The syntax of classes and inheritance

The class syntax in TypeScript is similar to ES6 but adds type annotations and access modifiers. Classes are defined using the `class` keyword and can include constructors, properties, and methods. They support three access modifiers: `public`, `private`, and `protected`. Inheritance is implemented using the `extends` keyword, allowing subclasses to inherit properties and methods from parent classes. Abstract classes are declared with the `abstract` keyword and cannot be instantiated directly. Interfaces can describe class structures, and classes implement interfaces using `implements`. Class members are divided into static members and instance members. Getters and setters are supported to control access. Class names can be directly used as type annotations. Polymorphism and method overriding are supported. Constructors can be overloaded. Classes can use generics to increase reusability. Decorators are supported to modify class behavior. Class expressions and private field syntax are also supported, using a `#` prefix for private fields.

Type compatibility rules

TypeScript's type compatibility is based on a structural type system rather than a nominal type system, meaning types are compatible as long as their structures match. For object types, compatibility requires that all properties of the target type exist in the source type and that their types are compatible. For function types, compatibility considers both parameters and return values: - Parameters follow bivariant rules. - Return values follow covariant rules. Class type compatibility is similar to objects, but private members from different declarations make them incompatible. Generic type compatibility depends on how the type parameters are used. Enums are compatible with numbers, but different enums are not compatible with each other. Union types must account for all possible types, while intersection types must satisfy all constituent types. Advanced types like index signatures, mapped types, and conditional types have specific compatibility rules. Type inference, contextual typing, and strict mode settings can also influence compatibility judgments.

The usage and limitations of enumeration types

TypeScript enumeration type is a special data type used to define named constant collections. It is declared via the `enum` keyword and supports numeric enums, string enums, and heterogeneous enums. Numeric enum members default to incrementing from 0 but can be manually assigned values. String enums require explicit initialization and do not generate reverse mappings. Enums compile into bidirectional mapping objects, allowing access by name or value. Const enums are declared with `const enum` and are removed during compilation, retaining only the used values to reduce code volume. Enum members are categorized as constant members or computed members. Enums are suitable for replacing magic numbers, managing states, and representing configuration options but have limitations in type safety and potential code bloat. Compared to union types, enums support reverse mapping and iteration. Compared to object constants, enums provide better type safety. Best practices include choosing the appropriate enum type, following naming conventions, and considering performance impacts. Enums support declaration merging and namespace extension for adding static methods.

Data mapping and encoding

Data mapping is the process of transforming raw data into visual elements, while encoding refers to the specific methods of mapping data attributes to visual channels. In ECharts, data mapping and encoding are the core mechanisms of chart presentation, determining how data is visually expressed. ECharts supports various visual channel encodings, including position encoding, size encoding, color encoding, shape encoding, and texture encoding. For continuous data, it provides strategies such as linear mapping, segmented mapping, and logarithmic mapping. When handling categorical data, considerations include color assignment, shape differentiation, and legend design. Multidimensional data can employ composite encoding strategies, such as position plus color or size plus color. ECharts allows custom data mapping through callback functions and offers performance optimization techniques like sampling strategies and aggregate mapping for large datasets. It supports dynamic data updates and interactive mapping adjustments. Global mapping rules can be uniformly managed through theme configuration. Special mapping considerations are required for geographic data, such as latitude-longitude mapping and region encoding. Time-series data can utilize timeline mapping and animation. Multidimensional data can be visualized using parallel coordinates, while hierarchical data is suitable for tree diagrams or sunburst charts.

Geographic data processing

Geographic data processing involves the entire process of spatial data collection, storage, analysis, and visualization. ECharts, as a powerful visualization library, provides robust capabilities for geographic data processing, supporting various geographic data formats such as GeoJSON and TopoJSON. Its core components include the geo and map series, enabling multiple visualization forms like heatmaps, scatter plots, and flow maps. Custom maps can be registered to meet specific requirements, offering interactive features such as region highlighting, data filtering, and linkage analysis. Performance optimization strategies include data simplification and layered rendering. Practical applications are demonstrated through case studies like air quality monitoring and population migration analysis. It can also be extended to specialized coordinate systems for diverse needs, such as indoor maps, virtual scenes, and astronomical data.

