aws_sdk_machinelearning::types::builders

Struct RedshiftDataSpecBuilder

Source 
#[non_exhaustive]
pub struct RedshiftDataSpecBuilder { /* private fields */ }
Expand description

A builder for RedshiftDataSpec.

Implementations§

Source§

impl RedshiftDataSpecBuilder

Source

pub fn database_information(self, input: RedshiftDatabase) -> Self

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

This field is required.
Source

pub fn set_database_information(self, input: Option<RedshiftDatabase>) -> Self

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Source

pub fn get_database_information(&self) -> &Option<RedshiftDatabase>

Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

Source

pub fn select_sql_query(self, input: impl Into<String>) -> Self

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

This field is required.
Source

pub fn set_select_sql_query(self, input: Option<String>) -> Self

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

Source

pub fn get_select_sql_query(&self) -> &Option<String>

Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

Source

pub fn database_credentials(self, input: RedshiftDatabaseCredentials) -> Self

Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

This field is required.
Source

pub fn set_database_credentials( self, input: Option<RedshiftDatabaseCredentials>, ) -> Self

Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

Source

pub fn get_database_credentials(&self) -> &Option<RedshiftDatabaseCredentials>

Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

Source

pub fn s3_staging_location(self, input: impl Into<String>) -> Self

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

This field is required.
Source

pub fn set_s3_staging_location(self, input: Option<String>) -> Self

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

Source

pub fn get_s3_staging_location(&self) -> &Option<String>

Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

Source

pub fn data_rearrangement(self, input: impl Into<String>) -> Self

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

  • percentBegin

    Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

  • percentEnd

    Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

  • complement

    The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

    For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

    Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}

    Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}

  • strategy

    To change how Amazon ML splits the data for a datasource, use the strategy parameter.

    The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

    The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}

    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}

    To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

    The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

Source

pub fn set_data_rearrangement(self, input: Option<String>) -> Self

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

  • percentBegin

    Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

  • percentEnd

    Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

  • complement

    The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

    For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

    Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}

    Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}

  • strategy

    To change how Amazon ML splits the data for a datasource, use the strategy parameter.

    The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

    The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}

    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}

    To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

    The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

Source

pub fn get_data_rearrangement(&self) -> &Option<String>

A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

  • percentBegin

    Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

  • percentEnd

    Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

  • complement

    The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

    For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

    Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}

    Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}

  • strategy

    To change how Amazon ML splits the data for a datasource, use the strategy parameter.

    The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

    The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}

    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}

    To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

    The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

    Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

    Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

Source

pub fn data_schema(self, input: impl Into<String>) -> Self

A JSON string that represents the schema for an Amazon Redshift DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

A DataSchema is not required if you specify a DataSchemaUri.

Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": \[

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } \],

"excludedVariableNames": \[ "F6" \] }

Source

pub fn set_data_schema(self, input: Option<String>) -> Self

A JSON string that represents the schema for an Amazon Redshift DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

A DataSchema is not required if you specify a DataSchemaUri.

Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": \[

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } \],

"excludedVariableNames": \[ "F6" \] }

Source

pub fn get_data_schema(&self) -> &Option<String>

A JSON string that represents the schema for an Amazon Redshift DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

A DataSchema is not required if you specify a DataSchemaUri.

Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": \[

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } \],

"excludedVariableNames": \[ "F6" \] }

Source

pub fn data_schema_uri(self, input: impl Into<String>) -> Self

Describes the schema location for an Amazon Redshift DataSource.

Source

pub fn set_data_schema_uri(self, input: Option<String>) -> Self

Describes the schema location for an Amazon Redshift DataSource.

Source

pub fn get_data_schema_uri(&self) -> &Option<String>

Describes the schema location for an Amazon Redshift DataSource.

Source

pub fn build(self) -> Result<RedshiftDataSpec, BuildError>

Consumes the builder and constructs a RedshiftDataSpec. This method will fail if any of the following fields are not set:

Trait Implementations§

Source§

impl Clone for RedshiftDataSpecBuilder

Source§

fn clone(&self) -> RedshiftDataSpecBuilder

Returns a duplicate of the value. Read more
1.0.0 · Source§

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
Source§

impl Debug for RedshiftDataSpecBuilder

Source§

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
Source§

impl Default for RedshiftDataSpecBuilder

Source§

fn default() -> RedshiftDataSpecBuilder

Returns the “default value” for a type. Read more
Source§

impl PartialEq for RedshiftDataSpecBuilder

Source§

fn eq(&self, other: &RedshiftDataSpecBuilder) -> bool

Tests for self and other values to be equal, and is used by ==.
1.0.0 · Source§

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.
Source§

impl StructuralPartialEq for RedshiftDataSpecBuilder

Auto Trait Implementations§

Blanket Implementations§

Source§

impl<T> Any for T
where T: 'static + ?Sized,

Source§

fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
Source§

impl<T> Borrow<T> for T
where T: ?Sized,

Source§

fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
Source§

impl<T> BorrowMut<T> for T
where T: ?Sized,

Source§

fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
Source§

impl<T> CloneToUninit for T
where T: Clone,

Source§

unsafe fn clone_to_uninit(&self, dest: *mut u8)

🔬This is a nightly-only experimental API. (clone_to_uninit)
Performs copy-assignment from self to dest. Read more
Source§

impl<T> From<T> for T

Source§

fn from(t: T) -> T

Returns the argument unchanged.

