Gartner Glossary

This article discusses the helpful Python environments features available in Visual Studio Code. An "environment" in Python is the context in which a Python program runs and consists of an interpreter and any number of installed packages.

Note: We are actively working on improving the Python environments documentation and experience in Visual Studio Code. Please follow this link and sign up to give your feedback to the program management, content, and research teams.

Python environments

Global environments

By default, any Python interpreter installed runs in its own global environment. They aren't specific to a particular project. For example, if you just run , , or at a new terminal (depending on how you installed Python), you're running in that interpreter's global environment. Any packages that you install or uninstall affect the global environment and all programs that you run within it.

Working in the global environment is an easy way to get started. If you install packages in that environment, though, in time it will become crowded and make it difficult to properly test an application.

Virtual environments

To prevent such clutter, developers often create a virtual environment for a project. A virtual environment is a folder that contains a copy (or symlink) of a specific interpreter. When you install into a virtual environment, any packages you install are installed only in that subfolder. When you then run a Python program within that environment, you know that it's running against only those specific packages.

Note: While it's possible to open a virtual environment folder as a workspace, doing so is not recommended and might cause issues with using the Python extension.

Python environment tools

Once you activate your virtual environment, you’ll need to identify how to manage it and its accompanying packages. The following table explains how to use these Python environments:

Conda environments

A conda environment is a Python environment that's managed using the package manager (see Getting started with conda (protomill.pt)). Whether to use a conda environment or a virtual one will depend on your packaging needs, what your team has standardized on, etc.

Where the extension looks for environments

The extension automatically looks for interpreters in the following locations:

• Standard install paths such as , , , , , etc.
• Virtual environments located directly under the workspace (project) folder.
• Virtual environments located in the folder identified by the setting (see General Python settings), which can contain multiple virtual environments. The extension looks for virtual environments in the first-level subfolders of .
• Virtual environments located in a folder for virtualenvwrapper.
• Interpreters installed by pyenv, Pipenv, and Poetry.
• Virtual environments located in the path identified by (as used by virtualenvwrapper).
• Conda environments found by . Conda environments which do not have an interpreter will have one installed for them upon selection.
• Interpreters installed in a folder for direnv under the workspace (project) folder.

You can also manually specify an interpreter if Visual Studio Code doesn't locate your interpreter automatically.

Note: Once the "select interpreter" flow is triggered, pipenv environments for the workspace folder will be searched for. If one is found, then no other interpreters are searched for or listed as pipenv expects to manage all aspects.

The extension also loads an environment variable definitions file identified by the setting. The default value of this setting is .

Creating environments

Create a virtual environment

To create a virtual environment, use the following command, where ".venv" is the name of the environment folder:

Note: To learn more about the module, see Creation of virtual environments on protomill.pt

When you create a new virtual environment, a prompt will be displayed to allow you to select it for the workspace.

This will add the path to the Python interpreter from the new virtual environment to your workspace settings. That environment will then be used when installing packages and running code through the Python extension. For examples of using virtual environment in projects, see the Python, Django, and Flask tutorials.

Tip: When you're ready to deploy the application to other computers, you can create a file with the command ( on macOS/Linux). The requirements file describes the packages you've installed in your virtual environment. With only this file, you or other developers can restore those packages using (or, again, on macOS/Linux). By using a requirements file, you need not commit the virtual environment itself to source control.

Create a conda environment

The Python extension automatically detects existing conda environments. We recommend you install a Python interpreter into your conda environment, otherwise one will be installed for you after you select the environment. For example, the following command creates a conda environment with the Python interpreter and several libraries, which VS Code then shows in the list of available interpreters:

In contrast, if you fail to specify an interpreter, as with , the environment won't appear in the list.

For more information on the conda command line, see Conda environments (protomill.pt).

Additional notes:

• If you create a new conda environment while VS Code is running, use the refresh icon on the top right of the Python: Select Interpreter window; otherwise you may not see the environment there.

• To ensure the environment is set up well from a shell perspective, one option is to use an Anaconda prompt with the activated environment to launch VS Code using the command. At that point you just need to select the interpreter using the Command Palette or by clicking on the status bar.

• Although the Python extension for VS Code doesn't currently have direct integration with conda protomill.pt files, VS Code itself is a great YAML editor.

• Conda environments can't be automatically activated in the VS Code Integrated Terminal if the default shell is set to PowerShell. To change the shell, see Integrated terminal - Terminal profiles.

• You can manually specify the path to the conda executable to use for activation (version +). To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set , which is in the Python extension section of User Settings, with the appropriate path.

Work with Python interpreters

Select and activate an environment

By default, the Python extension looks for and uses the first Python interpreter it finds in the system path. To select a specific environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

Note: If the Python extension doesn't find an interpreter, it issues a warning. On macOS, the extension also issues a warning if you're using the OS-installed Python interpreter, because you typically want to use an interpreter you install directly. In either case, you can disable these warnings by setting to in your user settings.

You can switch environments at any time; switching environments helps you test different parts of your project with different interpreters or library versions as needed.

The Python: Select Interpreter command displays a list of available global environments, conda environments, and virtual environments. (See the Where the extension looks for environments section for details, including the distinctions between these types of environments.) The following image, for example, shows several Anaconda and CPython installations along with a conda environment and a virtual environment () that's located within the workspace folder:

Note: On Windows, it can take a little time for VS Code to detect available conda environments. During that process, you may see "(cached)" before the path to an environment. The label indicates that VS Code is presently working with cached information for that environment.

If you have a folder or a workspace open in VS Code and you select an interpreter from the list, the Python extension will store that information internally so that the same interpreter will be used once you reopen the workspace.

The Python extension uses the selected environment for running Python code (using the Python: Run Python File in Terminal command), providing language services (auto-complete, syntax checking, linting, formatting, etc.) when you have a file open in the editor, and opening a terminal with the Terminal: Create New Terminal command. In the latter case, VS Code automatically activated the selected environment.

Tip: To prevent automatic activation of a selected environment, add to your file (it can be placed anywhere as a sibling to the existing settings).

Tip: If the activate command generates the message "protomill.pt1 is not digitally signed. You cannot run this script on the current system.", then you need to temporarily change the PowerShell execution policy to allow scripts to run (see About Execution Policies in the PowerShell documentation):

Note: By default, VS Code uses the interpreter selected for your workspace when debugging code. You can override this behavior by specifying a different path in the property of a debug configuration. See Choose a debugging environment.

The selected interpreter version will show on the right side of the Status Bar.

The Status Bar also reflects when no interpreter is selected.

In either case, clicking this area of the Status Bar is a convenient shortcut for the Python: Select Interpreter command.

Tip: If you have any problems with VS Code recognizing a virtual environment, please file an issue in the extension repository so we can help determine the cause.

Manually specify an interpreter

If VS Code doesn't automatically locate an interpreter you want to use, you can browse for the interpreter on your file system or provide the path to it manually.

You can do so by running the Python: Select Interpreter command and clicking on the Enter interpreter path option that shows on the top of the interpreters list:

You can then either enter the full path of the Python interpreter directly in the text box (for example, ".venv/Scripts/protomill.pt"), or you can select the Find button and browse your file system to find the python executable you wish to select.

If you want to manually specify a default interpreter that will be used once you first open your workspace, you can create or modify an entry for setting in your workspace with the full path to the Python executable.

For example:

• Windows:

• macOS/Linux:

You can also use to point to a virtual environment, for example:

• Windows:

• macOS/Linux:

Note: Changes to the setting are not picked up after an interpreter has already been selected for a workspace; any changes to the setting will be ignored once an initial interpreter is selected for the workspace.

Additionally, if you'd like to set up a default interpreter to all of your Python applications, you can add an entry for manually inside your User Settings. To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set , which is in the Python extension section of User Settings, with the appropriate interpreter.

You can also use an environment variable in the path setting using the syntax . For example, if you've created a variable named with a path to an interpreter, you can then use the following setting value:

Note: Variable substitution is only supported in VS Code settings files, it will not work in environment files.

By using an environment variable, you can easily transfer a project between operating systems where the paths are different, just be sure to set the environment variable on the operating system first.

Environments and Terminal windows

After using Python: Select Interpreter, that interpreter is applied when right-clicking a file and selecting Python: Run Python File in Terminal. The environment is also activated automatically when you use the Terminal: Create New Terminal command unless you change the setting to .

However, launching VS Code from a shell in which a certain Python environment is activated doesn't automatically activate that environment in the default Integrated Terminal. Use the Terminal: Create New Terminal command after VS Code is running.

