Plots Visual
Line plots are used to visualize data in 1, 2 or 3 dimensions. Data points are connected by straight lines. Learn more about Line Plots in C#, Visual Basic and .NET:
Deer Food Plot Tips: Creating Visual Barriers One really easy way to encourage whitetails to use a food source is making them feel safe by planting a food plot screen like BioLogic Blind Spot. The older age class of both bucks and does can be really shy of big, open fields or food plots.
In order to demonstrate the capabilities of ILNumerics for creating plots in C#, VB and .NET, we created some random data vector and provide it to a new LinePlot object. The Line Plot is than added to the plotting cube: Since we did only provide 1-dimensional data to the plot cube, the values are interpreted as Y values and the X values are automatically created.
Deer Food Plot Tips: Creating Visual Barriers One really easy way to encourage whitetails to use a food source is making them feel safe by planting a food plot screen like BioLogic Blind Spot. The older age class of both bucks and does can be really shy of big, open fields or food plots that are void of close cover. A box whisker plot uses simple glyphs that summarize a quantitative distribution with: the smallest and largest values, lower quantile, median, upper quantile. This summary approach allows the viewer to easily recognize differences between distributions and see beyond a standard mean value plots.
X-Y plots are generated by simply providing a matrix with two rows: X values in the first row, Y values in the second row:
The scheme applies similarly to 3D Line Plots, which expects 3 rows for X, Y and Z-coordinates.
The plots under correlation is used to visualize the relationship between 2 or more variables. That is, how does one variable change with respect to another. Iso-Contours Plot; Luminaire Classification System; Polar Plots; Roadway Summary; Zonal Lumen Summary; About. With the Visual Photometric Tool, you can search the entire Acuity Brands Lighting photometric database or select a photometric file from your computer to generate a customized photometric report. Help Information.
Handling NaN Values
Line Plots are able to handle NaN values. Those points, which have a coordinate component of NaN (Not a Number) are simply omited from rendering. One may utilize this property for non-continous lines and to realize several lines by using a single LinePlot object.
Auto-Coloring Line Plots
If no color was specified in the constructor, the color for each line is set automatically. Every subsequent new Line Plot receives an individual color.
In the next example, 64 Line Plots are created. The Line Plots get automatically colored by the keyframes in the Lines colormap, which is displayed eventually in the result:
Line Plots: Customizing Line Properties
Line Plots expose the LinePlot.Line property. Lines are made out of line strips (LineStrip) and provide the following options for configuration:
- Color
- DashStyle
- Width
The next example creates a whole bunch of Line Plots, all with different style combinations. We use a more efficient method than creating every line plot separately. LinePlot provides the static method LinePlot.CreateXPlots() for creating a number of regular Line Plots from the rows of a matrix. It allows the definition of style parameter collections for color, style and width of every line:
If any property of the line needs to get changed after creation, the LinePlot.Line property provides access to the line strip object for full configuration. If the need arises, the configuration may even include the modification of any vertex buffer of the line strip object. That way fully dynamic plots can be realized.
Markers
Line Plots allow the definition of markers for their data points. Markers can be configured either in the contructor of LinePlot …
Or, alternatively, the LinePlot.Marker property allows the (re-)configuration of markers:
All existing predefined marker shapes are displayed by the following code:
Customizing Markers
Markers can be arbitrarily customized. They allow the addition of custom shapes. LinePlot.Marker is a group node, which serves as a template for all markers to be drawn. All shapes added to the LinePlot.Marker group will be repeatedly displayed for every data point.
In the next example, the marker from the example above will be extended by a crosshair, which is made of two simple red lines, added to the marker node:
Coordinate System for Markers
A marker is a group node, which defines its own coordinate system. The node transforms vertex positions of its child shapes from the XY range [-1…1] to the pixel size defined by LinePlot.Marker.Size. If the Size property is set to 9 (default) this means, objects which range from -1 to 1 in X or Y will have a length of 9 pixels in X or Y direction, centered at the position of the data point.
Marker Billboarding
Markers are always oriented towards the camera. Even for 3D plots, with arbitrary rotation applied, the X-Y plane of a marker will always face the camera. The Z-coordinate of the vertices of marker shapes can be used to define the order of markers in Z-directions.
Line Plots in C# and VB (.NET): Further readings
The ILNumerics plotting library allows you to create fully customizable 2d and 3d line plots using C# code. Learn more about axis configurations and logarithmic axes in ILNumerics:
-->Plotting is a key part of a data scientist's workflow. In R Tools for Visual Studio (RTVS), all plotting activity centers around one or more plot windows, which are designed to improve your productivity with this key activity.