Source§

impl<T> Instrument for T

Source§

fn instrument(self, span: Span) -> Instrumented<Self>

Instruments this type with the provided Span, returning an Instrumented wrapper. Read more
Source§

fn in_current_span(self) -> Instrumented<Self>

Instruments this type with the current Span, returning an Instrumented wrapper. Read more
Source§

impl<T, U> Into<U> for T
where U: From<T>,

Source§

fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

Source§

impl<T> IntoEither for T

Source§

fn into_either(self, into_left: bool) -> Either<Self, Self>

Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
Source§

fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

Converts self into a Left variant of Either<Self, Self> if into_left(&self) returns true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
Source§

impl<Unshared, Shared> IntoShared<Shared> for Unshared
where Shared: FromUnshared<Unshared>,

Source§

fn into_shared(self) -> Shared

Creates a shared type from an unshared type.
Source§

impl<T> Paint for T
where T: ?Sized,

Source§

fn fg(&self, value: Color) -> Painted<&T>

Returns a styled value derived from self with the foreground set to value.

This method should be used rarely. Instead, prefer to use color-specific builder methods like red() and green(), which have the same functionality but are pithier.

§Example

Set foreground color to white using fg():

use yansi::{Paint, Color};

painted.fg(Color::White);

Set foreground color to white using white().

use yansi::Paint;

painted.white();
Source§

fn primary(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: Primary].

§Example
println!("{}", value.primary());
Source§

fn fixed(&self, color: u8) -> Painted<&T>

Returns self with the fg() set to [Color :: Fixed].

§Example
println!("{}", value.fixed(color));
Source§

fn rgb(&self, r: u8, g: u8, b: u8) -> Painted<&T>

Returns self with the fg() set to [Color :: Rgb].

§Example
println!("{}", value.rgb(r, g, b));
Source§

fn black(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: Black].

§Example
println!("{}", value.black());
Source§

fn red(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: Red].

§Example
println!("{}", value.red());
Source§

fn green(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: Green].

§Example
println!("{}", value.green());
Source§

fn yellow(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: Yellow].

§Example
println!("{}", value.yellow());
Source§

fn blue(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: Blue].

§Example
println!("{}", value.blue());
Source§

fn magenta(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: Magenta].

§Example
println!("{}", value.magenta());
Source§

fn cyan(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: Cyan].

§Example
println!("{}", value.cyan());
Source§

fn white(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: White].

§Example
println!("{}", value.white());
Source§

fn bright_black(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: BrightBlack].

§Example
println!("{}", value.bright_black());
Source§

fn bright_red(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: BrightRed].

§Example
println!("{}", value.bright_red());
Source§

fn bright_green(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: BrightGreen].

§Example
println!("{}", value.bright_green());
Source§

fn bright_yellow(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: BrightYellow].

§Example
println!("{}", value.bright_yellow());
Source§

fn bright_blue(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: BrightBlue].

§Example
println!("{}", value.bright_blue());
Source§

fn bright_magenta(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: BrightMagenta].

§Example
println!("{}", value.bright_magenta());
Source§

fn bright_cyan(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: BrightCyan].

§Example
println!("{}", value.bright_cyan());
Source§

fn bright_white(&self) -> Painted<&T>

Returns self with the fg() set to [Color :: BrightWhite].

§Example
println!("{}", value.bright_white());
Source§

fn bg(&self, value: Color) -> Painted<&T>

Returns a styled value derived from self with the background set to value.

This method should be used rarely. Instead, prefer to use color-specific builder methods like on_red() and on_green(), which have the same functionality but are pithier.

§Example

Set background color to red using fg():

use yansi::{Paint, Color};

painted.bg(Color::Red);

Set background color to red using on_red().

use yansi::Paint;

painted.on_red();
Source§

fn on_primary(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Primary].

§Example
println!("{}", value.on_primary());
Source§

fn on_fixed(&self, color: u8) -> Painted<&T>

Returns self with the bg() set to [Color :: Fixed].

§Example
println!("{}", value.on_fixed(color));
Source§

fn on_rgb(&self, r: u8, g: u8, b: u8) -> Painted<&T>

Returns self with the bg() set to [Color :: Rgb].

§Example
println!("{}", value.on_rgb(r, g, b));
Source§

fn on_black(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Black].

§Example
println!("{}", value.on_black());
Source§

fn on_red(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Red].

§Example
println!("{}", value.on_red());
Source§

fn on_green(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Green].

§Example
println!("{}", value.on_green());
Source§

fn on_yellow(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Yellow].

§Example
println!("{}", value.on_yellow());
Source§

fn on_blue(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Blue].

§Example
println!("{}", value.on_blue());
Source§

fn on_magenta(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Magenta].

§Example
println!("{}", value.on_magenta());
Source§

fn on_cyan(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: Cyan].

