{ "cells": [ { "cell_type": "markdown", "id": "4626da6c-d228-4270-8d8d-c98784a106ad", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [], "toc-hr-collapsed": true }, "source": [ "# 第一次体测" ] }, { "cell_type": "markdown", "id": "ba2deee7-9c63-4115-bcee-ad341175fc02", "metadata": {}, "source": [ "## 人员信息导入" ] }, { "cell_type": "code", "execution_count": null, "id": "dea5e486-6927-469f-aa57-e5efac4670be", "metadata": { "tags": [] }, "outputs": [], "source": [ "import openpyxl\n", "import json\n", "\n", "\n", "wb = openpyxl.load_workbook('data/农业银行测试名单.xlsx')\n", "sheet = wb.active\n", "# sheets = wb.sheetnames\n", "person = {}\n", "\n", "for n in range(2, sheet.max_row+1):\n", " if sheet.cell(n,1).value is not None:\n", " code = int(sheet.cell(n, 2).value)\n", " person.setdefault(code, {})\n", " dict1 = {}\n", " dict1['name'] = sheet.cell(n, 3).value\n", " if sheet.cell(n, 4).value ==1:\n", " sex = '男'\n", " else:\n", " sex = '女'\n", " dict1['sex'] = sex\n", " birth = str(sheet.cell(n, 5).value).split()[0]\n", " dict1['birth'] = birth\n", " dict1['unit'] = sheet.cell(n, 1).value\n", " if sheet.cell(n,10).value is not None:\n", " dict1['phone'] = sheet.cell(n, 10).value \n", " person[code] = dict1\n", "\n", "filename = 'data/北京农行人员名单.json'\n", "with open(filename, 'w') as fl:\n", " json.dump(person, fl, ensure_ascii=False)\n", "print('ok')" ] }, { "cell_type": "markdown", "id": "1c00e1e4-101a-4e9f-a7ed-9c65200d19df", "metadata": {}, "source": [ "## 成绩导入" ] }, { "cell_type": "code", "execution_count": null, "id": "fd73b8be-d3fe-49aa-99d2-3cc6df4a3eb0", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "re_ta = {}\n", "dict1 = {}\n", "list1 = []\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/places_result_20230918.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#print(list1)\n", "for result in list1:\n", " user = str(result[2])\n", " if user in dict1.keys(): \n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " re_ta[user]['sex'] = dict1[user]['sex'] \n", " re_ta[user]['unit'] = dict1[user]['unit']\n", " item_name = item[m_item]['name']\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", " re_ta[user][item_name]['得分'] = result[5]\n", "print(len(re_ta))\n", "filename = 'data/result_北京农行.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "markdown", "id": "928790e6-d8a4-4b2b-852a-3c323380a80f", "metadata": {}, "source": [ "## 成绩导出" ] }, { "cell_type": "code", "execution_count": null, "id": "b870bd6e-f972-4073-b13d-5fda8cc92bb7", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import openpyxl\n", "\n", "items = ['身高','体重','肺活量','握力','坐位体前屈','纵跳','俯卧撑','一分钟仰卧起坐','单脚站立','选择反应时','台阶指数']\n", "title = ['编号','姓名','性别','单位','身高','体重','肺活量','握力','坐位体前屈','纵跳','俯卧撑','一分钟仰卧起坐','单脚站立','选择反应时','台阶指数']\n", "\n", "filename = 'data/result_北京农行.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict2 = json.load(fl)\n", " \n", "list1 = []\n", "for k, v in dict1.items():\n", " list2 = []\n", " list2.append(str(k).rjust(5,'0'))\n", " list2.append(v['name']) \n", " list2.append(dict2[k]['sex'])\n", " list2.append(dict2[k]['unit'])\n", " for item in items:\n", " if item in v.keys():\n", " list2.append(v[item]['成绩']) \n", " list2.append(v[item]['得分']) \n", " elif item =='name':\n", " list2.append(v[item])\n", " else:\n", " list2.append('') \n", " list2.append('') \n", " list1.append(list2)\n", "filename = 'data/北京农行体测情况.xlsx'\n", "wb = openpyxl.Workbook()\n", "sheet = wb.active\n", "sheet.append(title)\n", "for row in list1:\n", " sheet.append(row)\n", " \n", "wb.save(filename)" ] }, { "cell_type": "markdown", "id": "dd2034ca-fe8b-4322-b06d-8c5eaf96373d", "metadata": {}, "source": [ "## 报告按部门分类更名" ] }, { "cell_type": "code", "execution_count": null, "id": "04967ec6-b9a0-4097-9e1f-8beaf8a9e75f", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os,sys,shutil\n", "import json\n", "import math\n", "import glob\n", "from pathlib import Path\n", "\n", "fi_path = 'file/2023-05-23'\n", "old = []\n", "dict2 = {}\n", "\n", "filename = 'data/result_北京农行.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "\n", "for k, v in dict1.items():\n", " m_name = v['name']\n", " m_depart = v['unit'] \n", " dict2[int(k)] = [m_name,m_depart]\n", "\n", "fls = glob.glob(f'{fi_path}/*.pdf')\n", "\n", "for fn in fls:\n", " old.append(os.path.basename(fn).split('.')[0])\n", "\n", "for n in old: \n", " o_name = f'{fi_path}/{n}.pdf'\n", " new_path = Path(fi_path,'new',dict1[n]['unit'])\n", " new_path.mkdir(parents = True, exist_ok = True)\n", " n_name = Path(new_path,f'{str(n).rjust(5,\"0\")}-{dict2[int(n)][0]}.pdf')\n", " if not os.path.exists(n_name):\n", " shutil.copyfile(o_name,n_name)\n", " print(n_name)\n", " \n", "print('ok')" ] }, { "cell_type": "markdown", "id": "515bc854-5745-4c38-8633-f56a078ea9a8", "metadata": { "execution": { "iopub.execute_input": "2023-05-25T09:28:28.615647Z", "iopub.status.busy": "2023-05-25T09:28:28.614774Z", "iopub.status.idle": "2023-05-25T09:28:28.619865Z", "shell.execute_reply": "2023-05-25T09:28:28.618821Z", "shell.execute_reply.started": "2023-05-25T09:28:28.615607Z" } }, "source": [ "## 生成人员电话信息表" ] }, { "cell_type": "code", "execution_count": null, "id": "c925d850-ff06-4be5-bf9f-31f4385c37c0", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import openpyxl\n", "wb = openpyxl.load_workbook('data/农业银行测试手机号码统计表.xlsx')\n", "sheet = wb.active\n", "# sheets = wb.sheetnames\n", "dict1 = {}\n", "\n", "for n in range(2, sheet.max_row+1):\n", " if sheet.cell(n,5).value is not None:\n", " phone = str(sheet.cell(n, 5).value)\n", " dict1.setdefault(phone,{})\n", " code = str(sheet.cell(n, 2).value)\n", " unit = sheet.cell(n, 1).value\n", " name = sheet.cell(n, 3).value\n", " sex = sheet.cell(n, 4).value\n", " dict1[phone]['code'] = code\n", " dict1[phone]['name'] = name\n", " #dict1[phone]['unit'] = unit\n", " #dict1[phone]['sex'] = sex\n", "filename = 'data/农业银行测试手机号码.