準備 啓用兩步驗證 創建程式密碼 授權外部訪問 Google Account。 發送電子郵件 發送文本電子郵件 import smtplib from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText username = "@gmail.com" password = "password" mail_from = "@gmail.com" mail_to = "@gmail.com" mail_subject = "Test Subject" mail_body

Install heroku CLI sudo snap install heroku --classic setup account heroku login Create app heroku create Create a new Git repository cd my-project/ git init heroku git:remote -a name Existing Git repository heroku git:remote -a name FLASK DEMO app.py from flask import Flask app = Flask(__name__) @app.route("/who") def who_am_i(): return "w0x7ce" if __name__ == '__main__': app.run(port=5000) wsgi.py from app import app if __name__ == "__main__": app.run() Procfile web: gunicorn wsgi:app

GCP Python 文檔 官方資料 Demo Installing the gcloud CLI Download #x86_64 curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-382.0.0-linux-x86_64.tar.gz #arm64 curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-382.0.0-linux-arm.tar.gz #x86 curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-382.0.0-linux-x86.tar.gz tar -xf google-cloud-*.tar.gz ./google-cloud-sdk/install.sh Initialize the gcloud CLI Run gcloud init: gcloud init If you are in a remote terminal session, you can use the –console-only flag to prevent the command from launching a browser-based authorization flow, if required: gcloud init --console-only Demo Deploy

字符串類 操作字符串 basic QString str1 = "a"; QString str2 = "b"; QString str = str1 + str2; append QString str1 = "Welcome"; QString str2 = "to"; str1.append(str2); str1.append(" you! "); sprintf Qstring str; str.sprintf("%s", "Welcome"); str.sprintf("%s %s", "Welcome","to you"); %n QString str; str=QString("%1 was born in %2.").arg("Bob").arg(1998); others insert() 特定位置插入字符串 prepend() 開頭插入

环境配置 import torch from torch import nn from torch.utils.data import DataLoader from torchvision import datasets from torchvision.transforms import ToTensor from torch.autograd import Variable 数据准备 df = pro.daily(ts_code='000001.SZ', start_date='20220101', end_date='20220326') df['date'] = pd.to_datetime(df['trade_date']) df['adj_close'] = df['close'] df['volume']=df['vol'] df['month'] = pd.DatetimeIndex(df['trade_date']).month df = df[['date','open','high','low','close','adj_close','volume','month']] print(df.head()) import numpy as np # x = torch.unsqueeze(torch.linspace(-1,1,100),dim=1) # y = x.pow(4)+0.1*torch.randn(x.size()) x_data = torch.unsqueeze(torch.tensor(df['high'].astype(np.float32).values),dim=1) x = (x_data-x_data.min())/(x_data.max()-x_data.min()) y_data = torch.unsqueeze(torch.tensor(df['close'].astype(np.float32).values),dim=1)