Tag: For Developers

Visual Studio Code, or VS Code, is an open source code editor that also includes tools for building and debugging an application. With the Python extension enabled, vscode becomes a great working environment for any Python developer. This article shows you which extensions are useful, and how to configure VS Code to get the most out of it.

If you don’t have it installed, check out our previous article, Using Visual Studio Code on Fedora:

Using Visual Studio Code on Fedora

Install the VS Code Python extension

First, to make VS Code Python friendly, install the Python extension from the marketplace.

Once the Python extension installed, you can now configure the Python extension.

VS Code manages its configuration inside JSON files. Two files are used:

• One for the global settings that applies to all projects
• One for project specific settings

Press Ctrl+, (comma) to open the global settings.

Setup the Python Path

You can configure VS Code to automatically select the best Python interpreter for each of your projects. To do this, configure the python.pythonPath key in the global settings.

// Place your settings in this file to overwrite default and user settings. { "python.pythonPath":"${workspaceRoot}/.venv/bin/python", }

This sets VS Code to use the Python interpreter located in the project root directory under the .venv virtual environment directory.

Use environment variables

By default, VS Code uses environment variables defined in the project root directory in a .env file. This is useful to set environment variables like:

PYTHONWARNINGS="once"

That setting ensures that warnings are displayed when your program is running.

To change this default, set the python.envFile configuration key as follows:

"python.envFile": "${workspaceFolder}/.env",

Code Linting

The Python extension also supports different code linters (pep8, flake8, pylint). To enable your favorite linter, or the one used by the project you’re working on, you need to set a few configuration items.

By default pylint is enabled. But for this example, configure flake8:

"python.linting.pylintEnabled": false, "python.linting.flake8Path": "${workspaceRoot}/.venv/bin/flake8", "python.linting.flake8Enabled": true, "python.linting.flake8Args": ["--max-line-length=90"],

After enabling the linter, your code is underlined to show where it doesn’t meet criteria enforced by the linter. Note that for this example to work, you need to install flake8 in the virtual environment of the project.

Code Formatting

VS Code also lets you configure automatic code formatting. The extension currently supports autopep8, black and yapf. Here’s how to configure black.

"python.formatting.provider": "black", "python.formatting.blackPath": "${workspaceRoot}/.venv/bin/black" "python.formatting.blackArgs": ["--line-length=90"], "editor.formatOnSave": true,

If you don’t want the editor to format your file on save,  set the option to false and use Ctrl+Shift+I to format the current document. Note that for this example to work, you need to install black in the virtual environment of the project.

Running Tasks

Another great feature of VS Code is that it can run tasks. These tasks are also defined in a JSON file saved in the project root directory.

Run a development flask server

In this example, you’ll create a task to run a Flask development server. Create a new Build using the basic template that can run an external command:

Edit the tasks.json file as follows to create a new task that runs the Flask development server:

{ // See https://go.microsoft.com/fwlink/?LinkId=733558 // for the documentation about the tasks.json format "version": "2.0.0", "tasks": [ { "label": "Run Debug Server", "type": "shell", "command": "${workspaceRoot}/.venv/bin/flask run -h 0.0.0.0 -p 5000", "group": { "kind": "build", "isDefault": true } } ] }

The Flask development server uses an environment variable to get the entrypoint of the application. Use the .env file to declare these variables. For example:

FLASK_APP=wsgi.py FLASK_DEBUG=True

Now you can execute the task using Ctrl+Shift+B.

Unit tests

VS Code also has the unit test runners pytest, unittest, and nosetest integrated out of the box. After you enable a test runner, VS Code discovers the unit tests and letsyou to run them individually, by test suite, or simply all the tests.

For example, to enable pytest:

"python.unitTest.pyTestEnabled": true, "python.unitTest.pyTestPath": "${workspaceRoot}/.venv/bin/pytest",

Note that for this example to work, you need to install pytest in the virtual environment of the project.

Twitter allows one to share blog posts and articles with the world. Using Python and the tweepy library makes it easy to create a Twitter bot that takes care of all the tweeting for you. This article shows you how to build such a bot. Hopefully you can take the concepts here and apply them to other projects that use online services.

Getting started

To create a Twitter bot the tweepy library comes handy. It manages the Twitter API calls and provides a simple interface.

The following commands use Pipenv to install tweepy into a virtual environment. If you don’t have Pipenv installed, check out our previous article, How to install Pipenv on Fedora.

$ mkdir twitterbot $ cd twitterbot $ pipenv --three $ pipenv install tweepy $ pipenv shell

Tweepy – Getting started

To use the Twitter API the bot needs to authenticate against Twitter. For that, tweepy uses the OAuth authentication standard. You can get credentials by creating a new application at https://apps.twitter.com/.

Create a new Twitter application

After you fill in the following form and click on the Create your Twitter application button, you have access to the application credentials. Tweepy requires the Consumer Key (API Key) and the Consumer Secret (API Secret), both available from the Keys and Access Tokens.

After scrolling down the page, generate an Access Token and an Access Token Secret using the Create my access token button.

Using Tweepy – print your timeline

Now that you have all the credentials needed, open a new file and write the following Python code.

import tweepy auth = tweepy.OAuthHandler("your_consumer_key", "your_consumer_key_secret") auth.set_access_token("your_access_token", "your_access_token_secret") api = tweepy.API(auth) public_tweets = api.home_timeline() for tweet in public_tweets: print(tweet.text)

$ python tweet.py

The above program calls the home_timeline API method to retrieve the 20 most recent tweets from your timeline. Now that the bot is able to use tweepy  to get data from Twitter, try changing the code to send a tweet.

Using Tweepy – send a tweet

To send a tweet, the API method update_status comes in handy. The usage is simple:

api.update_status("The awesome text you would like to tweet")

The tweepy library has many other methods that can be useful for a Twitter bot. For the full details of the API, check the documentation.

A magazine bot

Let’s create a bot that searches for Fedora Magazine tweets and automatically retweets them.

To avoid retweeting the same tweet multiple times, the bot stores the tweet ID of the last retweet. Two helper functions, store_last_id and get_last_id, will be used to save and retrieve this ID.

Then the bot uses the tweepy search API to find the Fedora Magazine tweets that are more recent than the stored ID.

import tweepy def store_last_id(tweet_id): """ Store a tweet id in a file """ with open("lastid", "w") as fp: fp.write(str(tweet_id)) def get_last_id(): """ Read the last retweeted id from a file """ with open("lastid", "r") as fp: return fp.read() if __name__ == '__main__': auth = tweepy.OAuthHandler("your_consumer_key", "your_consumer_key_secret") auth.set_access_token("your_access_token", "your_access_token_secret") api = tweepy.API(auth) try: last_id = get_last_id() except FileNotFoundError: print("No retweet yet") last_id = None for tweet in tweepy.Cursor(api.search, q="fedoramagazine.org", since_id=last_id).items(): if tweet.user.name == 'Fedora Project': store_last_id(tweet.id) tweet.retweet() print(f'"{tweet.text}" was retweeted'

In order to retweet only tweets from the Fedora Magazine, the bot searches for tweets that contain fedoramagazine.org and are published by the “Fedora Project” Twitter account.

Conclusion

In this article you saw how  to create a Twitter application using the tweepy Python library to automate reading, sending and searching tweets. You can now use your creativity to create a Twitter bot of your own.

The source code of the example in this article is available on Github.