Area charts show trends over time or different categories. They are similar to bar charts except the space between adjacent data points is filled.

Multi Series 3D Area Charts show how different measures change over time or across different categories. They allow you to visually compare those categories and spot patterns in the data.

Smooth Area charts are a data visualization tool similar to Standard Area charts. The main difference is that the data points are connected using cubic spline segments rather than straight lines. This creates a more visually appealing and fluid representation of the data trends.

Stacked area charts are a practical way to display cumulative data by showing how individual series contribute to the whole. The chart stacks the series on top of one another, with each new series starting where the previous one ends. This creates a visual representation of the total value and how it is divided between the different components. With stacked area charts, you can easily see which series contribute the most to the whole and how their contributions change over time. This makes it a valuable tool for analyzing trends and identifying patterns in your data.

Stacked percent area series effectively showcase the relative contribution of data series to the total. By displaying the data as a percentage of the whole, it is easy to see how each data series contributes to the overall picture. This chart type is beneficial for highlighting trends and changes over time.

The XY mode enables the visualization of non-regular sets of X-values with an area chart. This makes it possible to plot data series that are not evenly spaced on the X-axis, which is common for many financial and scientific charts.

The 2D categorical bar chart is probably the most commonly used charting type. This particular bar chart image shows a bar chart with applied image effects such as soft bar shadows and glow effect on the bar labels.

Nevron Chart fully supports 3D bar charts. On this image a categorical bar chart in 3D is displayed. The bars use a smooth bar shape and have a lighting effect for increased visual appeal.

Cluster bar charts allow visual comparison between corresponding values of synchronized data ranges. Nevron Chart supports cluster bars with both positive and negative values.

Stacked bar series provide the capability to present data in cumulative sections and their totals.

This image shows a bar chart that uses XZ Scatter bars, that in addition have stacked bar series.

Using multiple categorical bar series in 3D makes it easy to create Manhattan Bar charts. Those types of bar charts are perfect for comparing different items over a period of time or a number of categories.

Horizontal orientation of the bars is required in many practical applications of bar charts. This particular sample demonstrates the use of horizontal bar series in multiple charts positioned within the same canvas to display the difference in population structure between males and females in NYC.

Clustering and stacking may be combined in the same chart. In a 3D scene, clustered and stacked series may be distributed in different depths.

Stacked-percent bar charts are a type of stack bar that represents the contribution of data series in each category to the total.

This image shows a bar chart that uses XZ Scatter bars, that in addition have stacked and clustered bar series.

Box and Whiskers plots are very helpful in interpreting the distribution of data. Each box and whiskers item represents a set of values and displays statistical information like minimum, maximum, and median values, upper and lower quartiles, outliers, and optionally - average values.

A box and whiskers series with custom date time values. This chart lets you compare the statistical parameters of several datasets produced at different moments in time. Box and whiskers plots are very helpful in interpreting the distribution of data. Each box and whiskers item represents a set of values and displays statistical information about it, such as minimum, maximum, and median values, upper and lower quartiles, outliers, and, optionally - average value.

A data distribution chart is an effective way to summarize the overall distribution of data in a dataset. This chart type uses a box and whiskers series to display statistical information such as the minimum, maximum, and median values, upper and lower quartiles, and any outliers that may be present.

Bubble Charts are a convenient way for graphical representation of multi-dimensional data. In a bubble chart, the data points are represented as circles (or other shapes) with varying sizes and positions corresponding to the displayed data. One of the key advantages of a 2D bubble chart is its ability to represent up to four dimensions of data. This can include X and Y positions, size, and color. By incorporating these dimensions into the chart, you can gain deeper insights into the data and make more informed decisions based on the results. In particular, the size and color of each bubble can be used to convey important information about the data being displayed. For example, the size of the bubble can represent the magnitude of a particular variable, while the color can represent a categorical variable or another variable of interest.