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) \n", "print(len(dict1))\n" ] }, { "cell_type": "markdown", "id": "0f13e837-15d9-4dc6-b356-78b79bbb5b17", "metadata": {}, "source": [ "## 生成验证码" ] }, { "cell_type": "code", "execution_count": null, "id": "a87384c6-dc46-4d09-b757-b42462ed818d", "metadata": { "tags": [] }, "outputs": [], "source": [ "import random\n", "import json\n", "\n", "code_list = []\n", "for i in range(10): # 0~9\n", " code_list.append(str(i))\n", " \n", "list1 = []\n", "#print(code_num)\n", "i = 1\n", "while i < 165:\n", " code = random.sample(code_list,6) #随机取6位数\n", " code_num = ''.join(code)\n", " if code_num not in list1:\n", " list1.append(code_num)\n", " i +=1\n", "print(len(list1))\n", "print(list1)\n", "i = 0\n", "filename = 'data/农业银行测试手机号码.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " dict1[k]['yzm'] = list1[i]\n", " i +=1\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) " ] }, { "cell_type": "markdown", "id": "6efc1c2b-7af1-49ec-9b48-8930a8dd2801", "metadata": {}, "source": [ "## 生成下载链接" ] }, { "cell_type": "code", "execution_count": null, "id": "10845b90-48b4-4418-9dac-dd4a6f0147c0", "metadata": { "tags": [] }, "outputs": [], "source": [ "filename = 'data/农业银行测试手机号码.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "dict2 = {}\n", "for k, v in dict1.items():\n", " dict2.setdefault(v['yzm'],{})\n", " dict2[v['yzm']]['code'] = v['code']\n", " dict2[v['yzm']]['name'] = v['name']\n", "print(len(dict2))\n", "filename = 'data/yzm.json'\n", "with open(filename,'w') as fl:\n", " json.dump(dict2, fl, ensure_ascii=False) \n" ] }, { "cell_type": "markdown", "id": "fba0a0c4-edf2-45c2-9199-c6a86919f191", "metadata": {}, "source": [ "## 发送短信" ] }, { "cell_type": "code", "execution_count": null, "id": "62733f52-62c1-43aa-964a-232eab8b5251", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "from tencentcloud.common import credential\n", "from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException\n", "from tencentcloud.sms.v20210111 import sms_client, models\n", "from tencentcloud.common.profile.client_profile import ClientProfile\n", "from tencentcloud.common.profile.http_profile import HttpProfile\n", "try: \n", " secretId = \"AKID22rVyvSqbikXFFvav31ykc9YmqjN6KYc\"\n", " secretKey = \"51XVL1YxDKI6mJh8au6DDuTskS1RhwJX\"\n", " cred = credential.Credential(secretId, secretKey) \n", " httpProfile = HttpProfile()\n", " httpProfile.reqMethod = \"POST\" # post请求(默认为post请求)\n", " httpProfile.reqTimeout = 30 # 请求超时时间，单位为秒(默认60秒)\n", " httpProfile.endpoint = \"sms.tencentcloudapi.com\" # 指定接入地域域名(默认就近接入)\n", " clientProfile = ClientProfile()\n", " clientProfile.signMethod = \"TC3-HMAC-SHA256\" # 指定签名算法\n", " clientProfile.language = \"en-US\"\n", " clientProfile.httpProfile = httpProfile\n", " client = sms_client.SmsClient(cred, \"ap-guangzhou\", clientProfile)\n", " req = models.SendSmsRequest()\n", " req.SmsSdkAppId = \"1400140089\"\n", " req.SignName = \"坤铭教育\"\n", " req.TemplateId = \"1875765\" \n", " req.TemplateParamSet = [\"北京石油分公司体测者\",\"01740518\"]\n", " req.PhoneNumberSet = [\"+8613793180751\"]\n", " req.SessionContext = \"\"\n", " req.ExtendCode = \"\"\n", " req.SenderId = \"\"\n", " resp = client.SendSms(req)\n", " print(resp.to_json_string(indent=2))\n", "except TencentCloudSDKException as err:\n", " print(err)" ] }, { "cell_type": "code", "execution_count": null, "id": "e924a0a9-65a5-49c4-bdf2-b9152d278ad5", "metadata": { "tags": [] }, "outputs": [], "source": [ "dict1 = json.loads(resp.to_json_string(indent=2))\n", "print(dict1['SendStatusSet'][0][\"Message\"])" ] }, { "cell_type": "markdown", "id": "c55a8af0-4e4d-4bd4-b5b1-0a7d9170958c", "metadata": {}, "source": [ "## 批量发送短信（测试）" ] }, { "cell_type": "code", "execution_count": null, "id": "910d1881-d090-48dc-ac87-685ccaaf9553", "metadata": { "tags": [] }, "outputs": [], "source": [ "\n", "from tencentcloud.common import credential\n", "from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException\n", "from tencentcloud.sms.v20210111 import sms_client, models\n", "from tencentcloud.common.profile.client_profile import ClientProfile\n", "from tencentcloud.common.profile.http_profile import HttpProfile\n", "import json\n", "\n", "filename = 'data/短信测试手机号码.