a

compare_dir.py

@@ -0,0 +1,44 @@
+import zipfile
+import filecmp
+import os
+import shutil
+
+
+def extract_jar(jar_path, extract_path):
+    with zipfile.ZipFile(jar_path, 'r') as zip_ref:
+        zip_ref.extractall(extract_path)
+
+
+def compare_dirs(dir1, dir2):
+    comparison = filecmp.dircmp(dir1, dir2)
+    if comparison.left_only or comparison.right_only or comparison.diff_files:
+        return False
+    else:
+        for subdir in comparison.common_dirs:
+            if not compare_dirs(os.path.join(dir1, subdir), os.path.join(dir2, subdir)):
+                return False
+        return True
+
+
+def compare_jars(jar1, jar2):
+    temp_dir1 = 'temp_dir1'
+    temp_dir2 = 'temp_dir2'
+
+    # Extract jars
+    extract_jar(jar1, temp_dir1)
+    extract_jar(jar2, temp_dir2)
+
+    # Compare directories
+    are_same = compare_dirs(temp_dir1, temp_dir2)
+
+    # Clean up temp directories
+    shutil.rmtree(temp_dir1)
+    shutil.rmtree(temp_dir2)
+
+    return are_same
+
+
+# Test
+jar1 = '/Users/alvin/Downloads/tmp/rxdp-dm-tag-web.jar'
+jar2 = '/Users/alvin/Downloads/tmp/rxdp-dm-tag-web1.jar'
+print(compare_jars(jar1, jar2))  # If it prints True, jars are same; otherwise, jars are different.

project/compress_dir_img.py

@@ -4,15 +4,19 @@ import argparse
 
 
 def convert_to_webp(dir_path):
+    common_image_extensions = [".jpg", ".jpeg", ".png"]
     for subdir, dirs, files in os.walk(dir_path):
         for file in files:
             filepath = subdir + os.sep + file
             if filepath.endswith(".webp"):
                 continue
+            if not any(filepath.lower().endswith(ext) for ext in common_image_extensions):
+                continue
             try:
                 img = Image.open(filepath)
                 if img.format == "WEBP":
                     continue
+                print("检测到其他格式图片：" + filepath)
                 webp_path = filepath + ".webp"
                 img.save(webp_path, "WEBP")
                 os.remove(filepath)

tmp4.py

@@ -1,25 +1,273 @@
-import pandas as pd
-import numpy as np
-from scipy.stats import ttest_ind
+def exec(obj):
+    def data_pretreatment(map):
+        def get_value(str):
+            end = len(str)
+            if '.' in str:
+                end = str.index('.')
+            return int(str[:end])
 
-# 读取数据文件并创建 DataFrame 对象
-data = pd.read_csv('/Users/alvin/Downloads/sales.csv')
+        parameters = {k: get_value(v) for k, v in map.items()}
+        scores = []
 
-# 数据清洗和预处理
-data = data.dropna()  # 删除缺失值
-data = data[data['sales'] > 0]  # 删除销售数量为 0 的数据
+        for k, v in parameters.items():
+            result = 0
+            if k == 'bh10006':
+                if v == 0:
+                    result = 100
+                elif 0 < v <= 15:
+                    result = 80
+                elif 15 < v <= 25:
+                    result = 60
+                elif 25 < v <= 35:
+                    result = 40
+                elif 35 < v <= 50:
+                    result = 20
+                elif v > 50:
+                    result = 0
+                scores.append((0.069857492, result))
 
-# 计算销售总量和总价
-data['total_sales'] = data['sales'] * data['price']
+            elif k == 'bh10018':
+                if v == 0:
+                    result = 85
+                elif v == 1:
+                    result = 100
+                elif v == 2:
+                    result = 50
+                elif v == 3:
+                    result = 10
+                scores.append((0.048865349, result))
 
-# 对数据进行分组统计，按照 'product_type' 列分组，然后计算每个分组中 'total_sales' 列的总和、平均值和标准差
-grouped_data = data.groupby('product_type')['total_sales'].agg([np.sum, np.mean, np.std])
+            elif k == 'bh10025':
+                if v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 90
+                elif v == 2:
+                    result = 80
+                elif v == 3:
+                    result = 50
+                elif v == 4:
+                    result = 40
+                elif v == 5:
+                    result = 30
+                elif v == 6:
+                    result = 10
+                elif v > 6:
+                    result = 0
+                scores.append((0.0467602, result))
 