XYZ-scatter bubble charts are used to represent data that is distributed along all three axes. This makes it possible to identify correlations and patterns in the data that might not be immediately obvious with other charts. One of the key advantages of an XYZ-scatter bubble chart is its ability to incorporate bubble size and color as additional variables. This can help you represent more complex data sets with up to five dimensions of data. Using a range of bubble sizes and colors allows you to highlight different trends and patterns in the data.

XY scatter bubble charts provide a versatile and effective way to visualize data distributed along the X and Y axes. With this chart type, you can plot data points with freely distributed 2D positions. Additionally, you can incorporate bubble size and color as other variables to represent additional information. This allows you to provide deeper insights into complex data sets.

An Error Chart can be used to indicate estimated error in a measurement. The error values are shown as "T" and upside-down "T" symbols above and below the primary value. The most common Error Chart displays errors only in Y values.

An XY Error chart allows you to display errors in both X and Y directions.

An XYZ Error chart allows you to simultaneously display errors for X, Y, and Z dimensions. This chart type is advantageous in scientific research or engineering applications where it's crucial to understand the uncertainties in all three data dimensions.

A Heat Map is a 2D representation of data where the individual values are contained in a matrix and are represented as colors, which are taken from an associated palette.

The Heat Map can automatically generate contour lines. Those are lines that show areas in the heat map that have the same values.

This image shows how the heat map can be used to display a wafer chart. This type of chart is commonly used in the semiconductor industry.

A 2D High-Low chart displays an area connecting high and low value sets. This chart type helps represent changes in data over time and can be used to convey complex information simply and intuitively. High-Low charts typically display a vertical line representing the range of high and low values for a specific period. The area between the lines is filled in to indicate the magnitude or value of each category.

A 3D High Low chart is a powerful data visualization tool that provides a three-dimensional representation of high and low values over time. This chart type can display complex data sets and reveal patterns that might not be immediately apparent in two-dimensional charts. 3D High Low charts typically display a vertical line that connects the high and low values for a specific period, with the area between the lines filled in to indicate the magnitude or value of each category. The third dimension adds depth to the chart, allowing for a more comprehensive data view.

Positive and negative correlation between high and low data points may be represented by different fill effects of the current section of the area.

This example demonstrates a High Low chart with custom X positions for the data points.

The High-Low series display an area connecting sets of high and low values.

Positive and negative correlation between high and low data points may be represented by different fill effects of the current section of the area.

A standard 3D line chart is a visualization that represents data points connected by lines. It's a helpful tool for displaying trends and patterns in data. The chart can be customized with markers of various shapes and fill options to highlight specific data points. This makes it easier for viewers to interpret and understand the presented data.

A 2D line chart with different image effects such as soft shadows and gradients. To create visually appealing designs, the chart can be stylized with image effects like shadow, glow, bevel/emboss, and lighting.

Smooth line charts connect the distincst data points with cubic curves, making it easier to identify trends and patterns in the data. Each data point can be highlighted with markers of various shapes and fill options.

An XYZ line chart represents a line that connects data points with custom XYZ coordinates. With this chart, you can visualize how data points are distributed across the x, y, and z axes and detect trends and patterns that might be missed with other chart types. This charting type is commonly used in scientific charts.

Date-Time Step Line Chart is type of XY scatter line chart, where the X values represent dates or times when the measurement has happened. This chart is handy for visualizing trends and patterns in data collected over a period of time. The Date-Time Step Line Chart allows you to see the general direction of the movement while also showing the specific points at which changes occurred.

A stacked line chart is similar to a stacked bar chart and allows the user to compare the contribution of different measurements to a total over different categories or time spans. By stacking the lines on top of each other, you can easily see which categories are growing or declining and how they affect the overall trend.

Several categorical line series visualized a tapes in 3D. The chart can be useful when comparing data sets with similar or overlapping trends.

The multi series line chart allows the visualization of several trends over a common set of categories or time spans in a single chart. This allows you to easily compare and contrast the trends of each series. This approach can provide insights into complex data, enabling the user to spot different trends and patterns.