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " phone = '+86'+str(k)\n", " yzm = v['yzm']\n", " try: \n", " secretId = \"AKID22rVyvSqbikXFFvav31ykc9YmqjN6KYc\"\n", " secretKey = \"51XVL1YxDKI6mJh8au6DDuTskS1RhwJX\"\n", " cred = credential.Credential(secretId, secretKey) \n", " httpProfile = HttpProfile()\n", " httpProfile.reqMethod = \"POST\" # post请求(默认为post请求)\n", " httpProfile.reqTimeout = 30 # 请求超时时间，单位为秒(默认60秒)\n", " httpProfile.endpoint = \"sms.tencentcloudapi.com\" # 指定接入地域域名(默认就近接入)\n", " clientProfile = ClientProfile()\n", " clientProfile.signMethod = \"TC3-HMAC-SHA256\" # 指定签名算法\n", " clientProfile.language = \"en-US\"\n", " clientProfile.httpProfile = httpProfile\n", " client = sms_client.SmsClient(cred, \"ap-guangzhou\", clientProfile)\n", " req = models.SendSmsRequest()\n", " req.SmsSdkAppId = \"1400140089\"\n", " req.SignName = \"坤铭教育\"\n", " req.TemplateId = \"1808914\" \n", " req.TemplateParamSet = [yzm]\n", " req.PhoneNumberSet = [phone]\n", " req.SessionContext = \"\"\n", " req.ExtendCode = \"\"\n", " req.SenderId = \"\"\n", " #resp = client.SendSms(req)\n", " dict2 = json.loads(resp.to_json_string(indent=2))\n", "\n", " print(v['name'],dict2['SendStatusSet'][0][\"Message\"])\n", " except TencentCloudSDKException as err:\n", " print(v['name'],err)" ] }, { "cell_type": "markdown", "id": "59d38404-e097-4f08-a3fd-565e351f09bb", "metadata": { "tags": [], "toc-hr-collapsed": true }, "source": [ "# 第二次体测" ] }, { "cell_type": "markdown", "id": "deeb7606-5752-4337-9117-4ba9edc762e9", "metadata": {}, "source": [ "## 人员信息导入" ] }, { "cell_type": "code", "execution_count": null, "id": "d803a2b2-4a61-42ba-84a3-e9846e3e9f5c", "metadata": {}, "outputs": [], "source": [ "import openpyxl\n", "import json\n", "\n", "\n", "wb = openpyxl.load_workbook('data/北京分行员工体质检测情况表.xlsx')\n", "sheet = wb.active\n", "# sheets = wb.sheetnames\n", "person = {}\n", "\n", "for n in range(2, sheet.max_row+1):\n", " if sheet.cell(n,1).value is not None:\n", " code = int(sheet.cell(n, 1).value)\n", " person.setdefault(code, {})\n", " dict1 = {}\n", " dict1['name'] = sheet.cell(n, 3).value\n", " if sheet.cell(n, 4).value ==1:\n", " sex = '男'\n", " else:\n", " sex = '女'\n", " dict1['sex'] = sex\n", " birth = str(sheet.cell(n, 5).value).split()[0]\n", " dict1['birth'] = birth\n", " dict1['unit'] = sheet.cell(n, 2).value\n", " if sheet.cell(n,6).value is not None:\n", " dict1['phone'] = sheet.cell(n, 6).value \n", " person[code] = dict1\n", "\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename, 'w') as fl:\n", " json.dump(person, fl, ensure_ascii=False)\n", "print('ok')" ] }, { "cell_type": "markdown", "id": "ff97823f-fd67-45ae-bd78-f209317be6b9", "metadata": {}, "source": [ "## 成绩导入" ] }, { "cell_type": "code", "execution_count": null, "id": "4adf86dd-addf-490a-9954-5dcac4693868", "metadata": {}, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "re_ta = {}\n", "dict1 = {}\n", "list1 = []\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/places_result_20230825.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#print(list1)\n", "for result in list1:\n", " user = str(result[2])\n", " if user in dict1.keys(): \n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " re_ta[user]['sex'] = dict1[user]['sex'] \n", " re_ta[user]['unit'] = dict1[user]['unit']\n", " item_name = item[m_item]['name']\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", "print(len(re_ta))\n", "filename = 'data/result_北京农行0825.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "code", "execution_count": null, "id": "bb01b9a9-418d-4a1b-8ea9-1215af0b8285", "metadata": {}, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "re_ta = {}\n", "dict1 = {}\n", "list1 = []\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/places_result_20230825.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#print(list1)\n", "for result in list1:\n", " user = str(result[2])\n", " if user in dict1.keys(): \n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " re_ta[user]['sex'] = dict1[user]['sex'] \n", " re_ta[user]['unit'] = dict1[user]['unit']\n", " item_name = item[m_item]['name']\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", "print(len(re_ta))\n", "filename = 'data/result_北京农行0825.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "markdown", "id": "4d2a0d63-4124-4063-96a7-a02478b9eabe", "metadata": {}, "source": [ "## 查看项目不足人员" ] }, { "cell_type": "code", "execution_count": null, "id": "b26857af-5fa3-42d0-bf79-bc33d9f5e4a8", "metadata": {}, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = 'data/result_北京农行0825.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "i= 0\n", "for k, v in dict1.items():\n", " if len(v.keys())< 8:\n", " print(k,v['name'])" ] }, { "cell_type": "markdown", "id": "1966307c-4161-4c5b-b19a-b8a143972223", "metadata": {}, "source": [ "## 导出测试人员信息" ] }, { "cell_type": "code", "execution_count": null, "id": "288e39fb-b7e3-4566-ac13-ba98d7e72edb", "metadata": {}, "outputs": [], "source": [ "import json\n", "import openpyxl\n", "\n", "\n", "filename = 'data/result_北京农行0825.