Watch a video (youtube.com) on plotting with R (2m 02s).
The plot window
A plot window holds a series of plots, where each plot is generated by a plot command. For example, using plot(1:100) creates a new plot window if one isn't already available:
Technically speaking, R plot commands render their output to an R graphics device; a plot window renders the contents of an R graphics device, which is why each plot window is given a device number.
Plot windows are independent of Visual Studio projects, and remain open as you load and close projects.
Generating a plot uses the 'active' plot window, saving any previous plot it the plot history (see Plot history). For example, enter plot(100:1) and the first plot is replaced with a downward line.
Like all other Visual Studio windows. the plot window supports customized layouts (see Customize window layouts in Visual Studio. Plot windows can be docked at different locations within the Visual Studio frame, resized within that frame, or pulled out of the frame entirely for independent resizing.
Resizing a plot window always re-renders the plot to provide the best quality image. You typically want to resize a plot before exporting the plot to a file or to the clipboard using the commands described in the next section.
Plot window commands
The plot window's toolbar holds applicable commands, most of which are also available through the R Tools > Plots menu.
Visualization Plots Python
| Button | Command | Description |
|---|---|---|
| New plot window | Creates a separate plot window with its own history. See Multiple plot windows. | |
| Activate plot window | Sets the current plot window as the active window, so that subsequent plot commands are rendered to that window. See Multiple plot windows. See Multiple plot windows. | |
| Plot history window | Opens a window with all the plots in the history shown as thumbnails. See Plot history. | |
| Previous/Next Plot | Navigates to the previous or next plot in the history. You can also navigate the history with Ctrl+Alt+F11 (Previous) and Ctrl+Alt+F12 (Next). See Plot history. | |
| Save As Image | Prompts for a filename and saves the current plot (the window contents, at the window size) to an image file. Available formats are .png, .jpg, .bmp, and .tif. | |
| Save As PDF | Saves the current plot to a PDF file, using the current window size. The plot will re-render if the PDF is scaled. | |
| Copy As Bitmap | Copies the plot to the clipboard as a raster bitmap, using the current window size. | |
| Copy As Metafile | Copies the plot to the clipboard as a Windows metafile (Wikipedia). | |
| Remove Plot | Removes the current plot from the history. | |
| Clear All Plots | Removes all plots from the history (prompts for confirmation). |
Multiple plot windows
Plots In Visualizations
Because data scientists often work with many plots from many different datasets, RTVS lets you create as many independent plot windows. You can then arrange those windows however you like within the Visual Studio frame or outside that frame altogether. (See Customize window layouts in Visual Studio for general information on docking and resizing windows.)
You create a new plot window using the toolbar button or R Tools > Plots > New Plot Window. The new plot window becomes the active window, which is where new plots are rendered. To change the active window, switch to it and select the Activate Plot Window toolbar button or R Tools > Plots > Activate Plot Window.
Plots, too, are independent objects, which means you can copy or move them between plot windows using either drag-and-drop with the mouse, or by using the Copy, Cut, and Paste commands on the right-click context and Edit menus.
Plot Visual Aid
The default behavior for drag-and-drop is copy; to move, drag-and-drop while pressing the Shift key.
Plot history
Plot commands are maintained in a plot history for each window, ensuring that all your plotting within a session is preserved. To navigate the history, use the arrow buttons on the plot window toolbar, or Ctrl+Alt+F11 and Ctrl+Alt+F12. You can also remove single plots or clear all plots from the window again using toolbar buttons or the R Tools > Plots menu commands.
To see the entire collection of plots, open the plot history window using the toolbar button or R Tools > Plots > Plot History Window.The history gives you a list of thumbnails for the plots that have been displayed in that window, grouped by the different plot windows (or devices). Using the zoom buttons on the toolbar changes the size of the thumbnails.
To open a plot in its associated window, double-click that plot, select it, and then select the Show Plot toolbar button. Alternatively, right-click the plot and select Show Plot. You can also select an individual plot and copy, cut, or delete from the context or Edit menus.
The lifetime of your plot history across all windows is bound to the lifetime of your interactive R session. If you reset your R session, or exit and restart Visual Studio, your plot history is reset.
Programmatically manipulate plot windows
You can programmatically manipulate plot windows from R code, using device numbers to identify specific plot windows.
Plots Visual Basic
dev.list(): List all of the graphics devices within the current R session.dev.new(): Create a new graphics device (a new plot window).dev.set(<device number>): Set the active graphics device.dev.off(): Delete the active device.