XYZ Line Chart representing Lorenz Attractor – a complex fractal structure corresponding to the long-term behavior of a 3-dimensional dynamical system that exhibits chaotic flow and evolving without ever crossing itself.

3D Step line chart with dimension scale X-axis. Step line chart series display data points connected with HV segments. Markers with various shapes and fill options can be used for each data point.

Pie charts visually represent the relative contribution of each value to a total sum. Various pie shapes, labeling modes, and other options help arrange the chart and the labels within the scene, achieving optimal data readability and visual impression.

Pie chart Torus mode is a variation of the traditional pie chart that displays the data in a circular shape with a hole in the center, creating a donut-like appearance. The center hole can contain additional data or labels, while the outer circle still displays the relative values of the different categories.

Multiple pie charts are a way of displaying related data sets together. This type of visualization helps compare data sets side by side or analyze trends over time. Viewers can quickly identify similarities and differences between the data sets using multiple pie charts. However, it is crucial to remember that too many pie charts can be overwhelming and confusing to interpret. To ensure clarity, it is recommended to limit the number of pie charts used and to use consistent colors and labeling across all charts.

Exploded pie charts are charts where individual sectors are separated from the center for emphasis. They are available in 2D and 3D modes and can be sorted in ascending or descending order. The labels are displayed at the rim of the pie, and the smooth edge mode enhances the visual quality of the chart.

The Non-Overlapping Labels mode helps to automatically position pie labels so that they do not overlap and are positioned within the chart panel boundaries. This greatly improves the chart's readability by ensuring users can properly interpret all values in the pie chart.

Partial-angle pie charts display only a portion of the 360-degree circle, which can help highlight a specific category or segment. The angle can be adjusted to any value, allowing for chart customization. This mode includes various labeling options for clear data representation, including center, rim, and spider modes.

Candle Stock charts are used to plot daily stock data. Each data point resembles a candle, where the candle body is formed by the open and close prices for the day. The high and low prices are displayed by vertical lines above and below the candle body. The body fill color depends on whether the stock price goes up (the closing price is higher than the opening price) or down (the closing price is lower than the opening price).

Stick Stock Charts are used to plot daily stock data. Unlike Candle Charts, Stick Stock Charts do not draw a candle body. The open and close prices are marked by short horizontal lines, while the high-low range is presented as a single vertical line. The stroke style of a data point depends on whether the price goes up or down.

Point Charts display 1, 2 or 3-dimensional data as points in a Cartesian coordinate system. Values are represented by the position of the points on the chart. Point series is mainly intended to display discrete sets of independent values. However, it may also be used as an alternative to line or bar series.

In XYZ scatter mode, Point series may be used for presentation of data that is irregularly spread throughout 3D space using custom x and z values for the data points.

Multiseries XYZ scatter Point charts are suitable for representing the relative 3D distribution of several different datasets.

In XY scatter mode, Point series may be used for presentation of data that is irregularly spread throughout 2D space using custom x values for the data points. The point series supports a special rendering mode called clustered rendering, which allows you to render very large datasets. Clustering works by identifying densely populated regions of data, which are later visualized by a lesser number of points. This reduces the number of visual points on the screen and therefore improves performance.

The point series supports a special rendering mode called clustered rendering, which allows you to render very large datasets. Clustering works by identifying densely populated regions of data, which are later visualized by a lesser number of points. This reduces the number of visual points on the screen and therefore improves performance.

A Polar Area chart displays a filled closed contour in a 2-dimensional polar coordinate system. The contour is defined by a sequence of data points, where the position of each data point is specified by its distance from the center and its angle of rotation. The figure displays a Polar Area chart series.

A Polar Point Chart is a chart that displays data points in a 2-dimensional polar coordinate system. Each point on the chart is defined by its distance from the center and rotation angle, making it a useful tool for displaying data with both magnitude and direction.