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "list1 =[]\n", "for k, v in dict1.items():\n", " list2 = []\n", " list2 = [k,v['name'],v['sex'],v['unit']]\n", " list1.append(list2)\n", "filename = 'data/北京农行测试人员202308.xlsx'\n", "wb = openpyxl.Workbook()\n", "sheet = wb.active\n", "\n", "for row in list1:\n", " sheet.append(row)\n", " \n", "wb.save(filename)" ] }, { "cell_type": "markdown", "id": "a42cb587-8673-4655-8943-3cbd63f22b73", "metadata": {}, "source": [ "## 检查性别" ] }, { "cell_type": "code", "execution_count": null, "id": "6352b95c-0e19-4943-9e99-218138213060", "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "\n", "filename = 'data/result_北京农行0825.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " if v['sex'] == '男' and '一分钟仰卧起坐' in v.keys():\n", " print(k,'应为女性！')\n", " if v['sex'] == '女' and '俯卧撑' in v.keys():\n", " print(k,'应为男性！')" ] }, { "cell_type": "markdown", "id": "fddd68cd-b20c-4cf9-b503-5d59f52e3c1b", "metadata": {}, "source": [ "## 手工数据生成SQL" ] }, { "cell_type": "code", "execution_count": null, "id": "372294de-5370-41c0-9883-b18de9d8ef7e", "metadata": { "tags": [] }, "outputs": [], "source": [ "import openpyxl\n", "import json\n", "\n", "\n", "wb = openpyxl.load_workbook('data/北京农行测试人员202308.xlsx')\n", "sheet = wb.active\n", "# sheets = wb.sheetnames\n", "dict1 = {}\n", "\n", "data1 =list(sheet.values)\n", "del data1[0]\n", "list1 = []\n", "for item in data1:\n", " code = int(item[0])\n", " list1.append((309118,code,5,item[4],'2023-08-23','2023-08-23 22:00:00'))\n", "print(list1)" ] }, { "cell_type": "markdown", "id": "c32bdb5c-d987-4b0f-98e3-9ce4e3ef3671", "metadata": {}, "source": [ "## 核对问卷人员信息" ] }, { "cell_type": "code", "execution_count": null, "id": "c1b187e0-f566-43fa-88d8-090deb9ead19", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import csv\n", "\n", "\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", " \n", "filename = 'data/Survey_20230913.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " code = str(line[4])\n", " name = line[3]\n", " \n", " if code not in dict1.keys() or name!= dict1[code]['name']:\n", " print(code,name)\n", " \n", " " ] }, { "cell_type": "markdown", "id": "39cd284f-99fe-4ba0-928c-9da01042e394", "metadata": {}, "source": [ "## 合并人员信息" ] }, { "cell_type": "code", "execution_count": null, "id": "7ef51041-ad78-4c6a-af83-ed3e0473b199", "metadata": {}, "outputs": [], "source": [ "import json\n", "\n", "filename = 'data/北京农行人员名单2308.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/北京农行人员名单.json'\n", "with open(filename,'r') as fl:\n", " dict2 = json.load(fl) \n", "for k, v in dict2.items():\n", " dict1[k] = v\n", "filename = 'data/北京农行人员名单all.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "markdown", "id": "3877fc60-171c-434a-aa27-8f1fe9936dd2", "metadata": {}, "source": [ "## 报告按部门分类" ] }, { "cell_type": "code", "execution_count": null, "id": "80a85fcd-0597-4b58-898f-de420239919f", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os,sys,shutil\n", "import json\n", "import glob\n", "from pathlib import Path\n", "\n", "fi_path = '/home/songyi/python/mycrm/flask/pdf/files/309118'\n", "new_path = 'file/309118'\n", "old = []\n", "dict2 = {}\n", "\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "fls = glob.glob(f'{fi_path}/*.pdf')\n", "for fn in fls:\n", " fi_name =Path(fn).stem.split('-')[0]\n", " code = int(fi_name)\n", " unit_path = Path(new_path,dict1[str(code)]['unit'])\n", " unit_path.mkdir(parents = True, exist_ok = True)\n", " n_name = Path(unit_path,Path(fn).stem+'.pdf')\n", " if not os.path.exists(n_name):\n", " shutil.copyfile(fn,n_name)\n", " print(n_name)" ] }, { "cell_type": "markdown", "id": "3022dcdd-8700-468f-86ab-6ce43b118e1c", "metadata": {}, "source": [ "## 生成体测报告打印明细表" ] }, { "cell_type": "code", "execution_count": null, "id": "24433f75-6397-4b12-870e-611b77db8ded", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os,sys,shutil\n", "import json\n", "import glob\n", "from pathlib import Path\n", "import openpyxl\n", "\n", "fi_path = '/home/songyi/python/mycrm/flask/pdf/files/309118'\n", "new_path = 'file/309118'\n", "list1 = []\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "fls = glob.glob(f'{fi_path}/*.pdf')\n", "for fn in fls:\n", " list2 = []\n", " fi_name =Path(fn).stem.split('-')[0] \n", " code = int(fi_name)\n", " unit = dict1[str(code)]['unit']\n", " list2 = [fi_name,Path(fn).stem.split('-')[1],unit]\n", " list1.append(list2)\n", "filename = 'data/北京农行体测报告打印明细表.xlsx'\n", "wb = openpyxl.Workbook()\n", "sheet = wb.active\n", "sheet.append(title)\n", "for row in list1:\n", " sheet.append(row)\n", " \n", "wb.save(filename) \n", "print('ok')" ] }, { "cell_type": "markdown", "id": "d71a24d1-ab29-4852-9bfc-384deb8f57a5", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [] }, "source": [ "# 数据分析" ] }, { "cell_type": "markdown", "id": "d70ab6c9-4fb7-4f0d-864d-96e0ce05fb64", "metadata": {}, "source": [ "## 获取清理后数据" ] }, { "cell_type": "code", "execution_count": null, "id": "ac3b256e-7470-4cfa-b2ee-eb1110c9097c", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import time\n", "import csv\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "\n", "re_ta = {}\n", "list1 = []\n", "#print(\"\\n运动项目信息：\")\n", "filename = 'data/places_result_20230922-1695356859947.