Note: conda environments cannot be automatically activated in the integrated terminal if PowerShell is set as the integrated shell. See Integrated terminal - Terminal profiles for how to change the shell.

Any changes you make to an activated environment within the terminal are persistent. For example, using from the terminal with a conda environment activated installs the package into that environment permanently. Similarly, using in a terminal with a virtual environment activated adds the package to that environment.

Changing interpreters with the Python: Select Interpreter command doesn't affect terminal panels that are already open. You can thus activate separate environments in a split terminal: select the first interpreter, create a terminal for it, select a different interpreter, then use the split button (⌘\ (Windows, Linux Ctrl+Shift+5)) in the terminal title bar.

Choose a debugging environment

By default, the debugger will use the Python interpreter you've selected with the Python extension. However, if you have a property in the debug configuration of , that interpreter is used instead. To be more specific, VS Code will give precedence to the property of the selected debug configuration in . If it's not defined, then it will use the path to the Python interpreter you've selected for your workspace.

For more details on debug configuration, see Debugging configurations.

Limited support for Python

The Python extension no longer offers IntelliSense support for Python with Jedi as it only supports Python 3 at this point. When using Python with the Python extension you can customize the language server setting to either turn off auto-completions or select Pylance as your language server, as it may provide a good experience if the code is compatible enough with Python 3.

We currently support selecting Python as an interpreter in your workspace. Because Python is no longer maintained as of January , we strongly suggest you to upgrade your code to Python 3 as soon as you can. You can learn how to port your code to Python 3 if you need help.

Environment variables

Environment variable definitions file

An environment variable definitions file is a simple text file containing key-value pairs in the form of , with used for comments. Multiline values aren't supported, but values can refer to any other environment variable that's already defined in the system or earlier in the file. For more information, see Variable substitution. Environment variable definitions files can be used for scenarios such as debugging and tool execution (including linters, formatters, IntelliSense, and testing tools), but aren't applied to the terminal.

By default, the Python extension looks for and loads a file named in the current workspace folder, then applies those definitions. The file is identified by the default entry in your user settings (see General Python settings). You can change the setting at any time to use a different definitions file.

Note: Environment definition files are not used in all situations where environment variables are available for use. Unless Visual Studio Code documentation states otherwise, these only affect certain scenarios as per their definition. For example: The extension doesn't use environment definition files when resolving setting values.

A debug configuration also contains an property that also defaults to the file in the current workspace (see Debugging - Set configuration options). This property allows you to easily set variables for debugging purposes that replace variables specified in the default file.

For example, when developing a web application, you might want to easily switch between development and production servers. Instead of coding the different URLs and other settings into your application directly, you could use separate definitions files for each. For example:

protomill.pt file

protomill.pt file

You can then set the setting to , then set the property in the debug configuration to .

Note: When environment variables are specified using multiple methods, be aware that there is an order of precedence. All variables defined in the file will override variables contained in the file, specified by the setting (user or workspace). Similarly, variables defined in the file will override the environment variables defined in the that are specified in .

Variable substitution

When defining an environment variable in a definitions file, you can use the value of any existing environment variable with the following general syntax:

where means any other text as used in the value. The curly braces are required.

Within this syntax, the following rules apply:

• Variables are processed in the order they appear in the file, so you can use any variable that's defined earlier in the file.
• Single or double quotes don't affect substituted value and are included in the defined value. For example, if the value of is , then assigns the value to .
• The character can be escaped with a backslash, as in .
• You can use recursive substitution, such as (where is any other environment variable).
• You can use only simple substitution; nesting such as isn't supported.
• Entries with unsupported syntax are left as-is.

Use of the PYTHONPATH variable

The PYTHONPATH environment variable specifies additional locations where the Python interpreter should look for modules. In VS Code, PYTHONPATH can be set through the terminal settings (protomill.pt*) and/or within an file.

When the terminal settings are used, PYTHONPATH affects any tools that are run within the terminal by a user, as well as any action the extension performs for a user that is routed through the terminal such as debugging. However, in this case when the extension is performing an action that isn't routed through the terminal, such as the use of a linter or formatter, then this setting won't have an effect on module look-up.

When PYTHONPATH is set using an file, it will affect anything the extension does on your behalf and actions performed by the debugger, but it will not affect tools run in the terminal.

If needed, you can set PYTHONPATH using both methods.

An example of when to use PYTHONPATH would be if you have source code in a folder and tests in a folder. When running tests, however, those tests can't normally access modules in unless you hard-code relative paths.

To solve this problem, you could add the path to to PYTHONPATH by creating an file within your VS Code workspace.

Then set in your file to point to the file you just created. For example, if the file was in your workspace root, your would be set as shown:

The value of PYTHONPATH can contain multiple locations separated by : a semicolon () on Windows and a colon () on Linux/macOS. Invalid paths are ignored. If you find that your value for PYTHONPATH isn't working as expected, make sure that you're using the correct separator between locations for the operating system. For example, using a colon to separate locations on Windows, or using a semicolon to separate locations on Linux/macOS results in an invalid value for PYTHONPATH, which is ignored.

Note: PYTHONPATH does not specify a path to a Python interpreter itself. For additional information about PYTHONPATH, read the PYTHONPATH documentation.

Next steps

• Editing code - Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
• Debugging - Learn to debug Python both locally and remotely.
• Testing - Configure test environments and discover, run, and debug tests.
• Settings reference - Explore the full range of Python-related settings in VS Code.

More Python resources

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In addition to ensuring your app meets its functional requirements by building tests, it's important that you also ensure your code has no structural problems by running the code through lint. The lint tool helps find poorly structured code that can impact the reliability and efficiency of your Android apps and make your code harder to maintain.

For example, if your XML resource files contain unused namespaces, this takes up space and incurs unnecessary processing. Other structural issues, such as use of deprecated elements or API calls that are not supported by the target API versions, might lead to code failing to run correctly. Lint can help you clean up these issues.

To further improve linting performance, you should also add annotations to your code.

Overview

Android Studio provides a code scanning tool called lint that can help you to identify and correct problems with the structural quality of your code without having to execute the app or write test cases. Each problem detected by the tool is reported with a description message and a severity level, so that you can quickly prioritize the critical improvements that need to be made. Also, you can lower the severity level of a problem to ignore issues that are not relevant to your project, or raise the severity level to highlight specific problems.

The lint tool checks your Android project source files for potential bugs and optimization improvements for correctness, security, performance, usability, accessibility, and internationalization. When using Android Studio, configured lint and IDE inspections run whenever you build your app. However, you can manually run inspections or run lint from the command line.

Note: When your code is compiled in Android Studio, additional IntelliJ code inspections run to streamline code review.

Figure 1 shows how the lint tool processes the application source files.

Figure 1. Code scanning workflow with the lint tool

Application source files

The source files consist of files that make up your Android project, including Java, Kotlin, and XML files, icons, and ProGuard configuration files.

The file

A configuration file that you can use to specify any lint checks that you want to exclude and to customize problem severity levels.

The lint tool

A static code scanning tool that you can run on your Android project either from the command line or in Android Studio (see Manually run inspections). The lint tool checks for structural code problems that could affect the quality and performance of your Android application. It is strongly recommended that you correct any errors that lint detects before publishing your application.

Results of lint checking

You can view the results from lint either in the console or in the Inspection Results window in Android Studio. See Manually run inspections.

Run lint from the command line

If you're using Android Studio or Gradle, you can use the Gradle wrapper to invoke the task for your project by entering one of the following commands from the root directory of your project:

• On Windows: gradlew lint
• On Linux or Mac: ./gradlew lint

You should see output similar to the following:

When the lint tool completes its checks, it provides paths to the XML and HTML versions of the lint report. You can then navigate to the HTML report and open it in your browser, as shown in figure 2.

Figure 2. Sample HTML lint report

If your project includes build variants, running lint checks only the default variant. If you want to run lint on a different variant, you must capitalize the variant name and prefix it with `lint`.

To learn more about running Gradle tasks from the command line, read Build Your App from the Command Line.

Run lint using the standalone tool

If you're not using Android Studio or Gradle, you can use the standalone lint tool after you install the Android SDK Command-Line Tools from the SDK Manager. You can then locate the lint tool at .

To run lint against a list of files in a project directory, use the following command:

For example, you can issue the following command to scan the files under the directory and its subdirectories. The issue ID tells lint to only scan for XML attributes that are missing the Android namespace prefix.

To see the full list of flags and command-line arguments supported by the tool, use the following command:

The following example shows the console output when the lint command is run against a project called Earthquake.

The output above lists four warnings and no errors: three warnings (, , and ) in the project's file, and one warning () in the layout file.