Time series data processing

Time series data is a collection of data points arranged in chronological order, exhibiting strong temporal dependencies where adjacent data points are interrelated. It is commonly used in fields such as finance, IoT, and meteorology. ECharts handles time series by configuring the `type: 'time'` in the xAxis, supporting custom time formats. For large datasets, optimization strategies like data sampling and incremental rendering can be employed to achieve dynamic time range control, multi-time-series comparative analysis, and anomaly detection visualization. It can also display time series forecasting results, address cross-timezone data display issues, annotate significant events on the timeline, and implement real-time data updates. Enhanced interactive display functionalities for time points are provided, along with rich interactive details for specific time points.

Data grouping and aggregation

Data grouping and aggregation are common operations in data analysis, involving grouping data by specific conditions and then performing statistical calculations. ECharts, as a data visualization library, supports various methods to achieve grouped and aggregated displays. Grouping is based on discrete variables, while aggregation involves operations like summation and averaging on numerical data. ECharts allows data processing at both the data level and visual encoding level. For large datasets, pre-aggregation is recommended to improve performance. The `dataset` is the recommended data management approach, enabling the extraction of required dimensions from multidimensional data. Multi-series grouping is suitable for displaying comparisons between groups. ECharts 5.0 introduced the `transform` feature, allowing direct data aggregation within configurations. Complex scenarios can combine multiple `transform` operations, and custom aggregation functions can be registered for flexible processing. Interactive grouping enables dynamic changes to grouping dimensions via components. For large datasets, optimization strategies like pre-aggregation, sampling, or chunked loading can be applied. In real-world projects, complex aggregations are often handled on the backend, while the frontend focuses on display and interaction. Different chart types are suited for different forms of aggregated data: bar charts for comparing group summaries, pie charts for displaying proportions, and line charts for showing trends.

Multidimensional data analysis

Multidimensional data analysis is a method of cross-analyzing data from multiple perspectives to uncover patterns and trends. ECharts supports multidimensional data visualization through chart types such as line charts, bar charts, and scatter plots, offering features like dataset configuration, interactive analysis, and advanced functionalities such as custom dimension mapping, data aggregation, and multi-coordinate system combinations. Using sales data as an example, the article demonstrates the implementation of multidimensional analysis across time, region, and product, while also exploring performance optimization strategies for handling large datasets and the implementation of dynamic dimension switching, helping users comprehensively understand complex data structures.

Data transformation and preprocessing

ECharts, as a popular visualization library, offers robust data processing capabilities. Each step—from data cleaning and format conversion to aggregation calculations—impacts the final presentation. Standardizing data formats is recommended, with key-value pair arrays being the preferred form. Time data requires special attention to ensure uniform formatting. Data cleaning and filtering involve outlier handling and missing value interpolation. Data aggregation and grouping include time-based aggregation and categorical data grouping. Statistical data mapping and transformation cover color mapping and numerical range normalization. Time series processing encompasses weekly data conversion and discontinuous time series completion. Multidimensional data pivoting requires dimensionality reduction for display. Performance optimization involves sampling algorithms and incremental updates. Interactive data processing supports dynamic filtering. Geographic data conversion includes GeoJSON processing and coordinate transformation. Together, these techniques form ECharts' powerful data processing framework.

Optimization strategies for large data volumes

When dealing with large datasets, ECharts faces performance bottlenecks and requires multiple optimization strategies: data sampling and aggregation (reducing data volume through equidistant sampling or time-window aggregation), chunked loading and dynamic rendering (loading large datasets in batches), WebGL acceleration (enhancing rendering performance), visual mapping optimization (reducing rendering load via color encoding), data preprocessing (filtering, categorizing, and sorting on the server side), interaction optimization (using debounce techniques to minimize frequent operations), memory management (regularly clearing caches and properly destroying instances), and performance monitoring (recording metrics like rendering time with tools). Applying these methods comprehensively can significantly improve chart performance in big data scenarios.