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#print(list1)\n", "for result in list1:\n", " user = str(result[2])\n", " if user in dict1.keys(): \n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " \n", " re_ta[user]['sex'] = dict1[user]['sex']\n", " re_ta[user]['birth'] = dict1[user]['birth'].replace('-','/')\n", " item_name = item[m_item]['name']\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", " re_ta[user][item_name]['得分'] =result[5]\n", "\n", "print(len(re_ta))\n", "liwai = []\n", "for k, v in re_ta.items():\n", " if (len(v)<8 and '身高' in v and '体重' in v) or (len(v)<7 and '身高' not in v and '体重' not in v) :\n", " liwai.append(k)\n", "print(liwai)\n", "for k in liwai:\n", " del re_ta[k]\n", "print(len(re_ta))\n", "filename = 'data/result_北京农行all.json'\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print('ok')\n", "print(len(re_ta))" ] }, { "cell_type": "markdown", "id": "43c0dad1-9b96-4480-ae3d-d7fb4a2a8df5", "metadata": {}, "source": [ "## 计算人员年龄" ] }, { "cell_type": "code", "execution_count": null, "id": "567f512c-2f8a-4441-99c1-f091b40dfd3b", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import datetime\n", "\n", "filename = 'data/result_北京农行all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " #print(k,v)\n", " if '.' in v['birth']:\n", " birth = v['birth'].split()[0].split('.')\n", " else:\n", " birth = v['birth'].split()[0].split('/')\n", " \n", " nian = int(birth[0].strip())\n", " yue = int(birth[1].strip())\n", " ri = int(birth[2].strip())\n", " #print(k,nian,yue,ri)\n", " if int(k) < 199:\n", " days = (datetime.date(2023, 5, 11)-datetime.date(nian,yue,ri)).days\n", " else:\n", " days = (datetime.date(2023, 8, 24)-datetime.date(nian,yue,ri)).days\n", " v['age'] = int(days/365)\n", "filename = 'data/result_北京农行all.json'\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) \n", "print('ok') " ] }, { "cell_type": "markdown", "id": "7b2974f0-8b5e-40b9-a872-b0df27097096", "metadata": {}, "source": [ "## 汇总人员信息及成绩" ] }, { "cell_type": "code", "execution_count": null, "id": "576f9b57-237f-4376-b795-122a4cd2222a", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "\n", "items = ['体重','肺活量','握力','坐位体前屈','纵跳','俯卧撑','一分钟仰卧起坐','单脚站立','选择反应时','台阶指数']\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " print(k)\n", " score = 0\n", " i = 0 \n", " for k1,v1 in v.items(): \n", " if k1 in items:\n", " score = score + int(v1['得分'])\n", " i+=1\n", " dict1[k]['score'] = round(score/i,2) \n", "\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl, ensure_ascii=False) \n", "print('ok')" ] }, { "cell_type": "markdown", "id": "0fd37ce2-f965-4a46-8207-c5ce9a7b426a", "metadata": {}, "source": [ "## 计算测试等级" ] }, { "cell_type": "code", "execution_count": null, "id": "ec2773cb-16ff-4957-aa51-627bfe0caf95", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "dict2 = {}\n", "dict2['不合格'] = [0,255]\n", "dict2['合格'] = [256,332]\n", "dict2['良好'] = [333,367]\n", "dict2['优秀'] = [368,500]\n", "\n", "for k1, v1 in dict2.items():\n", " di = v1[0]\n", " gao = v1[1]\n", " i = 0 \n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if int(v['score']*100) in range(di,gao+1):\n", " dict1[k]['level'] = k1\n", " i+=1\n", " if v['sex'] == '男':\n", " m = m +1\n", " else:\n", " f = f+1\n", " print(f'{di}~{gao}分人数：{i}人,男性：{m}人,女性:{f}人')\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(dict1, fl) \n", "print('ok')" ] }, { "cell_type": "markdown", "id": "058f29fc-6de5-42b6-ba07-95d39e014418", "metadata": {}, "source": [ "## 根据年龄汇总人员信息及成绩" ] }, { "cell_type": "code", "execution_count": null, "id": "c605482a-3aa5-46ff-8648-2675408470a8", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,69]]\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " i = 0\n", " score = 0\n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1):\n", " score = score+v['score']\n", " i+=1\n", " if v['sex'] == '男':\n", " m = m +1\n", " \n", " print(f'{di}~{gao}岁平均成绩：{round(score/i,2)}分，人数：{i}人,男性：{m}人')" ] }, { "cell_type": "markdown", "id": "8a15ed82-93bb-4e25-a59e-2358c4d47ca5", "metadata": {}, "source": [ "### 根据年龄汇总人员信息及成绩（男）" ] }, { "cell_type": "code", "execution_count": null, "id": "a75f1748-444a-4b0f-a38c-79d814a16ae0", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " i = 0\n", " score = 0\n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1) and v['sex'] == '男':\n", " score = score+v['score']\n", " i+=1\n", " if i >0:\n", " print(f'{di}~{gao}岁平均成绩：{round(score/i,2)}分，人数：{i}人')\n", " else:\n", " print(f'{di}~{gao}岁平均成绩：0分，人数：0人') " ] }, { "cell_type": "markdown", "id": "9840c5a8-2a06-4d7b-91f5-d3c2d43d1d02", "metadata": {}, "source": [ "### 根据年龄汇总人员信息及成绩（女）" ] }, { "cell_type": "code", "execution_count": null, "id": "d735d1e2-02d3-4768-a5eb-22792791ca23", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " i = 0\n", " score = 0\n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1) and v['sex'] == '女':\n", " score = score+v['score']\n", " i+=1\n", " if i >0:\n", " print(f'{di}~{gao}岁平均成绩：{round(score/i,2)}分，人数：{i}人')\n", " else:\n", " print(f'{di}~{gao}岁平均成绩：0分，人数：0人') " ] }, { "cell_type": "markdown", "id": "f4a02b1d-2468-4430-8f55-3ffaf07efd34", "metadata": {}, "source": [ "## 计算平均成绩" ] }, { "cell_type": "code", "execution_count": null, "id": "fc897a81-7727-4f37-8ab3-8e9de72855d6", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "#filename = 'data/result_石家庄.