Configure lint to suppress warnings

By default when you run a lint scan, the tool checks for all issues that lint supports. You can also restrict the issues for lint to check and assign the severity level for those issues. For example, you can suppress lint checking for specific issues that are not relevant to your project and, you can configure lint to report non-critical issues at a lower severity level.

You can configure lint checking for different levels:

• Globally (entire project)
• Project module
• Production module
• Test module
• Open files
• Class hierarchy
• Version Control System (VCS) scopes

Configure lint in Android Studio

The built-in lint tool checks your code while you're using Android Studio. You can view warnings and errors in two ways:

• As pop-up text in the Code Editor. When lint finds a problem, it highlights the problematic code in yellow, or for more serious issues, it underlines the code in red.
• In the lint Inspection Results window after you click Analyze > Inspect Code. See Manually run inspections.

Configure the lint file

You can specify your lint checking preferences in the file. If you are creating this file manually, place it in the root directory of your Android project.

The file consists of an enclosing parent tag that contains one or more children elements. Lint defines a unique attribute value for each .

You can change an issue's severity level or disable lint checking for the issue by setting the severity attribute in the tag.

Tip: For a full list of lint-supported issues and their corresponding issue IDs, run the command.

Sample protomill.pt file

The following example shows the contents of a file.

Configure lint checking for Java, Kotlin, and XML source files

You can disable lint from checking your Java, Kotlin, and XML source files.

Tip: You can manage the lint checking feature for your Java, Kotlin, or XML source files in the Default Preferences dialog. Select File > Other Settings > Default Settings, and then in the left pane of the Default Preferences dialog, select Editor > Inspections.

Configuring lint checking in Java or Kotlin

To disable lint checking specifically for a class or method in your Android project, add the annotation to that code.

The following example shows how you can turn off lint checking for the issue in the method. The lint tool continues to check for the issue in other methods of this class.

The following example shows how to turn off lint checking for the issue in the class:

To suppress checking for all lint issues in the file, use the keyword, like this:

Configuring lint checking in XML

You can use the attribute to disable lint checking for specific sections of your XML files. Put the following namespace value in the file so the lint tool recognizes the attribute:

The following example shows how you can turn off lint checking for the issue in the element of an XML layout file. The attribute is inherited by the children elements of the parent element in which the attribute is declared. In this example, the lint check is also disabled for the child element.

To disable more than one issue, list the issues to disable in a comma-separated string. For example:

To suppress checking for all lint issues in the XML element, use the keyword, like this:

Configure lint options with Gradle

The Android plugin for Gradle allows you to configure certain lint options, such as which checks to run or ignore, using the block in your module-level file. The following code snippet shows you some of the properties you can configure:

Create warnings baseline

You can take a snapshot of your project's current set of warnings, and then use the snapshot as a baseline for future inspection runs so that only new issues are reported. The baseline snapshot lets you start using lint to fail the build without having to go back and address all existing issues first.

To create a baseline snapshot, modify your project's file as follows.

When you first add this line, the file is created to establish your baseline. From then on, the tools only read the file to determine the baseline. If you want to create a new baseline, manually delete the file and run lint again to recreate it.

Then, run lint from the IDE (Analyze > Inspect Code) or from the command line as follows. The output prints the location of the file. The file location for your setup might be different from what is shown here.

Running records all of the current issues in the file. The set of current issues is called the baseline, and you can check the file into version control if you want to share it with others.

Customize the baseline

If you want to add some issue types to the baseline, but not all of them, you can specify the issues to add by editing your project's , as follows.

After you create the baseline, if you add any new warnings to the codebase, lint lists only the newly introduced bugs.

Baseline warning

When baselines are in effect, you get an informational warning that tells you that one or more issues were filtered out because they were already listed in the baseline. The reason for this warning is to help you remember that you have configured a baseline, because ideally, you would want to fix all of the issues at some point.

This informational warning does not only tell you the exact number of errors and warnings that were filtered out, it also keeps track of issues that are not reported anymore. This information lets you know if you have actually fixed issues, so you can optionally re-create the baseline to prevent the error from coming back undetected.

Note: Baselines are enabled when you run inspections in batch mode in the IDE, but they are ignored for the in-editor checks that run in the background when you are editing a file. The reason is that baselines are intended for the case where a codebase has a massive number of existing warnings, but you do want to fix issues locally while you touch the code.

Manually run inspections

You can manually run configured lint and other IDE inspections by selecting Analyze > Inspect Code. The results of the inspection appear in the Inspection Results window.

Set the inspection scope and profile

Select the files you want to analyze (inspection scope) and the inspections you want to run (inspection profile), as follows:

1. In the Android view, open your project and select the project, a folder, or a file that you want to analyze.
2. From the menu bar, select Analyze > Inspect Code.
3. In the Specify Inspection Scope dialog, review the settings.

   Figure 3. Review the inspection scope settings

   The combination of options that appear in the Specify Inspection Scope dialog varies depending on whether you selected a project, folder, or file. You can change what to inspect by selecting one of the other radio buttons. See Specify inspection scope dialog for a description of all of the possible fields on the Specify Inspection Scope dialog.

   • When you select one project, file, or directory, the Specify Inspection Scope dialog displays the path to the Project, File, or Directory you selected.
   • When you select more than one project, file, or directory, the Specify Inspection Scope dialog displays a checked radio button for Selected files.
4. Under Inspection profile, keep the default profile (Project Default).
5. Click OK to run the inspection. Figure 4 shows lint and other IDE inspection results from the Inspect Code run:

   

   Figure 4. Select an issue to see its resolution

6. In the left pane tree view, view the inspection results by expanding and selecting error categories, types, and issues.

   The right pane displays the inspection report for the selected error category, type, or issue and provides the name and location of the error. Where applicable, the inspection report displays other information such as a problem synopsis to help you correct the problem.

7. In the left pane tree view, right-click a category, type, or issue to display the context menu.

   Depending on the context, you can do all or some of the following: jump to source, exclude and include selected items, suppress problems, edit settings, manage inspection alerts, and rerun an inspection.

For descriptions of the left-side Toolbar buttons, context menu items, and inspection report fields, see Inspection Tool Window.

Use a custom scope

You can use one of the custom scopes provided in Android Studio, as follows:

1. In the Specify Inspection Scope dialog, click Custom scope.
2. Click the Custom scope drop-down list to display your options.

   

   Figure 5. Select the custom scope you want to use

   • Project Files: All of the files in the current project.
   • Project Production Files: Only the production files in the current project.
   • Project Test Files: Only the test files in the current project. See Test types and location.
   • Open Files: Only the files you have open in the current project.
   • Module <your-module>: Only the files in the corresponding module folder in your current project.
   • Current File: Only the current file in your current project. Appears when you have a file or folder selected.
   • Class Hierarchy: When you select this one and click OK, a dialog appears with all of the classes in the current project. Use the Search by Name field in the dialog to filter and select the classes to inspect. If you do not filter the classes list, code inspection inspects all of the classes.
3. Click OK.

Create a custom scope

When you want to inspect a selection of files and directories that is not covered by any of the currently available custom scopes, you can create a custom scope.

1. In the Specify Inspection Scope dialog, select Custom scope.
2. Click the three dots after the Custom Scope drop-down list.

   

   Figure 6. Specify Inspection Scope dialog

   The Scopes dialog appears.

   

   Figure 7. Create a custom scope

3. Click Add to define a new scope.
4. In the resulting Add Scope drop-down list, select Local.

   Both the local and shared scopes are used within the project for the Inspect Code feature. A Shared scope can also be used with other project features that have a scope field. For example, when you click Edit Settings to change the settings for Find Usages, the resulting dialog has a Scope field where you can select a shared scope.

   

   Figure 8. Select a shared scope from the Find Usages dialog

5. Give the scope a name and click OK.

   The right pane of the Scopes dialog populates with options that enable you to define the custom scope.

6. From the drop-down list, select Project.

   A list of available projects appears.

   Note: You can create the custom scope for projects or packages. The steps are the same either way.

7. Expand the project folders, select what you want to add to the custom scope, and click one of the buttons on the right.

   

   Figure 9. Define a custom scope

   • Include: Include this folder and its files, but do not include any of its subfolders.
   • Include Recursively: Include this folder and all of its files and subfolders and their files.
   • Exclude: Exclude this folder and its files, but do not exclude any of its subfolders.
   • Exclude Recursively: Exclude ths folder and all of its files and subfolders and their files.

   Figure 10 shows that the main folder is included, and that the java folder is included recursively. The blue indicates partially included folders and the green indicates recursively included folders and files.

