162 lines
5.9 KiB
Python
162 lines
5.9 KiB
Python
import os
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import pandas as pd
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import math
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import shutil
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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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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
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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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config = THRESHOLDS.get(anion_type)
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if not config:
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return False
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try:
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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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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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c1 = perc > config["perc"]
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c2 = min_d < config["min_d"]
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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_result_file(group_name, anion_name, material_id):
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"""
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创建结果文件 (这里改为直接复制，软链接在跨文件系统或某些环境下可能不稳定，复制更稳妥)
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如果确实需要软链接，可以将 shutil.copy 换回 os.symlink
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"""
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# 1. 构建源文件路径
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# 正确路径: ../data/after_step1/Group/MaterialID/MaterialID.cif
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# 例如: ../data/after_step1/S/195819/195819.cif
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# 注意：如果原本结构是 S+O/S/ID... 这里会自动适配
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# 这里的路径逻辑要非常小心，取决于 extract_data.py 是怎么生成 CSV 目录结构的
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# 如果 CSV 在 output/S/S.csv -> 对应源文件在 after_step1/S/...
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# 如果 CSV 在 output/S+O/S/S.csv -> 对应源文件在 after_step1/S+O/S/...
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if group_name == anion_name:
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# 单阴离子情况 (如 output/S/S.csv -> after_step1/S/ID/ID.cif)
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rel_source_path = os.path.join(DATA_SOURCE_DIR, group_name, material_id, f"{material_id}.cif")
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else:
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# 混合阴离子情况 (如 output/S+O/S/S.csv -> after_step1/S+O/S/ID/ID.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 ../data/after_screening/Group/Anion
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if group_name == anion_name:
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target_subdir = os.path.join(TARGET_DIR, group_name)
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else:
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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. 构建目标文件路径
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target_file_path = os.path.join(target_subdir, f"{material_id}.cif")
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# 4. 执行复制 (改为复制以确保结果独立)
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try:
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if os.path.exists(target_file_path):
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os.remove(target_file_path)
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shutil.copy(abs_source_path, target_file_path)
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# 如果你非常确定要软链接，请注释上一行，解开下一行：
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# os.symlink(abs_source_path, target_file_path)
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except OSError as e:
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print(f"创建文件失败 {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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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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# --- 核心修正 1: 路径解析逻辑 ---
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# 获取相对于 output 根目录的路径部分
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# 例如 root = ../output/S -> rel_root = S
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# 例如 root = ../output/S+O/S -> rel_root = S+O/S
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rel_root = os.path.relpath(root, CSV_ROOT_DIR)
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path_parts = rel_root.split(os.sep)
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# 解析 Group 和 Anion
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if len(path_parts) == 1:
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# 单层目录: output/S -> Group=S, Anion=S
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group_name = path_parts[0]
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anion_name = path_parts[0]
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elif len(path_parts) >= 2:
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# 双层目录: output/S+O/S -> Group=S+O, Anion=S
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group_name = path_parts[0]
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anion_name = path_parts[1]
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else:
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# 根目录下直接有csv的情况 (不应该发生)
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continue
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if anion_name not in THRESHOLDS:
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print(f"跳过不支持的阴离子类型: {anion_name}")
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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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# --- 核心修正 2: 防止 Filename 被读取为浮点数 ---
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# dtypeStr={'Filename': str} 强制将第一列读取为字符串
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df = pd.read_csv(csv_path, dtype={'Filename': str})
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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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# 去除可能存在的 .0 后缀 (以防万一 CSV 里已经写成了浮点格式)
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material_id = str(row['Filename']).replace('.0', '')
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if check_requirements(row, anion_name):
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create_result_file(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} 个材料通过筛选并保存至 {TARGET_DIR}。")
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if __name__ == "__main__":
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process_all_csvs() |