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "i = 1\n", "m = 0\n", "f = 0\n", "score = 0\n", "t_score = 0\n", "for k,v in dict1.items():\n", " if v['sex'] == '男':\n", " m = m +1\n", " score = score+v['score']\n", "print(f'平均成绩：{round(score/m,4)}分，男性：{m}人')\n", "t_score = t_score + score\n", "score = 0\n", "for k,v in dict1.items():\n", " if v['sex'] == '女':\n", " f = f +1\n", " score = score+v['score']\n", "print(f'平均成绩：{round(score/f,4)}分，女性：{f}人')\n", "t_score = t_score + score\n", "print(f'平均成绩：{round(t_score/522,4)}分，总体：522人')" ] }, { "cell_type": "markdown", "id": "f4911bde-1a29-4fdf-a7f7-4ebef0905936", "metadata": {}, "source": [ "## 计算各年龄段测试等级(女）" ] }, { "cell_type": "code", "execution_count": null, "id": "43c8fd61-4bdb-4d8e-8061-f4f186bd1845", "metadata": { "tags": [] }, "outputs": [], "source": [ "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "dict2 = {}\n", "dict2['不合格'] = [0,255]\n", "dict2['合格'] = [256,332]\n", "dict2['良好'] = [333,367]\n", "dict2['优秀'] = [368,500]\n", "dict3 = {}\n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " age = f'{di}-{gao}'\n", " dict3.setdefault(age,{})\n", " i = 0 \n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1): \n", " dict3[age].setdefault(v['level'],0)\n", " if v['sex'] == '女':\n", " dict3[age][v['level']] = dict3[age][v['level']]+1\n", " \n", "for k, v in dict3.items():\n", " print(k,v)" ] }, { "cell_type": "markdown", "id": "6b5d0882-6c64-4e39-813b-a4c3ebb98006", "metadata": {}, "source": [ "## 计算各年龄段测试等级(男）" ] }, { "cell_type": "code", "execution_count": null, "id": "aefe62b7-8fcf-43e0-a525-a8584b064167", "metadata": { "tags": [] }, "outputs": [], "source": [ "nld = [[20,24],[25,29],[30,34],[35,39],[40,44],[45,49],[50,54],[55,80]]\n", "\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "dict2 = {}\n", "dict2['不合格'] = [0,255]\n", "dict2['合格'] = [256,332]\n", "dict2['良好'] = [333,367]\n", "dict2['优秀'] = [368,500]\n", "dict3 = {}\n", "for item in nld:\n", " di = item[0]\n", " gao = item[1]\n", " age = f'{di}-{gao}'\n", " dict3.setdefault(age,{})\n", " i = 0 \n", " m = 0\n", " f = 0\n", " for k,v in dict1.items():\n", " if v['age'] in range(di,gao+1): \n", " dict3[age].setdefault(v['level'],0)\n", " if v['sex'] == '男':\n", " dict3[age][v['level']] = dict3[age][v['level']]+1\n", " \n", "for k, v in dict3.items():\n", " print(k,v)" ] }, { "cell_type": "markdown", "id": "446217a9-4805-4627-a904-ca734e348433", "metadata": {}, "source": [ "## 计算各项目成绩" ] }, { "cell_type": "code", "execution_count": null, "id": "23b7518c-7434-467d-8d38-642f78bde7d9", "metadata": { "tags": [] }, "outputs": [], "source": [ "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "items = ['体重','肺活量','握力','坐位体前屈','纵跳','俯卧撑','一分钟仰卧起坐','单脚站立','选择反应时','台阶指数']\n", "for item in items:\n", " score = 0\n", " n = 0\n", " for k, v in dict1.items():\n", " if item in v.keys():\n", " n = n + 1\n", " score =score + int(v[item]['得分'])\n", " print(item,round(score/n,2),n)" ] }, { "cell_type": "markdown", "id": "2774523c-286d-460e-9003-434b4171f963", "metadata": {}, "source": [ "# 百日筑基" ] }, { "cell_type": "markdown", "id": "1f30663e-2790-4e0e-a5e5-9088941fe6d9", "metadata": {}, "source": [ "## 整理人员信息" ] }, { "cell_type": "code", "execution_count": null, "id": "4a99a23f-3b9e-438f-aaa3-79992112f9d2", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "import openpyxl\n", "\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict2 = json.load(fl)\n", "list1 = ['握力组', '平衡组', '平和质', '气虚质', '阳虚质', '阴虚质', '痰湿质', '湿热质', '血瘀质', '气郁质', '特禀质'] \n", "wb = openpyxl.load_workbook('data/北京农行体测分组情况.xlsx')\n", "#sheet = wb.active\n", "#sheets = wb.sheetnames\n", "for item in list1:\n", " sheet = wb[item]\n", " for n in range(2, sheet.max_row+1):\n", " if sheet.cell(n,1).value is not None:\n", " code = int(sheet.cell(n, 1).value)\n", " person.setdefault(code, {})\n", " dict1 = {}\n", " dict1['name'] = dict2[str(code)]['name']\n", " dict1['phone'] = dict2[str(code)]['phone']\n", " dict1['unit'] = item\n", " name = sheet.cell(n, 2).value\n", " person[code] = dict1\n", "\n", "filename = 'data/北京农行百日筑基.json'\n", "with open(filename, 'w') as fl:\n", " json.dump(person, fl, ensure_ascii=False)\n", "print('ok') \n", " \n", " " ] }, { "cell_type": "markdown", "id": "2afc474b-daa9-4524-afdf-eed2d7a9e40f", "metadata": {}, "source": [ "## 人员分组" ] }, { "cell_type": "code", "execution_count": null, "id": "4bd746d9-b573-4cdb-a536-008fe9cc1517", "metadata": { "tags": [] }, "outputs": [], "source": [ "import json\n", "\n", "item = {\n", " '血瘀质':'term_6547430d71970_aIWYoQ',\n", " '湿热质':'term_6547428956b35_BlEnpY',\n", " '痰湿质':'term_654742123c1bd_hPSDR4',\n", " '气虚质':'term_654741bc7c0fe_bf0PLC',\n", " '气郁质':'term_6547415695b85_5FAHbR',\n", " '阴虚质':'term_654740fdb2e6b_0pBU7m',\n", " '阳虚质':'term_654740777e521_tsKUsG',\n", " '特禀质':'term_65473fd157db4_FmXOE9',\n", " '平和质':'term_65473f50247a5_bGI0dX',\n", " '平衡组':'term_65473efbcc37f_dJgc2H',\n", " '握力组':'term_65472c788dbf6_5Hj3iw'\n", "}\n", "filename = 'data/北京农行百日筑基.