   

   Figure Example pattern for a custom scope

   • If you select the java folder and click Exclude Recursively, the green highlighting goes away on the java folder and all folders and files under it.
   • If you instead select the green highlighted protomill.pt file and click Exclude, protomill.pt is no longer green highlighted but everything else under the java folder is green highlighted.
8. Click OK. The custom scope appears at the bottom of the drop-down list.

Review and edit inspection profiles

Android Studio comes with a selection of lint and other inspection profiles that are updated through Android updates. You can use these profiles as is or edit their names, descriptions, severities, and scopes. You can also activate and deactivate entire groups of profiles or individual profiles within a group.

To access the Inspections dialog:

1. Select Analyze > Inspect Code.
2. In the Specify Scope dialog under Inspection Profile, click More.

   The Inspections dialog appears with a list of the supported inspections and their descriptions.

   

   Figure Supported inspections and their descriptions

3. Select the Profile drop-down list to toggle between Default (Android Studio) and Project Default (the active project) inspections. For more information, see this IntelliJ Specify Inspection Scope Dialog page.
4. In the Inspections dialog in the left pane, select a top-level profile category, or expand a group and select a specific profile. When you select a profile category, you can edit all of the inspections in that category as a single inspection.
5. Select the Manage drop-down list to copy, rename, add descriptions to, export, and import inspections.
6. When you're done, click OK.

This article discusses the helpful Python environments features available in Visual Studio Code. An "environment" in Python is the context in which a Python program runs and consists of an interpreter and any number of installed packages.

Note: We are actively working on improving the Python environments documentation and experience in Visual Studio Code. Please follow this link and sign up to give your feedback to the program management, content, and research teams.

Python environments

Global environments

By default, any Python interpreter installed runs in its own global environment. They aren't specific to a particular project. For example, if you just run , , or at a new terminal (depending on how you installed Python), you're running in that interpreter's global environment. Any packages that you install or uninstall affect the global environment and all programs that you run within it.

Working in the global environment is an easy way to get started. If you install packages in that environment, though, in time it will become crowded and make it difficult to properly test an application.

Virtual environments

To prevent such clutter, developers often create a virtual environment for a project. A virtual environment is a folder that contains a copy (or symlink) of a specific interpreter. When you install into a virtual environment, any packages you install are installed only in that subfolder. When you then run a Python program within that environment, you know that it's running against only those specific packages.

Note: While it's possible to open a virtual environment folder as a workspace, doing so is not recommended and might cause issues with using the Python extension.

Python environment tools

Once you activate your virtual environment, you’ll need to identify how to manage it and its accompanying packages. The following table explains how to use these Python environments:

Conda environments

A conda environment is a Python environment that's managed using the package manager (see Getting started with conda (protomill.pt)). Whether to use a conda environment or a virtual one will depend on your packaging needs, what your team has standardized on, etc.

Where the extension looks for environments

The extension automatically looks for interpreters in the following locations:

• Standard install paths such as , , , , , etc.
• Virtual environments located directly under the workspace (project) folder.
• Virtual environments located in the folder identified by the setting (see General Python settings), which can contain multiple virtual environments. The extension looks for virtual environments in the first-level subfolders of .
• Virtual environments located in a folder for virtualenvwrapper.
• Interpreters installed by pyenv, Pipenv, and Poetry.
• Virtual environments located in the path identified by (as used by virtualenvwrapper).
• Conda environments found by . Conda environments which do not have an interpreter will have one installed for them upon selection.
• Interpreters installed in a folder for direnv under the workspace (project) folder.

You can also manually specify an interpreter if Visual Studio Code doesn't locate your interpreter automatically.

Note: Once the "select interpreter" flow is triggered, pipenv environments for the workspace folder will be searched for. If one is found, then no other interpreters are searched for or listed as pipenv expects to manage all aspects.

The extension also loads an environment variable definitions file identified by the setting. The default value of this setting is .

Creating environments

Create a virtual environment

To create a virtual environment, use the following command, where ".venv" is the name of the environment folder:

Note: To learn more about the module, see Creation of virtual environments on protomill.pt

When you create a new virtual environment, a prompt will be displayed to allow you to select it for the workspace.

This will add the path to the Python interpreter from the new virtual environment to your workspace settings. That environment will then be used when installing packages and running code through the Python extension. For examples of using virtual environment in projects, see the Python, Django, and Flask tutorials.

Tip: When you're ready to deploy the application to other computers, you can create a file with the command ( on macOS/Linux). The requirements file describes the packages you've installed in your virtual environment. With only this file, you or other developers can restore those packages using (or, again, on macOS/Linux). By using a requirements file, you need not commit the virtual environment itself to source control.

Create a conda environment

The Python extension automatically detects existing conda environments. We recommend you install a Python interpreter into your conda environment, otherwise one will be installed for you after you select the environment. For example, the following command creates a conda environment with the Python interpreter and several libraries, which VS Code then shows in the list of available interpreters:

In contrast, if you fail to specify an interpreter, as with , the environment won't appear in the list.

For more information on the conda command line, see Conda environments (protomill.pt).

Additional notes:

• If you create a new conda environment while VS Code is running, use the refresh icon on the top right of the Python: Select Interpreter window; otherwise you may not see the environment there.

• To ensure the environment is set up well from a shell perspective, one option is to use an Anaconda prompt with the activated environment to launch VS Code using the command. At that point you just need to select the interpreter using the Command Palette or by clicking on the status bar.

• Although the Python extension for VS Code doesn't currently have direct integration with conda protomill.pt files, VS Code itself is a great YAML editor.

• Conda environments can't be automatically activated in the VS Code Integrated Terminal if the default shell is set to PowerShell. To change the shell, see Integrated terminal - Terminal profiles.

• You can manually specify the path to the conda executable to use for activation (version +). To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set , which is in the Python extension section of User Settings, with the appropriate path.

Work with Python interpreters

Select and activate an environment

By default, the Python extension looks for and uses the first Python interpreter it finds in the system path. To select a specific environment, use the Python: Select Interpreter command from the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)).

Note: If the Python extension doesn't find an interpreter, it issues a warning. On macOS, the extension also issues a warning if you're using the OS-installed Python interpreter, because you typically want to use an interpreter you install directly. In either case, you can disable these warnings by setting to in your user settings.

You can switch environments at any time; switching environments helps you test different parts of your project with different interpreters or library versions as needed.

The Python: Select Interpreter command displays a list of available global environments, conda environments, and virtual environments. (See the Where the extension looks for environments section for details, including the distinctions between these types of environments.) The following image, for example, shows several Anaconda and CPython installations along with a conda environment and a virtual environment () that's located within the workspace folder:

Note: On Windows, it can take a little time for VS Code to detect available conda environments. During that process, you may see "(cached)" before the path to an environment. The label indicates that VS Code is presently working with cached information for that environment.

If you have a folder or a workspace open in VS Code and you select an interpreter from the list, the Python extension will store that information internally so that the same interpreter will be used once you reopen the workspace.

The Python extension uses the selected environment for running Python code (using the Python: Run Python File in Terminal command), providing language services (auto-complete, syntax checking, linting, formatting, etc.) when you have a file open in the editor, and opening a terminal with the Terminal: Create New Terminal command. In the latter case, VS Code automatically activated the selected environment.

Tip: To prevent automatic activation of a selected environment, add to your file (it can be placed anywhere as a sibling to the existing settings).

Tip: If the activate command generates the message "protomill.pt1 is not digitally signed. You cannot run this script on the current system.", then you need to temporarily change the PowerShell execution policy to allow scripts to run (see About Execution Policies in the PowerShell documentation):

Note: By default, VS Code uses the interpreter selected for your workspace when debugging code. You can override this behavior by specifying a different path in the property of a debug configuration. See Choose a debugging environment.

The selected interpreter version will show on the right side of the Status Bar.

The Status Bar also reflects when no interpreter is selected.

In either case, clicking this area of the Status Bar is a convenient shortcut for the Python: Select Interpreter command.

Tip: If you have any problems with VS Code recognizing a virtual environment, please file an issue in the extension repository so we can help determine the cause.

Manually specify an interpreter

If VS Code doesn't automatically locate an interpreter you want to use, you can browse for the interpreter on your file system or provide the path to it manually.

You can do so by running the Python: Select Interpreter command and clicking on the Enter interpreter path option that shows on the top of the interpreters list:

You can then either enter the full path of the Python interpreter directly in the text box (for example, ".venv/Scripts/protomill.pt"), or you can select the Find button and browse your file system to find the python executable you wish to select.