-# 对两个产品类型的销售总量进行 T 检验，判断它们的均值是否有显著差异
-product1_sales = data[data['product_type'] == 'product1']['total_sales']
-product2_sales = data[data['product_type'] == 'product2']['total_sales']
-t_statistic, p_value = ttest_ind(product1_sales, product2_sales)
+            elif k == "bh10022":
+                if v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 70
+                elif v == 2:
+                    result = 35
+                elif v == 3:
+                    result = 5
+                scores.append((0.039446769, result))
+            elif k == "bh10019":
+                if v == 0:
+                    result = 80
+                elif v == 1:
+                    result = 100
+                elif v == 2:
+                    result = 40
+                elif v == 3:
+                    result = 5
+                scores.append((0.045120359, result))
+            elif k == "bh10023":
+                if v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 70
+                elif v == 2:
+                    result = 40
+                elif v == 3:
+                    result = 10
+                scores.append((0.043568957, result))
+            elif k == "bh10009":
+                if v == 0:
+                    result = 100
+                elif 0 < v <= 15:
+                    result = 80
+                elif 15 < v <= 25:
+                    result = 60
+                elif 25 < v <= 35:
+                    result = 40
+                elif 35 < v <= 50:
+                    result = 20
+                elif v > 50:
+                    result = 0
+                scores.append((0.092601792, result))
+            elif k == "bh10016":
+                if v == 0:
+                    result = 85
+                elif v == 1:
+                    result = 100
+                elif v == 2:
+                    result = 50
+                elif v == 3:
+                    result = 8
+                scores.append((0.056671171, result))
+            elif k == "bh10024":
+                if v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 70
+                elif v == 2:
+                    result = 50
+                elif v == 3:
+                    result = 25
+                elif v == 4:
+                    result = 5
+                scores.append((0.049505696, result))
+            elif k == "bh10017":
+                if v == 0:
+                    result = 75
+                elif v == 1:
+                    result = 100
+                elif v == 2:
+                    result = 40
+                elif v == 3:
+                    result = 10
+                scores.append((0.052926182, result))
+            elif k == "bh10027":
+                if v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 60
+                elif v > 1:
+                    result = 0
+                scores.append((0.054838899, result))
+            elif k == "cnName":
+                if v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 90
+                elif v == 2:
+                    result = 85
+                elif v == 3:
+                    result = 80
+                elif 4 <= v <= 8:
+                    result = 70
+                elif 8 < v <= 12:
+                    result = 50
+                elif 12 < v <= 15:
+                    result = 30
+                elif v > 15:
+                    result = 0
+                scores.append((0.071630387, result))
+            elif k == "cnNameLM":
+                if v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 90
+                elif v == 2:
+                    result = 85
+                elif v == 3:
+                    result = 80
+                elif 4 <= v <= 8:
+                    result = 70
+                elif 8 < v <= 12:
+                    result = 50
+                elif 12 < v <= 15:
+                    result = 30
+                elif v > 15:
+                    result = 0
+                scores.append((0.066097986, result))
+            elif k == "cnRuleType":
+                if v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 90
+                elif v == 2:
+                    result = 70
+                elif v == 3:
+                    result = 40
+                elif v == 4:
+                    result = 0
+                scores.append((0.072776513, result))
+            elif k == "personCheatIndices":
+                if v == -1:
+                    result = -1
+                elif v == 0:
+                    result = 100
+                elif v == 1:
+                    result = 80
+                elif v == 2:
+                    result = 60
+                elif v == 3:
+                    result = 40
+                elif v > 3:
+                    result = 0
+                scores.append((0.0736, result))
 
-# 输出统计结果
-print('销售数据统计结果：\n', grouped_data)
-print('两种产品类型的销售总量 T 检验结果：\n', 'T 统计量 =', t_statistic, 'p 值 =', p_value)
+        return scores
+
+    def coalesce(scores):
+        hb_score = 0
+        flag = False
+
+        for p in scores:
+            coefficient = p[0]
+            score = p[1]
+            if score == -1:
+                flag = True
+            hb_score += coefficient * score / 0.884267752
+            hb_score = round(hb_score, 10)
+
+        if flag:
+            return 70
+        else:
+            return hb_score
+
+    def is_numeric(value):
+        try:
+            float(value)
+            return True
+        except ValueError:
+            return False
+
+    keys = ['bh10022', 'cnRuleType', 'bh10025', 'bh10024', 'bh10023', 'bh10019', 'personCheatIndices', 'bh10018', 'bh10006', 'cnNameLM', 'bh10017', 'bh10016', 'bh10027', 'huhhuhuhuhuResult', 'cnName', 'bh10009']
+
+    obj = {key: obj[key] for key in keys if key in keys}
+
+    for key in obj.keys():
+        if not is_numeric(obj[key]):
+            obj['abc'] = 70
+            break
+
+    if obj["personCheatIndices"] == "-1":
+        obj['abc'] = 70
+    else:
+        result = coalesce(data_pretreatment(obj))
+        obj['abc'] = result
+    return obj
+
+
+print(exec({
+    "bh10022": "1",
+    "cnRuleType": "2",
+    "child_flow_base_url": "http://rxdp-flow:8080",
+    "bh10025": "1.38",
+    "bh10024": "3",
+    "bh10023": "2",
+    "bh10019": "3",
+    "personCheatIndices": "0",
+    "bh10018": "2",
+    "flow_reused_global_flag": "0.0",
+    "bh10006": "14",
+    "cnNameLM": "21",
+    "bh10017": "3",
+    "bh10016": "2",
+    "bh10027": "0",
+    "huhhuhuhuhuResult": "50.9627708497",
+    "current_flow_ip_address": "172.16.45.34",
+    "cnName": "21",
+    "bh10009": "23",
+    "flow_process_trace": {
+        "20014": []
+    },
+    "abc": "70.0"
+}))

tmp5.py

@@ -0,0 +1,23 @@
+import pymysql
+
+
+def execSql(host, port, user, password, database,sql):
+    try:
+        connection = pymysql.connect(host=host, port=port, user=user, password=password, database=database)
+        cursor = connection.cursor()
+        cursor.execute(sql)
+        result = cursor.fetchall()
+        cursor.close()
+        connection.close()
+        if result:
+            print(result)
+            return True
+        else:
+            return False
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return False
+
+
+
+print(execSql('172.16.104.106', 3322, 'cs_user', 'ceshimima.1', 'vered_system',""))