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "for k, v in dict1.items():\n", " url = 'https://appvaCALBPV5946.h5.xiaoeknow.com/p/course/camp/'+item[v['unit']]\n", " print(v['unit'],url)" ] }, { "cell_type": "markdown", "id": "2112bdec-bd4e-46b7-88cb-ec53e70ad844", "metadata": {}, "source": [ "## 报告添加水印" ] }, { "cell_type": "code", "execution_count": 137, "id": "ced8ec31-2b80-4ba1-901a-233f0c31c32c", "metadata": { "execution": { "iopub.execute_input": "2023-12-28T03:47:55.677670Z", "iopub.status.busy": "2023-12-28T03:47:55.677205Z", "iopub.status.idle": "2023-12-28T03:48:41.694191Z", "shell.execute_reply": "2023-12-28T03:48:41.693753Z", "shell.execute_reply.started": "2023-12-28T03:47:55.677631Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ok!\n" ] } ], "source": [ "import os,sys,shutil\n", "import json\n", "import glob\n", "from pathlib import Path\n", "import PyPDF2\n", "\n", "\n", "pdfWriter = PyPDF2.PdfWriter() \n", "\n", "fi_path = '/home/songyi/python/mycrm/flask/pdf/files/309118_1'\n", "new_path = 'file/309118_sy'\n", "\n", "filename = 'data/北京农行百日筑基.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "\n", "water = PyPDF2.PdfReader('data/气虚质.pdf').pages[0]\n", "fls = glob.glob(f'{fi_path}/*.pdf')\n", "for fn in fls:\n", " reader = PyPDF2.PdfReader(fn)\n", " writer = PyPDF2.PdfWriter()\n", " num = len(reader.pages)\n", " code =str(int(Path(fn).stem.split('-')[0]))\n", " sy_fn = 'data/'+dict1[code]['unit']+'.pdf'\n", " water = PyPDF2.PdfReader(sy_fn).pages[0]\n", " for i in range(num):\n", " page = reader.pages[i]\n", " if i == num -1:\n", " page.merge_page(water)\n", " writer.add_page(page)\n", " with open(f'file/309118_sy/{Path(fn).stem}.pdf', 'wb') as target_file:\n", " writer.write(target_file)\n", " \n", "print('ok!') " ] }, { "cell_type": "markdown", "id": "223fa825-da92-4667-bfc4-d359b87d3385", "metadata": {}, "source": [ "## 核对有体测无报告人员" ] }, { "cell_type": "code", "execution_count": 134, "id": "12155bbc-81c7-48c5-a6bf-228184e9382c", "metadata": { "execution": { "iopub.execute_input": "2023-12-28T03:13:06.270708Z", "iopub.status.busy": "2023-12-28T03:13:06.270214Z", "iopub.status.idle": "2023-12-28T03:13:06.288938Z", "shell.execute_reply": "2023-12-28T03:13:06.288393Z", "shell.execute_reply.started": "2023-12-28T03:13:06.270668Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['11', '27', '28', '29', '31', '32', '34', '42', '47', '52', '60', '64', '75', '84', '85', '87', '98', '103', '104', '105', '106', '111', '113', '114', '118', '119', '123', '137', '151', '162', '222']\n" ] } ], "source": [ "import os,sys,shutil\n", "import json\n", "import glob\n", "from pathlib import Path\n", "import PyPDF2\n", "\n", "\n", "pdfWriter = PyPDF2.PdfWriter() \n", "\n", "fi_path = '/home/songyi/python/mycrm/flask/pdf/files/309118'\n", "new_path = 'file/309118_sy'\n", "\n", "filename = 'data/北京农行百日筑基.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "\n", "list1 = []\n", "fls = glob.glob(f'{fi_path}/*.pdf')\n", "for fn in fls:\n", " code =str(int(Path(fn).stem.split('-')[0]))\n", " list1.append(code)\n", "list2 = []\n", "for k, v in dict1.items():\n", " if k not in list1 and k!='50':\n", " list2.append(k)\n", "print(list2)" ] }, { "cell_type": "markdown", "id": "95c57366-499f-4fa6-9504-2bb67e61d203", "metadata": {}, "source": [ "## 补充第一次测试人员报告" ] }, { "cell_type": "code", "execution_count": 133, "id": "edfbc320-a295-4564-b1e5-9165df5fbb22", "metadata": { "execution": { "iopub.execute_input": "2023-12-28T03:12:46.687698Z", "iopub.status.busy": "2023-12-28T03:12:46.687241Z", "iopub.status.idle": "2023-12-28T03:12:46.720241Z", "shell.execute_reply": "2023-12-28T03:12:46.719132Z", "shell.execute_reply.started": "2023-12-28T03:12:46.687658Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n" ] } ], "source": [ "import json\n", "import datetime\n", "import csv\n", "from datetime import date\n", "\n", "filename = '../item.json'\n", "item = {}\n", "unit = {}\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "for k,v in dict1.items():\n", " item[k] = v\n", "item['1']['en'] = 'lung'\n", "item['2']['en'] = 'grip'\n", "item['3']['en'] = 'flexion'\n", "item['4']['en'] = 'jump'\n", "item['5']['en'] = 'pushup'\n", "item['6']['en'] = 'balance'\n", "item['7']['en'] = 'reaction'\n", "item['8']['en'] = 'step'\n", "item['9']['en'] = 'situp'\n", "item['10']['en'] = 'height'\n", "item['11']['en'] = 'weight'\n", "\n", "\n", "re_ta = {}\n", "dict1 = {}\n", "list1 = []\n", "filename = 'data/北京农行人员名单all.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl) \n", "\n", "filename = 'data/places_result_20231228.csv'\n", "with open(filename,'r',newline='') as csv_file:\n", " fl = csv.reader(csv_file,delimiter=',')\n", " header = next(fl) \n", " for line in fl:\n", " #line = re.sub('[\\r\\n\\f ]{1,}', '', line)\n", " list1.append(line)\n", "#f_item = ['lung','grip','flexion','jump','balance','reaction','step','situp']\n", "#m_item = ['lung','grip','flexion','jump','pushup','balance','reaction','step']\n", "for result in list1:\n", " user = str(result[2])\n", " if user in list2:\n", " rq = date.fromisoformat(result[6].replace('/','-'))\n", " if user in dict1.keys():\n", " l_xm = []\n", " m_item = str(result[3]) \n", " re_ta.setdefault(user,{}) \n", " re_ta[user]['name'] = dict1[user]['name']\n", " re_ta[user]['sex'] = dict1[user]['sex']\n", " if