§Example
println!("{}", value.on_cyan());
Source§

fn on_white(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: White].

§Example
println!("{}", value.on_white());
Source§

fn on_bright_black(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightBlack].

§Example
println!("{}", value.on_bright_black());
Source§

fn on_bright_red(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightRed].

§Example
println!("{}", value.on_bright_red());
Source§

fn on_bright_green(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightGreen].

§Example
println!("{}", value.on_bright_green());
Source§

fn on_bright_yellow(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightYellow].

§Example
println!("{}", value.on_bright_yellow());
Source§

fn on_bright_blue(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightBlue].

§Example
println!("{}", value.on_bright_blue());
Source§

fn on_bright_magenta(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightMagenta].

§Example
println!("{}", value.on_bright_magenta());
Source§

fn on_bright_cyan(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightCyan].

§Example
println!("{}", value.on_bright_cyan());
Source§

fn on_bright_white(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightWhite].

§Example
println!("{}", value.on_bright_white());
Source§

fn attr(&self, value: Attribute) -> Painted<&T>

Enables the styling Attribute value.

This method should be used rarely. Instead, prefer to use attribute-specific builder methods like bold() and underline(), which have the same functionality but are pithier.

§Example

Make text bold using attr():

use yansi::{Paint, Attribute};

painted.attr(Attribute::Bold);

Make text bold using using bold().

use yansi::Paint;

painted.bold();
Source§

fn bold(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Bold].

§Example
println!("{}", value.bold());
Source§

fn dim(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Dim].

§Example
println!("{}", value.dim());
Source§

fn italic(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Italic].

§Example
println!("{}", value.italic());
Source§

fn underline(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Underline].

§Example
println!("{}", value.underline());

Returns self with the attr() set to [Attribute :: Blink].

§Example
println!("{}", value.blink());

Returns self with the attr() set to [Attribute :: RapidBlink].

§Example
println!("{}", value.rapid_blink());
Source§

fn invert(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Invert].

§Example
println!("{}", value.invert());
Source§

fn conceal(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Conceal].

§Example
println!("{}", value.conceal());
Source§

fn strike(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Strike].

§Example
println!("{}", value.strike());
Source§

fn quirk(&self, value: Quirk) -> Painted<&T>

Enables the yansi Quirk value.

This method should be used rarely. Instead, prefer to use quirk-specific builder methods like mask() and wrap(), which have the same functionality but are pithier.

§Example

Enable wrapping using .quirk():

use yansi::{Paint, Quirk};

painted.quirk(Quirk::Wrap);

Enable wrapping using wrap().

use yansi::Paint;

painted.wrap();
Source§

fn mask(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Mask].

§Example
println!("{}", value.mask());
Source§

fn wrap(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Wrap].

§Example
println!("{}", value.wrap());
Source§

fn linger(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Linger].

§Example
println!("{}", value.linger());
Source§

fn clear(&self) -> Painted<&T>

👎Deprecated since 1.0.1: renamed to resetting() due to conflicts with Vec::clear(). The clear() method will be removed in a future release.

Returns self with the quirk() set to [Quirk :: Clear].

§Example
println!("{}", value.clear());
Source§

fn resetting(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Resetting].

§Example
println!("{}", value.resetting());
Source§

fn bright(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Bright].

§Example
println!("{}", value.bright());
Source§

fn on_bright(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: OnBright].

§Example
println!("{}", value.on_bright());
Source§

fn whenever(&self, value: Condition) -> Painted<&T>

Conditionally enable styling based on whether the Condition value applies. Replaces any previous condition.

See the crate level docs for more details.

§Example

Enable styling painted only when both stdout and stderr are TTYs:

use yansi::{Paint, Condition};

painted.red().on_yellow().whenever(Condition::STDOUTERR_ARE_TTY);
Source§

fn new(self) -> Painted<Self>
where Self: Sized,

Create a new Painted with a default Style. Read more
Source§

fn paint<S>(&self, style: S) -> Painted<&Self>
where S: Into<Style>,

Apply a style wholesale to self. Any previous style is replaced. Read more
Source§

impl<T> Same for T

Source§

type Output = T

Should always be Self
Source§

impl<T> ToOwned for T
where T: Clone,

Source§

type Owned = T

The resulting type after obtaining ownership.
Source§

fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
Source§

fn clone_into(&self, target: &mut T)

Uses borrowed data to replace owned data, usually by cloning. Read more
Source§

impl<T, U> TryFrom<U> for T
where U: Into<T>,

Source§

type Error = Infallible

The type returned in the event of a conversion error.
Source§

fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
Source§

impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

Source§

type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
Source§

fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
Source§

impl<T> WithSubscriber for T

Source§

fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self>
where S: Into<Dispatch>,

Attaches the provided Subscriber to this type, returning a WithDispatch wrapper. Read more
Source§

fn with_current_subscriber(self) -> WithDispatch<Self>

Attaches the current default Subscriber to this type, returning a WithDispatch wrapper. Read more
Source§

impl<T> ErasedDestructor for T
where T: 'static,