VScode-jupyter: VS Code Jupyter Extension

Jupyter Extension for Visual Studio Code

A Visual Studio Code extension that provides basic notebook support for language kernels that are supported in Jupyter Notebooks today, and allows any Python environment to be used as a Jupyter kernel. This is NOT a Jupyter kernel--you must have Python environment in which you've installed the Jupyter package, though many language kernels will work with no modification. To enable advanced features, modifications may be needed in the VS Code language extensions.

NotebookUI

LinkDescription
File an issueReport problems and suggest enhancements
Go to docsJupyter extension and data science in VS Code documentation, tutorials, and more
DiscussionsPost questions, and engage in community discussions

Work in the browser

Editing Jupyter notebooks in VS Code can also be done on the browser in two ways.

  1. The Jupyter extension has support for a web based interface provided by vscode.dev (which includes github.dev, available by typing '.' when viewing a repo on github.com)
  2. The Jupyter extension can be installed on VS Code in GitHub Codespaces the same way it is done locally (or sync your VS Code settings to have your extensions installed automatically on Codespaces).

Installed extensions

The Jupyter Extension will automatically install the following extensions by default to provide enhanced Jupyter notebook experiences in VS Code.

You can also install the Jupyter PowerToys extension to try out experimental features (not installed by default). Extensions installed through the marketplace are subject to the Marketplace Terms of Use, and any or all of these extensions can be disabled or uninstalled.

Working with Python

Quick Start

  • Step 1. Install VS Code
  • Step 2. Install Anaconda/Miniconda or another Python environment in which you've installed the Jupyter package
  • Since not working with Python, make sure to have a Jupyter kernelspec that corresponds to the language you would like to use installed on your machine.
  • Step 3. Install the Jupyter Extension and the Python Extension
  • Step 4. Open or create a notebook file by opening the Command Palette (Ctrl+Shift+P) and select Jupyter: Create New Jupyter Notebook.
  • Step 5. Select your kernel by clicking on the kernel picker in the top right of the notebook or by invoking the Notebook: Select Notebook Kernel command and start coding!

Working with other Languages

The Jupyter Extension supports other languages in addition to Python such as Julia, R, and C#.

Quick Start

  • Step 1. Install VS Code
  • Step 2. Since not working with Python, make sure to have a Jupyter kernelspec that corresponds to the language you would like to use installed on your machine.
  • Step 3. Install the Jupyter Extension
  • Step 4. Open or create a notebook file and start coding!

Notebook support

The Jupyter Extension uses the built-in notebook support from VS Code. This UI gives a number of advantages to users of notebooks:

  • Out of the box support for VS Code's vast array of basic code editing features like hot exit, find & replace, and code folding.
  • Editor extensions like VIM, bracket colorization, linters and many more are available while editing a cell.
  • Deep integration with general workbench and file-based features in VS Code like outline view (Table of Contents), breadcrumbs and other operations.
  • Fast load times for Jupyter notebook (.ipynb) files. Any notebook file is loaded and rendered as quickly as possible, while execution-related operations are initialized behind the scenes.
  • Includes a notebook-friendly diff tool, making it much easier to compare and see differences between code cells, output and metadata.
  • Extensibility beyond what the Jupyter extension provides. Extensions can now add their own language or runtime-specific take on notebooks, such as the .NET Interactive Notebooks and Gather
  • While the Jupyter extension comes packaged with a large set of the most commonly used renderers for output, the marketplace supports custom installable renderers to make working with your notebooks even more productive. To get started writing your own, see VS Code's renderer api documentation.

Useful commands

Open the Command Palette (Command+Shift+P on macOS and Ctrl+Shift+P on Windows/Linux) and type in one of the following commands:

CommandDescription
Jupyter: Create New Jupyter NotebookCreates a new Jupyter Notebook
Notebook: Select Notebook KernelSelect or switch kernels within your notebook
Notebook: Change Cell LanguageChange the language of the cell currently in focus
Jupyter: Export to HTML Jupyter: Export to PDFCreate a presentation-friendly version of your notebook in HTML or PDF

To see all available Jupyter Notebook commands, open the Command Palette and type Jupyter or Notebook.

Context Keys for Key bindings

You can use the extension's context keys in 'when' clauses. Here's an example:

  {
    "key": "ctrl+i",
    "command": "jupyter.runAndDebugCell",
    "when": "!jupyter.webExtension"
  }

That keybinding states the jupyter.runAndDebugCell command should map to CTRL+I when not in the jupyter.webExtension.