A Polar Line Chart is a chart that displays a sequence of data points connected by a line in a 2-dimensional polar coordinate system. Each point on the chart is defined by its distance from the center and rotation angle, with consecutive points connected by straight line segments.
Polar Line Charts are useful for displaying data that has both magnitude and direction and can be used to visualize trends or patterns over time.

The image demonstrates polar angle axes positioning. In this case there are two angular axes - the first one denoting degree values (red) and the second one denoting grad values (purple). Note that the axes will be decorated by choosing a step that is a whole devisor of the range [0, 90] in degrees ([0, 100] in grads).

Here you have two different Value Axes (red and grey). You can dock polar value axes to one of the four cartesian zones attached to the polar - Left, Top, Right, and Bottom (in this example - Left and Bottom). You can control the orientation of the axes by changing the angle when in cross mode. You can display the value axis reflection using the paint reflection option (shown here).

This example demonstrates a 2D Vector Field implemented with the Vector Series. A 2D vector field is a visualization that uses arrows to represent forces acting at a specific point. The arrows are defined by four coordinates: Begin X, Begin Y, End X, and End Y. This series has many applications in scientific charts.

This example demonstrates a 3D Vector Field implemented with the Vector Series. It uses arrows to represent data points defined by six coordinates: Begin X, Begin Y, Begin Z, End X, End Y, and End Z. This type of chart is ideal for displaying complex 3D data sets and can be used in a variety of applications, from scientific research to engineering analysis.

Tree maps display hierarchical (tree-structured) data as a set of nested rectangles. Each branch of the tree is given a rectangle, which is then tiled with smaller rectangles representing sub-branches. Each leaf node is assigned a value. The area of each leaf node is proportional to its value versus the total aggregated value of all leaf nodes.

Tree maps enable you to easily identify patterns that might be challenging to detect using other methods. They effectively convey multi-dimensional information, such as size and direction, for numerous items while simultaneously comparing groups with hierarchical relationships. Another advantage of tree maps is their efficient use of space, allowing them to legibly display hundreds of items on the screen at once.

Tree Map Chart using Group Palette.

Tree Map Chart with Value Nodes Legend Mode. Each group tree map node can output information about itself or its descendants on a legend.

Tree Map Chart with Gradient Palette Legend Mode. Each group tree map node can output information about itself or its descendants on a legend.

A 2D float bar chart is a type of bar chart that allows you to display data consisting of ranges of values with bars. This is useful when you want to show the spread of values across categories or dates.
In a 2D float bar chart, each bar is represented by a rectangle that spans a vertical distance based on the range of values it represents. Nevron chart allows to embelish of the bars using rendering effects such as soft shadows, glow effects, bevel/emboss effects others. These features can enhance the visual appeal of your chart and make it easier to read and understand

Cluster Float Bar charts allow visual comparison between corresponding ranges of Y values with synchronized X values. This chart type displays multiple floating bar series side by side, with each series representing a specific category or data range. Cluster Float Bar charts are particularly useful in industries such as finance, where the ability to quickly compare data sets is crucial.

A Date Time Float Bar Chart is a bar chart that displays ranges of values using bars, with the bars' position corresponding to specific dates and times. It can also visualize data with a time component, such as stock prices or project timelines.
With the ability to customize the DateTime values along the X-axis, the chart allows for the accurate representation of time-based data. Additionally, image effects such as shadow, glow, bevel/emboss, and lighting can be applied to enhance the visual appeal of the chart.

Gantt charts are an effective tool for project management, allowing users to plan the use of resources over time. A Gantt chart can be created using a Floating Bar chart, where the Y-axis is set to date-time scale mode. Activities within the project are represented by their start and finish dates or times, allowing for easy visualization of the project timeline. Nevron's graphics engine provides high-quality image rendering and supports various visual effects like shadows, glow, and lighting, creating visually appealing and informative Gantt charts.

A 3D Float Bar Chart displays a sequence of horizontal or vertical bars in three dimensions, each representing a one-dimensional value range. This chart shows how multiple variables change over time and allows you to efficiently compare and contrast different data sets. Additionally, you can customize the chart with different colors and markers to highlight specific data points or trends.

