 {anion_type}，跳过筛选。")
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return False
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try:
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# 获取数值 (处理可能的空值或非数字情况)
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perc = float(row["Percolation Diameter (A)"])
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min_d = float(row["Minimum of d"])
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node = float(row["Maximum Node Length (A)"])
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# 检查是否为 NaN
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if math.isnan(perc) or math.isnan(min_d) or math.isnan(node):
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return False
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# --- 筛选逻辑 ---
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# Step 2: 连通孔径 > 阈值
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c1 = perc > config["perc"]
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# Step 3: 最短距离 < 3.0 (所有元素目前都是3.0)
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c2 = min_d < config["min_d"]
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# Step 4: 扩大锂离子节点 > 阈值
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c3 = node > config["node"]
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return c1 and c2 and c3
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except (ValueError, TypeError):
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return False
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def create_symlink(group_name, anion_name, material_id):
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"""
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创建软链接
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源: ../data/after_step1/Group/Anion/ID/ID.cif
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目: ../data/after_screening/Group/Anion/ID.cif
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"""
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# 1. 构建源文件路径 (必须使用绝对路径以确保软链接在任何地方都有效)
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# 注意：根据你修改后的 step1，文件在 ID 文件夹内，如 141/141.cif
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rel_source_path = os.path.join(DATA_SOURCE_DIR, group_name, anion_name, material_id, f"{material_id}.cif")
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abs_source_path = os.path.abspath(rel_source_path)
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if not os.path.exists(abs_source_path):
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print(f"源文件不存在: {abs_source_path}")
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return
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# 2. 构建目标文件夹路径
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target_subdir = os.path.join(TARGET_DIR, group_name, anion_name)
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if not os.path.exists(target_subdir):
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os.makedirs(target_subdir)
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# 3. 构建目标链接路径
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target_link_path = os.path.join(target_subdir, f"{material_id}.cif")
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# 4. 创建链接
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try:
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# 如果目标已经存在（可能是旧的链接），先删除
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if os.path.exists(target_link_path) or os.path.islink(target_link_path):
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os.remove(target_link_path)
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os.symlink(abs_source_path, target_link_path)
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# print(f"Link: {material_id} -> Passed")
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except OSError as e:
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print(f"创建软链接失败 {material_id}: {e}")
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def process_all_csvs():
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"""
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遍历 output 文件夹下的所有 CSV 并处理
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"""
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if not os.path.exists(CSV_ROOT_DIR):
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print(f"CSV 目录不存在: {CSV_ROOT_DIR}")
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return
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print("开始执行 Step 2-4 联合筛选...")
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# 遍历 output 目录
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# 结构预期: ../output/Group/Anion/Anion.csv (例如 ../output/O+S/O/O.csv 或 ../output/O/O.csv)
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for root, dirs, files in os.walk(CSV_ROOT_DIR):
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for file in files:
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if file.endswith(".csv"):
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csv_path = os.path.join(root, file)
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# 推断 Group 和 Anion
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# root 的末尾应该是 .../Group/Anion
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# 例如 root = ../output/O+S/O
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path_parts = os.path.normpath(root).split(os.sep)
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# 倒数第一级是 Anion (O), 倒数第二级是 Group (O+S)
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if len(path_parts) >= 2:
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anion_name = path_parts[-1]
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group_name = path_parts[-2]
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else:
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print(f"跳过路径结构异常的 CSV: {csv_path}")
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continue
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# 确保这是一个有效的阴离子类型
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if anion_name not in THRESHOLDS:
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continue
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print(f"正在处理: Group={group_name}, Anion={anion_name} ({file})")
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# 读取 CSV
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df = pd.read_csv(csv_path)
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pass_count = 0
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total_count = len(df)
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for index, row in df.iterrows():
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material_id = str(row['Filename'])
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if check_requirements(row, anion_name):
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create_symlink(group_name, anion_name, material_id)
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pass_count += 1
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print(f" - 完成: {pass_count}/{total_count} 个材料通过筛选并建立链接。")
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if __name__ == "__main__":
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process_all_csvs() |