The full list of context keys can be found here: https://github.com/microsoft/vscode-jupyter/wiki/Extensibility-for-other-extensions#context-keys-for-keybindings

Feature details

Learn more about the rich features of the Jupyter extension:

IntelliSense: Edit your code with auto-completion, code navigation, syntax checking and more.

Jupyter Notebooks: Create and edit Jupyter Notebooks, add and run code/markdown cells, render plots, create presentation-friendly versions of your notebook by exporting to HTML or PDF and more.

Supported locales

The extension is available in multiple languages: de, en, es, fa, fr, it, ja, ko-kr, nl, pl, pt-br, ru, tr, zh-cn, zh-tw

Questions, issues, feature requests, and contributions

If you have a question about how to accomplish something with the extension, please ask on Discussions. Our wiki can be a source of information as well.

Any and all feedback is appreciated and welcome! If you come across a problem or bug with the extension, please file an issue.

  • If someone has already filed an issue that encompasses your feedback, please leave a 👍/👎 reaction on the issue.

Contributions are always welcome, so please see our contributing guide for more details.

If you're interested in the development of the extension, you can read about our development process

Data and telemetry

The Microsoft Jupyter Extension for Visual Studio Code collects usage data and sends it to Microsoft to help improve our products and services. Read our privacy statement to learn more. This extension respects the telemetry.enableTelemetry setting which you can learn more about at https://code.visualstudio.com/docs/supporting/faq#_how-to-disable-telemetry-reporting.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Download Details:

Author: Microsoft
Source Code: https://github.com/microsoft/vscode-jupyter 
License: MIT license

#vscode #jupyter #machinelearning 

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Buddha Community

VScode-jupyter: VS Code Jupyter Extension
Nabunya  Jane

Nabunya Jane

1620290313

10 Most Useful VS Code Extensions For Web Development

Microsoft Visual Studio Code (VS Code) is one of the top code editors for software developers. Since its release, its popularity has surged not only because of the stable platform it provides, but also because of the extensible nature that Microsoft built into it.

T

his article will cover 10 developer problems those can be solved by below extensions and help you to become 10x engineer.

#vscode #extension #vs-code-extensions #web-development #vs code

Top 10 Dark VS Code themes

If you are getting bored with the dark theme, you are using right now. Or just want to explore what other exceptional themes are out there. I am here to help.

How to change your VS Code Color Theme

  1. In VS Code, open the Theme picker with File > Preferences > Color Theme. (Code > Preferences > Color Theme on macOS).
  2. You can also use the keyboard shortcut cmd + K, followed by cmd + T to display the picker.
  3. You can preview the theme by using the arrow keys.
  4. Press Enter to select your theme.

#vscode-tips #vscode #vs-code-extensions #vscode-theme

5 Best VS Code Extensions for Refactoring that Every Dev Should Know

If you’re looking at ways to clean up our code, reduce complexity and improve functionality - these refactoring extensions will help you move faster:

#refactoring #vscode #javascript #programming #legacy-code #coding #code

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

VScode-jupyter: VS Code Jupyter Extension

Jupyter Extension for Visual Studio Code

A Visual Studio Code extension that provides basic notebook support for language kernels that are supported in Jupyter Notebooks today, and allows any Python environment to be used as a Jupyter kernel. This is NOT a Jupyter kernel--you must have Python environment in which you've installed the Jupyter package, though many language kernels will work with no modification. To enable advanced features, modifications may be needed in the VS Code language extensions.

NotebookUI

LinkDescription
File an issueReport problems and suggest enhancements
Go to docsJupyter extension and data science in VS Code documentation, tutorials, and more
DiscussionsPost questions, and engage in community discussions

Work in the browser

Editing Jupyter notebooks in VS Code can also be done on the browser in two ways.

  1. The Jupyter extension has support for a web based interface provided by vscode.dev (which includes github.dev, available by typing '.' when viewing a repo on github.com)
  2. The Jupyter extension can be installed on VS Code in GitHub Codespaces the same way it is done locally (or sync your VS Code settings to have your extensions installed automatically on Codespaces).

Installed extensions

The Jupyter Extension will automatically install the following extensions by default to provide enhanced Jupyter notebook experiences in VS Code.

You can also install the Jupyter PowerToys extension to try out experimental features (not installed by default). Extensions installed through the marketplace are subject to the Marketplace Terms of Use, and any or all of these extensions can be disabled or uninstalled.

