Struct AnalysisResult 

pub struct AnalysisResult {
    pub statistics: BasicMetrics,
    pub shape_metrics: ShapeMetrics,
}

Contains comprehensive analysis results for a curve or dataset.

Overview

The AnalysisResult structure encapsulates both basic statistical metrics and shape-related characteristics of a dataset or curve. It serves as a complete analysis output that can be used to understand both the statistical properties and the geometrical behavior of the data.

This structure is particularly useful for comprehensive data analysis in financial modeling, statistical analysis, and geometric curve evaluation. It combines fundamental statistical insights with shape-based properties to provide a holistic view of the data.

Fields

• statistics: BasicMetrics

  • Contains fundamental statistical metrics like mean, median, mode, and standard deviation.
  • Provides insights into the central tendency and dispersion characteristics of the dataset.
  • Used for understanding the overall statistical properties of the analyzed data.

• shape_metrics: ShapeMetrics

  • Contains shape-related properties such as skewness, kurtosis, and critical points (peaks, valleys, and inflection points).
  • Provides insights into the geometrical behavior and distribution characteristics of the curve.
  • Used for understanding the asymmetry, tailedness, and notable features in the curve’s shape.

Applications

The AnalysisResult structure is utilized in various domains requiring both statistical and geometrical analysis:

• Financial Analysis:

  • Evaluate price distributions and identify critical price levels.
  • Detect patterns in market data through both statistical and shape-based observations.

• Data Science:

  • Provide comprehensive analysis of datasets, combining statistical insights with distributional shape characteristics.

• Geometric Modeling:

  • Analyze curve properties for more accurate modeling and representation.
  • Identify key points that define the geometric behavior of a curve.

Fields

statistics: BasicMetrics

The fundamental statistical metrics of the dataset, including measures of central tendency (mean, median, mode) and dispersion (standard deviation).

shape_metrics: ShapeMetrics

The shape-related metrics of the curve or dataset, including distribution characteristics (skewness, kurtosis) and critical geometric points (peaks, valleys, inflection points).

Trait Implementations

impl Clone for AnalysisResult

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl ComposeSchema for AnalysisResult

fn compose(generics: Vec<RefOr<Schema>>) -> RefOr<Schema>

impl Debug for AnalysisResult

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

Formats the value using the given formatter.

impl Display for AnalysisResult

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

Formats the value using the given formatter.

impl Serialize for AnalysisResult

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl ToSchema for AnalysisResult

fn name() -> Cow<'static, str>

Return name of the schema.

fn schemas(schemas: &mut Vec<(String, RefOr<Schema>)>)

Implement reference utoipa::openapi::schema::Schemas for this type.