If you want to manually specify a default interpreter that will be used once you first open your workspace, you can create or modify an entry for setting in your workspace with the full path to the Python executable.

For example:

• Windows:

• macOS/Linux:

You can also use to point to a virtual environment, for example:

• Windows:

• macOS/Linux:

Note: Changes to the setting are not picked up after an interpreter has already been selected for a workspace; any changes to the setting will be ignored once an initial interpreter is selected for the workspace.

Additionally, if you'd like to set up a default interpreter to all of your Python applications, you can add an entry for manually inside your User Settings. To do so, open the Command Palette (⇧⌘P (Windows, Linux Ctrl+Shift+P)) and enter Preferences: Open User Settings. Then set , which is in the Python extension section of User Settings, with the appropriate interpreter.

You can also use an environment variable in the path setting using the syntax . For example, if you've created a variable named with a path to an interpreter, you can then use the following setting value:

Note: Variable substitution is only supported in VS Code settings files, it will not work in environment files.

By using an environment variable, you can easily transfer a project between operating systems where the paths are different, just be sure to set the environment variable on the operating system first.

Environments and Terminal windows

After using Python: Select Interpreter, that interpreter is applied when right-clicking a file and selecting Python: Run Python File in Terminal. The environment is also activated automatically when you use the Terminal: Create New Terminal command unless you change the setting to .

However, launching VS Code from a shell in which a certain Python environment is activated doesn't automatically activate that environment in the default Integrated Terminal. Use the Terminal: Create New Terminal command after VS Code is running.

Note: conda environments cannot be automatically activated in the integrated terminal if PowerShell is set as the integrated shell. See Integrated terminal - Terminal profiles for how to change the shell.

Any changes you make to an activated environment within the terminal are persistent. For example, using from the terminal with a conda environment activated installs the package into that environment permanently. Similarly, using in a terminal with a virtual environment activated adds the package to that environment.

Changing interpreters with the Python: Select Interpreter command doesn't affect terminal panels that are already open. You can thus activate separate environments in a split terminal: select the first interpreter, create a terminal for it, select a different interpreter, then use the split button (⌘\ (Windows, Linux Ctrl+Shift+5)) in the terminal title bar.

Choose a debugging environment

By default, the debugger will use the Python interpreter you've selected with the Python extension. However, if you have a property in the debug configuration of , that interpreter is used instead. To be more specific, VS Code will give precedence to the property of the selected debug configuration in . If it's not defined, then it will use the path to the Python interpreter you've selected for your workspace.

For more details on debug configuration, see Debugging configurations.

Limited support for Python

The Python extension no longer offers IntelliSense support for Python with Jedi as it only supports Python 3 at this point. When using Python with the Python extension you can customize the language server setting to either turn off auto-completions or select Pylance as your language server, as it may provide a good experience if the code is compatible enough with Python 3.

We currently support selecting Python as an interpreter in your workspace. Because Python is no longer maintained as of January , we strongly suggest you to upgrade your code to Python 3 as soon as you can. You can learn how to port your code to Python 3 if you need help.

Environment variables

Environment variable definitions file

An environment variable definitions file is a simple text file containing key-value pairs in the form of , with used for comments. Multiline values aren't supported, but values can refer to any other environment variable that's already defined in the system or earlier in the file. For more information, see Variable substitution. Environment variable definitions files can be used for scenarios such as debugging and tool execution (including linters, formatters, IntelliSense, and testing tools), but aren't applied to the terminal.

By default, the Python extension looks for and loads a file named in the current workspace folder, then applies those definitions. The file is identified by the default entry in your user settings (see General Python settings). You can change the setting at any time to use a different definitions file.

Note: Environment definition files are not used in all situations where environment variables are available for use. Unless Visual Studio Code documentation states otherwise, these only affect certain scenarios as per their definition. For example: The extension doesn't use environment definition files when resolving setting values.

A debug configuration also contains an property that also defaults to the file in the current workspace (see Debugging - Set configuration options). This property allows you to easily set variables for debugging purposes that replace variables specified in the default file.

For example, when developing a web application, you might want to easily switch between development and production servers. Instead of coding the different URLs and other settings into your application directly, you could use separate definitions files for each. For example:

protomill.pt file

protomill.pt file

You can then set the setting to , then set the property in the debug configuration to .

Note: When environment variables are specified using multiple methods, be aware that there is an order of precedence. All variables defined in the file will override variables contained in the file, specified by the setting (user or workspace). Similarly, variables defined in the file will override the environment variables defined in the that are specified in .

Variable substitution

When defining an environment variable in a definitions file, you can use the value of any existing environment variable with the following general syntax:

where means any other text as used in the value. The curly braces are required.

Within this syntax, the following rules apply:

• Variables are processed in the order they appear in the file, so you can use any variable that's defined earlier in the file.
• Single or double quotes don't affect substituted value and are included in the defined value. For example, if the value of is , then assigns the value to .
• The character can be escaped with a backslash, as in .
• You can use recursive substitution, such as (where is any other environment variable).
• You can use only simple substitution; nesting such as isn't supported.
• Entries with unsupported syntax are left as-is.

Use of the PYTHONPATH variable

The PYTHONPATH environment variable specifies additional locations where the Python interpreter should look for modules. In VS Code, PYTHONPATH can be set through the terminal settings (protomill.pt*) and/or within an file.

When the terminal settings are used, PYTHONPATH affects any tools that are run within the terminal by a user, as well as any action the extension performs for a user that is routed through the terminal such as debugging. However, in this case when the extension is performing an action that isn't routed through the terminal, such as the use of a linter or formatter, then this setting won't have an effect on module look-up.

When PYTHONPATH is set using an file, it will affect anything the extension does on your behalf and actions performed by the debugger, but it will not affect tools run in the terminal.

If needed, you can set PYTHONPATH using both methods.

An example of when to use PYTHONPATH would be if you have source code in a folder and tests in a folder. When running tests, however, those tests can't normally access modules in unless you hard-code relative paths.

To solve this problem, you could add the path to to PYTHONPATH by creating an file within your VS Code workspace.

Then set in your file to point to the file you just created. For example, if the file was in your workspace root, your would be set as shown:

The value of PYTHONPATH can contain multiple locations separated by : a semicolon () on Windows and a colon () on Linux/macOS. Invalid paths are ignored. If you find that your value for PYTHONPATH isn't working as expected, make sure that you're using the correct separator between locations for the operating system. For example, using a colon to separate locations on Windows, or using a semicolon to separate locations on Linux/macOS results in an invalid value for PYTHONPATH, which is ignored.

Note: PYTHONPATH does not specify a path to a Python interpreter itself. For additional information about PYTHONPATH, read the PYTHONPATH documentation.

Next steps

• Editing code - Learn about autocomplete, IntelliSense, formatting, and refactoring for Python.
• Debugging - Learn to debug Python both locally and remotely.
• Testing - Configure test environments and discover, run, and debug tests.
• Settings reference - Explore the full range of Python-related settings in VS Code.

More Python resources

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In addition to ensuring your app meets its functional requirements by building tests, it's important that you also ensure your code has no structural problems by running the code through lint. The lint tool helps find poorly structured code that can impact the reliability and efficiency of your Android apps and make your code harder to maintain.

For example, if your XML resource files contain unused namespaces, this takes up space and incurs unnecessary processing. Other structural issues, such as use of deprecated elements or API calls that are not supported by the target API versions, might lead to code failing to run correctly. Lint can help you clean up these issues.

To further improve linting performance, you should also add annotations to your code.

Overview

Android Studio provides a code scanning tool called lint that can help you to identify and correct problems with the structural quality of your code without having to execute the app or write test cases. Each problem detected by the tool is reported with a description message and a severity level, so that you can quickly prioritize the critical improvements that need to be made. Also, you can lower the severity level of a problem to ignore issues that are not relevant to your project, or raise the severity level to highlight specific problems.

The lint tool checks your Android project source files for potential bugs and optimization improvements for correctness, security, performance, usability, accessibility, and internationalization. When using Android Studio, configured lint and IDE inspections run whenever you build your app. However, you can manually run inspections or run lint from the command line.

Note: When your code is compiled in Android Studio, additional IntelliJ code inspections run to streamline code review.

Figure 1 shows how the lint tool processes the application source files.

Figure 1. Code scanning workflow with the lint tool

Application source files

The source files consist of files that make up your Android project, including Java, Kotlin, and XML files, icons, and ProGuard configuration files.

The file

A configuration file that you can use to specify any lint checks that you want to exclude and to customize problem severity levels.