Working with Python

Quick Start

  • Step 1. Install VS Code
  • Step 2. Install Anaconda/Miniconda or another Python environment in which you've installed the Jupyter package
  • Since not working with Python, make sure to have a Jupyter kernelspec that corresponds to the language you would like to use installed on your machine.
  • Step 3. Install the Jupyter Extension and the Python Extension
  • Step 4. Open or create a notebook file by opening the Command Palette (Ctrl+Shift+P) and select Jupyter: Create New Jupyter Notebook.
  • Step 5. Select your kernel by clicking on the kernel picker in the top right of the notebook or by invoking the Notebook: Select Notebook Kernel command and start coding!

Working with other Languages

The Jupyter Extension supports other languages in addition to Python such as Julia, R, and C#.

Quick Start

  • Step 1. Install VS Code
  • Step 2. Since not working with Python, make sure to have a Jupyter kernelspec that corresponds to the language you would like to use installed on your machine.
  • Step 3. Install the Jupyter Extension
  • Step 4. Open or create a notebook file and start coding!

Notebook support

The Jupyter Extension uses the built-in notebook support from VS Code. This UI gives a number of advantages to users of notebooks:

  • Out of the box support for VS Code's vast array of basic code editing features like hot exit, find & replace, and code folding.
  • Editor extensions like VIM, bracket colorization, linters and many more are available while editing a cell.
  • Deep integration with general workbench and file-based features in VS Code like outline view (Table of Contents), breadcrumbs and other operations.
  • Fast load times for Jupyter notebook (.ipynb) files. Any notebook file is loaded and rendered as quickly as possible, while execution-related operations are initialized behind the scenes.
  • Includes a notebook-friendly diff tool, making it much easier to compare and see differences between code cells, output and metadata.
  • Extensibility beyond what the Jupyter extension provides. Extensions can now add their own language or runtime-specific take on notebooks, such as the .NET Interactive Notebooks and Gather
  • While the Jupyter extension comes packaged with a large set of the most commonly used renderers for output, the marketplace supports custom installable renderers to make working with your notebooks even more productive. To get started writing your own, see VS Code's renderer api documentation.

Useful commands

Open the Command Palette (Command+Shift+P on macOS and Ctrl+Shift+P on Windows/Linux) and type in one of the following commands:

CommandDescription
Jupyter: Create New Jupyter NotebookCreates a new Jupyter Notebook
Notebook: Select Notebook KernelSelect or switch kernels within your notebook
Notebook: Change Cell LanguageChange the language of the cell currently in focus
Jupyter: Export to HTML Jupyter: Export to PDFCreate a presentation-friendly version of your notebook in HTML or PDF

To see all available Jupyter Notebook commands, open the Command Palette and type Jupyter or Notebook.

Context Keys for Key bindings

You can use the extension's context keys in 'when' clauses. Here's an example:

  {
    "key": "ctrl+i",
    "command": "jupyter.runAndDebugCell",
    "when": "!jupyter.webExtension"
  }

That keybinding states the jupyter.runAndDebugCell command should map to CTRL+I when not in the jupyter.webExtension.

The full list of context keys can be found here: https://github.com/microsoft/vscode-jupyter/wiki/Extensibility-for-other-extensions#context-keys-for-keybindings

Feature details

Learn more about the rich features of the Jupyter extension:

IntelliSense: Edit your code with auto-completion, code navigation, syntax checking and more.

Jupyter Notebooks: Create and edit Jupyter Notebooks, add and run code/markdown cells, render plots, create presentation-friendly versions of your notebook by exporting to HTML or PDF and more.

Supported locales

The extension is available in multiple languages: de, en, es, fa, fr, it, ja, ko-kr, nl, pl, pt-br, ru, tr, zh-cn, zh-tw

Questions, issues, feature requests, and contributions

If you have a question about how to accomplish something with the extension, please ask on Discussions. Our wiki can be a source of information as well.

Any and all feedback is appreciated and welcome! If you come across a problem or bug with the extension, please file an issue.

  • If someone has already filed an issue that encompasses your feedback, please leave a 👍/👎 reaction on the issue.

Contributions are always welcome, so please see our contributing guide for more details.

If you're interested in the development of the extension, you can read about our development process

Data and telemetry

The Microsoft Jupyter Extension for Visual Studio Code collects usage data and sends it to Microsoft to help improve our products and services. Read our privacy statement to learn more. This extension respects the telemetry.enableTelemetry setting which you can learn more about at https://code.visualstudio.com/docs/supporting/faq#_how-to-disable-telemetry-reporting.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Download Details:

Author: Microsoft
Source Code: https://github.com/microsoft/vscode-jupyter 
License: MIT license

#vscode #jupyter #machinelearning