dict1[user]['sex'] == '男':\n", " l_xm = ['weight','height','lung','grip','flexion','jump','pushup','balance','reaction','step']\n", " else:\n", " l_xm = ['weight','height','lung','grip','flexion','jump','balance','reaction','step','situp']\n", " re_ta[user]['unit'] = dict1[user]['unit']\n", " birth = date.fromisoformat(dict1[user]['birth'].replace('/','-'))\n", " #nian = int(birth[0].strip())\n", " #yue = int(birth[1].strip())\n", " #ri = int(birth[2].strip())\n", " #print(k,nian,yue,ri)\n", " item_name = item[m_item]['en'] \n", " if item_name in l_xm: \n", " days = (rq-birth).days \n", " re_ta[user]['age'] = int(days/365)\n", " re_ta[user]['month'] = int(days/365*12)\n", " re_ta[user]['rq'] = result[6]\n", "\n", "\n", " re_ta[user].setdefault(item_name,{}) \n", " score = int(result[4])/item[m_item]['divisor'] \n", " re_ta[user][item_name]['成绩'] = f'{score} {item[m_item][\"unit\"]}'\n", " #print(len(re_ta))\n", "filename = 'data/result_北京农行补充人员.json'\n", "\n", "with open(filename,'w') as fl:\n", " json.dump(re_ta, fl, ensure_ascii=False) \n", "print(len(re_ta))" ] }, { "cell_type": "code", "execution_count": 135, "id": "8e578850-ea32-45e4-a78f-646c2a7aee1d", "metadata": { "execution": { "iopub.execute_input": "2023-12-28T03:20:49.080841Z", "iopub.status.busy": "2023-12-28T03:20:49.080377Z", "iopub.status.idle": "2023-12-28T03:20:59.726002Z", "shell.execute_reply": "2023-12-28T03:20:59.724866Z", "shell.execute_reply.started": "2023-12-28T03:20:49.080802Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n" ] } ], "source": [ "import requests\n", "import json\n", "import openpyxl\n", "\n", "\n", "headers = {\n", " \"Content-Type\": \"application/json; charset=UTF-8\"\n", " }\n", "filename = 'data/result_北京农行补充人员.json'\n", "with open(filename,'r') as fl:\n", " dict1 = json.load(fl)\n", "list1 = []\n", "fiie_path ='./北京农行补充/'\n", "list_item = ['lung','grip','flexion','jump','pushup','balance','reaction','step','situp','bmi']\n", "i=0\n", "list2 = []\n", "for k, v in dict1.items():\n", " list1 = []\n", " mydata = {}\n", " \n", " id = str(k).rjust(4,\"0\")\n", " mydata['path'] = fiie_path+id+'-'+ v['name']+'.pdf'\n", " mydata['title'] = '中国农业银行股份有限公司'\n", " mydata['subtitle'] = '北京市分行'\n", " mydata['id'] = id\n", " mydata['name'] = v['name']\n", " if v['sex'] == '男':\n", " mydata['gender'] = 'male'\n", " else:\n", " mydata['gender'] = 'female'\n", " \n", " mydata['month'] = v['month']\n", " mydata['fits'] = {}\n", " survey_list = ['tcm','psy','spine']\n", " for item in survey_list:\n", " if item in v.keys():\n", " mydata.setdefault('surveys',{})\n", " mydata['surveys'][item] = v[item]\n", " \n", " \n", " #mydata['fits'] = {}\n", " for item in list_item:\n", " if item in v.keys():\n", " mydata.setdefault('fits',{})\n", " if item in ['lung','pushup','step','situp']:\n", " mark = v[item]['成绩'].split()[0].split('.')[0]\n", " else:\n", " mark = v[item]['成绩'].split()[0]\n", " mydata['fits'][item] = {'mark':mark,'score':v[item]['score']}\n", " if len(mydata['fits']) >2 or len(mydata['surveys']) >0:\n", " \n", " list1.append(mydata)\n", " list2.append([k,v['name']])\n", " i+=1\n", " x = requests.post('http://localhost:3003', data = json.dumps(list1), headers=headers)\n", " #print(id,v['name'],x.text)\n", " #print(mydata)\n", " #x.close()\n", "print(i)" ] }, { "cell_type": "markdown", "id": "3fcb653d-8f2b-4f01-99e0-9e563631563b", "metadata": {}, "source": [ "## 生成手机验证码" ] }, { "cell_type": "code", "execution_count": 138, "id": "2fc466e3-80cf-4357-8910-94f8548cd3a5", "metadata": { "execution": { "iopub.execute_input": "2023-12-28T03:57:03.930890Z", "iopub.status.busy": "2023-12-28T03:57:03.930680Z", "iopub.status.idle": "2023-12-28T03:57:04.143869Z", "shell.execute_reply": "2023-12-28T03:57:04.143389Z", "shell.execute_reply.started": "2023-12-28T03:57:03.930868Z" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ok\n" ] } ], "source": [ "import openpyxl\n", "import os,sys,shutil\n", "import json\n", "import math\n", "import glob\n", "import random\n", "from pathlib import Path\n", "import pymongo\n", "\n", "myclient = pymongo.MongoClient('mongodb://localhost:27017/')\n", "mydb = myclient[\"baogao\"]\n", "\n", "place_id = 309118\n", "place_code = '03'\n", "dw_name = '中国农业银行股份有限公司北京市分行'\n", "fi_path = '/home/songyi/python/mycrm/flask/pdf/files'\n", "fls = glob.glob(f'{fi_path}/{str(place_id)}/*.pdf')\n", "\n", "code_list = []\n", "for i in range(10): # 0~9\n", " code_list.append(str(i))\n", " \n", "list1 = []\n", "i = 0\n", "while i < len(fls):\n", " code = random.sample(code_list,4) #随机取4位数\n", " code_num = ''.join(code) \n", " if place_code+code_num not in list1:\n", " list1.append(place_code+code_num)\n", " i +=1\n", "i = 0\n", "list2= []\n", "for fn in fls:\n", " dict2 = {}\n", " fi_name =Path(fn).stem.split('-')[0]\n", " code = int(fi_name)\n", " dxm = list1[i]\n", " dict2 = {'place_id':place_id,'code':dxm,'fn':Path(fn).name}\n", " list2.append(dict2)\n", " i +=1\n", "mycol = mydb[\"pdf\"]\n", "x = mycol.insert_many(list2)\n", "print('ok') \n" ] }, { "cell_type": "code", "execution_count": null, "id": "4be9bcbe-bfeb-450e-9863-1d49605979a3", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 }