The lint tool

A static code scanning tool that you can run on your Android project either from the command line or in Android Studio (see Manually run inspections). The lint tool checks for structural code problems that could affect the quality and performance of your Android application. It is strongly recommended that you correct any errors that lint detects before publishing your application.

Results of lint checking

You can view the results from lint either in the console or in the Inspection Results window in Android Studio. See Manually run inspections.

Run lint from the command line

If you're using Android Studio or Gradle, you can use the Gradle wrapper to invoke the task for your project by entering one of the following commands from the root directory of your project:

• On Windows: gradlew lint
• On Linux or Mac: ./gradlew lint

You should see output similar to the following:

When the lint tool completes its checks, it provides paths to the XML and HTML versions of the lint report. You can then navigate to the HTML report and open it in your browser, as shown in figure 2.

Figure 2. Sample HTML lint report

If your project includes build variants, running lint checks only the default variant. If you want to run lint on a different variant, you must capitalize the variant name and prefix it with `lint`.

To learn more about running Gradle tasks from the command line, read Build Your App from the Command Line.

Run lint using the standalone tool

If you're not using Android Studio or Gradle, you can use the standalone lint tool after you install the Android SDK Command-Line Tools from the SDK Manager. You can then locate the lint tool at .

To run lint against a list of files in a project directory, use the following command:

For example, you can issue the following command to scan the files under the directory and its subdirectories. The issue ID tells lint to only scan for XML attributes that are missing the Android namespace prefix.

To see the full list of flags and command-line arguments supported by the tool, use the following command:

The following example shows the console output when the lint command is run against a project called Earthquake.

The output above lists four warnings and no errors: three warnings (, , and ) in the project's file, and one warning () in the layout file.

Configure lint to suppress warnings

By default when you run a lint scan, the tool checks for all issues that lint supports. You can also restrict the issues for lint to check and assign the severity level for those issues. For example, you can suppress lint checking for specific issues that are not relevant to your project and, you can configure lint to report non-critical issues at a lower severity level.

You can configure lint checking for different levels:

• Globally (entire project)
• Project module
• Production module
• Test module
• Open files
• Class hierarchy
• Version Control System (VCS) scopes

Configure lint in Android Studio

The built-in lint tool checks your code while you're using Android Studio. You can view warnings and errors in two ways:

• As pop-up text in the Code Editor. When lint finds a problem, it highlights the problematic code in yellow, or for more serious issues, it underlines the code in red.
• In the lint Inspection Results window after you click Analyze > Inspect Code. See Manually run inspections.

Configure the lint file

You can specify your lint checking preferences in the file. If you are creating this file manually, place it in the root directory of your Android project.

The file consists of an enclosing parent tag that contains one or more children elements. Lint defines a unique attribute value for each .

You can change an issue's severity level or disable lint checking for the issue by setting the severity attribute in the tag.

Tip: For a full list of lint-supported issues and their corresponding issue IDs, run the command.

Sample protomill.pt file

The following example shows the contents of a file.

Configure lint checking for Java, Kotlin, and XML source files

You can disable lint from checking your Java, Kotlin, and XML source files.

Tip: You can manage the lint checking feature for your Java, Kotlin, or XML source files in the Default Preferences dialog. Select File > Other Settings > Default Settings, and then in the left pane of the Default Preferences dialog, select Editor > Inspections.

Configuring lint checking in Java or Kotlin

To disable lint checking specifically for a class or method in your Android project, add the annotation to that code.

The following example shows how you can turn off lint checking for the issue in the method. The lint tool continues to check for the issue in other methods of this class.

The following example shows how to turn off lint checking for the issue in the class:

To suppress checking for all lint issues in the file, use the keyword, like this:

Configuring lint checking in XML

You can use the attribute to disable lint checking for specific sections of your XML files. Put the following namespace value in the file so the lint tool recognizes the attribute:

The following example shows how you can turn off lint checking for the issue in the element of an XML layout file. The attribute is inherited by the children elements of the parent element in which the attribute is declared. In this example, the lint check is also disabled for the child element.

To disable more than one issue, list the issues to disable in a comma-separated string. For example:

To suppress checking for all lint issues in the XML element, use the keyword, like this:

Configure lint options with Gradle

The Android plugin for Gradle allows you to configure certain lint options, such as which checks to run or ignore, using the block in your module-level file. The following code snippet shows you some of the properties you can configure:

Create warnings baseline

You can take a snapshot of your project's current set of warnings, and then use the snapshot as a baseline for future inspection runs so that only new issues are reported. The baseline snapshot lets you start using lint to fail the build without having to go back and address all existing issues first.

To create a baseline snapshot, modify your project's file as follows.

When you first add this line, the file is created to establish your baseline. From then on, the tools only read the file to determine the baseline. If you want to create a new baseline, manually delete the file and run lint again to recreate it.

Then, run lint from the IDE (Analyze > Inspect Code) or from the command line as follows. The output prints the location of the file. The file location for your setup might be different from what is shown here.

Running records all of the current issues in the file. The set of current issues is called the baseline, and you can check the file into version control if you want to share it with others.

Customize the baseline

If you want to add some issue types to the baseline, but not all of them, you can specify the issues to add by editing your project's , as follows.

After you create the baseline, if you add any new warnings to the codebase, lint lists only the newly introduced bugs.

Baseline warning

When baselines are in effect, you get an informational warning that tells you that one or more issues were filtered out because they were already listed in the baseline. The reason for this warning is to help you remember that you have configured a baseline, because ideally, you would want to fix all of the issues at some point.

This informational warning does not only tell you the exact number of errors and warnings that were filtered out, it also keeps track of issues that are not reported anymore. This information lets you know if you have actually fixed issues, so you can optionally re-create the baseline to prevent the error from coming back undetected.

Note: Baselines are enabled when you run inspections in batch mode in the IDE, but they are ignored for the in-editor checks that run in the background when you are editing a file. The reason is that baselines are intended for the case where a codebase has a massive number of existing warnings, but you do want to fix issues locally while you touch the code.

Manually run inspections

You can manually run configured lint and other IDE inspections by selecting Analyze > Inspect Code. The results of the inspection appear in the Inspection Results window.

Set the inspection scope and profile

Select the files you want to analyze (inspection scope) and the inspections you want to run (inspection profile), as follows:

1. In the Android view, open your project and select the project, a folder, or a file that you want to analyze.
2. From the menu bar, select Analyze > Inspect Code.
3. In the Specify Inspection Scope dialog, review the settings.

   Figure 3. Review the inspection scope settings

   The combination of options that appear in the Specify Inspection Scope dialog varies depending on whether you selected a project, folder, or file. You can change what to inspect by selecting one of the other radio buttons. See Specify inspection scope dialog for a description of all of the possible fields on the Specify Inspection Scope dialog.

   • When you select one project, file, or directory, the Specify Inspection Scope dialog displays the path to the Project, File, or Directory you selected.
   • When you select more than one project, file, or directory, the Specify Inspection Scope dialog displays a checked radio button for Selected files.
4. Under Inspection profile, keep the default profile (Project Default).
5. Click OK to run the inspection. Figure 4 shows lint and other IDE inspection results from the Inspect Code run:

   

   Figure 4. Select an issue to see its resolution

6. In the left pane tree view, view the inspection results by expanding and selecting error categories, types, and issues.

   The right pane displays the inspection report for the selected error category, type, or issue and provides the name and location of the error. Where applicable, the inspection report displays other information such as a problem synopsis to help you correct the problem.

7. In the left pane tree view, right-click a category, type, or issue to display the context menu.

   Depending on the context, you can do all or some of the following: jump to source, exclude and include selected items, suppress problems, edit settings, manage inspection alerts, and rerun an inspection.

For descriptions of the left-side Toolbar buttons, context menu items, and inspection report fields, see Inspection Tool Window.

Use a custom scope

You can use one of the custom scopes provided in Android Studio, as follows:

1. In the Specify Inspection Scope dialog, click Custom scope.
2. Click the Custom scope drop-down list to display your options.

   

   Figure 5. Select the custom scope you want to use

   • Project Files: All of the files in the current project.
   • Project Production Files: Only the production files in the current project.
   • Project Test Files: Only the test files in the current project. See Test types and location.
   • Open Files: Only the files you have open in the current project.
   • Module <your-module>: Only the files in the corresponding module folder in your current project.
   • Current File: Only the current file in your current project. Appears when you have a file or folder selected.
   • Class Hierarchy: When you select this one and click OK, a dialog appears with all of the classes in the current project. Use the Search by Name field in the dialog to filter and select the classes to inspect. If you do not filter the classes list, code inspection inspects all of the classes.
3. Click OK.