A Stack Float Bar chart is useful for displaying a range of values in a stacked format. It's created by stacking one or more bar series over a FloatBar series. Each bar in the stack represents a portion of the overall value range, and the stack shows the total value for each data point. This type of chart is advantageous for comparing the relative sizes of different data points in a series and can be customized with various markers, colors, and labels to make the data easier to interpret.

A Date Time Float Bar Chart is a bar chart that displays data using vertical bars, with the bars' position corresponding to specific dates and times. It visualizes data with a time component, such as stock prices or project timelines. The chart displays a range of values for each date and time, with the bars floating at different heights to represent the range.
With the ability to customize the DateTime values along the X-axis, the chart allows for a precise and accurate representation of time-based data.

Radar Series, used to create a Radar Chart (Spider Chart, Star Chart), is rendered to a circular graph and is used primarily as a multi-parameter data comparison tool.

A multi-measure radar chart is an effective tool for comparing items based on different, unrelated measures. This type of chart displays data in a circular format, with each measure represented by a separate axis. By comparing the lengths of the different axes, users can instantly identify patterns and trends in the data.

Compared, areas covered by different data series form a general presentation of their relation, without losing the detailed information for every single parameter. Series that cover larger area are commonly considered to have a higher rating.

The Mesh Surface chart displays a grid of data points, where each data point specifies a distinct XYZ coordinate. This is in contrast to the Grid Surface series where only the y-position (elevation) is user-defined and the X/Z coordinates are shared for data points in the same row or column.

This example demonstrates a mesh representing a mathematically generated spiral surface.

The Mesh surface series supports a FillEffect for the whole surface. The user can apply images, gradients, advanced gradients and patterns to the surface, as well as modify material properties like Specular Color, Emissive Color and Shininess. In this example are added texture and mesh frame.

Mesh surface series may intersect. Using settings for surface filling allows higher readability of the represented data.

"Empty data points" is a feature allowing data points inside the mesh surface grid to contain invalid data. These data points are not rendered by the chart which allows you to create "holes" in the surface.

The Grid Surface chart displays a surface with user-defined elevation values for each data point and with X and Z coordinates from a rectangular grid. With the Grid Surface chart, you can create visualizations of 3D dimensional data which have many applications in the areas of geography, engineering, physics, and others.

This image demonstrates wire frame surface rendering. The surface filling is disabled and the frame style is "Mesh" - this way only mesh lines are displayed.

The Grid surface series offers a range of customizable FillEffect options for the surface, including images, gradients, advanced gradients, and patterns. Additionally, users can modify material properties such as Specular Color, Emissive Color, and Shininess to achieve their desired visual effect. This image shows a grid surface chart with applied texture.

This example demonstrates a contour chart displayed with the Grid Surface Series. Below - the surface is rendered in flat mode and above - with the actual surface elevation. This surface render mode allows you to easily create heat maps in 3D and use them as underlays or overlays of other 3D content.

Empty data points is a feature allowing the grid to contain areas that do not contain valid data and therefore should not be rendered.

This image demonstrates a contour chart displayed with the Grid Surface Series. The chart uses orthogonal projection with camera elevation of 90 degrees, the lighting is disabled and the surface is rendered in flat mode.

Here the Grid Surface chart is used to represent waves.

The Triangulated Surface chart series lets users visualize data points with custom X, Y, and Z coordinates as a 3D surface. Unlike the other surface series tyeps (like Mesh or Grid Surface), the Triangulated Surface does not necessitate the data points to be arranged in a grid. Instead, the chart automatically creates a Delaunay triangular network to render the surface. This chart type is beneficial when working with irregularly spaced data points or missing data in the set. It can help to fill in gaps and provide a complete representation of the data.

The Triangulated Surface allows you to assign a custom color for each data point. Custom Colors can be used both for the surface filling and the frame, thus adding a fourth dimension to the input data.