Create a custom scope

When you want to inspect a selection of files and directories that is not covered by any of the currently available custom scopes, you can create a custom scope.

1. In the Specify Inspection Scope dialog, select Custom scope.
2. Click the three dots after the Custom Scope drop-down list.

   

   Figure 6. Specify Inspection Scope dialog

   The Scopes dialog appears.

   

   Figure 7. Create a custom scope

3. Click Add to define a new scope.
4. In the resulting Add Scope drop-down list, select Local.

   Both the local and shared scopes are used within the project for the Inspect Code feature. A Shared scope can also be used with other project features that have a scope field. For example, when you click Edit Settings to change the settings for Find Usages, the resulting dialog has a Scope field where you can select a shared scope.

   

   Figure 8. Select a shared scope from the Find Usages dialog

5. Give the scope a name and click OK.

   The right pane of the Scopes dialog populates with options that enable you to define the custom scope.

6. From the drop-down list, select Project.

   A list of available projects appears.

   Note: You can create the custom scope for projects or packages. The steps are the same either way.

7. Expand the project folders, select what you want to add to the custom scope, and click one of the buttons on the right.

   

   Figure 9. Define a custom scope

   • Include: Include this folder and its files, but do not include any of its subfolders.
   • Include Recursively: Include this folder and all of its files and subfolders and their files.
   • Exclude: Exclude this folder and its files, but do not exclude any of its subfolders.
   • Exclude Recursively: Exclude ths folder and all of its files and subfolders and their files.

   Figure 10 shows that the main folder is included, and that the java folder is included recursively. The blue indicates partially included folders and the green indicates recursively included folders and files.

   

   Figure Example pattern for a custom scope

   • If you select the java folder and click Exclude Recursively, the green highlighting goes away on the java folder and all folders and files under it.
   • If you instead select the green highlighted protomill.pt file and click Exclude, protomill.pt is no longer green highlighted but everything else under the java folder is green highlighted.
8. Click OK. The custom scope appears at the bottom of the drop-down list.

Review and edit inspection profiles

Android Studio comes with a selection of lint and other inspection profiles that are updated through Android updates. You can use these profiles as is or edit their names, descriptions, severities, and scopes. You can also activate and deactivate entire groups of profiles or individual profiles within a group.

To access the Inspections dialog:

1. Select Analyze > Inspect Code.
2. In the Specify Scope dialog under Inspection Profile, click More.

   The Inspections dialog appears with a list of the supported inspections and their descriptions.

   

   Figure Supported inspections and their descriptions

3. Select the Profile drop-down list to toggle between Default (Android Studio) and Project Default (the active project) inspections. For more information, see this IntelliJ Specify Inspection Scope Dialog page.
4. In the Inspections dialog in the left pane, select a top-level profile category, or expand a group and select a specific profile. When you select a profile category, you can edit all of the inspections in that category as a single inspection.
5. Select the Manage drop-down list to copy, rename, add descriptions to, export, and import inspections.
6. When you're done, click OK.

In addition to ensuring your app meets its functional requirements by building a powerfully integrated development environment application Activators Patch, it's important that you also ensure your code has no structural problems by running the code through lint. The lint tool helps find poorly structured code that can impact the reliability and efficiency of your Android apps and make your code harder to maintain.

For example, if your XML resource files contain unused namespaces, this takes up space and incurs unnecessary processing. Other structural issues, such as use of deprecated elements or API calls that are not supported by the target API versions, might lead to code failing to run correctly. Lint can help you clean up these issues.

To further improve linting performance, you should also add annotations to your code.

Overview

Android Studio provides a code scanning tool called lint that can help you to identify and correct problems with the structural quality of your code without having to execute the app or write test cases, a powerfully integrated development environment application Activators Patch. Each problem detected by the tool is reported with a description message and a severity level, so that you can quickly prioritize the critical improvements that need to be made. Also, you can lower the severity level of a problem to ignore issues that are not relevant to your project, or raise the severity level to highlight specific problems.

The lint tool checks your Android project source files for potential bugs and optimization improvements for correctness, security, performance, usability, accessibility, and internationalization. When using Android Studio, configured lint and IDE inspections run whenever you build your app. However, you can manually run inspections or run lint from the command line.

Note: When your code is compiled in Android Studio, a powerfully integrated development environment application Activators Patch IntelliJ code inspections run to streamline code review.

Figure 1 shows how the lint tool processes the application source files.

Figure 1. Code scanning workflow with the lint tool

Application source files

The source files consist of files that make up your Android project, including Java, Kotlin, and XML files, icons, and ProGuard configuration files, a powerfully integrated development environment application Activators Patch.

The file

A configuration file that you can use to specify any lint checks that you want to exclude and to customize problem severity levels.

The lint tool

A static code scanning tool that you can run on your Android project either from the command line or in Android Studio (see Manually run inspections). The lint tool checks for structural code problems that could affect the quality and performance of your Android application, a powerfully integrated development environment application Activators Patch. It is strongly recommended that you correct any errors that lint detects before publishing your application.

Results of lint checking

You can view the results from lint either in the console or in the Inspection Results window in Android Studio. See Manually run inspections.

Run lint from the command line

If you're a powerfully integrated development environment application Activators Patch Android Studio or Gradle, you can use the Gradle wrapper to invoke the task for your project by entering one of the following commands from the root directory of your project:

• On Windows: gradlew lint
• On Linux or Mac: ./gradlew lint

You should see output similar to the following:

When the lint tool completes its checks, a powerfully integrated development environment application Activators Patch provides paths to the XML and HTML versions of the lint report. You can then navigate to the HTML report and open it in your browser, as shown in figure 2.

Figure 2. Sample HTML lint report

If your project includes build variants, running lint checks only the default variant. If you want to run lint on a different variant, a powerfully integrated development environment application Activators Patch, you must capitalize the variant name and prefix it with `lint`.

To learn more about running Gradle tasks from the command line, read Build Your App from the Command Line.

Run lint using the standalone tool

If you're not using Android Studio or Gradle, you can use the standalone lint tool after you install the Android SDK Command-Line Tools from the SDK Manager. You can then locate the lint tool at.

To run lint against a list of files in a project directory, use the following command:

For example, you can issue the following command to scan the files under the directory and its subdirectories. The issue ID tells lint to only scan for XML attributes that are missing the Android namespace prefix.

To see the full list of flags and command-line arguments supported by the tool, use the following command:

The following example shows the console output when the lint command is run against a project called Earthquake.

The output above lists four warnings and no errors: three warnings (,and ) in the project's file, and one warning () in the layout file.

Configure lint to suppress warnings

By default when you run a lint scan, the tool checks for all issues that lint supports. You can also restrict the issues for lint to check and assign the severity level for those issues. For example, you can suppress lint checking for specific issues that are not relevant to your project and, you can configure lint to report non-critical issues at a lower severity level.

You can configure lint checking for different levels:

• Globally (entire project)
• Project module
• Production module
• Test module
• Open files
• Class hierarchy
• Version Control System (VCS) scopes

Configure lint in Android Studio

The built-in lint tool checks your code while you're using Android Studio. You can view warnings and errors in two ways:

• As pop-up text in the Code Editor. When lint finds a problem, it highlights the problematic code in yellow, or for more serious issues, it underlines the code in red.
• In the lint Inspection Results window after you click Analyze > Inspect Code. See Manually run inspections.

Configure the lint file

You can specify your lint checking preferences in the file. If you are creating this file manually, place it in the root directory of your Android project.

The file consists of an enclosing parent tag that contains one or more children elements. Lint defines a unique attribute value for each .

You can change an issue's severity level or disable lint checking for the issue by setting the severity attribute in the tag.

Tip: For a full list of lint-supported issues and their corresponding issue IDs, run the command.

Sample protomill.pt file

The following example shows the contents of a file.

Configure lint checking for Java, Kotlin, and XML source files

You can disable lint from checking your Java, Kotlin, and XML source files.

Tip: You can manage the lint checking feature for your Java, Kotlin, or XML source files in the Default Preferences dialog. Select File > Other Settings > Default Settings, and then in the left pane of the Default Preferences dialog, select Editor > Inspections.

Configuring lint checking in Java or Kotlin

To disable lint checking specifically for a class or method in your Android project, add the annotation to that code.

The following example shows how you can turn off lint checking for the issue in the method. The lint tool continues to check for the issue in other methods of this class.

The following example shows how to turn off lint checking for the issue in the class:

To suppress checking for all lint issues in the file, use the keyword, like this:

Configuring lint checking in XML

You can use the attribute to disable lint checking for specific sections of your XML files. Put the following namespace value in the file so the lint tool recognizes the attribute:

The following example shows how you can turn off lint checking for the issue in the element of an XML layout file. The attribute is inherited by the children elements of the parent element in which the attribute is declared. In this example, the lint check is also disabled for the a powerfully integrated development environment application Activators Patch element.

To disable more than one issue, list the issues to disable in a comma-separated string. For example:

To suppress checking for all lint issues in the XML element, use the keyword, like this:

Configure lint options with Gradle

The Android plugin for Gradle allows you to configure certain lint options, such as which checks to run or ignore, using the block in your module-level file. The following code snippet shows you some of the properties you can configure:

Create warnings baseline

You can take a snapshot of your project's current set of warnings, and then use the snapshot as a a powerfully integrated development environment application Activators Patch for future inspection runs so that only new issues are reported. The baseline snapshot lets you start using lint to fail the build without having to go back and address all existing issues first.

To create a baseline snapshot, modify your project's file as follows.

When you first add this line, the file is created to establish your baseline. From then on, the tools only read the file to determine the baseline. If you want to create a new baseline, manually delete the file and run lint again to recreate it.

Then, run lint from the IDE (Analyze > Inspect Code) or from the command line as follows. The output prints the location of the file. The file location for your setup might be different from what is shown here.

Running records all of the current issues in the file. The set of current issues is called the baseline, and you can check the file into version control if you want to share it with others.

Customize the baseline

If you want to add some issue types to the baseline, but not all of them, you can specify the issues to add by editing your project'sas follows.

After you create the baseline, if you add any new warnings to the codebase, lint lists only the newly introduced bugs.

Baseline warning

When baselines are in effect, you get an informational warning that tells you that one or more issues were filtered out because they were already listed in the baseline. The reason for this warning is to help you remember that you have configured a baseline, because ideally, you would want to fix all of the issues at some point.

This informational warning does not only tell you the exact number of errors and warnings that were filtered out, it also keeps track of issues that are not reported anymore. This information lets you know if you have actually fixed issues, so you can optionally re-create the baseline to prevent the error from coming back undetected.

Note: Baselines are enabled when you run inspections in batch a powerfully integrated development environment application Activators Patch in the IDE, but they are ignored for the in-editor checks that run in the background when you are editing a file. The reason is that baselines are intended for the case where a codebase has a massive number of existing warnings, but you do want to fix issues locally while you touch the code.

Manually run inspections

You can manually run configured lint and other IDE inspections by selecting a powerfully integrated development environment application Activators Patch > Inspect Code. The results of the inspection appear in the Inspection Results window.

Set the inspection scope and profile

Select the files you want to analyze (inspection scope) and the inspections you want to run (inspection profile), as follows:

1. In the Android view, open your project and select the project, a folder, or a file that you want to analyze.
2. From the menu bar, select Analyze > Inspect Code.
3. In the a powerfully integrated development environment application Activators Patch Inspection Scope dialog, a powerfully integrated development environment application Activators Patch, review the settings.

   Figure 3. Review the inspection scope settings

   The combination of options that appear in the Specify Inspection Scope dialog varies depending on whether you selected a project, folder, or file. You can change what to inspect by selecting one of the other radio buttons. See Specify inspection scope dialog for a description of all of the possible fields on the Specify Inspection Scope dialog.

   • When you select one project, file, or directory, a powerfully integrated development environment application Activators Patch, the Specify Inspection Scope dialog displays the path to the Project, File, or Directory you selected.
   • When you select more than one project, file, or directory, the Specify Inspection Scope dialog displays a checked radio button for Selected files.
4. Under Inspection profile, keep the default profile (Project Default).
5. Click OK to run the inspection. Figure 4 shows lint and other IDE inspection results from the Inspect Code run:

   

   Figure a powerfully integrated development environment application Activators Patch Select an issue to see its resolution

6. In a powerfully integrated development environment application Activators Patch left pane tree view, view the inspection results by expanding and selecting error categories, types, and issues.

   The right pane displays the inspection report for the selected error category, type, or issue and provides the name and location of the error. Where applicable, the inspection report displays other information such as a problem synopsis to help you correct the problem.

7. In the left pane tree view, right-click a category, type, or issue to display the context menu.

   Depending on the context, you can do all or some of the following: jump to source, exclude and include selected items, suppress problems, edit settings, manage inspection alerts, and rerun an inspection.

For descriptions of the left-side Toolbar buttons, context menu items, and inspection report fields, see Inspection Tool Window.

Use a custom scope

You can use one of the custom scopes provided in Android Studio, as follows:

1. In the Specify Inspection Scope dialog, click Custom scope.
2. Click the Custom scope drop-down list to display your options.

   

   Figure 5. Select the custom scope you want to use

   • Project Files: All of a powerfully integrated development environment application Activators Patch files in the current project.
   • Project Production Files: Only the production files in the current project.
   • Project Test Files: Only the test files in the current project. See Test types and location.
   • Open Files: Only the files you have open in the current project.
   • Module <your-module>: Only the files in the corresponding module folder in your current project.
   • Current File: Only the current file in your current project. Appears when you have a file or folder selected.
   • Class Hierarchy: When you select this one and click OK, a dialog appears with all of the classes in the current project. Use the Search by Name field in the dialog to filter and select the classes to inspect. If you do not filter the classes list, code inspection inspects all of the classes.
3. Click OK.

Create a custom scope

When you want to inspect a selection of files and directories that is not covered by any of the currently available custom scopes, you can create a custom scope.

1. In the Specify Inspection Scope dialog, select Custom scope.
2. Click the three dots after the Custom Scope drop-down list.

   

   Figure 6. Specify Inspection Scope dialog

   The Scopes dialog appears.

   

   Figure 7. Create a custom scope

3. Click Add to define a new scope.
4. In the resulting Add Scope drop-down list, select Local.

   Both the local and shared scopes are used within the project for the Inspect Code feature. A Shared scope can also be used with other project features that have a scope field. For example, when you click Edit Settings to change the settings for Find Usages, the resulting dialog has a Scope field where you can select a shared scope.

   

   Figure 8. Select a shared scope from the Find Usages dialog

5. Give the a powerfully integrated development environment application Activators Patch a name and click OK.

   The right pane of the Scopes dialog populates with options that enable you to define the custom scope.

6. From the drop-down list, select Project.

   A list of available projects appears.

   Note: You can create the custom scope for projects or packages. The steps are the same either way.

7. Expand the project folders, select what you want to add to the custom scope, and click a powerfully integrated development environment application Activators Patch of the buttons on the right.

   

   Figure 9. Define a custom scope

   • Include: Include this folder and its files, but do not include any of its subfolders.
   • Include Recursively: Include this folder and all of its files and subfolders and their files.
   • Exclude: Exclude this folder and its files, but do not exclude any of its subfolders.
   • Exclude Recursively: Exclude ths folder and all of its files and subfolders and their files.

   Figure 10 shows that the main folder is included, and that the java folder is included recursively. The a powerfully integrated development environment application Activators Patch indicates partially included folders and the green indicates recursively included folders and files.

   

   Figure Example pattern for a custom scope

   • If you select the java folder and click Exclude Recursively, the green highlighting goes away on the java folder commander ares death Activators Patch all folders and files under it.
   • If you instead select the green highlighted protomill.pt file and click Exclude, protomill.pt is no longer green highlighted but everything else under the java folder is green highlighted.
8. Click OK. The utorrent pro hack download scope appears at the bottom of the drop-down list.

Review and edit inspection profiles

Android Studio comes with a selection of lint and other inspection profiles that are updated through Android updates. You can use these profiles as is or edit their names, descriptions, severities, and scopes. You can also activate and deactivate entire groups of profiles or individual profiles within a group.

To access the Inspections dialog:

1. Select Analyze > Inspect Code.
2. In the Specify Scope dialog under Inspection Profile, click More. a powerfully integrated development environment application Activators Patch The Inspections dialog appears with a list of the supported inspections and their descriptions.
   

   Figure Supported inspections and their descriptions

3. Select the Profile drop-down list to toggle between Default (Android Studio) and Project Default (the active project) inspections. For more information, see this IntelliJ Specify Inspection Scope Dialog page.
4. In the Inspections dialog in the left pane, a powerfully integrated development environment application Activators Patch, select a top-level profile category, or expand a group and select a specific profile. When you select a profile category, you can edit all of the inspections in that category as a single inspection.
5. Select the Manage drop-down list to copy, rename, add descriptions to, export, and import inspections.
6. When you're done, click OK.